CONSUMED-ENERGY-TYPE-AWARE ROUTING
FOR
WIRELESS SENSOR NETWORKS
A Thesis submitted
to the Graduate School
University of Arkansas at Little Rock
in partial fulfillment of requirements
for the degree of
MASTER OF SCIENCE
in Computer Science
in the Department of Computer Science
of the Donaghey College of Information Science and System Engineering
May, 2007
Shinya Ito
BS, University of Aizu, Fukushima, Japan, 2005
Ⓒ Copyright 2007 Shinya Ito
All Rights Reserved
UNIVERSITY OF ARKANSAS AT LITTLE ROCK
DONAGHEY COLLEGE OF IMFORMATION SCIENCE SYSTEM
ENGINEERING
DEPARTMENT OF COMPUTER SCIENCE
This thesis, “Consumed-Energy-Type-Aware Routing for Wireless Sensor Networks,” by
Shinya Ito, is approved by:
Thesis Adviser
________________________________________________
Kenji Yoshigoe
Assistant Professor of Computer Science
Thesis Committee
________________________________________________
Coskun Bayrak
Professor of Computer Science
________________________________________________
Xian Liu
Associate Professor of System Engineering
Program Coordinator
________________________________________________
Coskun Bayrak
Professor of Computer Science
Graduate Deans
________________________________________________
Ibrahim Nisanci
Dean of the Graduate School
Fair Use
This thesis is protected by the Copyright Laws of the United States (Public Law 94-533,
revised in 1976). Consistent with fair use as defined in the Copyright Laws, brief
quotations from this material are allowed with proper acknowledgement. Use of this
material for financial gain without the author’s express written permission is not allowed.
Duplication
I authorized the Head of Interlibrary Loan or the Head of Archives at the Ottenheimer
Library at the University of Arkansas at Little Rock to arrange for duplication of this
thesis for educational or scholarly purposes when so requested by a library user. The
duplication shall be at the user’s expense.
Signature
____________________________________________________
Acknowledgement
I would like to express my gratitude to those who gave me the possibility to complete this
thesis. First of all, I would like to thank my thesis advisor Dr. Yoshigoe for his generous
support and stimulating suggestions. His encouragement helped me throughout this
research. I would like to thank my thesis committee members Dr. Coskun Bayrak and Dr.
Xian Liu for all their help and valuable advice. I also would like to thank the University
of Arkansas at Little Rock for giving me a chance to learn computer science in the United
States and offering me a scholarship to continue the coursework. Finally, I would like to
give my special thanks to my parents. Without their support, it would have been
impossible to have studied in the United States.
i
Table of Contents
PAGE
List of Tables ..................................................................................................................... iii
List of Figures .................................................................................................................... iv
Glossary of Acronyms ...................................................................................................... vii
Abstract ............................................................................................................................ viii
Chapter 1 : Introduction ...................................................................................................... 1
1.1 Wireless Sensor Networks ................................................................................................... 1
1.2 Motivation ............................................................................................................................. 5
1.3 Contribution ......................................................................................................................... 7
1.4 Organization ......................................................................................................................... 7
Chapter 2 : Background and Related Works....................................................................... 8
2.1 Routing Algorithms .............................................................................................................. 8
2.1.1 Traditional Routing Algorithms .................................................................................. 8
2.1.2 Routing Algorithms in Mobile Ad-Hoc Networks (MANETs) .................................. 10
2.1.3 Routing Algorithms in WSNs ..................................................................................... 13
2.1.4 Energy Aware Routings in WSNs .............................................................................. 18
2.1.5 Hierarchical Technique in WSNs .............................................................................. 19
2.2 Other Energy Aware Protocols .......................................................................................... 22
2.3 Localization Algorithms ..................................................................................................... 27
Chapter 3 : Methodology .................................................................................................. 29
3.1 Consumed-Energy-Type-Aware Routing (CETAR) .......................................................... 29
3.2 CETAR with Biased Consumed Energy (BCE) ................................................................ 33
ii
3.3 CETAR with Aggressively-and-Adaptively Biased Consumed Energy (AABCE) .......... 35
3.4 Effect of Weighting Function on the BCE and AABCE ................................................... 38
3.5 CETAR for GEAR ............................................................................................................... 40
3.6 The Application of CETAR for the Other Type of WSN Protocol .................................... 46
Chapter 4 : Evaluation of CETAR .................................................................................... 49
4.1 Simulation Models ............................................................................................................. 49
4.1.1 Assumption for the Simualtion .................................................................................. 49
4.1.2 Common Experimental Settings ................................................................................ 51
4.1.3 Type of Traffics for CETAR ........................................................................................ 52
4.2 Evaluation for CETAR with BCE...................................................................................... 55
4.2.1 Experimental Settings for BCE ................................................................................. 55
4.2.2 Evaluation for BCE ..................................................................................................... 56
4.2.3 Effectiveness of β for BCE .......................................................................................... 60
4.3 Evaluation for CETAR with AABCE ................................................................................ 63
4.3.1 Experimental Setting for AABCE .............................................................................. 63
4.3.2 Evaluation for AABCE ............................................................................................... 64
4.3.3 Effectivity of Weighting Functions for AABCE ........................................................ 68
4.3.4 Effectiveness of AABCE over BCE in Dynamically Non-Uniform Traffic .............. 72
Chapter 5 : Conclusion and Future Works ........................................................................ 76
5.1 Contributions of This Research..................................................................................... 77
5.2 Discussions for Future Works ....................................................................................... 77
REFERENCES ................................................................................................................. 79
iii
List of Tables
PAGE
Table 1 - The average percentage of packet sent over GEAR for CETAR with BCE ......59
Table 2 - The percentage of packet sent over GEAR for CETAR with AABCE ..............68
Table 3 - Improvement of CETAR with AABCE over CETAR with BCE (1) .................68
Table 4 - The average of experimental result in Figure 4.18 and Figure 4.19...................75
Table 5 - Improvement of CETAR with AABCE over CETAR with BCE (2).................75
iv
List of Figures
PAGE
1.1 - Sensor nodes scattered in a sensor field ......................................................................1
1.2 - The components of sensor node ..................................................................................3
1.3 - A State Diagram for WSNs .........................................................................................4
1.4 - Power consumption of node subsystems (mW) ..........................................................5
2.1 - AODV algorithm .......................................................................................................12
2.2 - Greedy forwarding. y is x’s closest neighbor y to the destination node D ................15
2.3 - Node x faced routing hole for destination D..............................................................16
2.4 - Greedy Other Adaptive Face Routing (GOAFR) ......................................................17
2.5 - Packet forwarding with and without clustering and aggregation ..............................20
2.6 - S-MAC scheduling ....................................................................................................24
2.7 - Routing by COMPOW in a typical non-homogeneous networks .............................26
3.1 - The situation at the deployment of nodes in WSNs ..................................................30
3.2 - Intensive traffic from the fixed sets of nodes ............................................................30
3.3 - Intensive traffic from the semi-fixed sets of nodes ...................................................31
3.4 - An example of CETAR .............................................................................................33
3.5 - An example of CETAR with BCE ............................................................................35
3.6 - Graph of β and actually used square root β adapted by CEs(Ni) and CEr(Ni)............37
3.7 - Utility functions changes with increase in β ..............................................................39
3.8 - Greedy packet forwarding .........................................................................................41
3.9 - Load balancing and the shortest path routing ............................................................42
3.10 - An Example of GEAR incorporating CETAR ........................................................44
v
3.11 - Routing on the hierarchical network structure ........................................................47
4.1 - Relation of transmission range between fixed and adjustable power ........................50
4.2 - 10 given source and target region pairs .....................................................................52
4.3 - 10 sources out of 30 source candidates .....................................................................53
4.4 - 10 sources out of 10 source candidates .....................................................................54
4.5 - The network partitioning ...........................................................................................55
4.6 - CETAR with BCE (uniform traffic) ..........................................................................57
4.7 - CETAR with BCE (non-uniform traffic: A cluster of 10 out of 30 closest senders) 58
4.8 - CETAR with BCE (non-uniform traffic: A cluster of 10 closest senders) ................59
4.9 - CETAR with BCE for different β (uniform traffic) ..................................................61
4.10 - CETAR with BCE for different β (non-uniform traffic: A cluster of 10 out of 30
closest senders) ..........................................................................................................62
4.11 - CETAR with BCE for different β (non-uniform traffic: A cluster of 10 closest
senders) ......................................................................................................................63
4.12 - CETAR with AABCE (uniform traffic) ..................................................................65
4.13 - CETAR with AABCE (non-uniform traffic: A cluster of 10 out of 30 closest
senders) ......................................................................................................................66
4.14 - CETAR with AABCE (non-uniform traffic: A cluster of 10 closest senders) ........67
4.15 - CETAR with AABCE with utility functions (uniform traffic)................................69
4.16 - CETAR with BCE with utility functions (non-uniform traffic: A cluster of 10 out
of 30 closest senders) .................................................................................................70
4.17 - CETAR with BCE with utility functions (non-uniform traffic: A cluster of 10
closest senders) ..........................................................................................................71
vi
4.18 - CETAR with BCE for 2 stage sender selection (uniform traffic) ...........................73
4.19 - CETAR with BCE for 2 stage sender selection (non-uniform traffic: A cluster of
10 out of 30 closest senders) ......................................................................................74
vii
Glossary of Acronyms and Terms
ACK
AODV
CE
CETAR
COMPOW
CS
CTS
DATP
DSR
EAR
GEAR
GG
GOAFR
GPS
HEED
LEACH
MAC
MANET
MPR
OLSR
PAMAS
PEGASIS
RDG
RNG
RREP
RREQ
RTS
S-MAC
SYNC
TDMA
TDRPF
WSN
Acknowledgement
Ad-hoc on Demand Distance Vector Routing
Consumed Energy
Consumed-Energy-Type-Aware Routing
Common Power
Career Sensing
Clear To Send
Dynamically Adaptive Transmission Power
Dynamic Source Routing
Energy Aware Routing
Geographical and Energy Aware Routing
Gabriel Graph
Greedy Other Adaptive Face Routing
Global Positioning System
Hybrid Energy-Efficient Distributed Clustering
Low Energy Adaptive Clustering Hierarchy
Media Access Control
Mobile Ad-Hoc Network
Multi-Point Reply
Optimized Link State Routing
Power-Aware Multi-Access Protocol with Signaling
Power-Efficient Gathering in Sensor Information Systems
Restricted Delaunay Graph
Relative Neighborhood Graph
Route Reply
Route Request
Request To Send
Sensor-Media Access Control
Synchronization
Time Division Multiple Access
Topology Dissemination Based on Reverse-Path Forwarding
Wireless Sensor Network
viii
ABSTRACT
Wireless sensor networks (WSNs) typically operate with sensor nodes with
limited energy which is difficult or even impossible to be replaced due to the remote
environment in which they are deployed. Thus, power management of sensor nodes is
one of the important topics in WSNs. In this thesis, a new metric for energy aware
routing, consumed-energy-type-aware routing (CETAR), is proposed to extend the
lifetime of the WSNs. CETAR uses statistics of the energy consumed for each type of
node activities including sensing, data processing, data transmission as a source node,
and data reception/transmission as a routing node for routing decision. In particular,
CETAR selects a node with high residual energy which seldom plays a role of source
node as a routing node. Idea is to maintain the energy of active source nodes to prolong
the functionality of the WSNs.
Biased Consumed Energy (BCE), proposed in this thesis, derives a bias factor for
a node that frequently plays a role of routing node and a node that frequently plays a role
of source node. Simulation study demonstrates that the lifetime of the geographical and
energy aware routing (GEAR) can significantly extend with CETAR based on BCE.
Aggressively-and-Adaptively
BCE
(AABCE)
further
extends
BCE
by
dynamically adapting the extent of the bias factor among consumed-energy types as the
amount of consumed energy at each node changes. Simulation study demonstrates that
the lifetime of GEAR with CETAR based on AABCE can further extended that of GEAR
with CETAR based on BCE.
ix
This thesis also demonstrates the CETAR can be widely applied to the other type
of network architecture to extend the lifetime of WSNs. With its adaptability to
hierarchical network, the significance of CETAR to extend the lifetime of WSNs is clear.
1
Chapter 1
Introduction
1.1 1Wireless Sensor Networks
Wireless Sensor Networks (WSNs) have been receiving a great amount of
attention recently due to their substantial applicability to improve our lives [1]-[6]. They
aid us by extending our ability to accurately monitor, study, and control objects and
environments of various scales and conditions such as human bodies, geological surveys,
habitats, and security surveillance.
A WSN is composed of a large number of networked sensor nodes that are
densely deployed either inside the phenomenon or to its proximity as shown in Figure 1.1
[7].
Figure 1.2 - Sensor nodes scattered in a sensor field [7]
2
This figure shows sensed data is delivered to the user. Suppose data is sensed by
the sensor node A inside the sensor field. Since the transmission range of radio for each
sensor is short, A, at first, passes sensed data to the neighbor node B. In this example, this
data may be routed by the path A-B-C-D-E-Sink. Since sink is already connected to the
Internet, it can deliver sensed data to the user directly from sink. Sensor nodes in WSNs
can also autonomously process and cooperatively analyze sensed data inside networks so
that they can prune the redundant data observed inside a network and deliver only
necessary data to the user through sinks. Furthermore, WSNs can dynamically adapt its
topology. After sensor nodes are deployed in a sensor field, they autonomously find the
neighbor nodes and start communicating with each other in various ways, normally using
multi-hop communications. This allows random deployment of sensors in inaccessible
terrains or disaster relief operations.
Figure 1.2 shows components of a sensor node including sensor, processor,
storage and transceiver. Once the sensing unit captures an event, it converts the analog
signal in to digital signal and passes it to the processor for possible processing. The data
can be stored in the storage and later transmitted to a downstream sensor node. In this
figure, extra components are enclosed in dotted lines. For instance, a location finding
system such as GPS is not always necessary, and centroid localization calculated from the
location of sinks is used instead [8].
3
Figure 1.3 - The components of sensor node [7]
An operational sensor node can be in one of the three states; active, idle, or sleep
state. Figure 1.3 shows a node state transition diagram. As soon as node is powered on,
its state transits to the idle state. In the idle condition, node senses career and moves to
the active state if some signal is received (JOB_READY). On the other hand, if node
does not sense any signal and timer is expired, it goes to the sleep state (TIMEOUT). In
the sleep condition, node turns off most of its process and keeps remaining inactive. Even
when node is inactive, it still senses wake up signal to turn itself on when WAKE_UP
signal is received. In the active state, they perform activities such as processing,
transmission, and reception. Immediately after its job is done (JOB_DONE), it moves to
the idle state and wait a signal.
4
Figure 1.4 - A State Diagram in WSNs
Energy consumed by radio signal transmission and reception in sensor node is
dominant as shown in Figure 1.4. The amount of energy consumed for sensing and
processing are significantly smaller than that consumed for the radio activities. Among
the energy consumed by the node, energy used by the idle condition (listening) is also
dominant. This amount increases as the operation time of node increases. Significant
amount of literatures exist that efficiently schedule the sleep mode and listening mode
[9]-[13]. Thus, that is not the scope of this thesis.
With the advancement of technologies that has enabled a sensor device to have
higher cost performance and better capabilities like high resolution sensing, observations
in vast fields that require a number of sensor devices has become possible. Networking of
these intelligent yet low-cost sensor devices is expected to revolutionize information
gathering and processing in many situations. In contrast to various applicability of WSNs,
the capacity of batteries inside their nodes is highly limited, and it is unrealistic to
replenish battery of the nodes in many cases as described in Section 1.2. Therefore, the
preservation of such vital energy at each sensor node is significantly important in WSNs
5
1.2 Motivation
The study to reduce energy consumption rate in WSNs is significantly important
since energy of battery consumed by every single activity such as sensing, processing,
and transmission of data cannot be easily recharged due to geographical, environmental
or sometimes financial reasons. For instance, sensor nodes could be deployed around
volcanic areas that human beings cannot easily access. They could also be deployed in
very large area where the redeployment can be costly.
Even if their energy of nodes can be replenished in other cases, it is unrealistic
for the user to manage energy information for each sensor node individually due to the
size of WSNs. Thus, such management tasks are accomplished via collaboration among
sensor nodes which requires significant energy overhead. As the energy of sensor nodes
is depleted, the topology of the sensor network can be changed frequently. This would
require reorganization of the network that expends additional energy consumption by the
sensor nodes.
Figure 1.5 - Power consumption of node subsystems (mW) [14]
6
In these environments, nodes should autonomously construct their connection
after they are deployed, and the deployment of sensor nodes is one-time-only. This means
the lifetime of sensor nodes will directly determine the lifetime of sensor networks. This
study is motivated by the following observations regarding energy efficiency in sensor
nodes:
1) In many applications, the frequency of sensing activities among the deployed
sensor nodes in the network is not uniformly distributed. This is because, in
many cases, we cannot specifically identify a set of definite observation points
at the time of deployment phase of the network.
2) Energy consumed by radio signal transmission and reception in sensor node is
dominant as shown in Figure 1.4. Transmission and reception consume almost
70 percent of total energy used for all node activities [14]. Therefore, reducing
energy for transmission and reception activities has significant impact for
extending the lifetime of sensor nodes.
3) The set of actively sensing nodes, as sources of data origination, consume
extensive energy. Thus, their residual energy should be considered more
precious than the residual energy of the node which does not perform sensing
activities; however, no means are investigated to preserve actively sensing
node in the literature.
4) Energy-Aware Routing (EAR) algorithms attempt to minimize energy
requirements at each node or an overall network to transfer individual packets
and to maximize the operation time of a given network. It normally calculates
the least cost path based on several metrics including residual energy,
7
transmission power, and node location. Out of these metrics, residual energy
plays the primary role in the routing decision. Though various EAR algorithms
have been proposed and studied in literatures, none of the EAR algorithms take
in consideration the amount of energy each type of activities consume.
1.3 Contribution
This thesis proposes an approach to preserve the energy of actively sensing
nodes in the WSNs and investigates its effectiveness. The major contributions of this
thesis are as follows:
1) It proposes and investigates the method for preserving actively sensing source
node in the WSNs. This method, Consumed-Energy-Type-Aware Routing
(CETAR), improves the lifetime of the actively sensing nodes and the WSNs in
many cases.
2) It investigates the adaptability of CETAR in well-known energy aware routing.
1.4 Organization
The organization of the proposed work has the following structure layout.
Chapter 2 provides the overviews of earlier studies of routing algorithms, localization
algorithms, and methods that improve lifetime of WSNs. Chapter 3 describes CETAR,
and demonstrates its adaptability to geographical and energy aware routing (GEAR) [8].
Chapter 4 evaluates the performance of CETAR on GEAR simulator. Chapter 5 proposes
future work.
8
Chapter 2
Background and Related Works
2.1 Routing Algorithms
Routing is the process used by data communication networks to deliver a packets
from a source device to a destination device. For instance, the Internet dominantly uses a
hop-by-hop routing model where each host or router that handles a packet at first checks
the destination address in the IP header, computes the next hop that will bring the packet
one step closer to its destination, and delivers the packet to the next hops where the
process is continuously repeated. The rest of this section reviews a set of hop-by-hop
routing algorithms as they are currently dominant in the Internet.
2.1.1 Routing Algorithms on the Internet Protocol
There are mainly two types of routing algorithms in the Internet: distance vector
algorithm and link state algorithm. Distance vector algorithms use the Bellman-Ford
algorithm. The algorithm was developed by Richard Bellman and Lester R. Ford (Jr.)
independently in [15] and [16]. This approach assigns a number and the cost to each of
the links between each node in the network. Nodes will send information from point A to
point B via the path that results in the lowest total cost (i.e. the sum of the costs of the
links between the nodes used) [17]. At a initial point, each node only knows of its
immediate neighbors, and the direct cost involved in reaching them. The information; that
is, the list of destination, the total cost to each, and the next hop node for each destination
9
that consist of routing table, are passed. Each node send to each neighbor its own current
total cost to get to all the destinations it knows. The neighboring nodes examine this
information, and compare it to what they already know; that is, something which
represents an improvement on what they already have, they insert in their own routing
tables. Finally, all the nodes in the network will discover the best next hop for all
destinations and the lowest total cost.
When applying link-state algorithms, each node uses entire cost information of
the network by using graph. To produce this graph, each node floods the entire network
with information about what other nodes it can connect to, and each node then
independently assembles this information into a graph. Using this graph, each router then
independently determines the best route from itself to every other node. The algorithm
used to derive the best route in link-state algorithm is Dijkstra's algorithm, proposed by
Dijkstra in [18], Dijkstra's algorithm builds a tree data structure with the current node
itself as the root node, and establishing a path to every other node in the network. It starts
forming a tree containing only itself. Then, from the set of nodes which it has not yet
added to the tree, it adds the node which has the lowest cost to reach an adjacent node
which already appears in the tree. This continues until every node appears in the tree.
This tree then serves to construct the routing table which gives the best next hop which
has the least cost.
Distance-vector routing protocols are simple and efficient in small networks, and
require little management, if any. However, they do not scale well, and have poor
convergence properties. On the contrary, advantage of link-state routing is that it reacts
more quickly and in a bounded amount of time, to connectivity changes. Also, the
10
link-state packets that are sent over the network are smaller than the packets used in
distance-vector routing. However, it requires more storage and more computing to run
than distance-vector routing does. In the wireless sensor network, both type of traditional
routing algorithm does not fit because of processing ability, memory, and severe
bandwidth and power constraint. For instance, a large number of nodes might be
deployed in the sensor field. To exchange routing table for those nodes consumes so
much energy, bandwidth, and memory. Furthermore, it needs so much processing ability
to fuse or aggregate the information inside the network. It is unrealistic situation for the
energy-limited sensors in WSNs.
2.1.2 Routing Algorithms in Mobile Ad-Hoc Networks (MANETs)
Both Mobile Ad-Hoc Networks (MANETs) and WSNs consist of wireless
mobile nodes, and their nodes communicate with the other nodes in them by using
wireless signal and multi-hop data relay. However, WSNs have several different
characteristics. First, a battery of node in WSNs is not easily rechargeable because nodes
are likely to be deployed in such a hostile environment and/or with high volume that
access to these nodes can be risky and/or costly. This is not the case with MANETs
where the battery of a laptop computer, cell phone, and PDA, etc can be easily recharged,
Second, the high volume of WSN nodes require a significant amount of overhead during
packet transmission. Likewise, the employment of global addressing scheme is also
difficult in terms of energy efficiency and bandwidth utilization since the overhead of ID
maintenance is high. Thus traditional IP-based protocol, applicable to MANET, may not
be applicable for WSNs. Third, mobility of each node in WSNs are normally static after
11
deployment while that of MANET can be frequent. Thus, WSNs basically assume static
node configuration after node deployment while MANET assumes dynamic node
configuration. As known from their differences above, WSNs pursue energy efficiency,
on the other hand, MANETs pursue the efficiency of throughput and decreasing
communication delay. Regardless, the pursuit of MANET is also desirable and can be
used in WSN application since the determination of routing technique in WSN is
application specific and to balance energy-efficiency and communication efficiency is
important.
Routing protocol in MANET can be active or proactive. Reactive protocol
requires to retrieve route information before initiating communication. Dynamic Source
Routing (DSR) [19] and Ad Hoc on Demand Distance Vector Routing (AODV) [20] are
the examples of reactive protocols. Proactive protocol keeps route information by
exchanging periodic messages. Optimized Link State Routing Protocol (OLSR) [21] and
Topology Dissemination Based on Reverse-Path Forwarding (TBRPF) [22] are the
example of proactive protocols.
In DSR [19], proposed by Johnson et al., sender node contains all route
information to the destination in each packet header, called source routing. If sender
initiates communication with destination-unknown node, at first sender node flood Route
Request (RREQ) message inside the network. Each packet contains ID and each node
broadcasts a received packet only one time. Each node relays packet by attaching its
address information. Packet arriving at the destination has complete and ordered
information of address of nodes of the traced path. Destination node uses this route and
sends Route Reply (RREP) message to the sender.
12
AODV [19] proposed by Perkins et al., has routing table to determine which
node needs to send their data for which node. Specifically, each node only knows the
next relay node to route packet and it is not concerned about how the packet is delivered
to the destination. In Figure 2.1 (a), for example, sender node S which does not know the
routing path to a destination, at first floods a RREQ message inside the network. Relay
node records the information of a node that relayed the message as the next hop node to
the sender. By RREQ, all nodes inside the network come to have the route to send packet
for sender node as described by dotted line. By using this information, destination node
can send RREP message to the source node by unicast as shown in Figure 2.1 (b). At the
same time, relay nodes know the information to relay the node to destination of that
sender. By completing the processes mentioned above, bidirectional link is established
between sender and destination.
(a)
(b)
Figure 2.1 - AODV algorithm.
OLSR [20], proposed by Clausen et al. optimizes the flooding by using
Multi-Point Relay (MPR) set. MPR set consists of the previous relay nodes of packets to
13
a node. In the traditional flooding, each node needs to relay a packet at least one time; In
OLSR, on the contrary, uses MPR set so that only the minimum amount of nodes relays
are needed. Each node knows which node's MPR itself is for.
If the topology of the network is changed by locomotion of nodes, the routing
table in proactive type routing is affected. TBRPF [22], proposed by Ogier et al., uses the
stable link information and difference information to avoid this effect. The objective of
TBRPF is to create source tree as the shortest path for all nodes in the network. By
possessing the same tree created by the same topology, all nodes can communicate each
other. Since use of flooding technique is not efficient and creation of tree each time
consumes substantial amount of resource, it maintains this tree by exchanging periodic
reported sub tree information as difference information.
2.1.3 Routing Algorithms in WSNs
A plethora of routing protocols has been investigated in WSNs [23]-[26], [32].
The earliest work known as geographic routing was introduced by Finn [23]. Geographic
routing refers to a family of techniques to route data packets in a communication
network. The main idea is that packets should be aware of their destination and messages
will be routed hop-by-hop to nodes closer to the destination until the message reaches its
final destination, which could be a point or a region in the case of geocasting. This
implies that the hosts participating in the routing process should be aware of their
geographic positions. Face Routing, introduced by Kranakis et al. in [24], walks along
faces of planar graphs and proceeds along the line connecting to the source and the
destination. Besides guaranteeing to reach the destination, it does so with O(n) messages,
14
where n is the number of network nodes. However, this is unsatisfactory since even a
simple flooding algorithm can reach the destination with O(n) messages.
Cost of routing can be determined by the distance between the source and the
destination. Based on this observation, Intanagonwiwat et al. proposed directed diffusion
[25], a data-centric protocol for sensor network applications. It achieves energy savings
by cashing and processing data in network and by selecting empirically good paths.
However, without geographic routing support, there is initial and periodic interest that
results in low rate data flooding throughout the network. Since packet flooding consumes
substantial amounts of energy in network, Karp et al. proposed Greedy Perimeter
Stateless Routing (GPSR) [26], which elegantly avoided packet-flooding problems by
deriving a planar graph1 out of the original network graph. Though GPSR is one of the
most popular geographical routing protocols, it is originally designed for ad-hoc networks.
From the characteristics of the geographic routing, GPSR tries to take the shortest path to
forward packets to the next hop nodes as in Figure 2.2
1
A planar graph is a graph that can be drawn so that no edges intersect [27]
15
Figure 2.2 - Greedy forwarding. y is the x’s closest neighbor y to the destination node D [26]
In this figure, to sends packet from node x to D, x at first sends its packet to the nearest
neighbor node y, then y routes that packet to its nearest neighbor. GPSR assumes that
each node knows its neighbor’s location information by using the GPS or localization
method as described in [28]. The availability information of neighbors is exchanged by
using some periodic local communication protocol. It continues until the packet arrives at
destination node D. At the same time, it also follows the perimeter of the planer graph
(i.e., path x-w-v-D or x-y-z-D as described in Figure 2.4) to circumvent the routing holes2.
Greedy forwarding does not always work well. Figure 2.4 shows the situation which node
x faced routing hole to send packet to the destination D. In this figure, x has the packet
which destination is D. There are two ways to send this packet: the path w-v or y-z.
However, for node x, neighbor node w and y are farther position than itself to the
destination. So greedy forwarding will stop packet transmission at this time. To avoid this
2
A node in the routing hole has no neighbors closer to the destination than themselves as
in Figure 2.4.
16
problem, some protocols have been proposed. One of them is right hand rule incorporated
in GPSR. In right hand rule, after the node x received data, it sends this data to the first
existing node which direction is counterclockwise from the node from which x received
data. This approach requires a heuristic, the no-crossing heuristic, to force the right-hand
rule to find perimeters that enclose voids in regions where edges of the graph cross [26].
Though GPSR is using Relative Neighborhood Graph (RNG) [29] and Gabriel Graph
(GG) [30], it is recommended to use Restricted Delaunay Graph (RDG) [31] for gaining
theoretically or practically high performance. GPSR takes short path; however, traffic
will concentrate on the planar graph. Thus, only the nodes in that path will drain batteries
quickly while other nodes will remain intact. Furthermore, GPSR assumes nodes to
operate in promiscuous listening mode and is consequently always consuming energy.
Figure 2.3
Figure 2.4 - Node x faced routing hole for destination D [26]
17
Kuhn et al. proposed Greedy Other Adaptive Face Routing (GOAFR) [32], a
geographical routing algorithm that is both asymptomatically worst-case optimal and
average-case efficient. Similar to GPSR, GOAFR combines greedy routing and face
routing. As in Figure 2.6, starting at s, GOAFR proceeds in greedy mode until reaching
the n1 facing routing hole F. The algorithm switches to face routing mode and explores
the boundary of F to n2, the node closest to t on F's boundary. GOAFR falls back to
greedy mode again and finally reaches t. In the next section, a set of routing algorithms
especially suited for conserving energy of sensor nodes in WSNs are described.
Figure 2.5
Figure 2.6 - Greedy Other Adaptive Face Routing (GOAFR)
In [33], Gupta et al. investigated the performance of a new clustering algorithm
for sensor networks. The proposed approach balances the load among clusters and
simultaneously tries to cluster the sensor nodes close to high-energy cluster-heads, called
18
gateways. Simulation results have demonstrated the high efficiency of load balanced
clustering for sensor networks applying different routing methods. This clustering
algorithm is applied to the other WSN routing approaches and improves most of their
performances in terms of the lifetime of the sensor networks.
2.1.4 Energy Aware Routings in WSNs
Energy-Aware Routing (EAR) algorithms try to minimize energy requirements
at each node or an overall network to transfer each packet and to maximize the
operational time of a given network. It normally calculates least cost path based on
several metrics such as residual energy, transmission power, and node location. Out of
these metrics, residual energy plays the primary role in routing decision, and many
protocols has been proposed in [8], [25], and [34]. Woo et al. [25] first proposed energy
aware routing concept such as “maximize time to partition” and “minimize maximum
node cost.” Deployment of these concepts results in excellent solutions; however, it is
difficult to directly implement them in a local algorithm when even the centralized
version of the same problem is NP-complete3. Chang et al [34] obtained the result that in
order to maximize the lifetime of WSNs, the traffic should be routed such that the energy
consumption is balanced among nodes in proportion to their remaining energy.
Stojmenovic et al. [35] proposed a power-cost routing, which considers both the distance
to the destination and the consumed energy of a node in the route selection. The
particular power metric they used assumes arbitrary dense network and arbitrary adjusted
transmission power. However, the specific format of the cost function will not have
3
Any of a class of computational problems for which no efficient solution algorithm has
been found. Many significant computer-science problems belong to this class—e.g., the
traveling salesman problem, satisfiability problems, and graph-covering problems [36].
19
major impact on the performance mentioned in [8]. In addition, [35] did not address
routing with the presence of a communication hole. Yu et al. [8], elegantly avoided the
problem of GPSR (explained in the previous section) by proposing Geographical and
Energy Aware Routing (GEAR). GEAR will not deterministically follow a particular set
of nodes while routing around holes. GEAR uses two metrics for routing decision. One is
the distance between packet sending node and its neighbor nodes, and the other is the
remaining energy level of neighbor nodes. This simple heuristic in GEAR tends to avoid
nodes that have been frequently visited before to achieve load balancing.
2.1.5 Hierarchical Technique in WSNs
Many topologies have been proposed in WSNs. Topology types are mainly
classified into four parts in terms of a way for each node to deliver data to the sink.
20
Figure 2.7 - Packet forwarding with and without clustering and aggregation [37]
In a network which uses single hop transmission without clustering like (a) in
Figure 2.5 utilizes the nodes which use single hop transmission to the sink after sensing
information. This way is easy to implement because there is no special routing technique
to transfer data to the sink. However, if the size of network becomes large, it consumes so
much energy to transmit a signal to the sink. In Figure 2.5 (b) which uses multi hop
transmission without clustering, on the other hand, each sender uses its neighbor nodes as
a relay node for packet delivery. Packet is transmitted by multi-hop route and delivered to
the sink or target region. This approach is a more efficient way to save the energy
21
consumption and used in [8], [26], and [32] compared to (a) in Figure 2.5 since the
energy used for sending activity is only for sending their neighbors. However, traffic
concentrates normally on the nodes near by sink [38]. Some applications solve this
problem by allocating cluster head among the nodes [39]-[40]. The network which uses
single hop transmission with clustering like (c) in Figure 2.5 (c) splits the network into
sections so that each node inside each section can communicate each other by one hop.
One cluster head is chosen from each section and directly communicates with each node
inside the section where a cluster head is selected. A cluster head gathers and fuses
information for utilizing energy and bandwidth, and delivers it to the sink directly. This
topology is used in [39], [40], and [41]. However, the larger the network size, the greater
the consumed energy. Therefore, the network which uses single hop transmission with
clustering like (c) in Figure 8 is not suitable for the large size network. The network
which uses multi hop transmission with clustering like (d) in Figure 2.5 has a similar
approach with the network (c). The difference is that both nodes inside clusters and
cluster heads use multi-hop data transmission among nodes or among cluster heads to
relay packets. This approach can decrease the required energy to deliver packet from the
nodes to the cluster heads. Then, cluster heads use multi-hop data transmissions among
them to the sink. It can efficiently expand the size of cluster, and network more
efficiently compared to the approach like (a)-(c) because it does not need to use
long-range transmission to a sink. This approach is proposed by Younis et al. as Hybrid
Energy-Efficient Distributed Clustering (HEED) in [37].
22
2.2 Other Energy Aware Protocols
Heinzelman et al. [39], proposed Low Energy Adaptive Clustering Hierarchy
(LEACH), a clustering based protocol that uses randomized rotation of local cluster
heads to evenly distribute the energy load among the sensors in the network. LEACH
assumes that all nodes can transmit packets with enough power to reach the sink if
needed, and that each node has computational power to support different Media Access
Control (MAC) Protocol for scheduling. Therefore, it is not applicable to networks
deployed in large area [42]. In [40], Power-Efficient Gathering in Sensor Information
Systems (PEGASIS) [40] was proposed as an enhancement over the LEACH protocol.
PEGASIS is a near optimal chain-based protocol, and in order to extend network lifetime,
nodes need only communicate with their closest neighbors, and they take turns in
communicating with a sink. When the round of all nodes communicating with the sink
ends, a new round starts, and so on. This reduces the power required to transmit data per
round as the power draining is spread uniformly over all nodes. PEGASIS has two main
objectives. First, it increases the lifetime of each node by using collaborative techniques.
Second, it allows only local coordination between nodes that are close together so that the
bandwidth consumed in communication is reduced. Unlike LEACH, PEGASIS avoids
cluster formation and uses only one node, instead of multiple nodes, in a chain to transmit
to the sink. When a sensor node is idle, it is wasteful to keep sensors on and listening the
signal from other nodes. Motivated by this observation, Singh et al. [43] proposed an
Power-Aware Multi-Access Protocol with Signaling (PAMAS) for ad-hoc radio networks
by conserving battery power at nodes by intelligently powering off nodes that are not
actively transmitting or receiving packets.
23
On the contrary to the scheduling type MAC protocol used in LEACH, S-MAC
[44] proposed by Ye et al. combined benefits of TDMA and contention protocols. Since
idle listening consumes significant energy, it periodically turns on/off sensor's radio when
sleeping, and differs each node's active state in such a way that each node, except node’s
neighbors, has a different timing of active state. It reduces overhearing and message
passing. Together with overhearing avoidance and message passing, S-MAC obtains
significant energy savings over always-listening MAC protocols. At the same time, a
node wakes up for a short period of time at the end of each transmission (adaptive
listening). Adaptive listening largely reduces the latency due to periodic sleeping and
increases throughput. Figure 2.8 shows timing relationship between a receiver and
different senders. CS stands for carrier sense4. Synchronization (SYNC) packet is sent to
the other nodes at first to notify the existence of the sender for starting communication.
SYNC has two types of information; that is, an address and waiting time of a sender. If
the sender does not receive the reply for SYNC message within a defined waiting time, it
will randomly choose sleeping time and go to sleep state. For example, sender 1 at first
sends SYNC packet and does not receive any reply, thus it will sleep. The sender 2 at
first sends request-to-send (RTS) packet to win the medium, and the receiver will reply
with a clear-to-send (CTS) packet. If sender 2 receives CTS, it can start sending data. The
sender 3 sends SYSC packet since it is new node. While CS, it receive the reply for
4
Carrier Sense describes the fact that a transmitter listens for a carrier wave before
trying to send. That is, it tries to detect the presence of an encoded signal from another
station before attempting to transmit. If a carrier is sensed, the node waits for the
transmission in progress to finish before initiating its own transmission [45]
24
SYNC packet and send RTS packet. After receiving CTS packet from receiver, it can
send data to the receiver.
Figure 2.8 - S-MAC scheduling [44]
Position-based algorithm called SPAN [46] proposed by Cen et al. selects
some nodes as coordinators based on their positions. The coordinators form a network
backbone used to forward messages. A node should become a coordinator if two
neighbors of a non-coordinator node cannot reach each other directly or via one or two
coordinators.
In [47], Hull et al. investigated the end-to-end performance of various congestion
avoidance techniques in a sensor network. They proposed a method called Fusion that
combines three congestion control techniques that operate at different layers of the
traditional protocol stack. These techniques include a hop-by-hop flow control, a source
25
rate limiting scheme similar to the adaptive rate control mechanism that meters traffic
being admitted into the network, and a prioritized MAC layer that gives a backlogged
node priority over non-backlogged nodes for access to the shared medium.
Rodoplu et al. [48] proposed a general model in which the power consumption
between two nodes at distance d is u(d)=dα+c for some constants α and c, and described
several properties of power transmission that are used to find neighbors for which a direct
transmission is the best choice in terms of power consumption. Banerjee et al. [49]
investigated the transmission cost to reliably send packets from source to destination
node including re-transmission activity caused by link errors. Kubisch et al. defined a
strategy to select a minimum transmission power for each node in a given distributed
sensor network in [50]. Network operators define a target number of reachable neighbors
for all nodes. A node adjusts its transmit quality according to its current neighbor count.
If a node has more neighbors than necessary, it scales down its transmit power to the
target range. If too few neighbors exist, the node increases its power. Narayanaswamy et
al. [51] presents a power control protocol named Common Power (COMPOW). Their
goal is to choose the smallest common power level by each node that preserves
connectivity, maximizes traffic carrying capacity, reduces contention in the MAC layer;
and it requires low power to route packets. In their approach, several routing daemons run
in parallel at each node for each power level. Each routing daemon exchanges control
messages with its counterparts at the neighboring nodes to maintain its own routing table.
CLUSTERPOW [52] was designed to overcome some of the short comings of
COMPOW by accounting for non-uniform distributions of nodes. It introduces a
hierarchy, where by closely located nodes are allowed to form a cluster and choose a
26
small common power to interact with each other as in Figure 2.9. In this figure, a sender,
S, tries to send a packet to a destination node, D, via inter-cluster relay nodes, N1, N2,
and N3. Different clusters communicate among themselves at a different (higher) power
levels. Intentionally, most of the intra-cluster communication is done at a lower power
level, and the (possibly rare) inter-cluster communication is carried out with a higher
power. As before, each node runs multiple daemons, which constantly exchange
reachability information with neighbors. This incurs a significant message overhead.
Figure 2.9 - Routing by COMPOW in a typical non-homogeneous networks. S is the sender, N1
and N2 is the inter-cluster relay nodes, and the node D is the destination [51]
The PARO proposed by Gomez et al. in [53] maximizes the number of nodes
used between the source and destination within a signal transmission range between them
so as to minimize the energy consumption of the overall network. A scheme proposed by
the same authors in [54] shows the impact of an individual variable-range transmission
27
power control on the physical and network connectivity, network capacity, and power
saving of wireless multihop networks such as ad hoc and sensor networks.
2.3 Localization Algorithms
Typically, the aforementioned routing algorithms in WSNs mentioned above
assume that they already have some system to localize the location of nodes. Global
Positioning System (GPS) is the most popular way to get location information of nodes;
however, because of its size and cost, it is still not easy to deploy for a number of sensors.
The localization method proposed in [55] uses an idealized radio model and proposes a
simple connectivity-based localization method for such devices in unconstrained outdoor
environments. Rao et al. [56] focused mainly on the sensor network that no nodes have
location information. Their approach involved assigning virtual co-ordinates to each node
so that each node has virtual connection with its neighbor nodes by local connectivity,
and then it applies a standard geographic routing over these coordinates. Since nodes
always know their neighbors and keep connectivity with them, this technique can be
applied in most WSN settings. Their approach shows that greedy routing performs better
using virtual coordinates than using true geographic coordinates. The cricket compass
system proposed in [57] reports position and orientation indoors for a handheld device
and informs an application running on the device of the position and orientation in a local
coordinate system established by the fixed set of beacons. Though it is hard to achieve
the high accuracy of localization in indoors by interference of radio, it achieved an
accuracy with a few degrees of true value indoors. In [58], the angle of arrival technique
is applied in the ad-hoc network without reference points that transmit high power signals.
28
The nodes collect the angle information from neighbors and derive coordinates by using
angle-based triangulation.
29
Chapter 3
Methodology
In
this
section,
a
new
energy-aware-routing
metric
called
Consumed-Energy-Type-Aware Routing (CETAR) is proposed. Then, the CETAR is
employed to Geographic and Energy Aware Routing (GEAR) to demonstrate its
adaptability to a general energy-aware-routing algorithm.
3.1 Consumed-Energy-Type-Aware Routing (CETAR)
Some WSN applications constantly monitor and report environmental changes in
an entire region. For such WSN applications, the frequency of any sensor node being the
origin of data sending activity is more or less uniformly distributed throughout the WSN,
and survivability of each node is more or less equally important. In many other WSN
applications, however, some sensors actively capture and disseminate more information
than the rest of the sensors do. An example is that a collection of sample data is sent from
a target region whose exact location is unknown at the deployment of sensor nodes as
described in Figure 3.1. This figure describes the situation as some phenomena exits in
fixed regions inside the sensor field. Since an observer does not know the exact location
of phenomenon at the deployment of sensor nodes, he has to deploy sensor nodes to a
wider range of field than actual regions of his interest.
30
Figure 3.1 - The situation at the deployment of nodes in WSNs
Suppose nodes are uniformly deployed in the sensor field as described in Figure
3.2 and Figure 3.3. Figure 3.2 describes conceivable case in which the intensive traffics
are generated from the fixed sets of nodes. In this case, active sensors sense and transmit
more information than the rest of sensors do, and the location of data originator is easily
predicted since their location remains the same. Traffic in this figure is not uniformly
distributed throughout the sensor field, so we define it as a non-uniform traffic.
Figure 3.2 - Intensive traffic from the fixed sets of nodes
31
On the other hand, Figure 3.3 describes conceivable case in which the intensive
traffics are generated from the semi-fixed sets of nodes. In this case, active sensors also
sense and transmit more information than the rest of sensors do; however, the location of
data originator is not easily predicted since their location changes frequently time after
time as described specific time-span t1, t2, and t3 in the Figure 3.3. As seen in the figure,
the nodes inside the phenomenon A happened in the time-span t1 are painted gradated
gray. The other phenomena, happening during t2 and t3, are also painted gray and black.
Since traffic in this figure is not uniformly distributed throughout the sensor field and the
location of intensive traffic changes, we define it as a dynamic non-uniform traffic.
Figure 3.3 - Intensive traffic from the semi-fixed sets of nodes
As an actual example, let us suppose several braces of birds will migrate in some
sensor-deployed field in a forest during habitat monitoring observation. Then WNS will
start sensing some information about those birds. In this situation, either the observer
32
originates queries for the sensors which retrieve some data of environmental change or
the movement of birds triggers transmission of sensed information to the observer, bursts
of sensed information originated from a target area especially around birds happen. After
their young bird leave their nest, next bird will start migrating in a different place. We
can imagine that the most highly activated nodes are the data originating sensors. When
those sensors are used for routing activity of the other sensors, their energy can be
depleted rapidly. Replacement of those highly active sensor nodes so as to extend the
duration of monitoring activity may not be efficient or even not be practical in certain
cases such as a deployment of WSN on a wide and distant location like in a deep forest.
For this type of WSN application, special attention needs to be given to minimize the
energy consumption of the sensor nodes that actively disseminate the data. Lifetime of
active sensor nodes may be extended if the information regarding what type of activities
each sensor node consumes energy for, is available to its neighbor nodes so as to
accomplish intelligent packet forwarding decisions.
CETAR is the focus of this research. CETAR’s routing decision is based on the
frequency of the different activities (i.e., transmission, reception, sensing, and processing)
in which each node engages. The investigation of CETAR comprised of data sending
activity and data routing activity at each node because their energy consumptions are
dominated by transmission and reception operations which in turn dominate the total
energy consumed for node activities [14]. We define the data sending activity at a node as
it transmits data as a source node to its direct neighbor node. The data routing activity at a
node is defined as it receives and transmits data as an intermediate router. In the sensor
network activities, the importance of every node's role is sometimes not equal. There are
33
possibilities that only particular nodes could continue to originate collected data as shown
in Figure 3.4. In that case, keeping such sensor nodes active as much as possible is the
rational way to prolong the lifetime of sensor network rather than keeping them engaged
in both data sending and routing activities. CETAR is particularly suited for the
applications with this type of characteristics. The CETAR is a general solution to
preserve historically active source nodes and has a potential to improve existing
energy-aware routing algorithms in general to prolong the lifetime of the WSNs.
Figure 3.4 - An example of CETAR
3.2 CETAR with Biased Consumed Energy (BCE)
CETAR employs simple statistics for separate types of energy consuming
activities. For instance, each node keeps statistics of the energy consumed for data
transmissions as a source node and data transmission and reception as an intermediate
34
router. Since the transmission and reception operations dominate energy consumption of
sensor nodes in WSNs, such statistics are useful for identifying which nodes are primarily
active as a source node and which nodes are primarily active as a routing node. In
particular, the statistics can be used to establish routing paths so that CETAR selects a
sensor node with high residual energy as well as one that which has rarely consumed
energy as a source node. We can define a Biased Consumed Energy (BCE) at node i, Ni
as,
BCE ( N i )   (CEs ( N i ))  (1   )(CEr ( N i ))
(1)
where CEs(Ni) and CEr(Ni) are consumed energy of Ni used for data sending and that used
for routing activities, respectively. These statistics can be easily recorded by each node. β
is a tunable weight from 0 to 1. If β is 0.5, BCE(Ni) becomes equivalent to the total
consumed energy of Ni without bias. β value is shared in the whole network and each
node calculates BCE based on β.
A simple example in Figure 3.5 illustrates how CETAR can preserve energy of
the node which frequently plays a source node for potentially improving the lifetime of
WSNs. Suppose Ns tries to send a packet to Nd via one of the neighbor nodes, Nx and Ny.
Assuming the transmission cost among the direct neighbor nodes are the same, the least
cost path can be derived based on the total energy consumption at each node i, CEi. Since
CEx=0.55 and CEy=0.6, the least cost path from Ns to Nd is Ns-Nx-Nd. Thus, the node
which frequently plays a source node, Nx, will consume its energy which could be used in
future transmission of data originated from its sensor. When equation (1) with β=0.9, cost
will be computed as
BCE(Nx)=0.9(0.5)+0.1(0.05)≈0.46
35
and
BCE(Ny)=0.9(0.2)+0.1(0.4)≈0.21
respectively. Consequently, the least cost path from Ns to Nd is Ns-Ny-Nd. That is, Ni will
choose Ny as the next hop so as to preserve the energy of the active node, Nx.
Figure 3.5 - An example of CETAR with BCE
3.3 CETAR with Adaptively-and-Aggressively Biased Consumed Energy (AABCE)
Use of fixed β could abuse energy resources in the WSN since not enough
statistics are available to predict which nodes can be identified as sending nodes
immediately after the deployment of the network. For example, one node can be initially
performing sending activities only for a very small period of time. Even if the node has
no more future data to send it will be overprotected compared to its neighbor nodes that
could potentially become active at a later time. In this situation, BCE in equation (1) with
relatively high β is likely to increase the total energy consumption in the network without
36
a benefit. Based on this observation, instead of using predefined weight β, adaptively
BCE using β as an adaptive weight that can be adjusted dynamically based on the
statistics of consumed energy for sending, CEs(Ni) , and routing, CEr(Ni), in equation (2).
This statistical information of energy is used to calculate β. By using those values, β is
calculated as follows.
 
CEs ( N i )
CEs ( N i )  CEr ( N i )
(2)
where CEs(Ni)+ CEr(Ni) ≤ 1.0. When CEs(Ni) and CEr(Ni) consume none of their energy,
value of β is set 0 as a default value. In the equation (1), either side of the co-efficient, β
or 1-β, will be linearly increased/decreased if the other side of the co-efficient is linearly
decreased/increased. That means, the rate of change in bias for data sending activity part
 (CEs ( Ni )) and that for routing activity part (1   )(CEr ( Ni )) are moderate
throughout an entire range of β. Since we prefer to aggressively and rationally preserve
active nodes which are originating packets, we define aggressively-and-adaptively biased
consumed energy AABCE for data origination or routing at node i, Ni calculated as
follows.
AABCE ( N i )  sqrt ( )(CEs ( N i ))  (1  sqrt ( ))(CEr ( N i ))
(3)
where β is the tunable weight from 0 to 1. Linear and square root value for β and 1-β are
described in Figure 3.6. As seen in Figure 3.6, β changes linearly from 0 to 1. On the
other hand, square root β value sharply increases when β is close to 0 while its rate of
increase becomes moderate as β approaches 1.
37
Figure 3.6 - Graph of β and actually used square root β adapted by CEs(Ni) and CEr(Ni)
BCE sometimes excessively preserves nodes which perform data originating
activity compared to AABCE. AABCE can prevent this situation. For example, suppose
the case that Nx already consumed 10 percent of the energy for sending and 89 percent of
the energy for routing, and Ny consumed 20 percent of the energy for sending and 10
percent of the energy for routing so far in the Figure 3.5. In this case, CEs(Nx)=0.1,
CEr(Nx)=0.89, CEs(Ny)=0.2, and CEr(Ny)=0.2. It calculates
BCE(Nx) = 0.9(0.1) + 0.1(0.89)≈0.18
and
BCE(Ny) = 0.9(0.2) + 0.1(0.2)=0.21
where β is 0.9. In this example, even if the energy of node Nx is substantially depleted,
CETAR with BCE will choose the least cost path from Ns to Nd as Ns-Nx-Nd. CETAR
38
with BCE underestimates energy used for routing activity lets the node’s battery depleted
quickly, and it shortens the lifetime of the network. On the other hand, when AABCE is
used in the same scenario, β for Nx is 0.1/(0.1+0.89)=0.1 and β for Ny is 0.2/(0.2+0.2) =
0.5 and,
AABCE(Nx) = sqrt(0.1)(0.1)+(1-sqrt(0.1))(0.89)≈0.64
and
AABCE(Ny) = sqrt(0.5)(0.2)+(1-sqrt(0.5))(0.2)≈0.23
In this case, CETAR with AABCE will choose the least cost path from Ns to Nd as
Ns-Ny-Nd. Thus, CETAR with AABCE successfully avoids aforementioned situation by
adaptively using proper weight for each weight. In the section 3.5, GEAR is used as an
example to show how CETAR can be incorporated into existing energy-aware routing
protocols in general to gain the lifetime of the WSNs.
3.4 Effect of Weighting Function on the BCE and AABCE
Figure 3.6 shows the relationship between β and square root β on how the value
of squat root β changes with increase in β. Equation (3) used square root β as a coefficient
of each type of consumed energy. As seen in Figure 3.6, β changes linearly from 0 to 1.
On the other hand, square root β value sharply increases when β is close to 0 while its
rate of increase becomes moderate as β approaches 1. This type of function is called the
utility function5 for β. To use this function as a coefficient of equation (3), we need to set
the minimum/maximum value of utility function in the range of β as 0/1. Many utility
5
A mathematical expression that assigns a value to all possible choices
39
functions other than square root β can be used. Utility functions which are the

arctan(   ) and
2
1  (1   ) 2 where 0 ≤ β ≤ 1 is described in the Figure 3.7. In this

figure, as β increases from 0 to 1, arctan(   ) and
2
1  (1   ) 2 sharply increases as
square root β does. However, square root β sharply increases even when β is low enough.
This characteristic will contribute for putting more weight for data originating activity
from the early stage of WSN activity. Effect of utility function for AABCE compared to
linearly increased β is evaluated in Chapter 4.
We will consider which value of β in equation (1) results in extending the
lifetime of active sensor nodes for CETAR with BCE in the next chapter. The effect of
weighting function for using a coefficient in equation (3) for CETAR with AABCE will
also be considered.
Figure 3.7 - Utility Functions changed with increase in β
40
3.5 CETAR for GEAR
GEAR considers both the residual energy and the distance to the destination
when selecting a routing node. The idea of CETAR is incorporated into GEAR to
evaluate a relative performance improvement of CETAR. GEAR uses the following
assumptions [8].
1) It is assumed that locations of node in this simulator are determined by using
one of those existing localization algorithms as described in the section 2.3.
Knowledge of node location is necessary for the routing decision of GEAR.
2) There exists some periodic packet exchange protocol done in the underlying
layer to exchange the message containing information: location of a node
and its remaining energy. This information is shared among nodes and its
neighbors. It might be possible to be done in MAC layer, for example,
piggybacking the neighbor information in the MAC layer control packets,
such as RTS, CTS, and ACK.
3) Each radio-link is bi-directional. This is not an unreasonable choice as
unidirectional links can be selectively black-listed at the MAC layer
[59]-[60].
4) Sensor node consumes unnegligible amount of energy even when it is in a
listening or idle state as shown in Figure 1.4. Thus, TDMA-style MAC
protocol proposed in [61] or SMAC [44] that enables a sensor node switch
to sleep mode can be used when it is not transmitting or waiting packet.
GEAR controls the number of disseminated packets by only considering a
certain target region instead of flooding entire networks with the packets by using
41
energy-aware and geographically-informed neighbor-selection heuristics to route a packet
toward the destination region. Specifically, greedy forwarding as described in the Figure
3.8 and energy balancing technique as described in the Figure 3.9 is used.
Figure 3.9 explains the energy aware portion of GEAR. In this figure, there is
source node, S, and destination node, T, and the shortest path is S-A-B-C-D-T. If the
shortest path is used every time, the nodes A, B, C, D on the path S-A-B-C-D-T will be
depleted quickly since intermediate routing nodes consume energy for routing including
data transmission and reception in contrast to S and T, which concentrate on data
transmission/reception. If S and T on the path S-A-B-C-D-T are also depleted, there is no
way to send packets from P to Q, called a network partition. Therefore, instead of using
the path S-A-B-C-D-T, the alternate paths, in which the load balancing and the shortest
path routing can spread the load of energy consumption, is shown in Figure 3.9. This not
only for extends the lifetime of node S and T longer but also delays intermediate nodes
from quick depletion of their energy.
R
C
N
= Node
= Path
= Transmission range
Figure 3.8 - Greedy packet forwarding
42
Considering a node N trying to forward a packet whose destination is centroid C
in target region R, the node N routes the packet progressively toward the target region as
in Figure 3.8. At the same time, it tries to balance the energy consumption across all its
neighbors. The next hop determined by the smallest learned cost across all neighbors is:
h( N , R)  c( N , N min )  h( N min , R)
(4)
Learned cost is the combination of distance from sender to its neighbor node Ni, residual
energy of node N, and the learned cost of its neighbor Ni to the target region R, h(Ni,R). If
a node does not have h(Ni,R) for a neighbor Ni, it computes the estimated cost c(Ni, R) of
Ni as a default value for h(Ni, R) as follows:
αd(Ni, R) + (1-α)e(Ni)
(5)
where d(Ni,R) is the normalized distance from Ni to the centroid C of the region R and
expressed as
d ( N i , R) 
Distance ( N i , R )
Max N j Nei ( N j ) ( Distance ( N j , R ))
(6),
Figure 3.9 - Load balancing and the shortest path routing Error! Reference source not found.
43
and e(Ni) is the normalized consumed energy at node Ni, and expressed as
e( N i ) 
CE( N i )
Max N j Nei ( N j ) (CE( N j ))
(7),
where CE(Ni) is the consumed energy at Ni, Nei(Nj) is a set of neighbors of Nj, and α is a
tunable weight from 0 to 1. If α is 1, learned cost is purely determined by the distance
from the neighbors to the target region R. Since h(Ni,R) cannot be calculated for
non-adjacent neighbors Ni and R at the beginning, the estimated cost c(Ni,R) as a initial
learned cost of neighbors is used instead. From the result of learned cost calculation, the
next hop satisfying high residual energy which is also closer to the destination will be
selected.
If all neighbors are further than N to the destination C, this means N is in a
routing hole. That is, no nearer neighbors exist between N and C. In this case, N first
updates its learned cost by combining its neighbor’s learned cost and the cost of
forwarding a packet to the neighbor. Consequently, N will send the updated learned cost
to its neighbor to prevent subsequent packets from falling into the hole.
GEAR calculates a learned cost with fixed weighting factor, α, for the distance
between two nodes and a residual energy of node. We adopt CETAR scheme to GEAR so
that the residual energy component can be further categorized to different types of energy
consumption. Thus, when the consumed energy of an active source node is still high, the
node will receive greater weight so as to prevent it from being selected as a part of the
routing path. In particular, energy portion of the equation (5) is modified to
e( Ni ) 
BCE ( Ni )
MaxN j Nei ( N ) ( BCE ( N j ))
(8)
44
A simple example in Figure 3.10 illustrates how aggressive preservation of active source
nodes can potentially improve the lifetime of WSNs.
(a)
(b)
Figure 3.10 - An Example of GEAR incorporating CETAR
Suppose Ni is trying to send a packet to R via one of the neighbor nodes, Nx and Ny as an
intermediate
routing
node.
Based
on
GEAR,
h(Nx,R)=c(Nx,Nz)+h(Nz,R)
and
h(Ny,R)=c(Ny,Nz)+h(Nz,R). For α=0.5, CEs(Nx)=0.9, CEr(Nx)=0.01, CEs(Ny)=0.1, and
CEr(Ny)=0.2 are used in Figure 3.10 (a), the GEAR will compute
h(Nx,R)=0.5(1/3)+0.5(0.91/0.91)+10≈10.66
and
h(Ny,R)=0.5(3/3)+0.5(0.3/0.6)+10=10.75,
respectively. Thus, the highly active source node, Nx, will continue to be a part of routing
path between Ns and R. On the other hand, the GEAR with CETAR with β=0.9 will
compute
45
h(Nx,R) = 0.5(1/3)+0.5((0.9(0.9)+0.1(0.01))/0.811)+10≈10.66
and
h(Ny,R) = 0.5(3/3)+0.5((0.9(0.1)+0.1(0.2))/0.38)+10≈10.64.
Consequently, node Ni will choose Ny as the next hop
BCE sometimes excessively preserves nodes which perform data originating
activity. As an example, we show the particular case that AABCE is effective compared
to BCE. We assume the same situation with the section 3.3 such as α=0.5, β=0.9 for BCE,
and CEs(Nx)=0.1, CEr(Nx)=0.89, CEs(Ny)=0.2, and CEr(Ny)=0.2 in the Figure 3.10 (b). We
can know BCE(Nx)≈0.18, BCE(Ny)=0.21, AABCE(Nx)≈0.64, and AABCE(Ny) ≈0.23 from
the section 3.3. By using aforementioned BCE and AABCE, h(Nx,R) for GEAR using
CETAR with BCE is calculated as,
h(Nx,R)=0.5(2/3)+0.5(0.18/0.38)+10≈10.57
and
h(Ny,R)=0.5(3/3)+0.5(0.21/0.38)+10≈10.77
where β is 0.9. Similarly, learned cost for GEAR using CETAR with AABCE is
calculated as,
h(Nx,R)=0.5(2/3)+0.5(0.64/0.64)+10≈10.83
and
h(Ny,R)=0.5(3/3)+0.5(0.23/0.36)+10≈10.81
While CETAR with BCE could not avoid using energy depleted node Nx as the next hop,
CETAR with AABCE successfully choose Ny as the next hop.
Those examples demonstrate that CETAR with AABCE can adaptively and
rationally prevents active source nodes from being selected as a part of routing path in
46
GEAR. Thus, the lifetime of the heavily-used individual sensor nodes can be extended,
and that of entire WSNs can be extended accordingly. In the next chapter, CETAR is
incorporated into GEAR simulator and evaluated the performance in terms of the lifetime
of network.
3.6 The Application of CETAR for the Other WSN Topology
In this section, the possibility of CETAR for the other type of WSN topology is
discussed. The type of network protocol is classified according to their network structure
such as flat, hierarchical, or location-based [42]. In the flat network, each node has the
same role, on the contrary to the hierarchical network which has the cluster head and
normal node, and they serve different roles each other. Location-based network utilize the
location information of nodes acquired from a localization technique and use them in the
routing decision rather than flooding packets in the whole network. GEAR [8] proposed
in the previous section has the two attributes that are flat and location-based. Thus we
will seek the applicability of CETAR for the hierarchical network.
Hierarchical network is mainly classified into two types according to the type of
cluster’s role of whether they use the single hop routing or multi-hop routing for the data
delivery to the observer (sink) as described in the Figure 2.5 (c) and (d). We focus on the
Figure 2.5 (d) since CETAR needs multi-hop routing to save the energy of active sender
nodes. This idea cannot be applicable for the single hop routing as described in the Figure
2.5 (c).
Figure 3.11 shows that three clusters are sending their packet to the sink by
using inter-cluster head multi-hop routing. Also nodes in each cluster send packet to their
47
cluster head by using multi-hop data transmission. There are several assumptions for this
network as following:
1) Each node knows its absolute locations by some localization algorithm.
2) Network is separated into segments and nodes inside each segment will
form a cluster. Each cluster has one cluster head.
3) Nodes inside a cluster can only communicate with nodes inside the same
cluster or their cluster head by using multi-hop routing. Clusters heads can
communicate each other by using multi-hop routing.
4) Even if the cluster heads have a plenty of battery, their energy is not
unlimited. Also, the replenishment of battery is costly since nodes in WSNs
are deployed in distant or dangerous area where human can not easily enter.
5) All underlying layer used below or above network layer is abstracted.
Figure 3.11 – Routing on the hierarchical network structure
48
In this figure, we assumes that energy used inside each cluster is already
optimized by assuming existing multi-hop routing protocols like GEAR. In this case,
each node in a cluster will deliver their packet for their cluster head at first. After cluster
heads receives packets from their cluster, they deliver packets to the observer by using
multi-hop routing. Therefore, we can substitute routing in hierarchical network in Figure
3.11 as the overlay network among cluster heads. In this case, CETAR can be
incorporated to a routing algorithm used among the clusterheads. Consequently, the same
logic of applying CETAR for the routing scheme as described in the section 3.5 can be
used in this example. Locally-improved routing decisions among nodes inside the clusters
and cluster heads can create globally-improved routing decisions for the entire network.
49
Chapter 4
Evaluation of CETAR
4.1 Simulation Models
4.1.1 Assumptions for the simulation
Performance of power management in WSNs could be measured by total energy
consumed in a system or the number of packets being sent and/or received before the
network partitions. Latter metric is chosen over the former one in order to measure the
duration of operable time for the uniform and non-uniform traffic described in the section
4.1.3.
Many of routing algorithms proposed in recent literatures use dynamic adaptive
transmission power (DATP) described in Figure 4.1. Thus, GEAR is implemented with
DATP to save unnecessary transmission cost even though the GEAR did not provide it in
[8]. It is well known that the transmission power of Pt=PrDα is required to transmit signal
to the receiver where Pr is the receiving power and D is the distance from sender to
receiver. Pr needs to be higher than threshold Pthr. If the received signal is lower than Pthr,
receiver can not receive the signal properly. Since the transmission power of GEAR is
fixed, we can know at most Dα-Diα transmission power will be wasted at most from the
Figure 4.1. The value of α depends on the transmission media and antenna characteristics.
This value is typically around 2 for the short distance links which is less than 100 meters
and omni-directional antennae, and around 4 for longer distance in the 2.4 GHz
transmission band [49]. For the sake of research considered in this manuscript, α=2 is
50
assumed because wireless sensor nodes do not typically use in 2.4 GHz frequency band. A
margin of Dj (0 ≤ Dj ≤ D−Di), in addition to distance Di, can be applied to set a
transmission power level to (Di+Dj)α in order to reduce error rate in data link layer. Same
DATP is used for CETAR.
We assume link error-free environment since the relationship between signal attenuation
and error rate is complex. Thus, all signals are assured 100 % to be delivered to the next
hop.
Figure 4.1 - Relation of transmission range between fixed and adjustable power
51
4.1.2 Common Experimental Settings
The number of nodes in the network ranges from 400 to 4800 nodes while its
density is kept constant. For instance, the geometric area of a 600 node network is 1200
units by 1200 units square. This means one node exists per 2400 units2 of area. This node
density will be used for all evaluations of network configurations. To keep the density of
the number of nodes constant, the size of the sensor network area is doubled when the
number of nodes is doubled. After the number of nodes to be deployed in the sensor field
of simulator was entered, the length of the edge of the geometric area in the simulator is
automatically calculated as 20 6 p where p is the number of nodes in the network. If p
is 600, for instance, the area will be 1200 unit square. The energy level of each node is
initialized to 1 joule. For the GEAR with a fixed transmission power, 0.001 joule is
consumed for either transmission or reception of a packet. For the GEAR/CETAR with
DATP, the energy required for transmitting a packet can range from 0 to 0.001 joule
while the receiving energy is always fixed as 0.001 joule.
Node’s transmission range is fixed at a 100 unit distance across all simulations
for the GEAR. For GEAR/CETAR with DATP, transmission range was changed based
on the distance to the next node located within the maximum transmission range D. In
this experiment, an interference-free environment is assumed. That is, only the
transmission power to send for distance Di instead of Di+Dj is required. Value of α=0.5 is
used in equation (5) as used in [8]. Link error rate was ignored in this simulation
experiment since the experiments in [8] did not use link error metrics. Our intention was
to measure the optimal performance gain of the proposed schemes over GEAR.
52
4.1.3 Type of Traffics for CETAR
The following types of traffic to generate packets are used in our simulator.
1) Uniform traffic
Pairs of source nodes and target regions are uniformly distributed
throughout the entire network. Ten source and target region pairs are randomly
selected and paired with each other. This experiment measures the performance
of the network with applications requiring relatively uniformly distributed
communication patterns.
Figure 4.2 - 10 given source and target region pairs
2) Non-uniform traffic (a cluster of 10 moderately close nodes)
Source nodes are clustered so as to concentrate part of the traffic. An
initial source node is selected randomly out of all nodes in the network. Then the
rest of source nodes are randomly selected out of 29 nodes which are the closest
to the initially selected source node. Then, 10 target regions are randomly
selected and paired with the source nodes. This experiment measures the
53
performance of the network where active source nodes are moderately close to
each other. Such conditions exit in many circumstances in the real WSNs.
Figure 4.3 - 10 sources out of 30 source candidates
3) Non-uniform traffic a cluster of 10 closest nodes)
Source nodes are clustered so as to concentrate part of the traffic. An
initial source node is selected randomly out of all nodes in the network. Then a
set of nodes which are the closest to the initial source node is selected to form a
cluster of 10 source nodes. Ten target regions are randomly selected and paired
with the source nodes. This experiment measures the performance of the
network where active source nodes are adjacent to each other.
54
Figure 4.4 - 10 sources out of 10 source candidates
In the following sections, we conduct the high level simulations of GEAR,
GEAR with DATP, CETAR, and CETAR with DATP. We are only interested in how
packet can be routed to a target region, and the packet dissemination portion of GEAR is
not considered in our experiment. Experiments are conducted to measure the number of
packets successfully delivered to the target regions before the network partitioning in
both uniform and non-uniform traffic environments. The network is partitioned if all the
given sources are partitioned from their respective destinations as described in the Figure
4.5. This figure shows that the source node has no way to deliver its packet to the target
region because of the energy-depleted nodes (shown in gray).
55
Figure 4.5 - The network partitioning
4.2 Evaluation for CETAR with BCE
4.2.1 Experimental Setting for BCE
In this experiment, BCE of equation (1) described in the section 3.2 is
incorporated into the residual energy information of GEAR. The value of β is set to 0.9 in
section 4.2.2 intuitively since this value is good enough to avoid the data sending node
from routing activities. For example, when β is set to 0.9, the weight of data sending
activities is nine times larger than that of routing activities. The effect of this value is
further discussed in the section 4.2.3. Experimental assumptions and settings are the same
with the section 4.1.1., and traffic generators used in this experiment are the same with
4.1.2. We conducted the simulation experiment for 50 times on the network consisting of
400, 600, and 800 nodes; 30 times on the network consisting of 1200 and 2400 nodes;
and 20 times on the network consisting of 4800 nodes. Because of time constraint, the
number of measurements conducted in the evaluations for each node density is different.
56
For example, to conduct one simulation on 4800-node network, it takes 20 minutes on the
author’s machine (Pentium M 1.6 GHz, 1GB RAM). The experimental result which has
outside the range of 95% confidence interval from the average of experimental result is
eliminated.
4.2.2 Evaluation for BCE
Figure 4.7 shows the experimental result of the number of packets delivered
successfully before the network partitioning for the uniform traffic experiment. This
figure shows the relation between the node density and the total number of packets sent
from 10 randomly chosen sources to the target regions. Theoretically, the maximum
number of packet transmissions for the GEAR without transmission power control will be
10000 since each node has 1 joule (10000-unit) energy and they consume one unit of
energy per transmission or reception of a packet. Therefore, the results of GEAR and
CETAR with BCE have almost the similar results and do not exceed 10000 packets
delivery. GEAR with DATP can send 48.2% more packets on average than the GEAR
without transmission power control. The result indicates that significant energy saving
occurs at each node by dynamically adjusting transmission power. Furthermore, the
CETAR with BCE and DATP can send 70.5% more packets on average than the GEAR.
57
Figure 4.6
Figure 4.7 - CETAR with BCE (uniform traffic)
Figure 4.8 shows the results of the number of packets successfully delivered
before network partitioning for the non-uniform traffic experiment using a cluster of 10
out of 30 closest senders. In this experiment, CETAR with BCE can send 10.9% more
packets than GEAR. GEAR with DATP and CETAR with BCE and DATP can send on
average of 45.6% and 62.3% more packets, respectively, than GEAR.
58
Figure 4.8 - CETAR with BCE (non-uniform traffic: A cluster of 10 out of 30 closest senders)
Figure 4.9 shows the result of the number of packets successfully delivered
before network partitioning for the non-uniform traffic experiment using a cluster of 10
closest senders. In this experiment, CETAR with BCE can send 12.1% over GEAR.
GEAR with DATP and CETAR with BCE and DATP can send on average of 43% and
61.6% more packets, respectively, than GEAR.
59
FigureFigure 4.9 - CETAR with BCE (non-uniform traffic: A cluster of 10 closest senders)
Our experiments demonstrate that aggressively preserving active source nodes is
effective for extending the lifetime of WSNs regardless of network size. The result of the
experiment evaluating the performance of CETAR with BCE is summarized in the Table
1.
Table 1 - The average percentage of packet sent over GEAR for CETAR with BCE
Uniform
Nonuniform
(10/30)
Nonuniform
GEAR
CETAR with BCE
GEAR with DATP
0%
(9579)
0%
(5121)
0%
0%
(9529)
10.9 %
(5681)
12.1 %
48 %
(14206)
45.6 %
(7458)
43 %
CETAR with BCE
and DATP
70 %
(16338)
68.9 %
(8652)
61.6 %
60
(10/10)
(4815)
(5399)
(6890)
(7785)
4.2.3 Effectiveness of β for BCE
In this section, we investigate which value is most effective for CETAR with
BCE. Value of β (0.6, 0.7, 0.8, 0.9, and 0.95) is evaluated. If β become less than 0.5, the
weight of routing activity becomes higher than that of sending activity. We do not use β
which is less than 0.6 because our objective is to investigate the effect of
biased-consumed-energy type in data originating activities. This experiment is similar to
the experiments conducted in the section 4.2.1. The only difference is that we repeat
different value of β for each experiment.
Figure 4.10 shows the relation between the node density and the total number of
packet to be sent from 10 randomly chosen sources and target region pairs by using BCE
with several types of β. The simulation result of GEAR and GEAR with DATP is the
same with the section 4.2.1 in the Figure 4.10. Results of CETAR with BCE and DATP
for all β experimented exceed the result of GEAR and GEAR with DATP. CETAR with
BCE and DATP when β=0.6 and 0.95 have the worst result among measured β. CETAR
with BCE and DATP when β=0.7 and 0.9 have the second or nearly best results, and the
CETAR with BCE and DATP when β=0.8 has the best result.
61
Figure 4.10 - CETAR with BCE for different β (uniform traffic)
Figure 4.11 shows the result of the number of packets successfully delivered
before network partitioning for the non-uniform traffic experiment using BCE with a
cluster of 10 out of 30 closest senders. The results show the same trend as those of the
previous experiment as shown in Figure 4.10 Results of CETAR with BCE and DATP
for all β experimented exceed the result of GEAR/GEAR with DATP. CETAR with BCE
when β=0.6 and 0.95 have the worst result among measured β. CETAR with BCE and
DATP when β=0.7 and 0.9 have the second or nearly best results ,and the CETAR with
BCE and DATP when β=0.8 has the best result.
62
Figure 4.11 - CETAR with BCE for different β (non-uniform traffic: A cluster of 10 out of 30
closest senders)
Figure 4.12 shows the result of the number of packets successfully delivered
before network partitioning for the non-uniform traffic experiment using BCE with a
cluster of 10 closest senders. Most of tendencies are the same with previous experiment.
However, β=0.9 instead of β=0.8 has the best results. This result indicates that β=0.8 may
not be large enough for a sender node in a tight cluster to avoid participation to routing
activity since one or more sender nodes are adjacent to the sender.
63
Figure 4.12 - CETAR with BCE for different β (non-uniform traffic: A cluster of 10 closest
senders)
From the above experiments, we successfully investigated how the value of β
effects the simulation of CETAR with BCE. The result is sensitive based on the used
traffic model. So far we mentioned CETAR with BCE. We are going to evaluate CETAR
with AABCE in the next section.
4.3 Evaluation for CETAR with AABCE
4.3.1 Experimental Setting for AABCE
In this section, we evaluate CETAR with AABCE. BCE used in the previous
section is replaced with AABCE of equation (3) in the section 3.2. Experimental
64
assumptions and setting is the same with the section 4.1.1, and the traffic generators used
in this experiment are also the same with 4.1.2 except for the section 4.3.4.
4.3.2 Evaluation for AABCE
Figure 4.13 shows the relation between the node density and the total number of
packet sent from 10 randomly chosen sources to the target region pairs by using AABCE.
Theoretically, the maximum number of packet transmissions for the GEAR without
transmission power control will be 10000 as described in 4.2.2. Thus, GEAR and
CETAR with AABCE has almost the same result. However, GEAR with DATP and
CETAR with AABCE and DATP made clear difference. GEAR with DATP can send
over 48% more packets on average than the GEAR without transmission power control
throughout all sizes of networks measured. Furthermore, the CETAR with AABCE and
DATP can send over 80% more packets on average than the GEAR throughout all sizes
of networks measured.
65
Figure 4.13 - CETAR with AABCE (uniform traffic)
Figure 4.14 shows the result of the number of packets successfully delivered
before network partitioning for the non-uniform traffic experiment using AABCE with a
cluster of 10 out of 30 closest senders. In this experiment, CETAR with AABCE can
send 22.1% over GEAR. On the other hand, GEAR with DATP can send 45% more
packets on average than the GEAR without transmission power control throughout all
sizes of networks measured. Furthermore, the CETAR with AABCE and DATP can send
over 83.2% more packets on average than the GEAR throughout all size of networks
measured.
66
Figure 4.14 - CETAR with AABCE (non-uniform traffic: A cluster of 10 out of 30 closest
senders)
Figure 4.15 shows the result of the number of packets successfully delivered
before network partitioning for the non-uniform traffic experiment using AABCE with a
cluster of 10 closest senders. In this experiment, CETAR with AABCE can send 23.3%
over GEAR. On the other hand, GEAR with DATP can send 43% more packets on
average than the GEAR without transmission power control throughout all sizes of
networks measured. Furthermore, the CETAR with AABCE and DATP can send over
79.4% more packets on average than the GEAR throughout all size of networks
measured.
67
Figure 4.15 - CETAR with AABCE (non-uniform traffic: A cluster of 10 closest senders)
As a conclusion, all experiment about AABCE is improved compared to BCE as
described in the Table 2 and Table 3. For uniform traffic, CETAR with AABCE and
DATP can send 5.8% more packet than CETAR with BCE and DATP on average. For
the non-uniform traffic with 10 sources out of 30 sender candidate, CETAR with AABCE
can send 10% more packet than CETAR with BCE on average and CETAR with AABCE
and DATP can send 9.2% more packet than CETAR with BCE and DATP on average.
For the non-uniform traffic with a 10 closest nodes, CETAR with AABCE and DAPT
can send 9.9% more packet than CETAR with BCE and DATP on average and CETAR
with AABCE and DAPT can send 11.1% more packet than CETAR with BCE and DATP
on average. At this time, we used equation (3). β in equation (3) is weighted by the square
root. For further investigation, we evaluate the effect of weighting functions as a
68
coefficient of β in the section 4.3.3 and evaluate the adaptability of AABCE over BCE by
using different types of traffic generator named two-stage source node selection in the
section 4.3.4.
Table 2 - The percentage of packet sent over GEAR for CETAR with AABCE
Uniform
Nonuniform
(10/30)
Nonuniform
(10/10)
GEAR
CETAR with AABCE
GEAR with DATP
0%
(9579)
0%
(5121)
0%
(4815)
0%
(9613)
22.1 %
(6257)
23.3 %
(5939)
48 %
(14206)
45.6 %
(7458)
43 %
(6890)
CETAR with AABCE
and DATP
70 %
(17300)
83.2 %
(9386)
79.4 %
(8625)
Table 3 - Improvement of CETAR with AABCE over CETAR with BCE (1)
Uniform
Nonuniform
(10/30)
Nonuniform
(10/10)
CETAR with AABCE over
CETAR with BCE
0%
CETAR with AABCE and DATP over
CETAR with BCE and DATP
5.8 %
10 %
8.3 %
9.9 %
11.1 %
4.3.3 Effectivity of Weighting Functions for AABCE
In this section, the effectiveness of utility functions as a coefficient of β in
equation (3) is evaluated. In equation (3), square root is incorporated with the weighting
factor β. Additional utility functions are employed for CETAR with equation (3) and
compared to each other.
Figure 4.16 shows the relation between the node density and the total number of
packet sent from 10 randomly chosen sources to the target region pairs by using AABCE
with eight kinds of weighting functions. In this figure, AABCE with
 performs the
69
best in many cases. Occasionally, AABCE with
AABCE with
1  (1   ) 2 performs better than
 AABCE without weighting function performs the worst. AABCE

with arctan(   ) performs the second worst.
2
Figure 4.16 - CETAR with AABCE with utility functions (uniform traffic)
Figure 4.17 shows the result of the number of packets successfully delivered
before network partitioning for the non-uniform traffic experiment using AABCE with a
cluster of 10 out of 30 closest senders for eight weighting functions. The trend of the
results are identical to that of previous experiment shown in the Figure 4.16.
70
Figure 4.17 - CETAR with AABCE with utility functions (non-uniform traffic: A cluster of 10
out of 30 closest senders)
Figure 4.18 shows the result of the number of packets successfully delivered
before network partitioning for the non-uniform traffic experiment using CETAR with
AABCE with a cluster of 10 closest senders for eight weighting functions. In this
experiment, AABCE with
reason why AABCE with
1  (1   ) 2 performed the best. It is conceivable that the
1  (1   ) 2
performed the best is due to its network
configuration. In this experiment, source nodes are formed as a cluster as each node
adjoins each other. Therefore it is possible that sources cannot avoid using their neighbor
node from routing activities frequently. However, since AABCE with 1  (1   ) 2
highly preserve source node from using routing even when β is small as shown in Figure
71
4.18, it prevents adjacent source nodes from frequently using routing activities from the
initial stage.
Figure 4.18 - CETAR with AABCE with utility functions (non-uniform traffic: A cluster of 10
closest senders)
As a conclusion, AABCE with

performs the best. AABCE with

arctan(   ) which has less steep weighting function moderately increased their
2
experimental result compared with AABCE without square root. On the other hand, the
result of AABCE with
1  (1   ) 2 which has steep weight curve got better than
AABCE with  when highly non-uniform (10 sources out of 10 source candidates)
traffic is used. From the following result, we can conclude that AABCE with
 works
72
the best. On the other hand, too steep or moderate (linear) weighting function works
worse except for the particular situation.
4.3.4 Effectiveness of AABCE over BCE in Dynamically Non-Uniform Traffic
In this section, we verify if the AABCE is effective or not over BCE for
dynamically non-uniform traffic described in section 3.1. Two cases whose lifetime of
sending node would be susceptible to the effect of the value β are created.
1) Case 1
Nodes and target regions are uniformly distributed throughout the entire
network. From those nodes, (the number of nodes in the system)/10 of nodes are chosen
as the sender candidates. For each packet transmission in the simulation, one sender node
is chosen from the source node candidates and paired with one target regions. After all
sender candidates consumed 20% of their energy for sending activities, fixed set of 10
senders is chosen (those senders can send their packet for any target regions) and the
simulation is continued.
2) Case 2
A cluster of sender candidates which consists of 30 adjacent nodes is formed at
first and 10 sender nodes are chosen from the cluster. Those 10 sender nodes are matched
with any target regions for every packet transmissions. After all senders consumed 20%
of their energy for sending, we select a disjoint set of 10 sender node from the same
cluster of 30 adjacent nodes. and simulation is continued. Even 20% of energy is
consumed for sending, the value of
0.2 is relatively high (=0.447). By using this
biased β, original senders are prevented from routing activities well. For the CETAR with
73
BCE in this experiment, we used β=0.9 for the consistency to compare the experimental
result acquired in section 4.2.2.
Figure 4.19 shows the result for case 1. CETAR with AABCE performed better
than CETAR with BCE and CETAR with AABCE and DATP performed better than
CETAR with BCE and DATP. The numerical result of comparison among those
experiments is shown in the Table 4 as “Uniform”. CETAR with AABCE can send 16%
more packet than BCE. Moreover, AABCE with DATP can send 6.9% more packet than
BCE with DATP compared to the 5.8% improvement of section 4.3.2.
Figure 4.19 - CETAR with AABCE for 2 stage sender selection (uniform traffic)
Figure 4.20 shows the result of case 2. CETAR with AABCE performed better
than CETAR with BCE, and CETAR with AABCE and DATP performed better than
CETAR with BCE and DATP. The numerical result is shown in the Table 4 as
74
“Nonuniform (10/30)”. CETAR with AABCE can send 23.5% more packet than CETAR
with BCE compared to the 10% improvement of section 4.3.2. Moreover, CETAR with
AABCE and DATP can send 21.1% more packet than CETAR with BCE and DATP
compared to the 8.7% improvement of section 4.3.2.
Figure 4.20 - CETAR with AABCE 2 stage sender selection (non-uniform traffic: A cluster of 10
out of 30 closest senders)
The improvement of result from CETAR with BCE to CETAR with AABCE is
slightly better than the section 4.3.2. for the uniform traffic as described in the Table 5
because the traffic is well distributed and sender nodes are easily avoided from routing
activities even when there are many highly weighted senders in the system. On the other
hand, non-uniform traffic of CETAR with AABCE improved significantly over CETAR
75
with BCE that is 10% or more. This is because, the clustered senders have more chance
to directly avoid using active data originating node from routing activities because they
are adjacent to each other. The total number of packet sent is decreased in those
experiments since the number of sender nodes is fixed in the middle of the experiments.
Table 4 - The average of experimental result in Figure 4.19 and Figure 4.20
Uniform
Nonuniform
(10/30)
CETAR with
BCE
9482
CETAR with
AABCE
11009
CETAR with BCE
and DATP
15852
CETAR with AABCE
and DATP
16948
4212
5206
6706
8137
Table 5 - Improvement of CETAR with AABCE over CETAR with BCE (2)
Uniform
Nonuniform
(10/30)
CETAR with AABCE over
CETAR with BCE
16 %
CETAR with AABCE and DATP over
CETAR with BCE and DATP
6.9%
23.5 %
21.1 %
76
Chapter 5
Conclusion and Future Works
Consumed-energy-type-aware routing (CETAR), proposed in this thesis preserves
the energy of active source nodes by discouraging them to participate for routing tasks.
CETAR uses statistics of the energy consumed for each type of node activities including
sensing,
data
processing,
data
transmission
as
a
source
node,
and
data
receiving/transmission as a routing node for routing decision. In particular, CETAR
selects a node with high residual energy which seldom plays a role of source node as a
routing node. Idea is to maintain the energy of active source nodes to prolong the
functionality of the WSNs.
Two types of CETAR are proposed. Biased consumed energy (BCE) is derived
based on a bias factor, β, of pre-fixed value that can be applied to discourage an active
source node from joining a routing path. Aggressively-and-Adaptively BCE (AABCE) is
derived based on β whose value can be dynamically updated based on the amount of
energy consumed by sending and routing activities. Simulation model is developed to
evaluate the relative performance improvement of the CETAR over geographic and
energy aware routing (GEAR). Simulation results demonstrated that the lifetime of the
CETAR based on BCE improves that of the GEAR for all experiments conducted.
Furthermore, that of the CETAR based on AABCE further improves that of the CETAR
based on BCE. Our simulation evaluations demonstrate that CETAR can significantly
extend the lifetime of WSNs especially for non-uniform traffic in which sender nodes are
77
closely clustered and a active source is highly likely to have to route packets of its
neighbor if CETAR is not available.
5.1 Contributions of this research
This research addressed the new energy-aware routing scheme to extend the
lifetime of WSNs. The main contributions of my research presented in this thesis are as
follows:
1) A new metric used for energy aware routing to prolong the lifetime of
WSNs is proposed and investigated.
2) The adaptability of CETAR is demonstrated for a wide range of network
topology which use residual energy information for deciding routing path.
3) Dynamic power control is incorporated on GEAR simulator proposed by Yu
et al. in [8] and its performance gain is evaluated.
4) The effect of the new metric is evaluated using GEAR simulator.
5.2 Discussions for Future Works
Further evaluation is needed to obtain more accurate performance gain of the
proposed mechanisms by increasing the number of experiments. We intend to further
investigate the performance of the proposed energy-aware metric in a more dynamic
environment including interference and the energy consumptions incurred by the
exchange of information among neighboring nodes. Furthermore, we intend to
incorporate CETAR to different EAR simulators other than GEAR to enhance the
validity of its adaptability demonstrated in section 3.6.
78
Though CETAR with AABCE outperformed that with BCE, AABCE does not
always put appropriate biased cost for a sending activity in many cases. Suppose a node
consumes a great amount of energy for routing activities at first, and it suddenly becomes
active as a sender node. In this case, this node cannot immediately be prevented from
routing activity because the value of β would be significantly low due to the energy
consumed for routing in the past based on equation (2). One possible solution is to
consider not only the total statistics of consumed-energy type but also recent statistics of
consumed-energy type to control the bias factor.
79
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