ANTSENS – AN ANT BASED ROUTING PROTOCOL FOR
LARGE SCALE WIRELESS SENSOR NETWORKS
by
Balaji Polepalli Ramesh
A Thesis submitted in
Partial Fulfillment of the
Requirements for the Degree of
Master of Science
in Computer Science
at
The University of Wisconsin-Milwaukee
August 2009
ANTSENS – AN ANT BASED ROUTING PROTOCOL FOR
LARGE SCALE WIRELESS SENSOR NETWORKS
by
Balaji Polepalli Ramesh
A Thesis submitted in
Partial Fulfillment of the
Requirements for the Degree of
Master of Science
in Computer Science
at
The University of Wisconsin-Milwaukee
August 2009
_____________________________________________________________
Major Professor
Date
Graduate School Approval
Date
ii
ABSTRACT
ANTSENS – AN ANT BASED ROUTING PROTOCOL FOR
LARGE SCALE WIRELESS SENSOR NETWORKS
by
Balaji Polepalli Ramesh
The University of Wisconsin-Milwaukee, 2009
Under the Supervision of Professor Mukul Goyal
Large wireless sensor networks that consist of thousands of that nodes spread over a large
geographical area are gaining popularity. They will be widely used in building
automation, home automation and many controlling applications. The nodes that will be
used in these applications have very limited capabilities in terms of computation, memory
and battery life. Dynamic characteristics of these nodes like PHY/MAC level layer loss
rates and battery levels of the nodes require continuous monitoring. These nodes will be
used in different environments and there are a set of requirements which they should
satisfy and conform with. Hence an efficient protocol that suits the requirement of large
scale low power lossy networks is necessary. The protocol should have the ability to
scale the network in terms of memory, power consumption and processing overhead and
allow the nodes to communicate with each other through other nodes. The protocol
should be able to distribute the traffic uniformly throughout the network, continuously
monitor existing routes and discover new routes through which the traffic can be reliably
sent.
iii
This thesis discusses an ant based protocol, which has the ability to scale itself as
the number of nodes in the network increase. The protocol continuously monitors and
discovers new routes and dynamically reconfigures itself with changing network
conditions.
The ant protocol is based on how ants forage for their food, travelling from their
nest to the place of food and back to the nest. This protocol adapts itself with the
changing network conditions and thus performs better. The ant based protocol was
implemented on Network Simulator 2 (NS2) to run the simulations. Then the results of
the ant based protocol are compared with that of the shortest route protocol at different
data rates.
_________________________
_
Major Professor
____________________
Date
iv
TABLE OF CONTENTS
CHAPTER 1 ...................................................................................................................... 1
INTRODUCTION............................................................................................................. 1
CHAPTER 2 .................................................................................................................... 10
RELATED WORK ......................................................................................................... 10
2.1
FEW ANT-BASED ALGORITHMS .................................................................... 10
2.2
PROTOCOLS PROPOSED FOR ROLL .............................................................. 18
CHAPTER 3 .................................................................................................................... 21
ANTSENS: THE PROPOSED ROUTING PROTOCOL FOR LARGE SCALE
WIRELESS SENSOR NETWORKS ............................................................................ 21
3.1
NEIGHBORHOOD DISCOVERY ....................................................................... 21
3.2
PACKET FORWARDING AND RECEPTION ................................................... 22
3.3
MAINTAINING END-TO-END RELIABILITY OF REACHING THE
DESTINATION ................................................................................................................ 23
3.4
PROPERTIES OF THE PROPOSED AntSens ROUTING PROTOCOL ............. 25
3.4.1 MEMORY REQUIREMENTS .......................................................................... 25
3.4.2 ROUTING LOOPS ............................................................................................ 26
3.5
USE OF BROADCAST/MULTICAST ................................................................. 26
3.6
MULTIPLICATIVE ACCUMULATION OF LINK RELIABILITIES ............... 27
v
CHAPTER 4 .................................................................................................................... 28
SIMULATION BASED PERFORMANCE EVALUATION ..................................... 28
4.1
TOPOLOGY, EXPERIMENTAL SETUP AND ASSUMPTIONS ...................... 28
4.2
IMPACT OF THE FREQUENCY OF ACKR PACKETS ON CONVERGENCE
TO STEADY STATE ....................................................................................................... 32
4.3
AntSens VERSUS MINIMUM HOP ROUTING .................................................. 38
CHAPTER 5 .................................................................................................................... 39
CONCLUSION ............................................................................................................... 39
REFERENCES ................................................................................................................ 40
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LIST OF FIGURES
Figure 1. 1 : Pictorial Representation of ants foraging food ......................................... 7
Figure 4. 1 : Characteristics of 100 node topology used in the simulation ................ 30
Figure 4. 2 : Impact of Frequency of ACKR packets on the convergence of end-toend loss rates to their steady state values...................................................................... 34
Figure 4. 3 : Impact of the frequency of ACKR packets on the end-to-end loss rate
for each node ................................................................................................................... 37
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ACKNOWLEDGEMENTS
I extend my immeasurable gratitude to my advisor, Dr. Mukul Goyal, Ph.D, Assistant
Professor, Department of Computer Science, University of Wisconsin, Milwaukee for his
continued advice and support not only towards the completion of the thesis but also for
the guidance throughout the course of my program.
I thank Dr. Hossein Hosseini, Ph.D, Chair, Department of Computer Science, University
of Wisconsin,Milwaukee for upholding his direction and being involved in activities
targeted at the best interests of students.
I further thank Dr. Brian Armstrong, Ph.D, for his presence and valuable suggestions on
the thesis committee.
I dedicate my sincere gratitude to all the faculty of the Computer Science department who
have motivated me towards learning through their excellent instruction and
encouragement.
I offer earnest gratitude to my family for the never ending reassurance and comfort they
have given throughout my program.
I give all my dear and cherished friends credit for having kept me optimistic and buoyant
every day of me being a student at University of Wisconsin, Milwaukee
viii
1
CHAPTER 1
INTRODUCTION
Wireless devices are rapidly replacing existing wired infrastructure due to
their ease of use, ease of installation, portability and accessibility in many control and
monitoring applications. New monitoring and control applications based on large
scale wireless sensor networks are being conceptualized [1]. These applications would
involve a large number of wireless sensor nodes that spread over a vast geographical
area [2]. These nodes, which are usually battery powered, are limited in their
capability in terms of processing and memory. These wireless sensor nodes can
communicate only with other nodes in their radio range. But they also need to
communicate with nodes that are not in their radio range. This makes it necessary for
the sensor nodes to run a routing protocol which will help the source node
communicate with the destination node over a multi-hop path through other nodes in
the network.
There are several flavors of routing protocol. There are link state routing
protocols [3]-[5] which require each node maintains a map of all nodes that it can
reach and best routes through which it can reach them in the network. When a
network link state changes, the node floods the Link State Advertisement (LSA) and
all the nodes in the network recalculate the best route to reach one another in the
network. The link state routing protocols support the idea of area, where all the
routers in the same area share the same view of that area.
2
In contrast, the distance vector protocol [6]-[9] requires each node to
periodically exchange information regarding routing and topology changes with its
immediate neighbors and the nodes calculate the lowest cost for reaching other nodes
in the network.
There are protocols that are proactive, where each node maintains the route to
other nodes in the network irrespective of whether they will be needed or not and
some of the protocols are reactive or on-demand, where a route is discovered only
when there is a need for it to be discovered.
Internet Engineering Task Force (IETF) has formed a working group, called
Routing over Low-power and Lossy Networks (ROLL) [10] to standardize one or
more routing protocols for use in large scale wireless sensor networks. These low
power and lossy networks have additional constraints and requirements that other
networks typically do not possess along with already existing constraints of limited
resources and power sources which restrict the amount of traffic a protocol may
generate. These applications demand and embrace a range of heterogeneous node
capabilities and good support for specific traffic patterns.
The ROLL group has specified a set of criteria, although not sufficient and
complete, that the protocol should satisfy to evaluate its suitability to operate on large
scale wireless sensor networks. This protocol should satisfy at least all these criteria
so that it can be modified accordingly to suit the needs of particular requirements
[11]. The criteria are as follows:
3

Routing Table Scalability – This criterion indicates the scalability of the
protocol with the limited resources it has such as low power and memory. The
routing information that each node maintains should scale proportional to the
number of destinations in the network rather than the number of nodes in the
network or the number of nodes in the immediate neighborhood of the node.

Loss Response Scalability – This criterion refers to the reaction of the network
when the connectivity between the links fails. The response of the protocol to
these link failures should be limited. The protocol should always dynamically
recompute the paths and optimize the system. The protocol should not
propagate the link failure to the entire network; instead it should limit the
scope of link failure propagation to the nodes that are affected.

Control Cost Scalability – This criterion refers to the traffic of the packets that
are sent by the protocol such as route discovery, maintenance, etc.
Transmitting or receiving packets in a wireless network is a costly process and
energy is consumed for both transmission as well as reception of packets.
Wireless networks support very low data rates (popular IEEE 802.15.4
MAC/PHY protocol supports maximum data rate of 250Kbps [12]). Hence it
is more desirable to use the network capacity to transmit data packets rather
than control packets. As the control traffic increases, the energy consumption
will proportionately increase. Therefore the control traffic must be bounded by
the data rate. The route discovery/maintenance should not require network
wide broadcast of control messages.
4

Link and Node Cost – This criterion refers to how the protocol decides to
route the packets in the network. The protocol should allow characteristics of
node/link such as power, memory, ability to route other packets, processing
and management capabilities of other network devices to be taken into
consideration when discovering routes.
Adhoc On Demand Distance Vector (AODV) Protocol [7] is one such category
protocol which has the features that are required and specified by the above criterion.
In AODV, routes are discovered on demand i.e. when there is a need for a new route,
it is discovered. The source node sends Route Request packets to all the nodes. All the
other nodes except the destination node broadcasts the Route Request packets. Upon
receiving a Route Request packet, the destination node sends a Route Reply packet in
response, which travels in a backward direction of the corresponding Route Request
packet. At every node the Route reply packet accumulates the cost of traveling in that
route. Finally, when the source node receives all the Route Reply packets, it decides
which route to take based on the accumulated cost on each of the Route Reply
packets. The source node may use this information to update its routing table. Many
variants of the AODV are prevalent, where an intermediate node sends the Route
Reply if it knows the route to the destination node. AODV avoids routing loops if
there are any by using the minimum cost route.
The AODV routing is not suitable for large scale wireless sensor networks since
discovering the routes becomes costly. In worst case scenario, all the nodes in the
network may need to send the Route Request packet to discover new routes and
optimize the performance of the network with changing network conditions. Thus, it
5
would not satisfy the criteria of control cost which requires the protocol to send as
less number of packets as possible.
In this thesis, we propose a protocol based on the behavior of ants foraging for
their food. We have observed how ants find routes to their food and back to the nest.
Usually the route through which the ants travel is the shortest route from the nest.
Ants start from their nest and go in search of food. When an ant finds its food, it
travels back to its nest in the same route that it came in. Along the way these ants
deposit a substance called pheromone on the ground when they travel. Pheromone is a
volatile substance, so its concentration level decreases over time. Other ants sense this
pheromone and choose the route that has the highest levels of concentration of
pheromone. These ants also deposit pheromone on the way as they travel back. Figure
1.1 shows how ants forage food and find the shortest path from the nest to food.
(a) Ants start from the nest to find route to the food
6
(b) Ant reaches the food using the shortest path and deposit pheromone on the
way.
(c) Ant on the shortest path reaches the nest first and increase the pheromone
concentration on the shortest path
7
(d) As the shortest path has highest concentration of pheromone, all the ants use
the shortest path and it will be the only path remaining.
Figure 1.1: Pictorial representation of ants foraging for food.
The concentration level of the pheromone would be higher in the shortest path
as more ants would have travelled in this path as compared to other paths. Initially,
an ant has no preference on which path to chose and takes each of the paths with
equal probability.
However after a certain period, the ants would pick the path that has the
highest level of pheromone concentration. This would be the shortest path since more
ants would have travelled in this path than any other path in a given time interval. The
proposed ant based routing algorithm has several properties which makes it ideal for
the above specified requirements.
8

The algorithm has the capability to dynamically reconfigure itself with
changing network topology. This is done by making use of certain
number of data packets as ants which require the destination node to
send an acknowledgement back to the source node.

The Ant based routing algorithms does not exchange any routing table
information over the network and the routing is based entirely on the
local information stored in the node.

The Ant based algorithm can support multi path routing as each node
has certain number of neighbors with specified pheromone
concentration levels and the next hop is chosen based on the
concentration of pheromone. Hence it allows the node to choose
different routes each time.
Many ant based routing protocols have been proposed for traditional wired
networks [14]-[15] as well as wireless Mobile Adhoc Networks (MANETs) [16]-[19]
that have very different requirements and constraints than that of wireless sensor
networks. These protocols are discussed in the next chapter. Also several ant-based
routing protocols for wireless sensor networks have been proposed [21]-[23]. Most of
these algorithms focus on determining routes through the node which have minimum
battery consumption so as to minimize the energy consumption and maximize the
network life time. These algorithms do not meet the scalability requirements that were
9
mentioned earlier. The proposed AntSens routing protocol, is focused on providing a
simple, scalable routing protocol for the sensor nodes which provides the ability to
continuously discover new best quality routes. The proposed AntSens protocol can
enforce any local energy conservation policy. It may have; for example, a batterypowered node may refuse to forward packets originated at other nodes or periodically
go to sleep. AntSens would automatically take the local node policies into account
while determining the best quality routes available in the network.
10
CHAPTER 2
RELATED WORK
A number of routing algorithms based on ants have already been proposed.
Most of them are based on the concept of Ant Colony Optimization Algorithm (ACO),
which is a metaheuristic approach for solving computational problems based on
probability techniques. The idea behind the Ant Colony Optimization Algorithms is to
collect the routing information throughout the network by agents called ‘ants’ and use
this information for routing purposes and make other global decisions. These ants are
independently generated in a uniform concurrent manner by each node and are sent to
examine the quality of a route to a particular destination. On the way back, these ants
visit each of node they traversed in the reverse direction and update the routing
information at each intermediate node. These algorithms usually maintain a table
which holds the route’s goodness value called the pheromone value, which is
constantly updated based on the information these ants carry.
2.1 FEW ANT-BASED ALGORITHMS
Gianni Di Caro and Marco Dorigo, proposed AntNet [14], an ant routing
algorithm based on ACO. In the AntNet algorithm, each node maintains a routing
table and another table which holds network statistics about the traffic distribution
over the network. The routing table contains the goodness value normalized to one for
each destination and each next hop node. The goodness value is a measure of
11
choosing the next hop for forwarding the packet through it to reach the destination
node. These goodness values called the pheromone variables are used in stochastic
routing. The AntNet Algorithm makes use of two types of agents to find the most
optimal path: Forward Ant and Backward Ant. The source node creates forward ants
and dispatches them to the destination node. These forward ants use the heuristically
computed normalized goodness value to find the next hop. The forward ants
experience the same network conditions as normal data. The forward ants collect the
traffic distribution over the network and reach the destination. At the destination the
forward ant creates other agent, the backward ant, transfers all the network related
data collected to backward ant’s memory and dies. The backward ant takes the same
path as the forward ant but the opposite direction and reaches the source node.
Through each node the backward ant travels, it updates the additional table containing
the traffic distribution information at that node. AntNet proved that it can perform
better than many shortest path algorithms given varying traffic loads and topology.
Camara and Loureiro proposed the Global Positioning System ANT-Like
Routing Algorithm (GPSAL) [27]-[28]. It makes use of the Global Positioning System
and ant like agents for routing. Ant like agents are used to collect the data about the
network and update the network about a nodes location and GPS is used to get the
physical location of the destination node. This routing is based on the positioning
system and does not use the value of pheromone to compute the path to be taken.
Rajagopalan and Shen proposed the Adhoc Networking and Swarm
Intelligence (ANSI) [29].This protocol maintains a routing table which holds the list
of reachable nodes and the next best hop. The routing protocol also maintains a table
of pheromone values called the ant decision table. The protocol employs the forward
12
reactive ants and backward reactive ants as the mobile agents. When a node wants to
transmit data, it generates forward reactive ants and broadcasts them over the
network. The backward reactive ants then travel in the same path of the forward
reactive ants but in the reverse direction and update the value of the pheromone at
each node. When a node transmits the data packet, it chooses the path that has the
highest pheromone value and transmits the data over that path.
Gunes and Sorges proposed the Ant-Colony-Based Routing Algorithm for
Mobile Adhoc Networks (ARA) [18]. ARA is a reactive protocol. The protocol
maintains a routing table which contains the pheromone value of different paths to
reach the destination through each neighbor. The pheromone value in the table
deteriorates with time. When the value of the pheromone reaches below a certain
threshold, the node enters the sleep mode. The protocol makes use of two kinds of
mobile agents for route discovery: forward ants and backward ants. The forward and
backward ants use different sequence numbers and thereby avoid duplicate packets.
During the process of route discovery, the source and destination nodes flood the
network with forward and backward ants which update the values of the pheromone at
each of the nodes respectively. The route maintenance is done by the data packets.
The forward and backward ants are not originated further after the route discovery
phase.
Shivanjay Marwaha proposed a hybrid of ant based routing and AODV, the
Ant – AODV [30] technique, that overcome the inherent shortcomings of the AODV
technique. This technique decreases the end to end delay and latency in route
discovery and increases the connectivity between the nodes. This technique supports
on-demand route discovery to the destination node if they do not have recent route
13
entries. The use of ants increases the network connectivity (the number of destinations
for which a node has un-expired routes).This in turn reduces the amount of route
discoveries even if a node launches a RREQ (for a destination it does not have a fresh
enough route) as the probability of receiving quick replies from the nearby nodes for
the Route Request (RREQ) is very high due to increased connectivity. As the ants
constantly find better routes, the nodes send data packets in shorter routes. This
decreases the end to end delay in the network. The Ant – AODV technique uses Route
Error (RERR) packets to inform link failures in the network similar to that in AODV.
John Baras and Harsh Mehta proposed the Probabilistic Emergent Routing
Algorithm (PERA) [17]. The routing table stores the probability distribution for the
neighboring nodes. This probability associated with a neighbor reflects the relative
likelihood of that neighbor forwarding and eventually delivering the packet forwarded
to it to the destination node. The route discovery is done by two mobile agents –
forward ant and the backward ant. When a node does not have a route to the
destination, it creates a forward ant and sends it periodically on the network as long as
the route is required. The forward ant uses a structure which stores information
comprising the IP address of the source node, IP address of the destination node, a
sequence number, a hop count and a dynamically growing stack. The stack contains
information about the route traversed and the timestamp when that node was traversed
by the forward ant. Upon reaching the destination, the destination node creates a
backward ant, which uses the information in the forward ant to traverse back to the
source node. The backward node updates the probability distribution at each node on
its way. The drawback of this routing protocol is that at each node, both forward and
backward ants are broadcast which leads to huge number of duplicate ants in the
network.
14
Shabab Kamali and Jaroslav Optarny proposed an algorithm which finds an
optimum route for nodes having different transmission ranges in Mobile Adhoc
Networks (MANETs) based on Ant Colony Optimization routing and location of
nodes called the Position based Ant Colony Routing Algorithm for MANETs
(POSANT) [31].In POSANT, each node is assumed to be aware of its position or
location in the network. It is a reactive algorithm, hence routes are discovered only
when the source wants to send a collection of data packets to the destination and data
is sent only after the optimum route has been discovered and established. Information
about the position of the node is used as the heuristic value to keep the number of
forward and backward ants as low as possible. A node divides its neighbors into three
zones based on their position. Making use of location information as the heuristic
significantly reduces the time needed to establish the route between the source node
and the destination node. In addition to having a short route establishment time,
POSANT greatly reduces the number of generated control messages. POSANT has a
higher delivery rate with a shorter average packet delay than other position based
routing algorithm.
Lianggui Liu and Guangzeng Feng proposed the Ant colony based Multi-path
QoS aware Routing (AMQR) [32], an ant colony based multi-path reactive routing
protocol for Mobile Adhoc Networks. Most of the routing protocols proposed till then
were essentially single path routing protocols which loaded the links with data packets
along the shortest route from the source node to the destination node. This method
was unique because it provided a multi-path routing for the nodes in the network to
route the packets in different paths. This makes the protocol more robust to link
failures and network end to end delay. AMQR uses link disjoint multi path routing
along with swarm intelligence to distribute the traffic uniformly throughout the
15
network. AMQR establishes multiple routes and sends data packets throughout the
network utilizing multiple routes of the link-disjoint path and adapts the pheromone
value to distribute traffic. This protocol has the ability to adapt to changing network
conditions and topology in addition to supporting better QoS.
O.Hussein and T.Saadawi proposed the Ant Routing Algorithm for Mobile Adhoc networks (ARAMA) [19]. ARAMA makes use of two mobile agents: forward ant
and backward ant. When a source node wants to send data to the destination node, it
generates forward ant and sends it on the network to discover a route. The forward ant
collects not only collects basic information like hop count but also collects links local
heuristic along the route such as the node's battery power and queue delay. Once the
forward ant reaches the destination node, it is killed and a backward ant is generated
and the information from the forward ant is transferred to the backward ant. As
backward ants move in the reverse path, the intermediate nodes modify their
pheromone table values based on the grade value carried by the backward ant. The
value of grade is calculated by the backward ant. This value is a function of the path
information stored in the forward ant. ARAMA can configure itself to changing
network conditions, minimize the routing overhead by controlled flooding of the ants
into the network and increase the overall performance of the Mobile Adhoc Networks.
Laura Rosati et.al proposed the Distributed Ant Routing (DAR) [33]
Algorithm. It is an on-demand algorithm. A forward ant moves towards the
destination collecting information about identities of crossed nodes based on the value
of pheromone. Then the backward ant travels in the reverse direction and it deposits a
constant amount of pheromone on each of the links it travels. The simplicity of this
protocol can help seamless routing in networks that constitute heterogeneous
components. Each node maintains a routing table which holds weighted probability
16
values calculated on the basis of pheromone trails left by the backward ants. Node
uses the probability value to chose the next hop. When a node receives a data packet
to be transmitted to the destination, if it has a route to the destination the packet is
forwarded else the node buffers the data packets, creates forward ants and transmits
them at a constant rate. The forward ants are then forwarded on the basis of the
current nodes probability for the next hop that is stored in its routing table. Hence the
forward ants are sent out into the network in a probabilistic manner to discover new
routes, whereas the data packets are routed in a deterministic manner on the basis of
the maximum probability value stored in the current nodes routing table for the next
hop.
Xiangquan Zheng et al have proposed the Ant Based Distributed Routing
Algorithm for ad-hoc networks (ADRA) [34]. The ants move across the network
between randomly chosen pair of nodes. They deposit pheromone on the link
connecting these two random nodes. The pheromone value that is deposited is a
function of their hop distance from their source node, the quality of the link, the
congestion encountered on their journey, the current pheromones the nodes possess
and the velocity at which the nodes move. The nodes update the value of the
pheromone according to their link quality; they age the link by reducing the amount of
pheromone value with time. An ant selects the path through which it should travel at
each intermediate node based on the distribution of simulated pheromones at each
node. In order to avoid the shortcut problem and increase the convergence rate, the
parameters are given various weights to update the value of the probability. ADRA
has shown the features of distributed control while decreasing the number of call
failures.
17
Eseosa et al, have proposed a reactive protocol, the imProvised Ant Colony
Optimization algorithm for mobile Ad-hoc NETworks (PACONET) [35]. The protocol
uses two kinds of mobile agents, the Forward Ant (FANT) and the Backward Ant
(BANT). The FANTs are transmitted in a restricted broadcast manner to discover new
routes. The BANTs establish the path based on the information gathered by FANTs.
These mobile agents leave certain amount of pheromone at the time they depart from
the node. The data packets are routed on the path that has the highest pheromone
value along the path to destination stochastically. FANTs travel along the path of
unvisited nodes. They only take the path which has the highest level of pheromone
concentration if all the nodes in the routing table have been visited. Each node
maintains a routing table. Each row represents the neighboring nodes of the current
node and the columns hold all the nodes in the network. The (row, column) position
in the table holds two values
 A binary value to indicate if the node has been visited or not
 The Pheromone concentration value for the node pair
The FANT uses the value of the pheromone to determine its next hop only when all
the values in the column have been visited, thus ensuring that all the possible routes
through that node are examined before choosing the best path.
Gianni Di Caro, Frederick Ducatelle and Luca Maria Gambardella proposed
AntHocNet [16]. It is an Ant Colony Optimization Based hybrid algorithm for Mobile
Ad-hoc Networks which comprise of both proactive and reactive components. The
algorithm reactively finds the route to different nodes and proactively probes,
monitors and maintains the routes. The protocol does not maintain routes to
18
destination nodes, since these are discovered when they are needed, hence the forward
ants are sent only when there is a need for routes to be discovered. Then the backward
ants are created at the destination and as they work their way back to the source node,
they update the values in the pheromone table at each of the intermediate nodes. The
pheromone value represents the quality of that link to reach the destination. Data
packets are then routed stochastically based on the pheromone values. When data
packets are being sent, the source node periodically creates proactive forward ants and
sends them into the network. These proactive forward ants monitor and maintain the
routes. This algorithm is robust and handles link failures with either local route repairs
or by informing the preceding nodes in the path about the failure of the link.
AntHocNet outperforms AODV in terms of end-to-end delay and delivery ratio.
2.2 PROTOCOLS PROPOSED FOR ROLL
P. Thubert, T. Watteyne and Z. Shelby proposed a mechanisms using tree
structure that satisfies all the set of requirements for routing on a Low-power and
Lossy Network (LLN) for various scenarios identified by the ROLL working group
[36]. It has two phases, Tree Discovery phase and Route Dissemination phase. The
Tree discovery phase consists of finding the tree structure and a mechanism to find
the next neighbor. Each router in the tree structure stores a subset of its neighbors in
its memory. It advertises the structure of the tree below it only to the best parent so
that the protocol ends up in a simple tree representation which can be used for routing
traffic and making other decisions. The authors propose to unicast /anycast while
routing up the tree and unicast while routing down the tree. The mechanism to choose
the next best hop was left to the protocol which is developed using this mechanism. In
19
this mechanism each tree is rooted and is identified by a Tree ID. It uses a flavor of
distance vector protocol for tree discovery and forms directed graphs. The tree uses an
information structure called Tree Information Option (TIO) to do the tree discovery,
which travels down the tree. Each router decides which tree it wants to be a part of.
The Route Dissemination phase establishes the routing states and advertises the tree
structure that is formed by the Tree discovery phase. It defines a new information
structure Route Information Option (RIO) to spread the atomic route information to
the tree’s root. Hence the first stage forms the tree structure and the second stage
establishes the route from the leaf nodes to the root. Together both these stages
increase the reliability by allowing the node to discover different possible routes. The
upstream routing is done by routing the data packets through the parent nodes to avoid
loops; routing through the siblings is a possible option. The downstream routing can
be done using hop by hop forwarding or full source routing or loose source routing.
Multicasting is also supported by this mechanism.
R.Kelsey proposed the Label Information Protocol (LIP) [37] that satisfies
the set of requirements specified in the requirements survey of the ROLL working
group. LIP is an extension of the Multi-Protocol Label Switching (MPLS) in Lossy
Low Power Networks. MPLS involves the generation of a fixed length label which
acts as a representation of an IP packet’s header. The nodes use the information that is
present in the label to make routing and other global decisions. It transfers the data
between nodes with very high performance and makes it easy to create virtual links.
The LIP has two advantages

It enables quick and localized route change in response to network topology
changes.
20

It allows message fragments to traverse multiple hops without the need to
reassembled and refragmented at each hop.
The main extension of LIP over MPLS is the addition of an optional source
route and route record data to labeled packets. The lossy and low power network
makes use of these records. Thus making the labeled packets available allows the
node to use packets without losing the advantages of using label switching. LIP also
has optional methods to discover and maintain trees based on Route Information
Protocol (RIP). In summary, LIP can route packets based on either an included source
route or by using next hop information stored in a Label Map. There are three
methods for installing Label Map entries:

Out-of-scope Label distribution protocol; labels may be assigned to either
nodes or flows.

Tree discovery.

A source-routed message may optionally add its route to the Label Maps of
the forwarding nodes
LIP is compatible with both centralized and distributed routing.
Many ant-based protocols have been proposed for both wired networks and
wireless networks. In the next section we propose an ant-based algorithm for routing
in low power lossy networks that satisfy the requirements of the ROLL as described
earlier in Chapter1.
21
CHAPTER 3
AntSens: The Proposed Routing Protocol for
Large Scale Wireless Sensor Networks
The operation of the AntSens protocol consists of three tasks. They are as follows

Periodic neighborhood discovery

Packet forwarding/reception

Maintaining end-to-end reliability of reaching the destination(s).
3.1 NEIGHBORHOOD DISCOVERY
A sensor node sends a Hello message (heart beat message) via one-hop broadcast
to make its presence known to all the nodes in its radio range. The nodes which are in
the radio range are called neighbor nodes. A node may go to sleep mode to conserve
energy. When a node wakes up, it sends new hello packet is sent to all the neighbor
nodes along with the duration of time it will be in active state in the message. If a
node does not wish to be used for packet forwarding then it need not send a Hello
message to its neighbors. The hello message allows the node to keep track of all its
one-hop neighbors that are willing to forward packets and the duration the node is in
active or awake state. The protocol does not enforce the node to keep track of its
entire one-hop neighbors. A node can store a subset of its one-hop neighbors in its
memory due to possible memory constraints.
22
3.2 PACKET FORWARDING AND RECEPTION
Let the subset of neighbors that the node keeps track be represented as N.
Suppose a node s needs to send a packet, originated by some other node, to a
destination node d. For each neighbor node n ε N, the node s maintains a metric Rn,d ,
which represents the end to end reliability of forwarding a packet, going to destination
node d through the neighbor node n. The initialization and maintenance of the Rn,d
value are done by exponentially weighted moving average method which is discussed
in the later section. In the following discussion, Rn,d value is referred as just R where n
and d are obvious from the context.
If the destination node is not directly reachable, then the node forwards the
packet to an active and willing neighbor node. This neighbor node to which the data
packet is forwarded to will be henceforth referred to as the next-hop node. The nexthop node is chosen among the set of neighbors with highest R value. The data packets
are not forwarded to the node from which it received the packet or to the source node
which originated the packet. Every node in the network increments the hop-count
value present in the header by one. The packet is discarded if the value of the hopcount exceeds a certain value, thus avoiding infinite loops in the network.
The AntSens protocol distributes the traffic load throughout the network when
multiple high quality routes exist between the nodes.
23
3.3 MAINTAINING END-TO-END RELIABILITY OF
REACHING THE DESTINATION
The source node solicits an end to end acknowledgement packet henceforth
referred to as an ACK packet from the destination node for a fraction of packets that it
originates. The packets soliciting acknowledgement are called as ACK Request
(ACKR) packets. The objective of the ACKR packets is to sample the link quality and
end to end reliability of reaching the destination via each of the neighbor node. For
this purpose, the source node forwards these ACKR packets in a round robin fashion
to all active and willing nodes. However the ACKR packets are subsequently treated
in the same manner as data packets by all the other nodes to forward them to the nexthop node .i.e. the ACKR packets are treated similar to those of the data packets by
being forwarded to the node with the highest R value. On receiving an ACKR packet,
the destination node responds by sending an ACK packet to the source node in the
reverse path taken by the corresponding data packet.
The ACK packet maintains the end to end reliability value rel, initialized to 1
at the destination node. On receiving the ACK packet originated by the destination
node d through the neighbor node n, the node updates the value of rel value as well as
the Rn,d as shown in the equations below:
ack.rel = ack.rel × macSuccessRate
(1)
Rn,d = (1 − w) × Rn,d + w × ack.rel
(2)
24
where macSuccessRate is the current MAC-level success rate at the node and
parameter w < 1 is a suitable. Furthermore, a node forwards the ACK packet to the
node from which it received the corresponding ACKR packet. Hence the ACK packets
reflect the end to end reliability along the route taken by the ACKR packet originated
or forwarded by that node.
For every ACKR packet p a node generates or forwards, it needs to remember
the packet’s signature, sig(p), consisting of the original source node, the final
destination node, the sequence number of the packet, the previous node and next hop
node. The signature helps the ACK packet to be forwarded to the neighbor node from
which it received the ACKR packet. The signature also helps the node to keep track of
ACKR packets it received and eliminate any duplicate ACKR packets by discarding
them. A node periodically checks the number of signatures it stores and deletes part of
the old signatures if this number exceeds a certain threshold.
Each ACK packet carries the current value of end-to-end reliability of reaching
the destination node d through the neighbor node n along the route taken by the
corresponding ACKR packet. The loss of a huge portion of the ACK packets by these
transmissions is still acceptable as long as at least one ACK packet reaches the source
node within a reasonable amount of time t (e.g. every 10 minutes). If no ACK packets
are received in response to the ACKR packets forwarded to neighbor node n in time
interval t, then the value of the Rn,d is reduced in the following manner.
Rn,d = (1-w) * Rn,d
(3)
25
The ACKR/ACK packets can minimize the probability of their loss by making
use of the highest reliability service that is provided by the MAC layer such as
acknowledged transmission.
3.4
PROPERTIES OF THE PROPOSED AntSens ROUTING
PROTOCOL
3.4.1 MEMORY REQUIREMENTS
Let N be the set of all the nodes in the network and D be the set of all the
destination nodes in the network. Let η and Ө denote the cardinality of the sets N and
D respectively.
As the node need not keep track of all its one-hop neighbor nodes, η would be the
upper limit of the number of neighbor nodes a node keeps track. A node needs to
maintain the following things in its memory.

The set N of one-hop neighbors that the node tracks.

Rn,d , n ε N and  d ε D, will require O(ηӨ) memory.

{sig(p), n} , where sig(p) is the signature of an ACKR packet p forwarded to
node n that has not yet reached the destination node and acknowledged.
Each node restricts the number of packet signatures that it can store to some
threshold value. Therefore the total memory requirement of the node to store the
packet signatures is O(η). Thus, the memory requirement of the routing protocol is
26
O(ηӨ) or O(Ө) as η can be considered a constant. Clearly, the AntSens protocol
meets the Table Scalability requirement described earlier in Chapter1.
3.4.2 ROUTING LOOPS
The AntSens protocol can have routing loops, but these loops are not persistent.
Suppose, we consider a scenario where a node A forwards a packet to another node B.
Node B forwards this packet to node C, which in turn forwards this packet back to
node A. By imposing the limit on the hop count value ensures that the packet will
ultimately be discarded sometime once the hop count exceeds the limit. Also each
node keeps track of the signature for the ACKR packet that it has forwarded to its
neighbor nodes and has not yet been acknowledged yet. The node uses these
signatures to discard any of the duplicate ACKR packets that might be travelling in
the loop.
Since the node A does not receive the ACK packets in response to the ACKR
packets, it reduces the R value for node B. Therefore, any routing loops will not be
permanent in nature.
3.5 USE OF BROADCAST/MULTICAST
Packets forwarded via broadcast/multicast cannot utilize the MAC-level
acknowledgements and they are susceptible to loss due to MAC- level collisions and
PHY-level noise. The AntSens protocol avoids broadcast/multicast for data packets
forwarding or routing monitoring/discovery.
27
The protocol does not make use of broadcasting for route discovery and
maintenance as it hinders the scalability of the network.
One-hop broadcast is used for route discovery by sending hello packets to the
neighboring nodes.
3.6 MULTIPLICATIVE ACCUMULATION OF LINK
RELIABILITIES
Most of the traditional routing protocols such as AODV use an additive
accumulation of the link costs to determine the end to end cost of the path. Updating
the cost metric in the additive accumulation manner is not suitable for calculating
metrics like success rate (or reliability) of a path. Calculation of the reliability of the
route requires multiplication of the reliabilities of the links that constitute the route.
AntSens protocol inherently supports multiplicative accumulation of link reliabilities.
28
CHAPTER 4
SIMULATION BASED PERFORMANCE
EVALUATION
4.1 TOPOLOGY, EXPERIMENTAL SETUP AND
ASSUMPTIONS
In this section, we discuss the operation of the AntSens protocol and present
the results of comprehensive simulation based evaluation of the protocol. The AntSens
protocol was implemented on the NS2 [25] simulator. Each node in the network uses
AntSens as the protocol for routing and it operates in beaconless IEEE 802.15.4
default mode, 2.4 GHz range as the MAC/PHY layer as its configuration in the
simulations. The IEEE 802.15.4 module that was used in the NS2 is an improved
version of the native IEEE 802.15.4 module and represents an accurate
implementation of the standard specifications [26].
The topology of the network used for simulations consisted of 101 nodes
distributed over a 200m X 200m region. The positions x and y of each node was
determined individually in a uniform random manner between the range (0m, 200m)
under the constraint that the distance between any two nodes should be greater than
10m and less than 30m. This was done to ensure that the network is connected and
there are no isolated nodes in the network and the network is not partitioned i.e. a
node has at least one neighbor node in its radio range. The radio range of each node is
31.45m.
29
The simulation consisted of 100 nodes generating and forwarding packets to a
single destination using Poisson distribution at a rate of one packet per second (1pps)
and one packet for every 5 sec(0.2 pps). Figure 4.1(a) represents the topology of the
simulated network. Figure 4.1(b) shows the number of nodes having a given number
of neighbors in their radio range. Figure 4.1(c) shows the number of nodes with a
given minimum hop distance from the destination.
(a) The Topology
(b) Connectivity in the Topology
30
(c) Minimum Hop from the Destination
Figure 4. 1 : Characteristics of 100 node topology used in the simulation
The simulations performed had the following assumptions
 The PHY- layer was noise free where a packet transmission by a node is
correctly received by all the nodes that are in its radio range and was not
received by nodes outside the radio range.
 The IEEE 802.15.4 MAC layer of each node has completed its association
with some other node in the network before the start of the simulation.
The beaconless IEEE 802.15.4 MAC layer requires each and every packet it
transmits to be acknowledged except for the multicast hello packets. A packet loss
can be due to Channel access failures or Collision failures at the IEEE 802.15.4 MAC
layer [12].
31
Each packet transmitted by the node will be 133 bytes in size, the maximum size
allowed by the IEEE 802.15.4 PHY layer. This packet includes all the headers
including IEEE 802.15.4 MAC/PHY header and the AntSens header.
Here we evaluate the performance of the AntSens protocol by analyzing its
different aspects using the results obtained from the simulations. We use end-to-end
loss rate, i.e. the fraction of packets sent by a node during a particular time period
which is received by the destination node, as the performance metric. Since the
AntSens protocol was designed for use in large scale low power low rate wireless
networks where improving the reliability of receiving data from remote sensors is the
most important requirement, end-to-end loss rate was used as the performance metric .
However suitable modifications to the protocol can be done if necessary depending on
the requirement of the application (e.g. timely delivery of the data is more crucial than
to the reliable delivery of data).
The 1 pps simulations that are discussed ran for a period of 6.5 hours and the 0.2
pps simulations ran for a period of 20 hours. The simulation graphs show nodes
grouped according to their minimum hop distance from the destination. Figure 4.1(c)
shows the number of nodes with a given minimum hop distance from the destination.
The nodes in the simulations used 0.2 as the value of weight w for the “current”
reliability in Eq. 2 to update the R value.
A node sends ACKR packets to neighbor node n. If it does not receive ACK
packets back in response to the previous ACKR packets sent to neighbor node n over
last 10 minutes then it updates the R value for the neighbor node n using Eq. 3.The set
of neighbors for forwarding the packets is updated every time the R value is updated.
This set consists of neighbors whose R value is more than 90% of the maximum R
value.
32
4.2 IMPACT OF THE FREQUENCY OF ACKR PACKETS ON
CONVERGENCE TO STEADY STATE
The nodes in the network request ACK for a fraction of packets they originate
from the destination node for route monitoring and maintenance. These packets are
called ACKR packets. The destination node sends acknowledgement (ACK) packet
in response to ACKR packet. These ACK packets at each receiving node inform the
node the end-to-end reliability of reaching the destination along the route that was
taken by the corresponding ACKR packets.
A higher percentage of the ACKR packets would facilitate faster convergence
of the network to a steady state as the nodes receive more frequent reliability updates.
However higher percentage of ACKR packets would also result in higher number of
ACK packets generated in response and therefore more traffic load on the network.
This causes higher MAC level loss rate leading to increase in nodes end-to-end loss
rate.
We need to meticulously select the percentage of packets to send as ACKR
packets to strike a balance between the network’s need to converge quickly to a
steady state and minimize the increase in loss rate due to ACK packets.
33
(a) 5% ACKR, Scalable Forwarding 1pps simulations
(b) 1% ACKR, Scalable Forwarding 1pps simulations
34
(c) 5% ACKR Scalable Forwarding 0.2 pps simulations
(d) 1% ACKR Scalable Forwarding 0.2pps simulations
Figure 4. 2 : Impact of Frequency of ACKR packets on the convergence of endto- end loss rates to their steady state values
35
Figure 4.2 shows the impact of ACKR frequency on the speed of convergence
to steady state in 1pps and 0.2pps simulations. Requiring 5% of the originated packets
to be ACKR allow the network to converge to steady state in about 1 hour and 4 hours
respectively in 1pps and 0.2pps simulations. Reducing the fraction of ACKR packets
to 1% increases the convergence speed proportionately to 3 hours and 13 hours
respectively for 1pps and 0.2pps simulations. Figure 4.3(a) and Figure 4.3(b) show
how the end-to-end loss rates at steady state change with change in ACKR frequency
in 1pps and 0.2pps simulations respectively. From the figures we can infer there is an
increase in the end-to-end loss rate when the ACKR frequency was increased from
1% to 5%. The change of end-to-end loss rate is more evident in the case of 0.2pps
simulations. This is because of low loss rate values prevalent in 0.2pps simulations.
Figure 4.3(c) and Figure 4.3(d) show the MAC level loss rate calculated at each node
over the same period of time as that of end-to-end loss rate as shown in the figures
4.3(a) and 4.3(b). It is clear from figures 4.3(c) and 4.3(d) that the MAC level loss
rate is in fact more for 5% ACKR frequency than 1% ACKR frequency. Thus, a small
increase in MAC-level loss rates in low traffic load network causes much larger
increase in end-to-end loss rates than the same increase in MAC-level loss rates in
high traffic load networks.
36
(a) 5% ACKR versus 1% ACKR : 1pps Simulations
(b) 5% ACKR versus 1% ACKR : 0.2pps Simulations
37
(c) % ACKR versus 1% ACKR : 1pps Simulations
(d) 5% ACKR versus 1% ACKR : 0.2pps Simulations
Figure 4. 3 : Impact of the frequency of ACKR packets on the end-to-end loss
rate for each node
38
Clearly, the selection of ACKR frequency depends on the prevalent traffic load on
the network. Lightly loaded networks should use a lower ACKR frequency than
heavily loaded ones. It is possible to dynamically adjust the ACKR frequency based
on prevalent MAC-level loss rate at the node, which is an indicator of prevalent traffic
load in the node’s neighborhood. The simulations described below uses 1% of the
originated packets as ACKR packets.
4.3 AntSens VERSUS MINIMUM HOP ROUTING
The steady state end-to-end loss rates under AntSens routing against the endto-end loss rates obtained under the minimum hop routing were compared. In
minimum hop routing, the node forwards the packet along the minimum hop route(s)
i.e. the route which is the shortest to the destination from the source node. If multiple
neighbors lie along the route of minimum hop, then the node forwards the packets in
round robin fashion to distribute the traffic load among the multiple neighbors. Zigbee
is one such minimum cost routing protocol for wireless sensor networks, which
defaults to minimum hop routing when all the links have equal cost. It was observed
that the end-to-end loss rate of the minimum hop protocol is low compared to end-toend loss rate of the AntSens protocol. The minimum hop routing clearly performs
better than the AntSens protocol in terms of end-to-end loss rate. This might be
because the AntSens protocol does not converge to their best routes quick enough and
further investigation is required in this direction to resolve this issue.
39
CHAPTER 5
CONCLUSION
The AntSens is an ant-based routing protocol which satisfies the requirements
specified by the ROLL group. It has the ability to dynamically configure itself to
changing network conditions and topology. This protocol distributes the traffic
throughout the network. The protocol is robust; it continuously discovers new routes,
monitors and maintains existing routes. The simulation results show that a judicious
selection of frequency of packets that request acknowledgement (ACKR), which acts
as ants is necessary based on the prevailing traffic conditions. Networks with low
traffic load should use a lower ACKR frequency than the networks with high traffic
load.
The AntSens protocol does not converge quickly to their best routes, so the
end-to-end loss rates are relatively higher compared to the minimum hop scheme.
Further investigation is required to improve the speed of convergence to minimize the
loss rates.
40
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