OCO: OPTIMIZED COMMUNICATION AND ORGANIZING FOR TARGET
TRACKING IN WIRELESS SENSOR NETWORK
1. Introduction
Wireless sensor networks have significant impact to the efficiency of
military and civil applications such as environment monitoring, target
surveillance, industrial process observation, and tactical systems. In which,
target tracking is one of the most important applications of wireless sensor
networks.
So, there happen a demand of self-organizing and routing in the
network. However, the current computer network protocols could not apply to
sensor network because sensor nodes are constrained in energy supply,
performance, and band-width. Hence, optimized computation and energy
dissipation are the highest requirements to maximize lifetime of the sensor
network. Existing methods, however, suffer redundant in data and sensor
node deployment, or require complex computation on sensor node. Those
contribute to use energy inefficient or request complex calculation for sensor
nodes.
In this paper, we devise and evaluate a multiple target tracking
method, called OCO (Optimized Communication and Organizing), that ensures
equal accuracy as when all nodes are turn on. However, energy dissipation is
many times less than LEACH-based algorithm. Especially, OCO demands very
low computation on sensor nodes.
2. Survey of existing methods
a. Existing routing protocols:
(http://www.cs.umbc.edu/~kemal1/mypapers/Akkaya_Youni
s_JoAdHocRevised.pdf ,
http://vulcan.ee.iastate.edu/~kamal/Docs/kk04.pdf)
i. Data-centric protocols:
o
Direct communication:
o
How (one-hop communication): Change power of
the node transmitter to send the message
directly to destination nodes.
 Pros:
- Simple.
 Cons:
- Waste energy.
- Need auto-calibration for the
transmitter.
- Size of the monitoring zone is small.
Flooding:


How: Broadcast data to all neighbors. The
neighbors, then, continue broadcast the received
data to all their neighbors until the data reach


o
the destination or run out the message time-tolive.
Pros:
- Simple.
Cons:
- Data redundancy.
Gossip:(S. Hedetniemi and A. Liestman, “A survey of
gossiping and broadcasting in communication networks,”
Networks, Vol. 18, No. 4, pp. 319-349, 1988)

How: Try to reduce the data redundancy by
sending data to one randomly selected neighbor.

Pros:
- Reduce data overlapping compare to
flooding method.

Cons:
- Data is still overlapped
- Delay.
- Data is not guaranteed to reach the
destination.
- Inefficient energy consumption.
o
SPIN (SENSOR PROTOCOL FOR INFORMATION VIA
NEGOTIATION):(W. Heinzelman, J. Kulik, and H.
Balakrishnan, “Adaptive protocols for information
dissemination in wireless sensor networks,” in the
Proceedings of the 5th Annual ACM/IEEE International
Conference on Mobile Computing and Networking
(MobiCom’99), Seattle, WA, August 1999)

How: (Figure 1) The idea is to use meta-data (a
short description of data  small size). Before
transmission, meta-data are broadcasted to all
neighbors by advertisement message (ADV).
Interested neighbors (those who do not have the
data) retrieve the data by sending back a request
message (REQ). The node only sends the real
data to the interested neighbors.
Figure 1: SPIN phases

Pros:
- Consume energy 3.5 times less than
flooding.

Cons:
- How to make a meta-data?
- ADV messages (meta-data) are
redundant.
- Need meta-data caches at each node to
decide which new data should be
retrieved.
- Cannot guarantee the delivery of data
because if there is at least a node
between the source and the destination
that is not interested in that data, such
data will not be delivered to the
destination.
o
Directed Diffusion: (C. Intanagonwiwat, R. Govindan and
D. Estrin, "Directed diffusion: A scalable and robust
communication paradigm for sensor networks", in the
Proceedings of the 6th Annual ACM/IEEE International
Conference on Mobile Computing and Networking
(MobiCom'00), Boston, MA, August 2000)

How: (Used to query information) When the base
need information, it generates a message, called
INTEREST, including all attribute-value pairs. For
example: “Select all positions having
temperature greater than 30”. Then, the
message is sent to all neighbors. At each node,
when receiving the message, it considers if the
message is in the cache or not. If the message is
not in the cache, cache the message with
information (such as: timestamp, data rate,
duration, expiration, and etc). The reply path is
the neighbor from which the message was
received (gradient). Then, the message is
continue forwarded to all neighbors. In the case
the message is already in the cache, the cache
information (timestamp, data rate, and etc) is
compared with those values of the message to
select the best reply path. Finally, if the sensing
information meets the query (temperature
greater than 30), the answer is sent through the
reply path to reach the base. (Figure 2)
Figure 2: Directed diffusion phases


o
Pros:
- No need for maintaining global network
topology.
- Suitable for query applications.
Cons:
- On demand routing set up  Inefficient
for continuous data application as
environment monitoring.
- More computation (caching and
comparison) requirement for nodes.
- Extra overhead for data matching and
queries.
Energy-aware routing: (R. Shah and J. Rabaey, "Energy
Aware Routing for Low Energy Ad Hoc Sensor
Networks", in the Proceedings of the IEEE Wireless
Communications and Networking Conference (WCNC),
Orlando, FL, March 2002)

How: Similar to directed diffusion in the way
potential paths from data sources to the base are
discovered. However, network survivability is the
main metric of the approach. So, the cost each
path is calculated and stored in the routing table.
At each time, the reply path is selected randomly
from the routing table based on the cost of path
(the higher cost path has less probability of
selection).

Pros:
- Increase 44% of network lifetime
compare with directed diffusion.

Cons:
-
o
- On demand routing set up 
Inefficient for continuous data
application as environment monitoring.
More computation demands on node
than directed diffusion
Rumor routing:(D. Braginsky and D. Estrin, "Rumor
Routing Algorithm for Sensor Networks," in the
Proceedings of the First Workshop on Sensor Networks
and Applications (WSNA), Atlanta, GA, October 2002)

How: Rumor is another variation of Directed
Diffusion. It argues that flooding query to all
nodes as in directed diffusion is inefficient
because replying data is small. Its approach is
alternative, flooding the events if number of
events is small and number of queries is large.
To flood events through the network, the rumor
routing algorithm employs long lived packets,
called agents. When a node detects an event, it
adds such event to its local table and generates
an agent. Agents travel the network in order to
propagate information about local events to
distant nodes. When a node generates a query
for an event, the nodes that know the route, can
respond to the query by referring its event table
(Note: The idea of routing query is likely of
directed diffusion). Hence, the cost of flooding
the whole network is avoided.

Pros:
-

Cons:
-
o
Save energy significantly compare to
directed diffusion when number of
events is small.
Inefficient if number of events is large.
Hard to tune time-to-live for queries and
agents to prevent overhead.
On demand routing set up  Inefficient
for continuous data application as
environment monitoring.
Gradient-based routing:(C. Schurgers and M.B.
Srivastava, “Energy efficient routing in wireless sensor
networks,” in the MILCOM Proceedings on
Communications for Network-Centric Operations:
Creating the Information Force, McLean, VA, 2001)

How: It is another version of Directed Diffusion.
The idea is to keep the number of hops when the
interest is diffused through the network. Hence,
each node can discover the minimum number of
hops to the base, which is called height of the
node. When there are two or more next hops
with the same height, the node chooses one of
them at random. When a node’s energy drops
below a certain threshold, it increases its height
so that other sensors are discouraged from
sending data to that node.

Pros:
-

Cons:
-
Save energy compare to Directed
Diffusion.
Increase network lifetime.
Nodes have to consider height of their
neighbors before sending  exchange
asking height messages. So, when
number of neighbors is large  increase
the exchange messages  inefficient.
o
Information-driven sensor querying (IDSQ) and
Constrained anisotropic diffusion routing (CADR): ( M.
Chu, H. Haussecker, and F. Zhao, "Scalable InformationDriven Sensor Querying and Routing for ad hoc
Heterogeneous Sensor Networks," The International
Journal of High Performance Computing Applications,
Vol. 16, No. 3, August 2002 http://www2.parc.com/spl/members/zhao/stanfordcs428/readings/CollaborativeProcessing/zhao_idsq_2002
.pdf)

How: Generalized form of Directed Diffusion. Two
techniques namely information-driven sensor
querying (IDSQ) and constrained anisotropic
diffusion routing (CADR) are proposed. The idea
is to query sensors and route data in a network
in order to maximize the information gain, while
minimizing the latency and bandwidth. This is
achieved by activating only the sensors that are
close to a particular event and dynamically
adjusting data routes. The major difference from
Directed Diffusion is the consideration of
information gain in addition to the
communication cost. In CADR, each node
evaluates an information/cost objective and
routes data based on the local information/cost
gradient and end-user requirements. The
information utility measure is modeled using
standard estimation theory. IDSQ is based on a
protocol in which the querying node can
determine which node can provide the most
useful information while balancing the energy
cost. While IDSQ provides a way of selecting the
optimal order of sensors for maximum
incremental information gain, it does not
specifically define how the query and the
information are routed between sensors and the
sink. Therefore, IDSQ can be seen as a
complementary optimization procedure.

Pros:
-

o
Cons:
-
More energy efficient than Directed
Diffusion.
Too much calculation demand for nodes.
Cougar: (Y. Yao and J. Gehrke, “The cougar approach to
in-network query processing in sensor networks,” in
SIGMOD Record, September 2002)

How: The main idea is to use declarative queries
in order to abstract query processing from the
network layer functions such as selection of
relevant sensors etc. and utilize in-network data
aggregation to save energy. The abstraction is
supported through a new query layer between
the network and application layers. COUGAR
proposes architecture for the sensor database
system where sensor nodes select a leader node
to perform aggregation and transmit the data to
the gateway (sink). The architecture is depicted
in Figure 3. The gateway is responsible for
generating a query plan, which specifies the
necessary information about the data flow and
in-network computation for the incoming query
and send it to the relevant nodes. The query plan
also describes how to select a leader for the
query. The architecture provides in-network
computation ability for all the sensor nodes. Such
ability ensures energy efficiency especially when
the number of sensors generating and sending
data to the leader is huge.
Figure 3: Query plan at a leader node:
The leader node gets all the readings,
calculates the average and if it is greater
than a threshold sends it to the gateway
(sink).

Pros:
-

Cons:
-
Could save energy because the
aggregation.
Add new layer to node (query layer) ->
overhead, fault tolerant problems.
o
Need synchronization at leader node
(wait for packets from nodes before
aggregation).
Aggregation algorithm?
Acquire:(ACtive QUery forwarding In sensoR networks N. Sadagopan et al., “The ACQUIRE mechanism for
efficient querying in sensor networks,” in the
Proceedings of the First International Workshop on
Sensor Network Protocol and Applications, Anchorage,
Alaska, May 2003)

How: Similar to COUGAR, ACQUIRE views the
network as a distributed database where complex
queries can be further divided into several sub
queries. The operation of ACQUIRE can be
described as follows. The BS node sends a query,
which is then forwarded by each node receiving
the query. During this, each node tries to
respond to the query partially by using its precached information and then forward it to
another sensor node. If the pre-cached
information is not up-to-date, the nodes gather
information from their neighbors within a lookahead of d hops. Once the query is being
resolved completely, it is sent back through
either the reverse or shortest-path to the BS.
Hence, ACQUIRE can deal with complex queries
by allowing many nodes to send responses. Note
that directed diffusion may not be used for
complex queries due to energy considerations as
directed diffusion also uses flooding-based query
mechanism for continuous and aggregate
queries. On the other hand, ACQUIRE can provide
efficient querying by adjusting the value of the
look-ahead parameter d. When d is equal to
network diameter, ACQUIRE mechanism behaves
similar to flooding. However, the query has to
travel more hops if d is too small.

Pros:

Cons:
-
Solve complex queries.
No validation result for energy
efficiency.
ii. Hierarchical protocols
o LEACH: (W. Heinzelman, A. Chandrakasan and H.
Balakrishnan, "Energy-Efficient Communication Protocol
for Wireless Micro-sensor Networks," Proceedings of the
33rd Hawaii International Conference on System
Sciences (HICSS '00), January 2000)

How: There are 2 phases: set-up phase and
steady-phase.
Set-up phase: Sensors may elect themselves to
be a local cluster head at any time with a certain
probability (reason: to balance the energy
dissipation). A sensor node chooses a random
number between 0 and 1. If this random number
is less than the threshold T (optimal is 5%), the
sensor node becomes a cluster-head.
After the cluster heads are selected, the cluster
heads advertise to all sensor nodes in the
network that they are the new cluster heads.
Each node accesses the network through the
cluster head that requires minimum energy to
reach. Once the nodes receive the
advertisements, they decide which head they
belong to. The, the nodes inform the appropriate
cluster heads that they will be member of the
cluster. Finally, the cluster heads assign the time
slot on which the sensor nodes can send data to
them.
Steady-phase: Sensors begin to sense and
transmit data to the cluster heads which
aggregate data from the nodes in their clusters.
After a certain period of time spent on the steady
state, the network goes into start-up phase again
and enters another round of selecting cluster
heads.


Pros:
Cons:
-
o
Save energy.
Increase network lifetime.
LEACH assumes that all nodes have
enough power to direct communicate
with the base  Can not apply for large
areas.
LEACH cannot show how a node know if
the numbers of cluster heads reach the
threshold.
Some area has no cluster head.
Cluster heads directly communicate with
the base  Need too many channels.
Aggregation algorithm?
Power-Efficient Gathering in Sensor Information
Systems (PEGASIS): (S. Lindsey and C. S Raghavendra,
"PEGASIS: Power Efficient GAthering in Sensor
Information Systems," in the Proceedings of the IEEE
Aerospace Conference, Big Sky, Montana, March 2002)

How: An enhancement over LEACH. The basic
idea of the protocol is that in order to extend
network lifetime, nodes need only communicate
with their closest neighbors and they take turns
in communicating with the base-station. When
the round of all nodes communicating with the
base-station ends, a new round will start and so
on. To locate the closest neighbor node in
PEGASIS, each node uses the signal strength to
measure the distance to all neighboring nodes,
then, adjust the signal strength so that only one
node can be heard. The chain in PEGASIS will
consist of those nodes that are closest to each
other and form a path to the base-station. The
aggregated form of the data will be sent to the
base-station by any node in the chain and the
nodes in the chain will take turns in sending to
the base-station. The chain construction is
performed in a greedy fashion.

Pros:

-
Elimination of the overhead caused by
dynamic cluster formation in LEACH.
-
Simulation results showed that PEGASIS
is able to increase the lifetime of the
network twice as much the lifetime of
the network under the LEACH protocol.
Cons:
-
-
o
Need time and energy to estimate the
closest node.
PEGASIS introduces excessive delay for
distant node on the chain.
The single leader can become a
bottleneck.
Require dynamic topology adjustment
since a sensor node needs to know
about energy status of its neighbors in
order to know where to route its data.
PEGASIS assumes that all nodes have
enough power to direct communicate
with the base  Can not apply for large
areas.
Threshold-sensitive Energy Efficient Protocols (TEEN):
(A. Manjeshwar and D. P. Agarwal, "TEEN: a routing
protocol for enhanced efficiency in wireless sensor
networks," In 1st International Workshop on Parallel and
Distributed Computing Issues in Wireless Networks and
Mobile Computing, April 2001)

How: TEEN pursues a hierarchical approach along
with the use of a data-centric mechanism. The
sensor network architecture is based on a
hierarchical grouping where closer nodes form
clusters and this process goes on the second
level until base station (sink) is reached. The
model is depicted in Figure 4. After the clusters
are formed, the cluster head broadcasts two
thresholds to the nodes. These are hard and soft
thresholds for sensed attributes. Hard threshold
is the minimum possible value of an attribute to
trigger a sensor node to switch on its transmitter
and transmit to the cluster head. Thus, the hard
threshold allows the nodes to transmit only when
the sensed attribute is in the range of interest,
thus reducing the number of transmissions
significantly. Once a node senses a value at or
beyond the hard threshold, it transmits data only
when the value of the attribute changes by an
amount equal to or greater than the soft
threshold. As a consequence, soft threshold will
further reduce the number of transmissions if
there is little or no change in the value of sensed
attribute. One can adjust both hard and soft
threshold values in order to control the number
of packet transmissions.
Figure 4: Hierarchical Clustering in TEEN/APTEEN

Pros:
-

Cons:
-
Soft threshold will further reduce the
number of transmissions if there is little
or no change in the value of sensed
attribute.
TEEN is not good for applications where
periodic reports are needed since the
user may not get any data at all if the
thresholds are not reached.
-
o
Adaptive Threshold sensitive Energy Efficient sensor
Network protocol (APTEEN): (A. Manjeshwar and D. P.
Agarwal, "APTEEN: A hybrid protocol for efficient routing
and comprehensive information retrieval in wireless
sensor networks," Parallel and Distributed Processing
Symposium., Proceedings International, IPDPS 2002,
pp. 195-202)

How: APTEEN is an extension to TEEN and aims
at both capturing periodic data collections and
reacting to time-critical events. The architecture
is same as in TEEN. When the base station forms
the clusters, the cluster heads broadcast the
attributes, the threshold values, and the
transmission schedule to all nodes. Cluster heads
also perform data aggregation in order to save
energy. APTEEN supports three different query
types: historical, to analyze past data values;
one-time, to take a snapshot view of the
network; and persistent to monitor an event for a
period of time.

Pros:

Cons:
-
o
Overhead and complexity of forming
clusters in multiple levels, implementing
threshold-based functions and dealing
with attribute-based naming of queries.
Better than TEEN
Overhead and complexity of forming
clusters in multiple levels, implementing
threshold-based functions and dealing
with attribute-based naming of queries.
Aggregation algorithm?
Energy-aware routing for cluster-based sensor
networks: ( M. Younis, M. Youssef and K. Arisha,
“Energy-Aware Routing in Cluster-Based Sensor
Networks”, in the Proceedings of the 10th IEEE/ACM
International Symposium on Modeling, Analysis and
Simulation of Computer and Telecommunication
Systems (MASCOTS2002), Fort Worth, TX, October
2002)

How: Sensors are grouped into clusters prior to
network operation. The algorithm employs cluster
heads, namely gateways, which are less energy
constrained than sensors and assumed to know
the location of sensor nodes. Gateways maintain
the states of the sensors and sets up multi-hop
routes for collecting sensors’ data. The base
communicates only with the gateways. The
routing is based on the cost (energy
consumption, delay, and etc) of links between
node and the gateway.

Pros:

Cons:
-
o
Increase lifetime of the network.
Overhead of the cost computation and
re-routing.
Two types of sensor nodes: gateway and
sensor  Lost generality.
Self-organizing protocol: (L. Subramanian and R. H.
Katz, "An Architecture for Building Self Configurable
Systems," in the Proceedings of IEEE/ACM Workshop on
Mobile Ad Hoc Networking and Computing, Boston, MA,
August 2000)

How: The routing architecture is hierarchical
where groups of nodes are formed and merge
when needed. In order to support fault tolerance,
Local Markov Loops (LML) algorithm, which
performs a random walk on spanning trees of a
graph, is used in broadcasting. The algorithm for
selforganizing the router nodes and creating the
routing tables consists of four phases:
• Discovery phase: The nodes in the
neighborhood of each sensor are discovered.
• Organization phase: Groups are formed and
merged by forming a hierarchy. Each node is
allocated an address based on its position in the
hierarchy. Routing tables of size O(log N) are
created for each node. Broadcast trees that span
all the nodes are constructed.
• Maintenance phase: Updating of routing
tables and energy levels of nodes is made in this
phase. Each node informs the neighbors about its
routing table and energy level. LML are used to
maintain broadcast trees.
• Self-reorganization phase: In case of
partition or node failures, group reorganizations
are performed.

Pros:

Cons:
-
Save energy compare to SPIN.
Complex computations are required to
nodes.
iii. Location-based protocols
o
Minimum Energy Communication Network (MECN): (V.
Rodoplu and T.H. Ming, "Minimum energy mobile
wireless networks," IEEE Journal of Selected Areas in
Communications, Vol. 17, No. 8, pp. 1333-1344, 1999)

How: The protocol has two phases:
• It takes the positions of a two dimensional
plane and constructs a sparse graph (enclosure
graph), which consists of all the enclosures of
each transmit node in the graph. This
construction requires local computations in the
nodes. The enclose graph contains globally
optimal links in terms of energy consumption.
• Finds optimal links on the enclosure graph. It
uses distributed Belmann-Ford shortest path
algorithm with power consumption as the cost
metric. In case of mobility the position
coordinates are updated using GPS.

Pros:
-

Cons:
-
o
MECN is self-reconfiguring and thus can
dynamically adapt to nodes failure or
the deployment of new sensors
Required complex computation for
nodes.
The network is assumed to be full
connected.
Small Minimum Energy Communication Network
(SMECN): (L. Li and J. Y Halpern, “Minimum energy
mobile wireless networks revisited,” in the Proceedings
of IEEE International Conference on Communications
(ICC’01), Helsinki, Finland, June 2001)

How: SMECN is an extension to MECN. In MECN,
it is assumed that every node can transmit to
every other node, which is not possible every
time. In SMECN possible obstacles between any
pair of nodes are considered. The subnetwork
constructed by SMECN for minimum energy
relaying is provably smaller (in terms of number
of edges) than the one constructed in MECN if
broadcasts are able to reach to all nodes in a
circular region around the broadcaster. As a
result, the number of hops for transmissions will
decrease. Simulation results show that SMECN
uses less energy than MECN and maintenance
cost of the links is less. However, finding a sub-
network with smaller number of edges introduces
more overhead in the algorithm.

Pros:

Cons
-
More energy efficient compare to MECN.
-
The proposed algorithm is local in the
sense that it does not actually find the
minimum-energy path, it just constructs
a sub-network in which it is guaranteed
to exist.
Finding a sub-network with smaller
number of edges introduces more
overhead in the algorithm.
The network is still assumed to be full
connected.
-
o
Geographic Adaptive Fidelity (GAF): (Y. Xu, J.
Heidemann, and D. Estrin, "Geography-informed energy
conservation for ad hoc routing," in the Proceedings of
the 7th Annual ACM/IEEE International Conference on
Mobile Computing and Networking (MobiCom’01), Rome,
Italy, July 2001)

How: GAF conserves energy by turning off
unnecessary nodes in the network without
affecting the level of routing fidelity. It forms a
virtual grid for the covered area. Each node uses
its GPS-indicated location to associate itself with
a point in the virtual grid. Nodes associated with
the same point on the grid are considered
equivalent in terms of the cost of packet routing.
Such equivalence is exploited in keeping some
nodes located in a particular grid area in sleeping
state in order to save energy. Thus, GAF can
substantially increase the network lifetime as the
number of nodes increases. A sample situation is
depicted in Figure 5. In this figure, node 1 can
reach any of 2, 3 and 4 and nodes 2, 3, and 4
can reach 5. Therefore nodes 2, 3 and 4 are
equivalent and two of them can sleep. Nodes
change states from sleeping to active in turn so
that the load is balanced. There are three states
defined in GAF. These states are discovery, for
determining the neighbors in the grid, active
reflecting participation in routing and sleep when
the radio is turned off. The state transitions in
GAF are depicted in Figure 6. Which node will
sleep for how long is application dependent and
the related parameters are tuned accordingly
during the routing process. In order to handle the
mobility, each node in the grid estimates its
leaving time of grid and sends this to its
neighbors. The sleeping neighbors adjust their
sleeping time accordingly in order to keep the
routing fidelity. Before the leaving time of the
active node expires, sleeping nodes wake up and
one of them becomes active. GAF is implemented
both for non-mobility (GAF-basic) and mobility
(GAF-mobility adaptation) of nodes.
Figure 5: Example of virtual grid in GAF
Figure 6: State transitions in GAF

Pros:
-

Cons:
-
Simulation results show that GAF
performs at least as well as a normal ad
hoc routing protocol in terms of latency
and packet loss and increases the
lifetime of the network by saving
energy.
Considered as a hierarchical protocol
without aggregation  Having same
weaknesses of hierarchical protocol as
talk above.
o
Geographic and Energy Aware Routing (GEAR): (Y. Yu,
D. Estrin, and R. Govindan, “Geographical and EnergyAware Routing: A Recursive Data Dissemination Protocol
for Wireless Sensor Networks,” UCLA Computer Science
Department Technical Report, UCLA-CSD TR-01-0023,
May 2001)

How: The idea is to restrict the number of
interests in Directed Diffusion by only considering
a certain region rather than sending the interests
to the whole network. In GEAR, each node keeps
an estimated cost and a learning cost of reaching
the destination through its neighbors. The
estimated cost is a combination of residual
energy and distance to destination. The learned
cost is a refinement of the estimated cost that
accounts for routing around holes in the network.
A hole occurs when a node does not have any
closer neighbor to the target region than itself. If
there are no holes, the estimated cost is equal to
the learned cost. The learned cost is propagated
one hop back every time a packet reaches the
destination so that route setup for next packet
will be adjusted. There are two phases in the
algorithm:
• Forwarding packets towards the target region:
Upon receiving a packet, a node checks its
neighbors to see if there is one neighbor, which is
closer to the target region than itself. If there is
more than one, the nearest neighbor to the
target region is selected as the next hop. If they
are all further than the node itself, this means
there is a hole. In this case, one of the neighbors
is picked to forward the packet based on the
learning cost function. This choice can then be
updated according to the convergence of the
learned cost during the delivery of packets.
• Forwarding the packets within the region: If the
packet has reached the region, it can be diffused
in that region by either recursive geographic
forwarding or restricted flooding. Restricted
flooding is good when the sensors are not
densely deployed. In high-density networks,
recursive geographic flooding is more energy
efficient than restricted flooding. In that case, the
region is divided into four sub regions and four
copies of the packet are created. This splitting
and forwarding process continues until the
regions with only one node are left. An example
is depicted in Figure 7.
Figure 7: Recursive Geographic
Forwarding in GEAR

Pros:

Cons:
-
Better in term of packet delivery.
Complex computation demands for
nodes.
iv. Network flow and QoS-aware protocols
o
Maximum lifetime energy routing: (J.-H. Chang and L.
Tassiulas, "Maximum Lifetime Routing in Wireless
Sensor Networks," in the Proceedings of the Advanced
Telecommunications and Information Distribution
Research Program (ATIRP'2000), College Park, MD,
March 2000)

How: The main objective of the approach is to
maximize the network lifetime by carefully
defining link cost as a function of node remaining
energy and the required transmission energy
using that link. By using Bellman-Ford shortest
path algorithm for the above link costs, the least
cost paths to the destination (gateway) are
found. The least cost path obtained is the path
whose residual energy is largest among all the
paths.

Pros:

Cons:
-
Increase lifetime of network
Using Bellman-Ford leads to require
high-performance nodes when the
number of node is large.
o
Maximum lifetime data gathering:(K. Kalpakis, K.
Dasgupta and P. Namjoshi, “Maximum lifetime data
gathering and aggregation in wireless sensor networks,”
in the Proceedings of IEEE International Conference on
Networking (NETWORKS '02), Atlanta, GA, August 2002)



o
How: The lifetime “T” of the system is defined as
the number of rounds or periodic data readings
from sensors until the first sensor dies. The datagathering schedule specifies for each round how
to get and route data to the sink. A schedule has
one tree for each round, which is directed from
the sink and spans all the nodes in the system.
The system lifetime depends on the duration for
which the schedule remains valid. The aim is to
maximize the lifetime of the schedule. An
algorithm called Maximum Lifetime Data
Aggregation (MLDA) is proposed. The algorithm
considers data aggregation while setting up
maximum lifetime routes. In this case, if a
schedule “S” with “T” rounds is considered, it
induces a flow network G. The flow network with
maximum lifetime subject to the energy
constraints of sensor nodes is called an optimal
admissible flow network. Then, a schedule is
constructed by using this admissible flow
network.
Pros:
- System lifetime is significantly better
than hierarchical-PEGASIS.
Cons:
- Delay is slightly greater that PEGASIS.
- Computation is so complex.
Minimum cost forwarding: (M. Chu, H. Haussecker, and
F. Zhao, "Scalable Information-Driven Sensor Querying
and Routing for ad hoc Heterogeneous Sensor
Networks," The International Journal of High
Performance Computing Applications, Vol. 16, No. 3,
August 2002)

How: Minimum cost forwarding protocol aims at
finding the minimum cost path in a large sensor
network. The cost function for the protocol
captures the effect of delay, throughput and
energy consumption from any node to the sink.
There are two phases in the protocol. First phase
is a setup phase for setting the cost value in all
nodes. It starts from the sink and diffuses
through the network. Every node adjusts its cost
value by adding the cost of the node it received
the message from and the cost of the link. Such
cost adjustment is not done through flooding.
Instead, a back-off based algorithm is used in
order to limit the number of messages
exchanged. The forwarding of message is
deferred for a preset duration to allow the
message with a minimum cost to arrive. Hence,
the algorithm finds optimal cost of all nodes to
the sink by using only one message at each
node. Once these cost fields are set, there will be
no need to keep next hop states for the nodes.
This will ensure scalability. In the second phase,
the source broadcasts the data to its neighbors.
The nodes receiving the broadcast message, adds
its transmission cost (to sink) to the cost of the
packet. Then the node checks the remaining cost
in the packet. If the remaining cost of the packet
is not sufficient to reach the sink, the packet is
dropped. Otherwise the node forwards the packet
to its neighbors.

Pros:
-

o
Cons:
-
The protocol does not require any
addresses and forwarding paths.
Simulation results show that the cost
values for each node obtained by the
proposed protocol is same as flooding.
The average number of advertisement
messages in flooding could be reduced
by a factor of 50 using the back off
based algorithm with a proper setting of
the back off timer.
Computation is expensive.
Sequential Assignment Routing (SAR): (I. F. Akyildiz et
al., “Wireless sensor networks: a survey”, Computer
Networks, Vol. 38, pp. 393- 422, March 2002 & K.
Sohrabi, et al., "Protocols for self-organization of a
wireless sensor network,” IEEE Personal
Communications, Vol. 7, No. 5, pp. 16-27, October
2000)

How: the first protocol for sensor networks that
includes the notion of QoS in its routing
decisions. It is a table-driven multi-path
approach striving to achieve energy efficiency
and fault tolerance. The SAR protocol creates
trees rooted at one-hop neighbors of the sink by
taking QoS metric, energy resource on each path
and priority level of each packet into
consideration. By using created trees, multiple
paths from sink to sensors are formed. One of
these paths is selected according to the energy
resources and QoS on the path. Failure recovery
is done by enforcing routing table consistency
between upstream and downstream nodes on
each path. Any local failure causes an automatic
path restoration procedure locally.

Pros:
-

o
Cons:
-
Simulation results show that SAR offers
less power consumption than the
minimum-energy metric algorithm,
which focuses only the energy
consumption of each packet without
considering its priority.
SAR maintains multiple paths from
nodes to sink. Although, this ensures
fault-tolerance and easy recovery, the
protocol suffers from the overhead of
maintaining the tables and states at
each sensor node especially when the
number of nodes is huge.
Energy-Aware QoS Routing Protocol: (K. Akkaya and M.
Younis, “An Energy-Aware QoS Routing Protocol for
Wireless Sensor Networks,” in the Proceedings of the
IEEE Workshop on Mobile and Wireless Networks (MWN
2003), Providence, Rhode Island, May 2003)

How: The proposed protocol extends the routing
approach and finds a least cost and energy
efficient path that meets certain end-to-end delay
during the connection. The link cost used is a
function that captures the nodes’ energy reserve,
transmission energy, error rate, and other
communication parameters. In order to support
both best effort and real time traffic at the same
time, a class-based queuing model is employed.
The queuing model allows service sharing for
real-time and non-real-time traffic. The
bandwidth ratio r, is defined as an initial value
set by the gateway and represents the amount of
bandwidth to be dedicated both to the real-time
and non-real-time traffic on a particular outgoing
link in case of a congestion. As a consequence,
the throughput for normal data does not diminish
by properly adjusting such “r” value. The queuing
model is depicted in Figure 8. The protocol finds
a list of least cost paths by using an extended
version of Dijkstra’s algorithm and picks a path
from that list which meets the end-to-end delay
requirement.
Figure 8: Query model in a particular sensor node

Pros:
-

o
Cons:
-
Simulation results show that the
proposed protocol consistently performs
well with respect to QoS and energy
metrics.
How to define r-value for each node.
Computation is so expensive.
SPEED: (T. He et al., “SPEED: A stateless protocol for
real-time communication in sensor networks,” in the
Proceedings of International Conference on Distributed
Computing Systems, Providence, RI, May 2003)

How: The protocol requires each node to
maintain information about its neighbors and
uses geographic forwarding to find the paths. In
addition, SPEED strive to ensure a certain speed
for each packet in the network so that each
application can estimate the end-to-end delay for
the packets by dividing the distance to the sink
by the speed of the packet before making the
admission decision. Moreover, SPEED can provide
congestion avoidance when the network is
congested. The routing module in SPEED is called
Stateless Geographic Non-Deterministic
forwarding (SNFG) and works with four other
modules at the network layer, as shown in Figure
9. The beacon exchange mechanism collects
information about the nodes and their location.
Delay estimation at each node is basically made
by calculating the elapsed time when an ACK is
received from a neighbor as a response to a
transmitted data packet. By looking at the delay
values, SNGF selects the node, which meets the
speed requirement. If such a node cannot be
found, the relay ratio of the node is checked. The
Neighborhood Feedback Loop module is
responsible for providing the relay ratio which is
calculated by looking at the miss ratios of the
neighbors of a node (the nodes which could not
provide the desired speed) and is fed to the
SNGF module. If the relay ratio is less than a
randomly generated number between 0 and 1,
the packet is dropped. And finally, the
backpressure-rerouting module is used to
prevent voids, when a node fails to find a next
hop node, and to eliminate congestion by sending
messages back to the source nodes so that they
will pursue new routes.
Figure 9: Routing components of SPEED

Pros:
-

Cons:
-
-
SPEED performs better in terms of endto-end delay and miss ratio.
The total transmission energy is less due
to the simplicity of the routing algorithm
(i.e. control packet overhead is less).
SPEED does not consider any further
energy metric in its routing protocol.
Therefore, for more realistic
understanding of SPEED’s energy
consumption, there is a need for
comparing it to a routing protocol, which
is energy-aware.
Expensive computation.
b. Existing target tracking techniques:
(http://mnet.cs.nthu.edu.tw/paper/934355tbl/050526-Survey%20on%20Target%20Tracking%20in%20wireless%20
sensor%20newworks.pdf)
i. Tree-based:
o Scalable Tracking Using Networked Sensors (STUN): (H.
T. Kung and D. Vlah. “Efficient Location Tracking Using
Sensor Networks.” WCNC, March 2003 & Chih-Yu Lin
and Yu-Chee Tseng “Structures for In-Network Moving
Object Tracking in Wireless Sensor Networks”
BROADNETS’04 -- -- Manish Kochhal, Loren Schwiebert,
and Sandeep Gupta (2004). "Integrating Sensing
Perspectives for Better Self Organization of Ad Hoc
Wireless Sensor Networks". JOURNAL OF INFORMATION
SCIENCE AND ENGINEERING 20, 449-475 (2004))

How: Use Voronoi diagram to build a hierarchy
tree as Figure 10 and 11. The leaves are sensors,
the querying point as the root, and the other
nodes are communication nodes. The routing is
based on the tree. The communication nodes can
aggregate data to reduce redundancy.
Figure 10: Using Voronoi diagram to build graph
Figure 11: Hierarchy tree T from the graph


Pros:
Cons:
-
o
Data could be aggregated.
Routing follows the tree.
All nodes are ON  waste energy and
noise sensitive.
Nodes are supposed direct communicate
to the base (root)  Can not apply for
large area.
Data can be aggregated only when there
is one intruder.
Build the tree is so expensive when
number of nodes is large.
Dynamic Convoy Tree-Based Collaboration (DCTC):
(Wensheng Zhang and Guohong Cao, “DCTC: Dynamic
Convoy Tree-Based Collaboration for Target Tracking in
Sensor Networks” IEEE TRANSACTIONS ON WIRELESS
COMMUNICATIONS, VOL. 3, NO. 5, SEPTEMBER 2004 &
Wensheng Zhang and Guohong Cao, “Optimizing Tree
Reconfiguration for Mobile Target Tracking in Sensor
Networks” Infocom 2004)

How: DCTC relies on a tree structure called
“convoy tree”. The tree is dynamically configured
to add some nodes and prune some nodes as the
target moves. When a target shows up for the
first time, an initial convoy tree is constructed
(Figure 12). The root collects data from nodes
surrounding the target, process the data. When
the target moves, the membership of the tree is
changed. The structure of the tree is reconfigured
if necessary.
Figure 12: Using convoy tree to track the target

Pros:

Cons:
-
-
Save energy
DCTC did not mention how to detect the
target at first time. If all nodes are turn
ON  Inefficient and noise sensitive.
Nodes in DCTC can create, maintain or
reconfigure the tree  demand high
performance for the nodes and energy
consumption.
At each target moving, the tree and the
routes are re-calculated  Inefficient.
Calculation booms when there appear
many targets.
ii. Cluster-based:
o Dynamic Clustering for Acoustic Target Tracking: (WeiPeng Chen, Jennifer C. Hou, and Lui Sha, Fellow, IEEE
“Dynamic Clustering for Acoustic Target Tracking in
Wireless Sensor Networks” IEEE TRANSACTIONS ON
MOBILE COMPUTING, VOL. 3, NO. 3, JULY SEPTEMBER
2004 -- Xiang Ji, Hongyuan Zha, John J. Metzner, and
George Kesidis , “Dynamic cluster structure for object
detection and tracking in wireless ad-hoc sensor
networks” ICC 2004)

How: Based on LEACH algorithm. However, it
aims to select the cluster head (CH) that closest
to the target (ideally). To do that, it uses the
strength of a received acoustic signal to estimate
the distance from a node to target. A CH
volunteers to become active when the strength
exceeds a predetermined threshold. If there are
more than one CH volunteers, a random delaybased broadcast mechanism is used to select
one. When a CH is active, it will broadcast a
packet that contains the energy and the
extracted signature of the detected signal to
sensors, receive replies from sensors, construct
two Voronoi diagram (one for set of neighbor
sensors and one for set of neighbor CHs) to
estimate the location of the target based on
replies, and send the result to subscriber(s).

Pros:
-

Cons:
-
More energy efficiency than original
LEACH.
Estimate target position more accuracy.
Still require all node turn ON.
Noise sensitive because it estimates
distance by strength of acoustic signal.
High performance required for nodes,
especially, when number of nodes is
huge.
Still have LEACH weaknesses.
iii. Prediction-based:
o Prediction-based: (Yingqi Xu Winter, J. Wang-Chien Lee
“Prediction-based strategies for energy saving in object
tracking sensor networks” Mobile Data Management,
2004. Proceedings. 2004 IEEE International Conference
-- Xu, Y., Winter, J., Lee, W.-C. “Dual predictionbased
reporting for object tracking sensor networks”
MOBIQUITOUS 2004)

How: Cluster-based with prediction models:
-
-
Heuristics INSTANT: Current node
assumes that moving objects will stay in
the current speed and direction for the
next (T-X) seconds.
Heuristics AVERAGE: By recording some
history, the current node derives the
object’s speed and direction for the next
-
(T-X) seconds from the average of the
object movement history.
Heuristics EXP_AVG: Assigns different
weights to the different stages of
history.

Pros:

Cons:
-
Could save energy.
Have the weaknesses of the based
protocol.
Expensive computation for node.
Inaccuracy and unstable results.
Did not mention how to detect intruders
at the first time.
3. Detailed description of OCO
a. Assumptions:
i. The base station: The base station is likely a node with
unlimited energy, high performance computer, and can one
way direct communication to any node.
ii. Sensor node ID and location: Nodes are assumed having a
unique ID and knowing their location (by attached GPS or etc).
iii. Data package size: 2000-bit per data packet, 64-bit per
signal (advertising or neighbor wake-up) packet.
iv. Data rate: It is assumed 2Kbits/s.
v. Energy consumption formula:
o
Energy consumption for module: Based on the
MICA2DOT (MPR500) of UCLA as in Figure 13 (re-drawn
from
http://www.xbow.com/Support/Support_pdf_files/MPRMIB_Series_Users_Manual.pdf page 21):
Figure 13: MOTE’s specification at 38.4Kbits/s and
Vcc=3v (page 4)
From the Figure 13, we can determine that current
consumption of sensor board (Esensor) is equal to 2/3 of
the current of the radio in receive mode.
o
Energy consumption formula for transmitter:
To transmit k bits to a distance d, we need:
Etx= Eelec* k(bits) + Eamp*k(bits)*d^2
Eelc: Energy is used for transmit electronic circuit.
Eamp: Energy is used for transmit amplifier.
Figure 13 does not tell clearly about current
consumption of electronic board and the amplifier. We
get the value from LEACH simulation (
http://academic.csuohio.edu/yuc/mobile03/0403heinzelman.pdf):
Eelc = 50nJ/bit
Eamp = 100pJ/bit/m2 = 0.1nJ/bit/m2
Esensor = Epro = Eelc = 50nJ/bit
(Epro = Energy consumption for processor module. And,
we assume that the modules take zero energy in sleep
mode).
vi. Sensor node modes: There are 3 modes:
o
ACTIVE: All module of the node is turned on (Processor
= ON, Radio = ON, Sensor Board =ON/full operation). In
practical, the processor is in sleep mode, it becomes
active when having an eternal interrupt. So, in the
simulation, we assume that in ACTIVE mode, the
processor is in sleep mode. It just switches to full
operation when having an event. It means when
creating a new message, an energy as follow is
consumed:
Epro_data = 2000(bit)*50(nJ) = 100 µJ/message
Epro_signal = 64(bit)*50(nJ) = 3.2 µJ/message ~
3 µJ/message
The radio board in this mode is assumed in idle mode (~
receive mode). Hence, energv is supposed to take:
2*10^3(bit)*50(nJ) = 100 µJ/s
100 µJ * 2/3 (assumed that current of sensor board is
2/3 of radio board) = 66 µJ is also the energy for sensor
board (Esensor) at each second.
The transiver board is likely processor board
assumption. It just takes energy for each sent message:
Erecv_data = 2000(bit)*50(nJ) = 100 µJ
Erecv_signal = 64(bit)*50(nJ) = 3.2 µJ ~ 3 µJ
Etrans_data = 2000(bit)*(50(nJ) + 0.1(nJ)*d^2)
Etrans_signal = 64(bit)*(50(nJ) + 0.1(nJ)*d^2)
We assume that, the optimized distance is 60m. So:
Etrans_data = 2000(bit)*(50(nJ) + 0.1(nJ)*60*60) =
820 µJ/message
Etrans_signal = 64(bit)*(50(nJ) + 0.1(nJ)*60*60) =
26.2 µJ/message ~ 26 µJ/message
It also means that the transmitter consumes 820
µJ/message for all distances d <= 60m.
Create/Receive a data message
Create/Receive a signal message
Send a data message (d<= 60m)
Send a signal message (d<=60m)
Send a message (d > 60m)
Sensor board (full operation)
Radio board (idle mode)
Summary table
100 µJ
3 µJ
820 µJ
26 µJ
100 µJ + 0.1*d^2
66 µJ/s
100 µJ/s
o
FORWARD: In this mode, the sensor module of the node
is in sleep mode. The node only forwards all messages
received from its neighbors.
o
SLEEP: In this mode, the node is totally turned off and
almost consumes zero energy. It just turns on each long
period to processes commands from the base.
b. Collecting positions phase: (Happen at nodes)
i. Purpose: The base collects all reachable nodes in the network.
ii. How: When millions of sensor nodes are thrown randomly,
they are all in FORWARD mode. The base station assigned its
ID as a father to all nodes in its coverage (neighbor nodes) and
asks them about their positions. At the neighbors, after sending
their IDs and positions to their father (the base), they mark
itself as recognized and do as the base does with their
neighbors and so on. Note that, when a node gets the
information (position and ID) from its neighbor, it just forwards
the message to its father and by this way the message will
reach the base. So, after this step, the base got all information
about reachable nodes (ID and positions) in the network.
c. Processing phase: (Happen at the base ~ high performance
computer and unlimited energy)
i. Purpose: Clean up the redundant nodes, assign mission for
nodes, and route.
ii. How:
o Clean up the redundant nodes:
o

Define: A redundant node is a node that its
sensing coverage zone is occupied by one or
more other nodes

Algorithm:
-
Initialize a list of node that is supposed
to cover all network area (a rectangular
of xmin, ymin, xmax, ymax), called
Area_List.
-
Assign Area_List = null.
-
Add the base node to the Area_List.
-
For each point in the network area. If
the point isn’t covered by the node in
the Area_List -> Add the node that
contains the point to the Area_List.
-
Nodes aren’t in the Area_list called
redundant nodes.
Assign missions for nodes:

Define: Classify redundant nodes (SLEEP mode),
border nodes (ACTIVE mode), and forwarding
nodes (FORWARD mode). The base assigns tasks
for nodes by broadcasting with node ID.

Algorithm:
- Redundant nodes are classified in part 1.
-
Border nodes: Nodes that stay at the
border of the network area. To find out
these nodes, firstly, we build a
geographical image about the coverage
zone of the network. Then, we apply
border detection for the image to find
out a list of points that stay at the
border of the image, called border
points. Finally, find all nodes in the
Area_List that contain at least one
border point. These nodes are called
border node. See Figure 14, 15, 16 for
more detail
Figure 14: Nodes in the network area
Figure 15: Coverage zone and border.
Figure 16: Border nodes
-
o
Forwarding node: Nodes are in the
Area_List but not a border node.
Route:

Purpose: Find the shortest path (the least hops)
from every node in the Area_List to the base.

Algorithm:
- Work only with nodes in the Area_List.
-
Assign father_ID = 0 for all nodes.
-
Initialize 2 processing list, called
Process_List[2]. A boolean variable,
called active, is used to recognize which
processing list is in active.
-
Assign active = 0. Add the base to
Process_List[0]. Assign Process_List[1]
= null.
-
While Process_List[active] != null
{
Foreach node pn in Process_List[active]
{
Neighbor_list = all neighbors of pn
Foreach node nn in Neighbor_list
If(nn.father_ID == 0)
{
nn.father_ID = pn.ID
add nn to Process_List[1- active]
}
}
Process_List[active].Clear
active = 1- active
}
-
After the while loop, each node in the
Area_list has a father_ID. When a node
want to send a message to the base, it
just delivery the message to its
father_ID and so on. The algorithm
ensures that by this way, all the
messages will reach the base with
minimum hops. Figure 17 shows the
routing paths from nodes to the base by
following father_ID.
Figure 17: Routes follow father_ID.
d. Tracking phase:
Objects are assumed coming from outside. Normally, only the
border nodes are ACTIVE. When a border node detects an object, it
periodically sends its position information to the base by using
father_ID (as said above). When it lost the object, it will turn all its
neighbors (forwarding nodes) to ACTIVE (assumed that the delay time
is smaller than sensing radius / object speed). If a neighbor detects
the object, it will send its position to the base and right after it lost the
object, it turns all its neighbors to ACTIVE and so on. If activated
neighbors detect nothing, they automatically switch to the previous
state (FORWARD) after a short interval. By this way, the objects are
tracked during the time in the network area.
e. Maintenance phase: (Happen at nodes)
i. Purpose: Reconfigure the network when events happen.
ii. Define events and estimate:
o Nodes in Area_List run out energy  frequently.
o Nodes are broken suddenly  rarely.
o Nodes change position by physical events, such as
earthquake, explosives, or etc  rarely.
iii. Algorithm:
o Event No. 1: When energy level of a node is below a
threshold, it turns all its sons to SLEEP and sends a
report to the base. When the base gets the report, it
performs the processing phase with dead node are
deleted and re-assign tasks for nodes.
o Event No. 2: After an interval (long period: hours or
days), nodes require their sons to send their ID (small
size message) to them  They can detect dead node
IDs.
o Event No. 3: When a node changes position, it
automatically turns to SLEEP mode  Become event No.
2.
4. Design experiments to compare OCO to LEACH & direct
communication
a. Scenario: 200  1000 sensor nodes are thrown randomly in area of
640m x 540m. Each node has 2J (2*10^6 µJ) of energy with sensing
radius = 30m and communication radius = 60m. Intruder objects are
supposed moving specific paths. No data aggregation is allowed.
b. Utilized tools and module descriptions
i. Tools: OMNET++, C#, Matlab.
ii. Module description under OMNET++:
Sensor node module: (Figure 18)
Application
Sensor
Coordinator
Module
MAC
Radio
Figure 18: A node structure
o
Layer 0 module: Represented for physical layer. It
consists of gates (in/out) and be responsible for making
connection between the node and its neighbors. Its
behaviors include forward messages from higher layer to
its neighbors and vice versa.
o
MAC module: Represented for pre-processing packet
layers. It consists of gates (in/out) and queues
(incoming queue and outgoing queue). When the queue
is full, it deletes some latest messages to make sure
that there is enough room in the queue for new
messages. It helps to evaluate performance of the node.
(Note: In current simulation, this module is temporary
eliminated to speech up the performance)
o
Application module: Represented for application layer
consisting of gates (in/out). Note that, at anytime, after
sending a message, the module automatically sends a
DECREASE_ENERGY message to energy module
(through the coordinator) to let the module decrease the
energy by one unit.
o
Coordinator module: An interface to connect all modules
together. It categories incoming messages to delivery
them to the right module. For example, when receiving
a DECREASE_ENERGY message, it will forward the
message to energy module.
o
Sensor module: Represented for sensor board in a
sensor node. If SENSOR_SWITCH parameter is ON (=1),
the module consumes energy, so, after an interval
(timer), the module send DECREASE_ENERGY message
to the energy module (through the coordinator). When
the timer ticks, the waiting timer decreases. The waiting
timer is set by SENSOR_REFRESH messages from
application module. If the waiting timer is zero, the
module will turn off (SENSOR_SWITCH parameter is set
to 0).
o
Radio module: Represented for the radio board in a
sensor node. If RADIO_SWITCH parameter is ON (=1),
the module consumes energy, so, after an interval
(timer), the module send DECREASE_ENERGY message
to the energy module (through the coordinator).
o
Energy module: Represented for battery in a sensor
node. At the beginning, each sensor node is set to a
specific energy level (ENERGY parameter). If the module
receives a DECREASE_ENERGY message, it decreases
the energy level by one.
o
Parameters:
 CNNCTVTY: Maximum connections a node has.
 OCCUPATION: Task of the node
 PX: Position by X.
 PY: Position by Y.
 ID: ID of node.
 FATHER: ID of node for forwarding message.
 SENSING_RADIUS: Radius of zone that node can
sensing.
 COMMUNICATION_RADIUS: Radius of zone that
node can communication to.
 ENERGY: Energy level.
 SENSOR_SWITCH: Turn ON/OFF the sensor
module.
 POWER_SWITCH: Turn ON/OFF the node.
Object module: (figure 19)
objectApplication
Layer 0
Figure 19: An object structure
o
Layer 0 module: Similar to layer 0 of the sensor node.
However, the connections are re-created after each
moving (the manager module handles this task).
o
objectApplication module: The object is moved by
reading position from a text file. The sensing is
simulated by creating connection between the object
and the sensor nodes in ACTIVE mode, then, the object
sends SENSOR_INFO messages to all the connected
nodes after an interval.
Manager module: This module aims to help the
simulation. Firstly, it read the network.txt (file stores all
information of position, task, routing for all nodes) to place
the nodes. Then, it makes connections among nodes (it
checks the coverage zone of all nodes to see if any node is
in the coverage and make connections between the node
and the covered nodes). Secondly, at each time of the
object movement, it will consider if any node is in the object
zone (nodes can sense the object) to create the connection
among them so that the object can send SENSOR_INFO
messages to the nodes. Also, it manages the broadcast
from the base to all nodes (create connections from the
base to all nodes). Finally, it controls the power switch
(POWER_SWITCH parameter) for all nodes in the network.
Network module: The simulation network consists of
sensor nodes, objects, and a manager module and place in
the area of a rectangular (0, 0, xmax, ymax).
c. Apply direct communication for target tracking on the scenario:
In this case, all nodes are in ACTIVE mode and send to information
about intruders to the base by direct communicating.
d. Apply LEACH-based for target tracking on the scenario:
In this case, nodes are ACTIVE all the time and organized by LEACHbased algorithm. The number of cluster head is selected 5% (the
optimal cluster head number of LEACH) of the total number of nodes.
e. Apply OCO for target tracking on the scenario:
In this case, nodes are organized as the proposed method above.
5. Simulation results
a. Metrics
i. Energy consumption: Count the total energy consumption
after the simulation.
ii. Accuracy: The number of detected positions of methods is
compared to the number of detected positions in the case when
all sensor nodes in the network are ACTIVE.
iii. Cost per detected position: Count the ratio between Energy
consumption and number of detected positions.
b. Diagrams
The object path is supposed as Figure 20. The energy
consumption of methods is collected in the cases of 200, 400,
600, 800, and 1000 nodes (area: 640x540). The result is as
Figure 21, Figure 22, and Figure 23.
Figure 20: The testing object path
Figure 21: Energy consumption for each method
Figure 22: Accuracy of methods
Figure 23: Cost per detected point of methods
c. Explanations and comparisons
The Figure 21 shows that proposed method consumed less
energy. When the number node is smaller than 400, the border
in this case is longer so he OCO consumes more energy. When
the number node increases, the energy consumption of OCO
goes toward a threshold. Meanwhile, the energy consumption of
the direct communication and LEACH-based method increase
forever.
6. Related problems
a. Living time: The border nodes seem to drain energy first. However,
we can reassign task for nodes (the network coverage may be shrunk
down by the time). In addition, the neighbors of the base are used to
forward messages for all network, so, they could run out energy faster
than others. To cope with this problem, skeleton routing can be used.
b. Noise sensitive: The proposed method is very noise insensitive
because, normally, only the border nodes are active. The noise effects
just at the border.
c. Security: The proposed method can support shared key. For other
security stuff, we need node-node communication support. The
skeleton-based protocol could do that.
7. Summary:



The proposed method seems to consume less energy than others.
The new method demands less computation on node. The nodes is
only keep father ID, son ID (for maintenance phase).
Noise insensitive is also a strong feature of the method.
8. Future work:
 Do more testing.
 Simulate the skeleton method.
 Evaluate network life time for the OCO method and the skeleton
method.