AN ENERGY EFFICIENT DISTRIBUTED PROTOCOL FOR ENSURING COVERAGE AND CONNECTIVITY (E3C 2 ) OF WIRELESS SENSOR NETWORKS

International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.3, No.1, February 2012
DOI : 10.5121/ijasuc.2012.3101 1
AN ENERGY EFFICIENT DISTRIBUTED PROTOCOL
FOR ENSURING COVERAGE AND CONNECTIVITY
(E3
C
2
) OF WIRELESS SENSOR NETWORKS
D.G.Anand1
, Dr. H.G. Chandrakanth2
and Dr.M.N.Giriprasad3
1,2
Sri Krishna Institute of Technology, Bangalore, Karnataka,
3
Jawaharlal Nehru Technological University College of Engineering,
Anantpur, Andra Pradesh
dg_anand2003@sifymail.com,ckgowda@hotmail.com,
mahendran_gp@rediffmail.com
ABSTRACT
As wireless sensor networks (WSNs) continue to attract more and more researchers attention, new ideas for
applications are continually being developed, many of which involve consistent coverage with good
network connectivity of a given area of interest. For the successful operation of the wireless Sensor
Network, the active sensor nodes must maintain both coverage and also connectivity. These are two closely
related essential prerequisites and they are also very important measurements of quality of service (QoS)
for wireless sensor networks. This paper presents the design and analysis of novel protocols that can
dynamically configure a sensor network to result in guaranteed degrees of coverage and connectivity. This
protocol is simulated using NS2 simulated and compared against a distributed probabilistic coverage-
preserving configuration protocol (DPCCP) with SPAN [1] protocol in the literature and show that it
activates lesser number of sensor nodes, consumes much lesser energy and maximises the network lifetime
significantly.
KEYWORDS
Coverage, Connectivity ,energy conservation ,power nodes power control
1. INTRODUCTION
During the last decade, with the development of Very Large Scale Integration (VLSI) and Micro
Electro Mechanism System (MEMS), embedded computation ,wireless sensor network have
attracted a lots of attentions by both researchers and also by developers [2].This wireless sensor
network (WSN) is a collection of a large number of tiny devices each with sensing (collection of
surrounding information), processing and wireless communication (transmission of the report
data to a sink node/base station) capability. A tiny node by itself has severe resource problems,
such as limited memory, battery energy, signal processing, and computation and transmitting
capabilities; hence it can sense only a small area of interest the environment. But, a group of
sensors coordinating with neighbouring nodes can perform a much bigger applications effectively
& efficiently. In wireless sensor networks, there are two important prerequisits.They are (a.)
Sufficient Sensing Coverage (b.) Sufficient Network Connectivity.
The coverage problem is to study and solve how to make sure that the coverage range meets the
desired application requirements. The connectivity problem concentrates on how to make sure

2
that all the active sensor nodes are able to communicate with each other. The Connected coverage
(CC) is the integration of coverage problems and also connectivity problems.
A wide range of applications have been envisioned using WSNs [2], such as temperature and
environmental conditions monitoring, security surveillance in military and battle-fields, industrial
diagnosis, wildlife habitat monitoring, fire detection, object tracking, distant health care and so
on.
In most of these applications the environment could be hostile (such as a volcano crater); the
replacement of these nodes may not be possible once they are deployed. This will remain
untouched for months or years without any battery replacement. So, the development of energy-
saving algorithm for the establishment of these sensor networks can prolong their lifetime, what is
very essential. Our main goals are:
• To turn on only minimal number of sensor nodes, controlling the density to avoid
collision of packets which may lead for packets loss and intern for retransmission and
indirectly reducing some problems like radio interference, congestion
• To ensure that this available number of active nodes can cover the area of interest;
• To ensure that the information can flow outside, that is, the active sensor nodes are
connected;
• To increase the network lifetime.
The rest of the paper is organized as follows. The related work is reviewed in Section 2. In
Section 3 Problem definition & discussions on connected coverage mathematical model are
discussed. Performance Evaluation & Simulation result are presented in Section 4. Finally, it is a
concluded in Section 5.
II. RELATED WORK
Tian et al. [10] presented the first protocol that guarantees full coverage. They defined the
“sponsored area” of sensor node ‘j’ by node ‘i ’ as the maximal sector of sensor node ‘j’s
coverage disk that is covered by sensor node ‘i’. Whenever a sensor node receives a
packet/message from one of its active neighbours, it calculates its covered sponsored region by
that sensor node. If the union of all the sponsored region of a sensor node covers the coverage
disk of the sensor node, the sensor node turns itself off and opts to sleep. Normally, this method
requires good number of working sensors. Moreover, the authors address only the coverage issues
without touching the network connectivity problem.
In [13], describes a hybrid approach to study sensor coverage by using a Voronoi diagrams. They
provide protocol to determine sensor coverage quality with worst case runtime estimations for
different sensor network models and coverage criteria in 2-D or 3-D. This paper does not consider
deployment connectivity and the according trade-offs.
SPAN [5] is a randomized with distributed algorithm in which sensor nodes make local decisions
on whether the nodes should sleep or actively participate in the process. A node that opts to stay
awake and maintain sensor network connectivity is known as coordinators. A non-coordinator
sensor node elects itself as a coordinator if any two of its neighbours cannot communicate with
each other. The non-coordinator sensor node broadcasts locally, its willingness of being a
coordinator, delayed by an interval that gives the residual power of a sensor node. The
information required for coordinator election is exchanged mutually among neighbours through
HELLO messages

3
The authors [11], proposed an Integer Linear Programming (ILP) formulation and algorithms to
solve the Density, Coverage and Connectivity Control Problem (DCCCP) in flat networks subject
to sensor either node failures due to mechanical problems or draining of energy from the
battery. They present a global algorithm which has a better vision of the sensor network and so
can build up good routing topologies using the available sensor nodes. However, spreading the
solutions generated by this algorithm can be very expensive in terms of energy, time and sensor
network load.
In paper [12], the author presented the problem to improve networks lifetime (in terms of rounds)
while preserving both target coverage and connectivity. This not only gives satisfied quality of
service (QoS) in sensors networks, but also presents more options to changes the design a power
efficient sensor scheduling. They studied and presented the Connected Set Covers (CSC)
problem, used to solve the connected target coverage problem. Later they proposed a Three Phase
Iterative algorithm to solve the CSC problem named TPICSC.
In [3], Author proposed that the recent research result that significant energy savings achieved by
dynamic management of node duty cycles in sensor networks with high node density. In this
method, some of the sensor nodes are scheduled to sleep (or enter a power saving mode) while the
remaining active nodes provide service without disturbing the network functionality. A
fundamental problem is to reduce the number of nodes that remain active mode, while still
achieving acceptable quality of service for applications. In particular, maintaining sufficient
communication coverage and network connectivity with the active nodes is a critical requirement
in sensor networks. The authors provided a geometric analysis that 1) proves sensing coverage
implies network connectivity when the sensing range is no more than half of the communication
range. Their Simulation results demonstrate that Coverage Configuration Protocol (CCP) and
CCP+SPAN+2Hop can effectively configure the network to achieve both requested coverage
degrees and satisfactory communication capacity under different ratios of sensing /
communication ranges as predicted by geometric analysis.
In [4], author presented a Coverage Configuration Protocol (CCP) that results different degrees of
coverage and also maintain communication connectivity. The authors propose that coverage can
imply connectivity only when sensors’ communication ranges are not less than twice of their
sensing ranges (Rc ≥ 2Rs). Apart from that, they proved that the desired connectivity of boundary
sensing nodes and interior nodes are equal to the degree of coverage and twice the degree of
coverage, respectively. Each deployed tiny node runs the k-coverage eligibility protocol to
determine the targeting coverage of a sensor network by observing at how intersection points
between sensors’ sensing/communicating ranges are covered by their neighbors. For the case
when Rc < 2Rs, Coverage Configuration Protocol does not guarantee network connectivity. But,
the authors integrated CCP with SPAN [5] to provide sensing coverage and along with network
communication connectivity.
Figure 1: The relation between Sensing Range ‘Rs’ and Communication Range ‘Rc’
Tian Ying, Zhang Shu-Fang and Wang Ying ,presented [6] ,which differs from existing protocols
in four key ways: (1) They proposed a distributed probabilistic coverage-preserving configuration

4
protocol (DPCCP) based on Neyman-Peason probabilistic detection model; (2) A simplified
algorithm on coverage check is developed using Voronoi diagram; (3) Considering the network
connectivity, after that, they integrated DPCCP with SPAN to ensure both probabilistic coverage
and network connectivity; (4) To evaluate the coverage percentage of their protocol, they
proposed an approximate algorithm. Simulation results shows that distributed probabilistic
coverage-preserving configuration protocol DPCCP+SPAN can effectively reduce the number of
active sensors and prolong the network lifetime on the precondition of probabilistic coverage-
preserving and network connectivity. The authors also presented an approximate algorithm to
evaluate the coverage percentage of their proposed algorithm. Simulation results showed that
their protocol out performs CCP+SPAN presented in [5], which can effectively prolong network
lifetime on the precondition of probabilistic coverage preserving and network connectivity.
In [7], the authors consider the target coverage and connectivity issues in wireless sensor
networks (WSNs) with multiple sensing units. They reduced the problem to a connected set cover
problem and further formulated it as integer programming (IP) constraints. Two distributed
schemes, Remaining Energy First Scheme (REFS) and Energy Efficient First Scheme (EEFS), are
proposed to solve the coverage and connectivity problems. Both REFS and EEFS have similar
inclination that the network lifetime increases with the increase of the number of sensors, but
reduces with the increase of the number of targets. However, the EEFS has better performance
than REFS.
In [8], authors present an ILP formulation and protocols to solve the Coverage and Connectivity
Control Problem (CCCP) in Wireless Sensor Networks subject to sensor node failures due to
mechanical problems or when battery dries out of energy. They present an algorithm which has
full information of the sensor network and so can build up good topologies using the available
active nodes. However, spreading the solutions generated by this protocol can be costly in terms
of power, time and load. Thus, they also proposed a local protocol, which is called every time a
sensor node failure occurs and solves the problem considering only the failure neighbourhood. By
combining both of these protocols the authors obtained a hybrid approach which results from the
best features of each one of them.
III. PROBLEM: Design Goals
In order to extend the whole wireless sensor network lifetime and also to maintain the connected
area coverage problem, we present an energy-efficient Connected Coverage Preserving Protocol
to select the group of active sensor nodes.
Definition 1: A deployment pattern is called fully optimal if it employs the minimum number of
sensors nodes to produce the given sensor coverage and network connectivity requirement,
among all patterns.
Definition 2: If any sensor node in network is among the communication range of one active
node, then the network is said to be covered.
Definition 3: Connected Neighbouring Sensors: Two sensor situated at points u and v are
said to be connected with neighbours, if
1. D(u) ∩ D(v) ≠ ϕ , i.e. their sensing convex disc intersect , and
2. d (u,v) ≤ Rc , where Rc denotes coverage range

5
Assumption Done
• We assume each node knows it location information in terms of coordinates.
• We assume a heterogeneous network with powerful nodes uniformly distributed in a
ratio.
Algorithm Steps
• Each Power node will Query the Normal Nodes to send it location information. A node
can be reached by two powerful nodes but it always replies only to the first Power node.
• Each Normal Node sends its location information (x, y) to the power node.
• The power node maintain a list of (X, Y) and broadcasts this to all power nodes
neighboring.
• The neighbor power nodes randomly elect a leader to do coverage calculation for
particular Set (X, Y). Say which node has highest id will do the coverage calculation.
• Coverage Calculation algorithm will work by choosing the optimal nodes to cover the
area using (X, Y) points and switch on schedule.
• Power node will send the Border Sensor and coverage area information to all power
nodes.
• Two Power Nodes will determine if they are properly connected via the normal nodes
else the node with higher id will choose the border node to ensure connectivity and sends
the chosen node to the other power node. This is covered in the connectivity ensured
algorithm.
• Once connectivity ensured algorithm runs between all the power nodes, then whole
network wide connectivity is ensured.
• Due to this step we know the optimal nodes to ensure connectivity and coverage.
The use of Power nodes
A power node runs the coverage and connectivity algorithm to ensure optical nodes to be active
and their duty cycle.
But once this operation is complete how can they be used.
If a power nodes can formed a network to sink, then it can used a backplane for sending any
urgent messages to sink.
Power nodes can sleep and wake up after some configured period of time and query the sensors,
In case some sensors have drained in particular area, it can change the duty cycle of the nodes in
that area, or it can even enable the sensors in intersection area of two squares.
In this paper, we assumed that nodes know their location. But for some networks when it is not
known, we can make it known as following.
Power nodes can be equipped with GPS, so they know location; they can broadcast their locations
to the normal sensor nodes. Sensor nodes can easily find their own location by triangulation.
IV. SIMULATION PARAMETERS
We evaluate our (E3
C2
) through NS2 simulation [9]. We use a bounded region of 1000 x 1000
square meters, in which we place nodes using a uniform distribution. We assign the power levels

6
of the nodes such that the transmission range and the sensing range of the nodes are all 250
meters. We use the distributed coordination function (DCF) of IEEE 802.11 for wireless LANs as
the MAC layer protocol. In our simulation, sensor nodes of sizes 50, 100 and 150 are deployed in
a 1000 m x 1000 m rectangular region for 50 seconds of simulation time. The simulated traffic is
Constant Bit Rate (CBR). To measure the performance of different protocols under different
ratios of communication range/sensing range, we varied the communication range by 250,300,350
and 450m, in the network interface. All experimental results presented in this section are averages
of ten runs on different randomly chosen scenarios.
The following table summarizes the simulation parameters used
No. of Nodes 50,100,150 and 200
Area Size 1000 X 1000
MAC Protocol 802.11
Simulation Time 50 sec
Traffic Source CBR
Packet Size 512
Transmit Power 0.360W
Receiving Power 0.395W
Idle Power 0.335W
Transmission Range 250,300,350 and 400
Routing Protocol AODV
Performance Metrics
During experimentation, our proposed E3
C2
protocols’ performance is compared with
DPCCP+SPAN [AA].In this evaluation; the experimentation is focused mainly on average energy
consumption and connected Coverage of sensor.
Experiment 1: In experiment 1, we vary the no. of sensor nodes as 50,100,150 and 200 and
measure the average energy consumption.
From Fig.2, we can observe that, our proposed E3
C2
protocol consumes less energy when
compared with the DPCCP+SPAN algorithm. The transmission range is fixed as 250m.

7
Figure 2.Performance: Total Nodes vs. Number of Active Nodes
Experiment 2: In experiment 2, we focused on the connected sensor node coverage in terms of
active nodes. Fig.3 shows the results of coverage sensor nodes, for network sizes of 50,100,150
and 200.
Figure 3.Comparison of Efficiency: Communication Range vs. No. Of Active Nodes
Experiment 3: In this experiment 3, we changed the transmission range as 250,300,350 and 400,
and measured the average power consumption at fixed the no. of nodes as 100. From Fig. 4, we
noted that, the energy consumption is significantly reduced in our algorithm E3
C2
, when
compared with DPCCP+SPAN.
Figure 4. System Life Time: Round No. Vs. No. of Active Nodes

8
Experiment 4: In this evaluation, the results of sensor node coverage in terms of active nodes are
measured. Fig.5 shows the sensor coverage results for different sensor network sizes for changed
transmission range from 250 to 1600.
Figure 5.System Normalised rest energy: Normalised rest energy vs. Round No.
V. CONCLUSION & FUTURE WORK
In this work, we presented (E3
C2
), a randomized algorithm which is run locally at a sensor node to
govern its operation. In this paper, we presented recent energy-efficient connected coverage
problems proposed in literature, their formulations and assumptions as well as solutions proposed.
Sensor coverage, connectivity and energy are the three important elements for QoS in
applications with WSNs. We examined the performance of the protocols using NS2 simulators
and showed that our presented results in significant reduction of energy, with strongly connected
coverage. Most recent works on the sensor connected coverage problem are still limited to
theoretical study. In future, more and more research work will be concentrated on distributed and
localized solutions for practical deployment.
REFERENCES
[1] T.Ying, Z.Shu-Fang W.Ying” A Distributed Protocol for Ensuring Both Probabilistic Coverage and
Connectivity of High Density Wireless Sensor Networks” IEEE Communications Society, WCNC
2008 proceedings,2008, pp.2069-2075.
[2] I.F. Akyildiz, W. Su, Y. Sankara subramaniam, E. Cayirci, “Wireless sensor networks: A survey,
Computer Networks” 38 (2) (2002), pp.393–422.

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[3] G.Xing, X.Wang, Y.Zhang, C.Lu, R.Pless, C. Gill” Integrated Coverage and Connectivity
Configuration in Wireless Sensor Networks” ACM Transactions on Sensor Networks, Vol. 1, No. 1,
August 2005, Pages 36–72.
[4] X.Wang, G.Xing, Y.Zhang, C.Lu, R.Pless and C.Gill,”Integrated Coverage and Connectivity
Configuration in Wireless Sensor Networks,” in ACM International Conference on Embedded
Networked Sensor Systems (SenSys), pp.28-39, 2003.
[5] Benjie Chen, Kyle Jamieson, Hari Balakrishnan, and Robert Morris” Span: An Energy Efficient
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[6] Tian Ying, Zhang Shu-Fang and Wang Ying” A Distributed Protocol for Ensuring Both Probabilistic
Coverage and Connectivity of High Density Wireless Sensor Networks” IEEE Communications
Society, WCNC proceedings, 2008, pp. 2069-2075
[7] Kuei-Ping Shih, Hung-Chang Chen, and Bo-Jun Liu” Integrating Target Coverage and Connectivity
for Wireless Heterogeneous Sensor Networks with Multiple Sensing Units” IEEE, ICON proceedings,
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[8] Meenakshi Bansal, Iqbal Singh, Parvinder S. Sandhu “Coverage and Connectivity Problem in Sensor
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[9] Network Simulator: www.isi.edu/nsnam/ns
[10] D. Tian and N. D. Georganas. A coverage-preserving node scheduling scheme for large wireless
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[11] F.P. Quintao, F.G. Nakamura, and G. R. Mateus.” Model and Algorithms for the Density, Coverage
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[12] Mohammad ali Jamali, Navid Bakhshivand, Mohammad Easmaeilpour, Davood Salami”AN
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Authors:
D. G. Anand is native of Madikeri, Karnataka, India. He received his B.E Degree
from AIT, Chikamagalore, Mysore University and ME from UVCE Bangalore
University, Bangalore and currently he is perusing his PhD from Jawaharlal Nehru
Technological University Anantapur, Andra Pradesh. Presently he is serving as
Professor and HOD in the department of ECE at Sri Krishna Institute Of
Technology, Bangalore. His areas of interest are wireless communication, sensor
networks.
(dg_anand2003@sifymail.com)
Dr. H. G. Chandrakanth is native of Bangalore, Karnataka, India. He received B.E
Degree from UVCE, Bangalore University, Bangalore, India in 1991, MSEE from
Southern Illinois University Carbondale, USA in 1994 and PhD from Southern
Illinois University Carbondale, USA in 1998. Presently he is working as Principal
in Sri Krishna Institute Of Technology, Bangalore.
(ckgowda@hotmail.com)
Dr. M. N. Giri Prasad is native of Hindupur town of Anantapur District of Andhra
Pradesh, India. He received B.Tech degree from J.N.T University College of
Engineering, Anantapur, Andhra Pradesh, India in 1982, M.Tech degree from Sri
Venkateshwara University, Tirupati, Andhra Pradesh, India in 1994 and PhD
degree from J.N.T University, Hyderabad, Andhra Pradesh, India in 2003.
Presently he is working as Professor and Head, department of Electronics and
Communication at J.N.T University College of Engineering, Pulivendula, and
Andhra Pradesh, India. His research areas are Wireless Communications and
Biomedical Instrumentation. He is a member of ISTE, IE & NAFEN
(e-mail:mahendran_gp@rediffmail.com).

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