IRJET- Load Optimization with Coverage and Connectivity for Wireless Sensor Networks

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 05 | May 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1697
Load Optimization with Coverage and Connectivity for Wireless
Sensor Networks
Shreyas R1, Abdul Haq N2
1,2Dept. of ECE Engineering, REVA University, Karnataka, Bangalore-India, 560064
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Abstract - The main important function of wireless sensor
networks is coverage, sensing and connectivity. Lifetimeofthe
network is mainly reduced due to the coverage problem
imposed by the densely congested sensors in the wireless
sensor networks (WSNs) .To overcome such issues, in this
paper the proposed novel maximum connectedloadbalancing
cover tree(MCLCT) to get entire coverage and base station
connectivity of every node by formulating load balanced
routing cover trees. For the limited energy of sensor nodes, we
are gathering these nodes in to subgroups, which is efficientof
monitoring all discrete point of interest (DPOIs).And it is a
maximum cover tree problem, which is non-probabilistic
complete problem. The MCLCT consists of two strategies: a
coverage optimizing recursive heuristic for coverage and the
probabilistic load balancing for the determination of the
routing path. By using the MCLCT less amount of energy
consumption can be done by balancing the load evenly on the
nodes. The simulation results show that the energy efficiency
and the connectivity of the nodes.
Key Words: (Size 10 & Bold) Key word1, Key word2, Key
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1.INTRODUCTION
Fast growing [10] and demanding in the field in today’s
world of computing is data gathering. Sensors give a easy
solution to these applications particularly in the remote and
low-maintenance areas where conservative approaches
prove to be very expensive. Networking of these unattended
sensors is expected to have significant impact on the
effectiveness of many military and civil applications, suchas
combat field observation,securityanddisastermanagement.
Power is a very scarce reserve for such sensorsystems.Even
in WSN power is considered as scarce resource and should
be efficiently used.
The main aim of this paper to provide the better coverage
and connectivity [1] of the WSNs. The deployment of the
wireless sensor nodes near the discrete point of interest
(DPOIs), the region where there is no efficient signal or
remote area. And how each DPOI will be covered is the main
issue. This is one of the problem in WSNs. And scheduling
the process is the main feature which will determine the
time of activation [1] and time of inactivation of the nodes.
By using the localization technologies, it is possible to locate
the sensor nodes. Grouping the sensor nodes in to a
maximum no. of cover sets which are disjoint sets or non-
disjoint sets is proved to be NP-complete problem. This
nodes [1] in the cover sets can monitor all the DPOIs. To
achieve the longer life time alternate activation of this cover
set will be done.
The connected set cover (CSC) [2] which targets to find a
maximum number of cover sets. The sensor nodes will be
activated only when it is connected to the base station and it
is a non-probabilistic complete problem. To overcome this
issues they have come up with two algorithms: an integer
programming based heuristicanda breadthheuristic.Andto
deal with connected cover set problem distributed heuristic
was also implemented which is on minimum spanning tree.
Sleep scheduling mechanisms andcoverage mechanisms are
used to extend the life time of the network. Virtual robust
spanning tree and modified virtual robust spanning tree is
used for connectivity preservation[3]. This is best when
compared to cardei method because it reduces energy
consumption than the cardei method. By using VRST and
MVRST has less depth and data latency. And uses existing
point coverage methods to reduce energy consumption of
sensor node and to construct a load balanced tree to fight
against network failure.
And some solutions [4] to forming k-connected coverage of
targets with minimum number of active nodes .This is a k-
connected argumentation problem which is a target
coverage problem, it is a NP-hard. To overcome this issues,
heuristic algorithm is used. This aforementioned [2-3-4]
studies tells about the CSC problem. They are only practical
about sensing and communications. And it will not effect to
the distance between the nodes.And equal amountofenergy
is consumed for both sensing and relaying. Based on thesize
of the data to be transmitted only energy consumption
should be done.
Connected target coverage problem [5] is similar to
maximum cover. Algorithm known as communication
weighted greedycover(CWGC)isused.Optimizedconnected
coverage heuristic (OCCH) is one of the algorithm which is
based on general coverage method. Generate the connected
cover sets, where the critical nodes would not serve as
relaying nodes by assigning different weights to edges
between nodes. By doing so, [5] the major energy of the
critical nodes could be conserved so as to prolong the
network lifetime. The experimental result showed that the
network lifetime acquired by the OCCH is longer than those
of the aforementioned approaches, including CWGC [6],
Greedy-CSC [2], and GIECC [2].

Large no. of sensors are deployed for the surveillance
applications to get [15] efficient coverage. Due to the
unexpected node failures, they focus on the fault tolerance
and energy efficiency in sensor coverage. To overcome the
node failures, the proposed distributed technique to
facilitate switching between active covers without the urge
for node synchronization. This [15] framework is called as
“resilient online coverage” framework. It allows only sensor
node to take control over the degree of redundancy and
surveillance in its region according to the current network
condition.
2. DESIGN EQUATIONS AND ANALYSIS
This section [1] tells about the main optimizationproblem is
maximum cover tree problem. The load of sensor nodes are
formulated and providing load balanceamongthenodes. Let
n number of sensor nodes are thereinWSN whichisdenoted
by S, Where S={S1,S2,S3…………..Sn} and m number of DPOIs
is denoted by P. And P={P1,P2,P3,……..Pm}.Assume at least
one sensor node will cover one dpoi. And initial energy is
denoted by E0.And let τ denotes the operational time of the
cover tree. The nodes[1] undergoes three modes of
operations sensing, relaying and sleep mode/inactive mode.
And ts(si, τ ), tr(si, τ ), and tinact (si, τ) will also denote the
operational period of time of the cover tree.
ts(si, τ )+tr(si, τ )+ tinact(si, τ)= τ (1)
For the energy consumption for communicationandsensing
operations both free space and multipath fading channels
are used depending on the distance betweenthetransmitter
and receiver. So the energy consumed for transferring
information depends on the transmitter and receiver. The
free space model is used when the distance is less than the
threshold, or otherwise the multipath model is used.
‘ect’ is represented as consumption ofenergy bytransmitted
circuit per data bit. ‘ecfs’ and ‘ecmp’ denote the energy
consumption for the power amplifier per data bit for free
space and multipath model respectively. Euclidian distance
is denoted by dij between the node Si and Sj. d0 denoted as,
d0= (3)
2.1 MAXIMUM CONNECTED LOAD BALANCING
COVER TREE ALGORITM
The main agenda of the MCT problem is to build several
connected cover trees. [10] By doing so, a longer network
lifetime and full coverage can be acquired. TheMCTproblem
is a complicated NP-Complete problem, so finding a
suboptimal solution is a generic approach in order to
decrease the time of computation. The proposed MCLCT is
composed of two sub-strategies: a coverage-optimizing
recursive (COR) heuristic and a probabilistic load-balancing
(PLB) strategy. The CORheuristicaimsatfindinga maximum
number of disjoint sets of nodes, which can be achieved by
one of the sensor nodes (such as the sink node). In each
disjoint set, the nodes are able to monitor all the DPOIs
together. That is, the COR heuristic focuses on dealing with
the full coverage preservation issue. Moreover, the PLB
strategy is used to figure out the appropriate path from each
node to the BS after the disjoint sets are initiated.
Fig.1 Flowchart of methodology used in MCLCT
The main aim[1] of this paper to provide full coverage and
connectivity in the wireless sensor networks and which is
comprises of:
Lost coverage and base station connectivityofsensing nodes
can be dynamically recovered because some of the factors
such as weather change, man-made interference,
congestion.. etc., some set nodes are reservedwhichiscalled
as residual nodes which are used recover the lost data by
coverage optimizing recursive heuristic strategy.
Load balance among the wireless sensor nodes, when the
nodes will try to choose the best candidate parent nodes by
using probabilistic load balancing strategy, they will also
help to build the overall load-balanced dynamic cover trees.
And so the network hot spot and congestion can be avoided,
and a longer network lifetime can be increased. At the same
tier maintaining connections between the neighbor nodes.

Size of every disjoint set is minimized by the existing
maximum load balancing cover tree selects as few nodes as
possible and put them into the disjoint sets using the
coverage optimizing recursive heuristic. Only necessary
sensing nodes will be activatedduringtheoperationinorder
to reduce energy waste.
And by avoiding the simultaneous operations playing as
sensing and relaying by the nodes, a node plays one role at
one time as sensing or relaying.
Fig.2 Coverage recovery by MCLCT
Fig. 2(a). Tells about the [1]dynamic coverage recovery in
which P1, P2, P3 are the DPOIs and which there are 3
sensing nodes that will monitor this DPOIs.8 relaying nodes
used to transfer the sensed data. And s1, s10, ands11arethe
sensing nodes that form the disjoint set
S2,S3,S4,S5,S6,S7,S8,S9 form the set Srsd. ) At the T(τ) at τ'
there will be connection established between s9 and
s10.And s10 has a sole candidate parent node (s7) in the
lower tier
(Tier-2). Under such circumstances, maximum connected
load balancing cover tree algorithm will conduct a dynamic
coverage recovery, becausethep1will not becovered,which
is due to the malfunction of S1. Fig. 2 (b) tells about the new
topology of T(τ) at τ' + 1. As seen in the fig.2(b) node S5
originally serving as a relaying node would be changed to
serve as a sensing node at τ' + 1, because its coverage
resembles with that of S1.And without connecting to S10 at
τ' + 1, the sensed data of S10 will not be transferred to the
base station. And by the dynamic compensation mechanism
of the maximum connected load balancing cover tree for
coverage and connectivity, the roles of sensor nodes can be
efficiently determined.
The main objective of the probabilistic load balancing
strategy is to fulfill the load balance among the wireless
sensor nodes by sharing load equally among the nodes. The
main function of the sensing nodes to monitortheDPOIsand
transfer the data that sensed by it. By undergoing this, there
will be less amount of energy consumption can be
maintained. And equation (4), the residual energy is
represented as ef(τ') of the node Si at τ'.’α’ denotes
exponential factor used to set the response strength of the
curve from energy to distance ratio. The sensing node si to
the candidate parent node,theforwardingprobabilitycanbe
expressed as,
Equation (4), is further simplified. Before performing the
forwarding probability for the relaying node, the
normalization of the residual energy of the candidateparent
nodes using their expected loads. It is done with the
expected load, the weight of the one-hop transmission path
from the relaying node Sk to its candidate parentnode[srh
Prt(sk) at τ'].
And by using equation(5),theforwardingprobabilitythat Sk
tries to relay data to the candidate parent node Srh with
bigger positive weight deviation will be high. By doing so,
forwarding probability [1] that Sk attempts to transfer data
to Srh with a larger negative weight deviation will be too
smaller. Therefore, tendency of load balance can be found
when Sk chooses the best one from the candidate parent
nodes to relay data, by using the weight deviations. Relaying
on serving as sensing node or a relaying node, the
forwarding probabilities for candidate parent nodes can be
computed according to the equation (4) or (5). By this idea
of probability assignment is suitable for wireless sensor
networks. Without depending on every sensor node instead
of on a sink node or a BS with a higher computation
capability and unlimited resource.

3. RESULTS AND COMPARISON
In this section, the simulation results regarding the
performance of the proposed MCLCT and compare it with
previously mentioned algorithms which include CWGC [1],
OCCH [2], GIECC [4]. All the simulations are carried out on
the network simulation -2 platform.
Fig 3. Network lifetime when various numbers of nodes
are considered (10 to 100 nodes).
Fig 4. Network lifetime when various numbers of nodes
are considered
Fig 5.Network lifetime when various number of DPOIs
are considered.
4. CONCLUSION
The simulation results show that network life duration is
efficiently increased because of the traffic, the burden
generated by the sensing nodes is dynamically organized to
various relaying nodes. Further, for power conservation, the
MCLCT will definitely performs well-arranged power
utilization for nodes through the construction of dynamic
cover tree.
REFERENCES
[1] [1]. Chia-Pang Chen, Member, IEEE, S.C.
Mukhopadhyay, Fellow, IEEE, Cheng-Long Chuang,
Member, IEEE, Maw-Yang Liu, Member, IEEE, and Joe-
Air Jiang, Senior Member, IEEE “Efficient Coverage and
Connectivity Preservation with Load Balance for
Wireless Sensor Networks”
[2] [2] I. Cardei and M. Cardei “Energy-efficient connected–
coverage in wireless sensor networks,” Int. J. Sens.
Netw., vol. 3, no. 3, pp. 201–210, 2008..
[3] [3]. P. Ostovari, M. Dehghan, and J. Wu,“Connectedpoint
coverage in wireless sensor networks using robust
spanning trees,” in Proc. 31st International Conference
on Distributed Computing Systems Workshops
(ICDSC’11), 2011, pp. 287–293.
[4] [4]. N. Jaggi and A. A. Abouzeid, “Energy-efficient
connected coverage in wireless sensor networks,” in
Proc. 4th Asian International Mobile Computing
Conference (AMOC’06), 2006, pp. 77–86.
[5] [5]. D. Zorbas and C. Douligeris, “Connected coverage in
WSNs based on critical targets,” Comput. Netw., vol. 55,
pp. 1412–1425, 2011.
[6] [6]. Q. Zhao and M. Gurusamy, “Lifetime maximization
for connected target coverage in wireless sensor
networks,” IEEE/ACM Trans. Netw., vol. 16, no. 6, pp.
1378–1391, 2008.

[7] [7]. X. Han, X.G. Cao, E. L. Loyd, and C.-C. Shen, “Fault-
tolerant relay node placement in heterogeneous
wireless sensor networks,” IEEET.MobileComput.,vol.
9, no. 5, pp. 643–656, 2010.
[8] [8]. A. Krause, R. Rajagopal, A. Gupta, and C. Guestrin,
“Simultaneous optimization of sensor placements and
balanced schedules,” IEEE Trans. Autom. Control, vol.
56, no. 10, pp. 2390–2405, 2011.
[9] [9]. D. Yang, S. Misra, X. Fang, G. Xue, and J. Zhang, “Two-
tiered constrained relay node placement in wireless
sensor networks: computational complexity and
efficient approximations,” IEEE Trans. Mobile Comput.,
vol. 11, no. 8, pp. 1399–1411, 2012.
[10] [10]. P.Gowtham1, P.Vivek Karthick2, “Optimized
Coverage and Efficient Load Balancing Algorithm for
WSNs-A Survey [11].H. M. Ammari and S. K. Das,
“Centralized and clustered k-coverage protocols for
wireless sensor networks,” IEEE T. Comput., vol. 61, no.
1, pp. 118–133, 2012.
[11] [12]. M. Ashouri, Z. Zali, S.R. Mousavi, and M.R. Hashemi,
“New optimal solution to disjoint set K-coverage for
lifetime extension in wireless sensor networks,” IET
Wirel. Sens. Syst., vol. 2, no. 1, pp. 31–39, 2012
[12] [13]. Y. Lin, J. Zhang, H.S.-H. Chung, W. H. Ip, Y. Li, and Y.-
H. Shi, “An ant colony optimization approach for
maximizing the lifetime of heterogeneous wireless
sensor networks,” IEEE Trans. Syst., Man, Cybern. C,
Appl. ,Rev., vol. 42, no. 3, pp. 408–420, 2012.
[13] [14]. X. He, H. Yanh, and X. Gui, “The maximum coverage
set calculated algorithm for WSN area coverage,” J.
Netw., vol. 5, no. 6, pp. 650–657, 2010.
[14] [15]. O. M. Younis, M. M. Krunz, and S.
Ramasubramanian, “ROC: resilient online coverage for
surveillance applications,” IEEE/ACM Trans. Netw., vol.
19, no. 1, pp.251–264, 2011.

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