THIS ARTICLE HAS BEEN RETRACTED: Reliable and Energy Efficient Topology Control Algorithm Based on Connected Dominating Set for Wireless Sensor Network"/> THIS ARTICLE HAS BEEN RETRACTED: Reliable and Energy Efficient Topology Control Algorithm Based on Connected Dominating Set for Wireless Sensor Network" />

## Journal of Green Engineering

Vol: 4    Issue: 3

Published In:   April 2014

### THIS ARTICLE HAS BEEN RETRACTED: Reliable and Energy Efficient Topology Control Algorithm Based on Connected Dominating Set for Wireless Sensor Network

Article No: 3    Page: 211-234    doi: 10.13052/jge1904-4720.433

 1 2 3 4 5

Reliable and Energy Efficient Topology Control Algorithm Based on Connected Dominating Set for Wireless Sensor Network

Received 10 January 2015; Accepted 15 January 2015; Publication 19 March 2015

Manisha Bhende and Sanjeev Wagh

• Research Center, Pad. Dr. Y. Patil Institute of Engineering an Technology, University of Pune, India Email: manisha.bhende@gmail.com; sjwagh1@yahoo.co.in

## Abstract

Energy consumption in wireless sensor network is of paramount importance, which is demonstrated by a large number of algorithms, techniques and protocols that have been developed to save energy and to extend lifetime. Poly is the topology construction protocol. It is based on the idea of a polygon. Lifetime extension is one of the most critical research issues in the area of Wireless Sensor Network due to the severe resource limitation of sensor nodes. One of the key approaches for prolonging the sensor network’s operable lifetime is to deploy an effective topology control protocol. We propose a Topology control algorithm for intelligent and reliable clustering, introducing energy harvesting nodes for maximizing the lifetime of network by supporting energy backup in the sensor field, proper localization of base station in the field to minimize the communication distance between cluster heads and the base station. Our simulation results demonstrate that IPoly performs consistently better in terms of energy efficiency message overhead, Energy overhead and reliability.

## Keywords

• Reliable
• Connected domination set
• Energy Efficient
• Static and Dynamic
• Topology Maintenance

## 1 Introduction

Due to advancement in technologies and reduction in cost of technologies and reduction in size, sensors are becoming involved in almost every field of life. WSNs can be widely used such as agriculture, Industry, Medicine, Horticulture and Military [11, 39]. In mission critical application; packet loss is not acceptable. Generally it is assumed that packet, [23] when nodes in WSN are connected to their neighbor, there is a possibility of packet loss, and therefore reliability should be achieved while improving energy efficiency. Topology construction and maintenance are two phases of topology control. Topological property is established in the construction phase.

Connectivity should be maintained in the construction phase. Second phase is the topology maintenance phase. In CDS based Topology control scheme, some nodes [1, 7] are part of virtual backbone. Non CDS node conserves energy by turning off radios. To achieve reliability and energy efficiency CDS size is an important parameter. For small CDS network traffic is handled by very few nodes, resulting into draining the battery. This is disadvantages of CDS. The advantage of this system is more nodes can go to sleep mode. “Saving energy compromises reliability”. Poly is semi distributed graph theoretic topology control protocol for WSN. It finds the number of polygon present in the network, by modeling network as connected graph. To achieve energy efficiency, the protocol forms a CDS like polyphonic network, which in turn provide reliability in the case of random link failure. It adapts to topological changes in the remaining energy of nodes. The problem of maximizing the wireless sensor network lifetime is broadly categorized into direct approach and indirect approach [37, 38]. In indirect approach minimize energy consumption is managed through various intelligent algorithms, while the other approach directly supports external support to maximize network lifetime. Though the indirect approach can help extend the network lifetime, it does not focus on the problem of maximizing network lifetime. [23] Wireless sensor devices are cheap devices with fairly high failure rates. Further, in many applications, these devices have to be thrown into the area of interest from a helicopter, or similar vehicle. As a result, several nodes break or partially breaks affecting their normal functionality. Node reliability is also affected by crucial levels of available energy.

Many energy harvesting devices are proposed to supplement for the battery power of sensors to manage the power and control. The energy harvesting enabled networks mainly focus on the power management issue to estimate the amount of energy that can be harvested in the future [1719], so as to optimize duty cycles and the scheduling of tasks [2022] to maximize system performance, such as latency [23].

## 2 Related Work

In the literature, there has been some work that protects previously existent topology control algorithms. Waltenegu Dargie et al [2010] proposed topology Control protocol [1, 23]. The developed protocol enables nodes to exhaust their energy fairly. The algorithm tries to preserve shortest path connecting itself to nearby nodes and the minimum-energy paths. In [2] concept of distributed topology control algorithm to conserve energy is introduced. In this paper localized distributed Topology control algorithm is presented. It calculates optimal transmission power to active network connectivity. It reduces node transmission power to cover nearest neighbor. A node uses only the locally available information to determine nodes. The majority of work has been done in fault tolerant topology control algorithm to minimize the total power consumption [16, 22]. It provides k-vertex connectivity between two vertices. Mihaela Cardei et al [4] propose a new architecture to achieve minimum energy consumption by using k-approximation, centralized greedy, distributed and localized algorithm. It provides reliable data gathering infrastructure from sensors to super node. In [13] summary of recent research results on Topology control techniques for extending the lifetime of battery powered wireless network is given. To increase the network lifetime, the design of efficient topology control of communication is very important.

EBC is based on SNA (Social network analysis) and measure the importance of each node in the network. QoS is achieved by evaluating relationships between entities of the network (i.e. edges) and identifying different roles among them (e.g. Brokers, outliers) to control information flow, message delivery, latency, and energy dissipation among them. This algorithm is applicable in homogeneous network and proposes a different line of research: Topology control in terms of QoS requirement. Given a set of nodes performing specific task, e.g. sink node in environmental sensor networks. The topology control algorithm is to select from the target network appropriate logical neighbors’ of the former nodes, namely a subset of the physical neighbors’ of former node that can be used to perform application specific procedure, without the need of involving the rest of physical neighbors’ during execution of these procedures. QoS based topology control algorithm selects a suitable set of logical neighbors’ such that input QoS requirements can be satisfied. EBC is bidirectional, weighted topology control algorithm. It is compared with GG, RNG and closeness centrally. In [17] authors proposed a self stabilizing algorithm for efficient topology control in Wireless sensor networks. It reduces the transmission power of each node so as to maintain network connectivity while saving maximum energy. The goal of the optimization is to minimize the average path length from source to destination to minimize the transmitted power. In [28] authors proposed novel topology control solution on the concept of betweenness centrally. This information allows us to achieve high quality of service. By studying the available literature we have found that still energy optimization can be done in topology control. Figure 1(a, b) and Figure 2(a, b) shows the Polygon formation for Dense and Sparse environment.

Figure 1 a) Polygon formation for dense network

Figure 1 b) Polygon formation for dense network

Figure 2 a) Polygon formation for sparse network

Figure 2 b) Polygon formation for sparse network

## 3 Material and Method

In the developed Topology Control Protocol some nodes are selected from the given set to create virtual backbone. Let V and D be be the set of nodes D ∈ V Where all nodes in V are in D. The set of nodes is one hop neighbor to other nodes

$d∈D(∀v∈V∇∃d∈V:(v,d)∈E)$

Redundancy is defined as the expected number of functional spanning trees in a graph G. Every edge must be considered as a bridge in the spanning tree. Network Reliability is achieved with the help of spanning tree. There should be one spanning tree in the network to handle random link failure. An adjacency matrix of a graph G is denoted by A = (Ai.j)n.n

Then

The degree of vertices is represented by diagonal matrix. If D denotes diagonal matrix of graph G then

The energy harvesting node, transmit power control and maintain topology of the network as well as the achievable throughput of the network. The minimum number of sinks required to keep the network connected is analyzed [25].

## 4 Proposed Algorithm Working

The resulting topology obtained by IPOLY provides a desired level of packet delivery and energy consumption is less than CDS. It has low message overhead. Among a set of nodes, poly protocol forms a closed path. It [2] provides reliable and energy efficient topology because it allows nodes to use an alternative in case of random link failure. Position or orientation information is not considered by this protocol. For energy saving dormant nodes are entered into sleep mode.

### A. Description of Control Messages

Three types of messages are used by Poly at the time of the polygon formation process:

• Hello
• Create topology
• Finish discovery

Parent id of the sender is contained in hello message. To announce the end of topology discovery – finish discovery message uses a create topology message containing the IDS of the active node set is propagated in the network. Hassaan Khaliq Qureshi et el[23] proposed POLY [23]. We have consider the same assumption defined by the authors for implementation of protocols.

### B. Topology Construction Protocol

The neighbor discovery process is initiated by sink node and CDS is created in this first phase of topology construction. In the second phase sink node received neighbor list. Discovery of polygons in the graph is done in the last phase. Polygon nodes are informed that they are part of the active node set [13]. Poly algorithm selected a random node as an initiator node. If more than one node initiates a process, the performance will be given to node having largest ID [38][39]. The hello of node A is received by B, F and H. These are the uncovered nodes.

### C. Complexity Analysis of Poly and Proposed Protocol

Complexity of the CDS discovery process is same for A3, EECDS and CDS rule protocol but POLY have lower CDS discovery message complexity because it uses wireless broadcast for parent discovery. Figures 36 shows the comparison of CDS based protocols. In A3 children recognition messages contain ordered list of all the children of the sender. This list is used by children to set a timer to compete for an active node. When network is dense, this list increases with the increase in message size. And hence consume more energy. The more the children, the more length of message and it will result in more energy consumption per children recognition messages. Due to this reason, A3 uses a 100 byte size for children recognition message. Apart from other messages of size 25 bytes EECDS uses broadcast packet size of 25 bytes with 6 types of messages for topology construction, which does not exceed broadcast packet size, CDS rule K also uses 25 byte broadcast packet.

Figure 3 Comparison of message overhead

Figure 4 Comparison of residual energy

Figure 5 Comparison of network connectivity

Figure 6 Comparison of connectivity

After CDS discovery A3, EECDS and CDS Rule protocols do not have any additional overhead, Poly introduces additional complexity. To reduce this additional complexity, Author discovers a subset of the cycle, they haven’t considered all the cycles in the network. Therefore sink node processes a reduced subset of a message’s path and few cycles. Author also utilized cycle merging smaller cycles are combined to form larger cycles. The additional complexity of poly protocol represents a tradeoff between reliability and energy efficiency. The size of the polygon in the protocol is a critical parameter for evaluation of the algorithmic metrics is:

Message overhead- It is defined as total no of packets sent-received generated in the whole network during an experiment. Message overhead is directly proportional to energy consumption. Lower the message overhead, lower energy will be consumed. Every protocol designed in WSN is always trying to minimize this overhead.

Energy overhead- it is defined as the fraction of network energy expended during construction of topology. In case of topology maintenance this metric calculates overhead during reconstruction of topology under dynamic condition.

Residual energy- it is defined ratio of energy in the active set of nodes to the total network energy at the end of an experiment. Residual energy is a measure of network lifetime. As residual energy falls below a certain threshold value the probability of network partitioning increases.

Connectivity – connectivity refers to the number of nodes which are disconnected from the sink node after the activation of topology maintenance technique. This parameter measures the effectiveness of the topology construction protocol. If the connectivity value equals to zero, protocol is the best one. Higher value of connectivity shows that the protocol is unable to provide the backbone.

The message and energy overhead of EECDS, CDS-rule and A3 protocol compared with POLY. Among these three A3 has a low message and energy overhead due to its three way handshake protocol. Poly protocol has low energy overhead and greater message overhead than A3.A3 uses signal strength as selection metric for node selection in CDS. In grid topologies nodes are placed at equal distances which results in more energy overhead [11]. For a selection of node in proportion to the size of the network broadcast mechanism is used by poly. It results in better residual energy as compared to other protocol.

An increase in the node degree leads to an increase in the number of messages exchanged. Poly has been providing better residual energy because.

1. The active node set is proportional to network size.
2. Rebroadcast mechanism is used by poly; it consumes battery of node at an equal rate.
3. The active node set is proportional to network size.
4. Rebroadcast mechanism is used by poly; it consumes battery of node at an equal rate.

To reduce this additional complexity, discovers a subset of the cycle, haven’t considered all the cycles in the network. Therefore sink node processes a reduced subset of a message’s path and few cycles. Cycle merging has been done: smaller cycles are combined to form larger cycles. The additional complexity of poly protocol represents a tradeoff between reliability and energy efficiency. Figures 710 shows the Comparison of Static and Dynamic Implementation using energy harvesters. We compared results for message overhead, Energy overhead, connectivity and residual energy. The size of the polygon in the protocol is a critical parameter for evaluation of the algorithmic metrics:

Figure 7 Comparison of network lifetime for static and dynamic topology control with energy harvester

Figure 8 Comparison of energy overhead for static and dynamic topology control with energy harvester

Figure 9 Comparison of residual energy for static and dynamic topology control with energy harvester

Figure 10 Comparison of message overhead for static and dynamic topology control with energy harvester

In grid topologies nodes are placed at equal distances which results in more energy overhead [11]. For a selection of node in proportion to the size of the network broadcast mechanism is used by poly. It results in better residual energy as compared to other protocol. An increase in the node degree leads to an increase in the number of messages exchanged [40]. Poly has been providing better residual energy because.

## 5 Simulation Setup and Result

Poly is implemented in MATLAB environment. In the experiment we consider that sensor nodes are deployed in 600m*600m area randomly. 50 to 250 nodes are used to perform different network topology. We have considered energy based topology maintenance technique. Data packet size of 25 bytes and ideal Medium Access Control layer is used. There is no packet loss due to channel contention/collisions.

Following Matlab code shows the placement of energy harvesting nodes function [node]=EHNode_Deployment(AREA,N,RR)

global DEPLOYED_NODES

NODE.ID=0;
NODE.CURRENT_ROLE=‘Energy Harvester’;
NODE.DISTANCE=0;
NODE.POSITION=0;
NODE.RESERVED_ENERGY=2;
NODE.NBHRS=0;

%% Create random positions of the nodes
a=0;
x_cord = a + (AREA.X-a).*rand(N,1)-eps(RR/sqrt(5));
y_cord = a + (AREA.Y-a).*rand(N,1)-eps(RR/sqrt(5));
vertices=[x_cord y_cord];
clear a b x_cord y_cord

%% Use above Structure defination to create ‘N’ number of nodes
node=NODE;
node(N).ID=0;
for i=1:N
node(i).ID=i;
node(i).POSITION= vertices(i,:);
node(i).CURRENT_ROLE=‘Energy Harvester’;
node(i).RESERVED_ENERGY=1;
end

%%SINK AT POSITION (Xmax,Ymax)
% node(N+1).POSITION= [AREA.X+20 AREA.Y+20];
%% DISCOVER NEIGHBOURS

Distance=[];
all_Distance=[];
for i=1:N
a=node(i);
for j=1:length(DEPLOYED_NODES)

d_ij=cal_dist(vertices(i,:),DEPLOYED_NODES(j).POSITION); %Cal Dist b/w nodes
if d_ij<=RR           %Whether the node is within range?
a.NBHRS(j)=j;         %Add the id of node in neighbor list
% a.N_L(j)=1;
% Energy=[Energy e_ij];
end


Table 1 No of nodes and performance metric values

In static topology maintenance technique, performance is dependent on efficient topology construction protocol. We have considered the results of the dynamic topology maintenance technique based on energy threshold. Polygon size depends on the network size. 10 to 50 nodes are used to construct polygon.

Controlled indoor deployments are consider to evaluate all five protocols. two ideal grid environment observed are:

The grid H-V.

The grid H-V-D topology.

In grid H-V nodes can communicate horizontally and vertically. In H-V-D nodes can be communicated horizontally, vertically and diagonally. Figures 1114 shows the final comparison of proposed and the existion CDS based protocols. Result shows that Proposed protocol performs well in terms of message overhead and energy overhead and it is providing strong connectivity between the backbone nodes. So it is providing higher reliability with energy efficiency.

Figure 11 Comparison of poly and improved poly for message overhead

Figure 12 Comparison of poly and improved poly for message overhead

Figure 13 Comparison of poly and improved poly for residual energy

Figure 14 Comparison of poly and improved poly for connectivity

## 6 Conclusion

We computed the reliability for CDS based Poly protocol and compared with static and dynamic deployment of node deployment. By considering EH as a backup, the lifetime of wireless sensor network field can be maximize the lifetime of sensor fields with clustering and base station placement. Existing CDS-based protocols have considerably lower network reliability because each edge (link) in these topologies serves as a bridge edge, and therefore does not provide any redundancy in the network. Improved Algorithm has fewer message overhead. It has less energy consumption compare to other available CDS based technique. Developed algorithm performs well in static as well as dynamic environment.

## References

[1] Waltenegus Dargiea, Rami Mochaourabb, Alexander Schill a, LinGuanc, “A topology control protocol based on eligibility and efficiency metrics,” The Journal of Systems and Software, PP 1–10, August 2010.

[2] R. S. Komali, R. W. Thomas, L. A. DaSilva, and A. B. MacKenzie, “The Price of Ignorance: Distributed Topology Control in Cognitive Networks,” IEEE Transactions On Wireless Communications, Vol. 9, No. 4, April 2010.

[3] Chi-Tsun Cheng, Member, Chi K. Tse, Francis, C. M. Lau “A Clustering Algorithm for Wireless Sensor Networks Based on Social Insect Colonies,” IEEE Sensors Journal, Vol. 11, No. 3, March 2011.

[4] Mihaela Cardei, Shuhui Yang, Jie Wu, “Algorithms for Fault-Tolerant Topology in Heterogeneous Wireless Sensor Networks,” IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, PP 545–558 VOL. 19, NO. 4, APRIL 2008

[5] Tahiry Razafindralambo, Member, IEEE, and David Simplot-Ryl, Member, IEEE “Connectivity Preservation and Coverage Schemes for Wireless Sensor Networks,” IEEE Transactions On Automatic Control, Vol. 56, No. 10, October 2011.

[6] Tao Shu and Marwan Krunz “Coverage-Time Optimization for Clustered Wireless Sensor Networks: A Power-Balancing Approach,” IEEE/ACM Transactions On Networking, PP 202–215 Vol. 18, No. 1, February 2010.

[7] Ioannis Ch. Paschalidis, Senior Member, IEEE, and Binbin Li, Student Member, IEEE “Energy Optimized Topologies for Distributed Averaging in Wireless Sensor Networks” IEEE Transactions On Automatic Control, PP 2290–2304 Vol. 56, No. 10, October 2011.

[8] Pham Tran Anh Quang and Dong-Sung Kim, Member, IEEE “Enhancing Real-Time Delivery of Gradient Routing for Industrial Wireless Sensor Networks” IEEE Transactions On Industrial Informatics, Vol. 8, No. 1, February 2012.

[9] Yunhuai Liu, Qian Zhang, and Lionel M. Ni, “Opportunity Based Topology Control in wireless sensor network” IEEE Transactions On Parallel And Distributed Systems, Vol. 21, No. 3, March 2010.

[10] Antonio-Javier Garcia-Sanchez, Felipe Garcia-Sanchez, Joan Garcia-Haro, “Wireless sensor network deployment for integrating video-surveillance and data-monitoring in precision agriculture over distributed crops”, Computers and Electronics in Agriculture 75 (2011) 288–303.

[11] Tapiwa M. Chiwewe, Student Member, IEEE, and Gerhard P. Hancke, Senior Member, IEEE “A Distributed Topology Control Technique for Low Interference and Energy Efficiency in Wireless Sensor Networks” IEEE Transactions On Industrial Informatics, Vol. 8, No. 1, February 2012

[12] Hiroki Nishiyama, Member, IEEE, Thuan Ngo, Nirwan Ansari, Fellow, IEEE, and Nei Kato, Senior Member, IEEE “On Minimizing the Impact of Mobility on Topology Control in Mobile Ad Hoc Networks” IEEE Transactions On Wireless Communications, PP 1158–1166 Vol. 11, No. 3, March 2012.

[13] Azrina Abd Aziz, Y. Ahmet Şekerciŏglu, Paul Fitzpatrick, and Milosh Ivanovich “A Survey on Distributed Topology Control Techniques for Extending the Lifetime of Battery Powered Wireless Sensor Networks,” IEEE Communications Surveys & Tutorials, PP 121–141, Vol 15, No.1, First quarter 2013.

[14] Yunhuai liu, lionel ni, and chuanping hu, “A Generalized Probabilistic Topology Control for Wireless Sensor Networks,” IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, PP 1780–1788 VOL. 30, NO. 9, OCTOBER 2012

[15] Ningxu Cai, Mohammad Golami, Robert Bennan, “Application Oriented Intellegent Middleware for Distributed Sensing and Control,” IEEE TRANSACTIONS ON SYSTEMS AND CYBERNATICS, PP 947–955 VOL. 5, NO.11, NOVEMBER 2012.

[16] Sajjad Rizvi, Hassan Khaliq Querishi, “A1: An Energy Efficient Topology Control Algorithm for Connected Area Coverage in Wireless Sensor Networks,” Journal of network and Computer Application, PP 597–605, NO. 53, NOVEMBER 2011.

[17] Jalel ben Othman, Karim Bessaoud, Alain Bui, Laurence Pilard, “Self Stabilizing Algorithm for Efficient Topology Control in Wireless Sensor Networks,” Journal of Computational science, PP 1–10, NO. 117, JANUARY 2012.

[18] Hui Wang, Nazim Agoulmine, Maode Ma,Yanliang Jin, “Network Lifetime Optimization in Wireless Sensor Networks”, IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, PP 1127–1136 VOL. 28, NO. 7, SEPTEMBER 2010

[19] Yang Xiao, Miao Peng, John Gibson, Geoffrey G. Xie, Ding-Zhu Du, Athanasios V. Vasilakos, “Tight Performance Bounds of Multihop Fair Access for MAC Protocols in Wireless Sensor Networks and Underwater Sensor Networks,” IEEE TRANSACTIONS ON MOBILE COMPUTING, PP 1538–1553 VOL. 11, NO. 10, OCTOBER 2012

[20] Stefania Sardellitti, Sargio Barbarossa, Ananthram Swami “Optimal Topology Control and Power Allocation for Minimum Energy Consumption in Consensus Networks,” IEEE TRANSACTIONS ON SIGNAL PROCESSING, PP 383–399 VOL. 60, NO. 1, JANUARY 2012.

[21] Masoumeh Haghpanahi, Mehdi Kalantari, Mark Shayman, “Topology control in large-scale wireless sensor networks: Between information source and sink” Journal of, Ad Hoc Networks, article in press NOVEMBER 2012.

[22] Chuan Zhu, Chunlin Zheng, Lei Shu, Guangjie Han, “A survey on coverage and connectivity issues in wireless sensor networks,” The Journal of Network and Computer Application, 35, PP 619–632, December 2011.

[23] Hassaan Khaliq Qureshi, Sajjad Rizvi, Muhammad Saleem, Syed Ali Khay, m Veselin Rakocevic, Muttukrishnan Rajarajan, “Poly: A reliable and energy efficient topology control protocol for wireless sensor networks,” The Journal of Computer Communications, 34, PP 1235–1242, January 2011.

[24] Luo Xiaoyuan a, Yan Yanlin a, Li Shaobao b, Guan Xinping, “Topology control based on optimally rigid graph in wireless sensor networks” The Journal of Computer Networks, 34, PP 1235–1242, DECEMBER 2012.

[25] Linfeng Liu, Ruchuan Wang, FuXiao “Topology control algorithm for underwater wireless sensor networks using GPS-free mobile sensor nodes” The Journal of Computer Network and Application, 35, PP 1953–1963, August 2012.

[26] Halit Üster, Hui Lin “Integrated topology control and routing in wireless sensor networks for prolonged network lifetime” The Journal of Ad Hoc Network, 35, PP 835–851, September 2010.

[27] De-gan Zhang, Ya-nan Zhu, Chen-peng Zhao, Wen-bo Dai “A new constructing approach for a weighted topology of wireless sensor networks based on local-world theory for the Internet of Things (IOT)” Computers and Mathematics with Applications, 64, PP 1044–1055, Dec 2012.

[28] Alfredo Cuzzocrea, Alexis Papadimitriou, Dimitrios Katsaros, Yannis Manolopoulos “Edge betweenness centrality: A novel algorithm for QoS-based topology control over wireless sensor networks,” Journal of Network and Computer Applications, 64, PP 1210–1217, July 2011.

[29] J. Guadalupe Olascuaga-Cabrera, Ernesto López-Mellado, Andres Mendez-Vazquez, and Félix Francisco Ramos-Corchado “A Self-Organization Algorithm for Robust Networking of Wireless Devices,” IEEE Sensors Journal, Vol. 11, No. 3, March 2011.

[30] P. J. Wan, K. M. Alzoubi, O. Frieder, Distributed construction of connected dominating sets in wireless ad hoc networks, IEEE INFOCOM (2002).

[31] H. K. Qureshi, S. Rizvi, M. Saleem, S. A. Khayam, V. Rakocevic, and M. Rajarajan, An Energy Efficient Clique-based CDS Discovery Protocol for Wireless Sensor Networks, in: proceedings of 44th Annual conference on Information Sciences and Systems(CISS), March, 2010.

[32] M. Gerla, J. T. Tsai, Multicluster mobile multimedia radio network, ACM Wireless Networks 1 (3) (1995) 255–265.

[33] P. M. Wightman and M. A. Labrador, A3: A Topology Construction Algorithm for Wireless Sensor Network, in: Proceedings IEEE Globecom, 2008.

[34] Z. Yuanyuan, X. Jia, and H. Yanxiang, Energy efficient distributed connected dominating sets construction in wireless sensor networks, in: Proceedings of the ACM International Conference on Communications and Mobile Computing, pp. 797–802, 2006.

[35] J. Wu, M. Cardei, F. Dai, S. Yang, Extended dominating set and its applications in ad hoc networks using cooperative communication, IEEE Transactions on Parallel and Distributed Systems 17 (8) (2006) 851–864.

[36] F. Wang, M. T. Thai, D. Z. Du, On the construction of 2-connected virtual backbone in wireless network, IEEE Transactions on Wireless Communications 8 (3) (2009) 1230–1237.

[37] Y. Wu, F. Wang, M. T. Thai, Y. Li, Constructing k-connected m-dominating sets in wireless sensor networks, in: Military Communications Conference, Orlando, FL, October 29–31, 2007.

[38] Sanjeev Wagh, Ramajee Prasad, 2013, “Heuristic Clustering for wireless sensor networks using genetic approach”, International Journal of Wireless and Mobile Networking (IJWAMN) Vol. 1, No. 1(November 2013).

[39] Sanjeev Wagh, Ramajee Prasad, 2013, “Power backup density based clustering algorithm for maximizing the lifetime of wireless sensor network”, IEEE Xplore.

[40] Wagh S., Prasad R: “Energy Optimization in wireless sensor network through natural science computing: A Survey”, Journal of Green Engineering, Vol. 3, No. 4, 01.07.2013, p. 383–402., DOI: 10. 13052/jge1904–4720.342.

## Biographies

M. Bhende received her ME from University of Pune and bachelors degree from government college of engineering, Amravati, India. Currently She is pursuing PhD from University of Pune. Her research interest include Wireless Sensor Network, Network Security, Cloud Computing and Operating system. She has published more than 20 papers in International, National conferences and Journals. She is working as reviewer for various International conferences and Journals.

Prof. Dr. S. Wagh received his Bachelors & PhD in Computer Science & Engineering from SRT Marathwada University, Nanded and Masters degree from University of Pune, India. He was guest researcher at Center for TeleInfrastruktur (CTIF) at Aalborg University (AAU), Denmark during March 2013 to May 2014. His research interest includes Computer Networks, Network Algorithmics, wireless sensor networks, IoT etc. He is currently involved in the research work of intelligent wireless communication, evaluation and energy optimization in wireless sensor networks. Sanjeev Wagh is a member of IEEE, Fellow IE, LMISTE & Fellow IETE. He is active technical committee member for the various top-quality conferences and journals in wireless networking.