Multi objective energy efficient node deployment in wireless sensor networks

Multi-Objective Energy Efficient Node Deployment in Wireless Sensor Networks Ruby Saini1,Mohit Lalit2 1 Dept. of CSE, Geeta Institute of Management and Technology, Kurukshetra, India 2 Dept. of CSE, Geeta Institute of Management and Technology, Kurukshetra, India Abstract—A wireless sensor network consists of a collection of sensor nodes that sense the application environment, collect the information and send it to the base station. Many studies have been done in the area of Wireless Sensor Networks (WSNs). In this kind of networks, some of the key parameters that need to be satisfied are connectivity, number of sensor nodes and energy consumed by nodes. In this paper, we propose a multi-objective optimization algorithm based on energy and connectivity to prolong WSN lifetime. The efficiency of our algorithm can be shown as finding the optimal solutions among the least energy consumption, and the minimum number of active nodes while maintaining the connectivity of the network.

Keywords—Wireless Sensors Network, Multi-objective optimization, Lifetime.

I. INTRODUCTION Wireless sensor networks (WSN) can be used in wireless communication technology, sensing technology, micro-electronics technology and embedded system, for a wide variety of applications and systems with excessively varying requirements and characteristics. A wireless sensor network design is affected by many factors such as transmission errors, network topology and power consumption. One of the most fundamental issues in WSN designing is the deployment problem.

There are different specific problems in the literature, e.g. deployment, layout, coverage or positioning problem in WSNs. The term positioning seems to be more general, so we propose a classification illustrated in Figure 1. On the left is localization – its aim is to location of nodes where the nodes are placed. On the right is deployment – its aim is to determine where the nodes should be placed.

WSN positioning

Localization

Deployment

Figure 1. A classification for positioning in WSN

The most important step of design to selectively decide the locations of the sensors to optimize the desirable objectives, e.g., maximize the area coverage or minimize the use of energy. Fundamental questions in this case include [1]:  How many sensor nodes needed to meet the overall system objectives?  For a given network with a certain number of sensor nodes, how do we deploy these nodes in order to optimize network performance?  When data sources change, how do we adjust the topology and sensor deployment of the network?

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II. WSN PARAMETERS There are various significant parameters which are included in WSN which are the following [2]: 1.

Coverage: In WSN, when the nodes are deployed randomly the position of the nodes is out of control and as a result some nodes which are places in the target region are known as coverage holes remaining are uncovered.

2.

Connectivity: In WSN, network connectivity is measured by connectivity graph of the network. If the network connectivity graph is connected as the whole graph, the network is assumed to be connected, else partitioned. If there is more than one distinct path between every two nodes of the network, the connectivity degree is defined as the minimum number of these paths.

3.

Scalability: Some WSN applications need the order of deployment of sensor nodes for monitoring and detecting events in the target environment to be as high as hundreds or thousands. In some specific applications this number may even reach to the extreme value of millions. The new scheme in the wireless sensor networking must be applicable to such large values. These new scheme should network grow or develop without major changes to the design.

4.

Lifetime: Network lifetime is directly related to the battery lifetime of the sensor nodes. The increase in node’s activities leads to a network node’s power consumption that results in lower battery and less network lifetime.

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Latency: The delay in transmitting data, data aggregation and routing is defined as latency. These parameters can be measured as the time between arrival of data packets at the destination and its departure from the source node.

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Cost: In WSNs, the cost of a wireless sensor network starts from the construction phase of sensor nodes.

Depending on the application and sensor devices used in the node, the cost of the node can be variable. Some applications do not consider node cost because of the low price of node used.

III. LITERATURE SURVEY Swagatam Das et al., 2012 [3] proposed the objectives of sensor nodes deployment that needed to be satisfied were monitored coverage of the area, energy consumed by the WSN, lifetime of the network, and connectivity and number of deployed sensors. Multi-objective optimization problem was to find a deployed sensor node arrangement to maximize the coverage area and network lifetime, to minimize the energy consumption and node deployment.

Seyed Mahdi Jameii et al., 2013 [4] proposed a NSGA-II based multi-objective algorithm for optimizing area coverage, number of active sensor nodes and energy consumption. The efficiency of algorithm could be shown as finding the optimal results among the maximum rate of coverage, the least energy consumption, and the minimum number of active nodes while maintaining the connectivity of the network.

Shikha Swaroop et al., 2013 [5] presented the features of the environment in which the sensor networks may deploy. Wireless Multimedia sensor nodes were able to process real time multimedia content such as video and audio streams data from the environment. Node deployment in wireless multimedia sensor network was application dependent.

Baldeep Kaur Brar, 2014 [6] proposed a wireless sensor network consists of sensor nodes which were capable to perform sensing and transmission. These sensor nodes had limited battery power

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International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 08; August - 2016 [ISSN: 2455-1457]

which is difficult to replace due to resistant environment. The relay node deployment problem for wireless sensor networks was concerned with placing the minimum number of relay nodes into the wireless network to meet certain connectivity requirements.

Shobha et al., 2015 [7] described wireless sensor network was built with many sensor nodes.

Different approaches were used for the deployment of sensor nodes were, random deployment and the deterministic deployment. The efficient coverage techniques used in wireless sensor networks were static and dynamic coverage strategy.

KiranKumar Y. Bendigeri et al., 2015 [8] proposed the optimized network developed by effective placement of nodes in circular and grid pattern. Each of the nodes was in optimized positions at uniform distance with neighbors, followed by running energy efficient routing algorithm that saves an additional energy to provide connectivity management by connecting all the nodes.

Sasmita Manjari Nayak, 2015 [9] investigate random and deterministic sensor node deployments in WSN. The Sensor Node deployment was a fundamental issue to be solved in Wireless Sensor Networks (WSNs).A proper node deployment could reduce the complexity of problems in WSN. It could extend the lifetime of WSNs by minimizing energy consumption.

Xuemei Sun, 2015 [10] proposed node deployment algorithm in wireless sensor networks based on Steiner tree algorithm. This algorithm considered the two aspects algorithm complexity and practical topology to design improvement strategies. The algorithm could ensure high arithmetic speed and better results to solve node deployment based on network connectivity.

IV. PROPOSED ALGORITHM Aim of the proposed algorithm is to increase the network lifetime by minimizing the energy consumption and maximizing the connectivity using multi-objective optimization algorithm. A multi-objective optimization problem involves a number of objective(fitness) functions which are to be either minimized or maximized. The proposed algorithm is combination of two algorithms one is NSGA-II algorithm and other is DE algorithm. And the differential evolution (DE) algorithm is used for cluster head election. The proposed algorithm describe in the following steps.

Step 1: NSGA-II algorithm: NSGA-II is termed as “Non-Dominated Sorting Genetic Algorithm-II�. It is one of the most efficient multi-objective optimization algorithm. By introducing the fast non dominated sorting approach, the Pareto-optimal front can be searched effectively. The main role of NSGA-II algorithm is to find the optimal locations of node deployment into the network.

The optimal locations of node deployment are find using NSGA-II fitness function. The fitness function is calculated on the basis of two main multi-objective parameters such as energy consumption and connectivity. According to NSGA-II algorithm to minimize the energy consumption and maximize the connectivity of wireless network to improve the lifetime of a network.

Step 2: Cluster Head Election: Cluster heads are elected with the help of differential evolution fitness function and using DE algorithm process. The differential evolution uses distance and direction information of node deployment into the network.

The fitness evaluation function is designed according to the requirement of the objectives of the work. In the fitness function the remaining energy of the node is added, so that we can take account

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of the energy of the node’s energy before its selection as the cluster head. The fitness function used is the negative fitness function and it considers the energy of node for the fitness evaluation.

V. SIMULATION RESULTS The algorithms are compared on the basis of these graphs which are the result of their simulation.

The comparison of dead nodes between algorithms and number of rounds are shown with the help of graphs.

A. Lifetime estimation using HND and LND:The figure 2 shows graph of half node dead (HND) in optimal deployment DE algorithm happens after 1000 rounds in spite of random deployment DE algorithm which is having its half dead after 800 rounds. Hence optimal deployment algorithm is energy efficient than random deployment algorithm. The figure 3 shows the graph of last node dead (LND) in optimal deployment DE algorithm happens after 1600 rounds in spite of random deployment DE algorithm which is having its last dead node after 1200 rounds. Hence optimal deployment algorithm is energy efficient than random deployment algorithm.

B. Number of Dead Nodes per Round:Figure 4 gives the graph which compares the performance of random and optimal deploy DE in terms of number of dead nodes with total number of rounds. Green line represents the optimal deployment DE and blue line represents the random deployment DE. Graph shows that optimal deployment shows improved performance over random deployment after 800 rounds.

C. Remaining Energy per Round:Figure 5 gives the graph which compares the performance of random and optimal deploy DE in terms of number of remaining energy with total number of rounds. Green line represents the optimal deployment DE and blue line represents the random deployment DE. Graph shows that random deployment have almost same residual energy up to initial 100 rounds as optimal deployment is having. The optimal deployment shows improved performance of the remaining energy over random deployment after 100 rounds.

Figure 2. Comparison of optimal and random deployment in terms of half node dead @IJRTER-2016, All Rights Reserved

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International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 08; August - 2016 [ISSN: 2455-1457]

Figure 3. Comparison of optimal and random deployment in terms of last node dead

Figure 4. Comparison of the performance of random and optimal deploy-DE in terms of number of dead nodes & number of rounds

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Figure 5. Comparison of the performance of optimal and random deploy-DE in terms of number of dead remaining energy with total number of rounds

VI. CONCLUSION In this paper, proposed a multi-objective optimization algorithm based on energy and connectivity to prolong WSN lifetime. To maximize the network lifetime, minimizing the connectivity of nodes and maximizing the energy consumption of nodes. The results obtained from the simulations showed that with the same number of active sensor nodes, optimal deployment DE can obtain better results than random deployment DE algorithm. In future NSGA-II algorithm can be further implemented for other parameters such as coverage, reliability and quality of service (QoS).

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REFERENCES Michał Marks. "A Survey on Multi-Objective Deployment in Wireless Sensor Networks."Journal of Tele communication and Information Technology, 2010.

Kamal Jadidy Aval and Shukor Abd Razak."A Review on Implementation of Multiobjective Algorithms in Wireless Sensor Networks." World Appl. Sci. J., 19 (6): 772-779, 2012.

Soumyadip Sengupta , Swagatam Das, M.D. Nasir, B.K. Panigrahi. "Multi-Objective Node Deployment in WSN: In Search of An Optimal Trade-off among Coverage, Lifetime, Energy Consumption, Connectivity." 2012.

Seyed Mahdi Jameii and Seyed Mohsen Jameii. " Multi- Objective Energy Efficient Optimization Algorithm for Coverage Control in Wireless Sensor Networks." International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.3, 2013.

Shikha Swaroop,Prashant Krishan, Ayashasiddiqua." A Survey of Architecture and Node Deployment in Wireless Multimedia Sensor Network." Intenational Journal of Scientific and Research Publications ,Volume 3, 2013.

Baldeep Kaur Brar. "Review on Relay Node Placement Techniques to Increase System Capacity in WSN." International Journal of Engineering Research And General Science ,Volume 2, 2014.

Shobha and Kiran Kumari Patil. "Survey Paper on Node Deployment and Coverage Strategies in Wireless Sensor Networks." In Proceedings of 35th IRF International Conference, 2015.

Kirankumar Y. Bendigeri, Jayashree D. Mallapur." Multiple Node Placement Strategy for Efficient Routing in Wireless Sensor Networks." 2015.

Sasmita Manjari Nayak."Node Deployment and Coverage in Wireless Sensor Networks." International Journal of Innovative Research in Advanced Engineering (IJIRAE), Volume 2 , 2015.

Xuemei Sun."Node Deployment Algorithm Based on Improved Steiner Tree."International Journal of Multimedia and Ubiquitous Engineering ,Vol.10, 2015.

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