Energy Aware Node Placement Algorithm for Wireless Sensor Network

Transcription

1 Advance in Electronic and Electric Engineering. ISSN , Volume 4, Number 6 (2014), pp Research India Publications Energy Aware Node Placement Algorithm for Wireless Sensor Network Kirankumar Y. Bendigeri and Jayashree D. Mallapur Basaveshwar Engineering College, Vidyanagar, Bagalkot Karnataka, India. Abstract Wireless Sensor Network (WSN) is a technology, which has great invention for public and the defense. The wireless sensor communication technology allows random participation of sensor nodes with particular applications to take part in the network, which results in most of the uncovered simulation area, where fewer nodes are located at far distances. The drawback of such network would be, additional energy is spent by the nodes located in a pattern of dense location, using more number of nodes for a smaller distance of communication adversely in a region with less number of nodes, additional energy is again spent by the source node in order to transmit a packet to neighbors there by transmitting the packet to reach the destination. The proposed work is intended to develop Energy Efficient Node Placement Algorithm (EENPA) in order to place the sensor node efficiently in simulated area, where all the nodes are equally located on a radial path to cover maximum area at equidistance. The total energy consumed by each node compared to random placement of nodes is less by having equal burden on fewer nodes of far location, having distributed the nodes in whole of the simulation area. There by calculating the network lifetime which also proves to be efficient as compared to random placement of nodes, hence increasing the network lifetime too. Simulation is been carried out in a qualnet simulator, results are obtained on par with random placement of nodes with EENP algorithm. 1. Introduction In recent years use of wireless sensor networks (WSN s) has increased in numerous applications such as wild life forest monitoring, disaster management, space

2 542 Kirankumar Y. Bendigeri & Jayashree D. Mallapur exploration, industry automation, secure installation, border protection, and battlefield surveillance. The demand for such application is to use sensors of tiny size. These sensor devices may be called as sensor nodes or simply nodes that operate independently in a environment. Each node has got a sensing range and has ability to connect itself with the neighbor nodes and a base station. Sensor networks are different from other wireless networks due to the number of limitations like battery power, node densities, node deployment, security issues, bandwidth and huge data volume etc. WSNs utilize a large number of militarized sensor nodes with sensing, processing and wireless communicating capabilities to implement the said tasks in the detection area. Sensor nodes are placed close to a particular application or very near to it. Nodes may self organize into clusters of different size and different types of nodes to complete a task issued by the user. These node positions are predefined as a result, they fit for many applications. Sensor network considered with large number of nodes is divided into clusters, and within a cluster there are several number of nodes, the advantage of WSN is that instead of sending individual data from of each node, combined result from all the nodes from each of the cluster can be sent to the base station, thus reducing the energy consumed by each node. WSN s are designed to operate on a small battery power say 100 nj. A node stops working out when it runs out of energy and thus we consider that WSN may be physically damaged, if many sensors exhaust their battery power source which is in terms of energy. The lifetime of WSN is mainly effected by node position, pattern of topology, routing protocol. One of the major task and challenge in WSN s is to place the wireless sensor nodes in specified field of a simulation area. Node placement by optimization can improve the energy consumption and a network lifetime. Node placement is a prime concern as many applications of WSN demands for longer life over the years. Proposed EENP algorithms mainly considers energy oriented strategy to minimize total energy consumption by considering a circular pattern to deploy the nodes in a simulation area, and secondly lifetime oriented strategy, that mainly focuses more on lifetime of a nodes which directly increases the lifetime of a network. As the information collected by all the nodes is to be forwarded to base station which is called as sink node. It is required to have a sufficient energy for each node. An efficient sensor location algorithm gives us the minimum intersection of sensor s range and maximizes the total area covered by sensors. 2. Related Work In paper [1] node placement is considered as an important task in wireless sensor network and is a multi-objective combinatorial problem. A multi-objective ACO (Ant Colony Optimization) algorithm based framework has been proposed here. The framework optimizes the operational modes of the sensor nodes along with clustering schemes and transmission signal strengths. In paper [2] author formulates a constrained multivariable nonlinear programming problem to determine both the locations of the sensor nodes and data transmission pattern. The two objectives studied in the paper are to maximize the network lifetime and to minimize the application-

3 Energy Aware Node Placement Algorithm for Wireless Sensor Network 543 specific total cost, given a fixed number of sensor nodes in a region with certain coverage requirement. In paper [3], the relationship between the network lifetime and the coverage problem is been discussed. It is shown that controlling the density function, relying on efficient sensor nodes placement, can improve significantly network lifetime. Hence an efficient node placement algorithm addressing the case where the monitored area density is equal to two is done. In paper [4] novel ideas to save energy through extra relay nodes is done by eliminating geometric deficiencies of the given topology. Given the sensing locations, the problem is to determine the optimal locations of relay nodes together with the optimal energy provided to them so that the network is alive during the desired lifetime with minimum total energy. In paper [5] a strategy for optimizing the placement of mobile sensor nodes, and energy metric of communication is presented for energy efficiency. Dijkstra s algorithm is utilized to obtain the optimal paths from which energy metric is derived. Tradeoff between coverage and energy consumption is achieved by optimizing both metrics and adjusting the metric weight of optimization. Simulations of mobile target tracking are executed to verify that energy efficiency is enhanced by deployment optimization strategy and dynamic power management mechanism. In paper [6] a novel algorithm for autonomous deployment of active sensor networks is analyzed. The algorithm aims to enhance the sensing coverage based on an initial placement of sensor nodes. Based on the fact that a unique circle packing exists satisfying any given set of combinatorics and boundary conditions of a sensor network. Minimum sensing range required for every interior node to fulfill such packing conditions is found. Based on a number of numerical simulations, algorithm always yields sensor deployments of wide coverage and minimize the sensing ranges required for every interior sensing node to satisfy the packing and boundary conditions. 3. Node Placement Algorithm Node placement is a technique to place the nodes effectively in a simulation area so as to conserve the minimum energy from each node that is intended for transmission of packets or a data. Most of the network considers a communication by deploying the nodes randomly. When the nodes are deployed randomly, there are three issues that mainly are, some nodes are densely deployed at particular region while the other region has got fewer nodes located at farer distances and leaving region will not at all be with the single coverage of node. The drawbacks of such random node deployment is that nodes with dense location, where routing is to take place. All the hops between source and destination in a region of dense location of nodes take part in routing leading to additional utilization of energy from each node, where as in the case of nodes at far location, additional energy again is to be spent in transmitting the data to neighbors as well to the destination located at far distances. On a similar way it is difficult to manage the routing in a region where no nodes are located leading in all the three case, an uneven distribution of energy source and node deployment. Fig. 2 shows the random deployment of sensor nodes, the node pattern is set up in Qualnet

4 544 Kirankumar Y. Bendigeri & Jayashree D. Mallapur simulator. Fig. 3 shows the circular deployment of nodes which provides mainly two objectives. First one is, careful node placement can be a very effective optimization means for achieving the desired design goals, and is classified as static approaches. On the other hand, some schemes have advocated dynamic adjustment of node location, since the optimality of the initial positions may become void during the operation of the network depending on the network state and various external factors, We categorize the placement strategies into static and dynamic depending on whether the optimization is performed at the time of deployment or while the network is operational, this approach considers positions of node metrics that are independent of the network state or assume a fixed network operation pattern that stays unchanged throughout the lifetime of the network. Fig. 1: Showing random deployment of sensor nodes.

5 Energy Aware Node Placement Algorithm for Wireless Sensor Network 545 Fig. 2: Showing circular deployment of nodes. 4. Proposed Work The proposed algorithm considers the drawback of wireless sensor network, in which nodes are deployed randomly. In this paper, the optimized placement of the sensor nodes and relay nodes are done by the circular node deployment technique which is used for reducing the power consumption and thereby increasing the lifetime of the WSN. To ensure the coverage of the network, a sensor placement should satisfy the following constraint. D is the sensing region given by, with R as the radius of the network 0 D; d n (1)

6 546 Kirankumar Y. Bendigeri & Jayashree D. Mallapur 1 i 1 d R i for(1 i n) (2) A sensor consumes E sur =50nJ for transmitting unit of data and k = 100 pj/bit/m2 for the transmitting amplifier. So the energy consumption of sensor S i in order to be active in the network is given by, E E sur ene As the energy consumed depends on the number of bits transmitted and hence if sensor node transmits k bits, then energy consumed in order to be active in the network is given by, E Actual) K E (4) s ( ene At the destination nodes will receive packets; practically the received packets are less than k. Hence energy consumed for receiving packets is given by, E Actual ) ( T P ) (5) r( p l Where T p is the energy due to transmission and P L is the energy lost due to packet loss. Similarly energy consumption of sensor S i in transmitting a unit of data packet to neighbor node is given by E nr E 2 s( Actual ) n EI di Where n n is the number of neighboring nodes, E i is the energy of each node and d i is the distance of each neighbor from source node. Note that node S i also relays the data collected by nodes s1 to node si-1, we assume sensors relay data only to next nodes along the radius, the power consumption of the ith node to relay of all the collected data can be denoted as: (3) (6) E E r E ( n 1) d 2 s( Actual) i i (7) 5. Simulation The simulation is carried out in a qualnet simulator. AODV is the routing protocol that mainly concentrates on node deployment technique. After several rounds of routing packets through AODV, simulation results demonstrate that energy consumption with circular deployment of nodes certainly is efficient and less as compared with random deployed nodes. Proposed algorithm tries to save the energy of a network, covering maximum area with increase in lifetime of a network. Results obtained are plotted for random and circular deployment of nodes on various parameters. Fig. 3 and 4 gives the amount residual energy spent by circular and random node deployment topology. It is clear from the simulation that amount energy left for the circular pattern is more.

7 Energy Aware Node Placement Algorithm for Wireless Sensor Network 547 Fig. 3: Showing a transmit energy for circular node deployement. Fig. 4: Showing a transmit energy for random node deployment. 6. Conclusions This paper outlined energy efficient node placement algorithm by comparing the energy consumed by the nodes of random placement with that optimized placement of nodes. Simulation results shows that with the help of node placement algorithm energy consumption of nodes with proposed algorithm of node optimization using circular deployment are less compared to random deployment of node, this definitely help to increase the network lifetime. Work can be extended to compare the network lifetime by placing the nodes in triangular as well as hexagonal pattern. Best pattern then can be evaluated.

8 548 Kirankumar Y. Bendigeri & Jayashree D. Mallapur Reference [1] Rabindra, Ku Jena, Energy Aware Node Placement in wireless sensor network aco Journal of Theoretical and Applied Information Technology, Vol. 53 No.2 20th July [2] Peng Cheng, Chen-Nee Chuah, Xin Liu, Energy-aware Node Placement in Wireless Sensor Networks, Globecom [3] Moez Esseghir, Nizar Bouabdallaht and Guy Pujolle, A Novel Approach for Improving Wireless Sensor Network Lifetime, IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications [4] Sinem Coleri Ergen and Pravin Varaiya, Optimal Placement of Relay Nodes for Energy Efficiency in Sensor Networks, IEEE ICC 2006 proceedings [5] Xue Wang, Junjie Ma, Sheng Wang, Collaborative Deployment Optimization and Dynamic Power Management in Wireless Sensor Networks, Fifth International Conference on Grid and Cooperative Computing (GCC'06), IEEE [6] Miu-ling Lam Yun-hui Liu, Active Sensor Network Deployment and Coverage Enhancement using Circle Packings, Proceedings of the 2006 IEEE International Conference on Robotics and Biomimetics December 17-20, 2006, Kunming, China [7] M. Dhanaraj and C. Siva Ram Murthy, On Achieving Maximum Network Lifetime Through Optimal Placement of Cluster-heads in Wireless Sensor Networks, ICC 2007 proceedings.