Performance Optimization of WSN Using Multiple Sinks

As the name indicates, wireless sensor network is very useful our day to day life. The term mobile technology is happening in recent years. And thus this is challenging job for industry to develop an efficient network which suits in today’s technology. The users are main factor in this technology, without users the network is incomplete or rather incomplete technology without users. The main aim behind this paper is to simulate and analyse performance in a multi sink environment with static infrastructure and mobile sources. We modified traditional aodv protocol and developed a new protocol called as MSAODV. To improve performance parameters for static sink infrastructure as well as for dynamic sink infrastructure this protocol is useful. Will compare results of MSAODV protocol with the traditional AODV protocol, and note down performance of MSAODV which will be greater than AODV. The paper is divided into 5 sections. First one is introduction, second one is related work, third one is system model, and fourth one is results and observations, final one is conclusion and future scope. Index Terms Remote user, Mobile communication, Mobile users, Wireless sensor networks, multiple sink. I.INTRODUCTION Wireless mobile technology has more demand in today’s culture. Mobile network has more popular now days because of its availability and ease of use. To overcome the limitations of single sink AODV protocol, there is need to develop a new protocol called as MSAODV. The new modified protocol (MSAODV) is a routing protocol for mobile ad hoc networks and other wireless ad hoc networks. MSAODV is, as the name indicates, a multiple sink ad hoc on demand distance vector. This protocol uses more than one sink for data gathering. By using more number of sinks in the network, the route request is divided and packets are distributed through sharing queries. The resolve functions for routing table management doing a great work for MSAODV protocol. Basically this function check for hop count, on the basis of hop count sink movement changes from static to dynamic. It will check if the hop count is greater than two than it will do a route request, add the packets to the queue and sending packets to default sink node. If this condition fails, again it will ask for rout request, add the packets to the queue and sending packets to nearest sink. In this way, MSAODV protocol distributes packets to different sinks which results in less congestion of packets near to sink node. That helps to improve packet delivery ratio of network. The node nearer to sink consumes more energy, due to that such nodes die at an early stage but in MSAODV protocol, by using multiple sink less energy consumption nearer to sink node that ultimately increases node life. This helps to improve overall energy consumption of network. II.RELATED WORK While referring the paper [1], author introduce a novel communication model that solves the disconnection problem of both static and mobile users, due to of data gathering tree in the mobile sink model and the dynamic sink model, and the hotspot problem and data delivery with both low delivery ratio and high latency of the single static sink, named the multiple static sinks based communication model, and propose a novel protocol for supporting the mobile users based on the multiple static sinks model. The paper [2], deals with the effect of a non uniform traffic pattern consisting of a single hot spot of higher access rate superimposed on a background of uniform traffic. Due to hot spot traffic, memory access is decreases. They call this problem as tree saturation. The technique [2], understands this problem arises due to lock or synchronization contention. The study of data collection capacity [3], has concentrated on large-scale random networks. The author of the paper gives idea of data collection technique, and how it is useful to collect sensing data from all sensor nodes. The paper [3], aim to understand the theoretical limits of data collection in a TDMA-based sensor network in terms of possible and achievable maximum capacity. This paper [3], first derive the upper and lower bounds for data collection capacity in arbitrary networks under protocol interference and disk graph models. They show that a simple BFS tree based method can lead to order-optimal performance for any arbitrary sensor networks. Also they study the capacity bounds of data collection under a general graph model, where two nearby nodes may be unable to communicate due to barriers or path fading, and discuss performance implications. Data can be collected from sensor nodes through multi hop technique. Generally, each sensor has the task to monitor and International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 4 Issue 3, March 2015 607 ISSN: 2278 – 1323 All Rights Reserved © 2015 IJARCET measure ambient conditions and disseminate the collected data toward a base station, or sink, for data post-analysis and processing. The paper [6] proposed data dissemination protocol to allow the dissemination of the collected data toward a static sink. Recently, mobile sinks were shown to be more energy-effective than static ones. In the article [6], existing data dissemination protocols supporting mobile sinks are summarized. In addition, sink mobility is analyzed, as well as its impact on energy consumption and the network lifetime. The paper [10] uses technique called as foot-print chain, to adapt the strategy for supporting mobile sink groups. In the paper [10], they propose a novel strategy for data dissemination decoupled with any member sink of a mobile sink group. In order to independently deal with a mobile sink group, the strategy is composed of three mechanisms: representative location update, distributed data collection, and per-group foot-print chaining. This paper [8] deals with new term called as, jumping sensors. They are mobile sensors that provide relocation capabilities and a temporary increase in elevation can be utilized for improving communication. The paper [8] provides a comprehensive multidimensional analysis for jumping sensors. It studies the main factors that impact the Received Signal Strength (RSS) in sensor communication, and performs a comparative analysis between theoretical and experimental results. The paper [7], proposes distributed energy-efficient deployment algorithms for mobile sensors and intelligent devices that form an Ambient Intelligent network. The term cluster structure is used by this paper. These algorithms [7] employ a synergistic combination of cluster structuring and a peer-to-peer deployment scheme. An energy-efficient deployment algorithm based on Voronoi diagrams is also proposed here. Performance of papers algorithms is evaluated in terms of coverage, uniformity, and time and distance travelled until the algorithm converges. According to author those algorithms are shown to exhibit excellent performance. In the paper [11], gives results for aodv protocol for multiple sink. But the results are varying from scenario to scenario. Some scenario it will give better, in some little bit vary. Individual node wise energy is also calculated. The paper compares single sink results with the multiple sink results by using same protocol AODV. The results are evaluated with the help of some parameters like delay, data delivery ratio, energy consumption. Here the base stations are static. Dynamic stations can be used for better results. III.SYSTEM MODEL The system model of my communication protocol is as follows. It is observed that from fig.1 number of nodes is taken to simulate with the help of speed. To decide the topology of network, network size is taken as parameter. Multiple sink is act as a gateway between users and sensor field. During the simulation process, queries of mobile users as well as mobile user’s record are stored in mobile user management table. This helps to recognize the information of mobile users in the network. After simulation process will get the parameters output, with the help of trace n analysis process will plot the graph against energy consumption, packet delivery ratio, delay, throughput by using xgraph. Simulation can be done by using ns2 simulation tool. Fig.1 Block diagram of MSAODV model IV.RESULTS AND OBSERVATIONS In result analysis section we compared performance of both protocols (AODV and MSAODV) with respect to their parameters. We use four metrics to evaluate the performance of AODV and MSAODV protocol, say Packet delivery ratio, throughput, delay, and energy consumption. We have used Network Simulator (NS-2.34) on fedora 13 system to collect results. We have created following wireless networking scenarios and have recorded the values for above mentioned parameters. Scenario 1:-Wireless Topology with Random Node movement Simulation Time: 100 s Node Movement: Dynamic Source Node: 1(Movable) Number of Sink Node: 1(AODV), 2(MSAODV) MAC Type: 802.11 Application Traffic: UDP Here performance is calculated on basis of number of technical stuffs. A.Packet Delivery Ratio Packet delivery ratio is nothing but the number of successfully received data packets at a user to the total number of data packets generated by every sensor node. Table 1 Number of Nodes Vs PDR Number of Nodes Packet delivery ratio for AODV and MSAODV AODV MSAODV 50 99.6669 99.9167