INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET)

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1 INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET) International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN ISSN (Print) ISSN (Online) Volume 3, Issue 2, July- September (2012), pp IAEME: Journal Impact Factor (2012): (Calculated by GISI) IJECET I A E M E THE ROUTING ALGORITHMS FOR WIRELESS SENSOR NETWORKS THROUGH CORRELATION BASED MEDIUM ACCESS CONTROL FOR BETTER ENERGY EFFICIENCY P.T.Kalaivaani MNM Jain Engineering College, Department of Electronics and Communication Engineering, Jyothi Nagar,Thorapakkam, Chennai , India kalaivani.p.t@gmail.com] A.Rajeswari Coimbatore Institute of Technology Department of Electronics and Communication Engineering Coimbatore , India rajeswari@cit.edu.in] ABSTRACT In Wireless Sensor Networks (WSN), Computation and Sensing, Communication are the three basic functionalities performed in Wireless Sensor Networks. Generally in any application, large number of sensors are deployed in Wireless Sensor Networks. These Sensors are systemized and they form a Cooperative Communication Network. In Wireless Sensor Networks, one of the Challenges is Energy Efficiency. In this paper the Routing Algorithms for Wireless Sensor Networks through Correlation Based Medium Access Control for better Energy Efficiency is proposed. A new model of WSN with Spatial Correlation Based MAC is developed with Collaborative based Medium Access Control (CC MAC) and AODV and DSR. Simulations have been obtained using NS2 for analyzing the performance of the protocols such as AODV and DSR in the proposed model. The results show that AODV performs well than that of DSR in parameters such as End to End delay, Packet Delivery Ratio, Packet Loss, Energy Consumption. Key Words : Wireless Sensor Network (WSN), Spatial Correlation, Collaborative based Medium Access Control (CC MAC), Adhoc On Demand Distance Vector Routing (AODV), Dynamic Source Routing (DSR) 294

2 I. INTRODUCTION Wireless Sensor Networks (WSN) comprise sensors which performs special tasks, such that Sensing, Processing, Storage and consigns the energy at different locations to achieve sensing and information gathering. Random deployment and dense nature and unattended mode of operation, varying nature of radio links, course limited energy resource are the major features of the WSNs. The energy consumption in Wireless Sensor Network is increased due to sensing process. To save the energy of the network, the total number of sensor nodes sending data is to be reduced thereby decreasing the transmission of redundant data. The main goal of energy minimization in WSNs is to extract event features from the collective information provided by sensor nodes [2]. II. RELATED WORK A brief literature survey is presented in the following section: A theoretical framework has been developed for transmission regulation of sensor nodes under distortion constraint exploiting spatial correlation on MAC [1]. A draw back of the work is that it doesn t take the signal strength as the major constraint [1]. A Spatial Correlation Based Medium Access Control is developed resulting in higher performance in the aspect of energy consumption, transmission latency, packet delivery rate, packet drop rate [2]. An energy aware spatial correlation based on a cluster protocol [3]. In this approach, only the cluster-heads are responsible for exploiting spatial correlation of their member nodes and selecting the appropriate member nodes to remain active. The gridiron spatial correlation mechanism is adapted to achieve the required reliability by dynamically changing the correlation region. The draw back of this work is the Correlation radius is fixed and the correlation radius value between the sensor nodes cannot be resized. An Energy Efficient Routing protocols with AODV and DSR in Wireless Sensor Network are compared [14]. The strength and Weakness of different MAC protocols are analyzed for energy wastage in Wireless Sensor Networks [7]. III. PROPOSED MODEL FOR WIRELESS SENSOR NETWORK Each node in a sensor network integrated with sensors, processors, transceivers with limited resources and low capacity battery is associated with each node [15]. Primary source of energy to sensor node is the battery. At regular interval of time, the nodes available in a sensor network collect the data points and transform all the data points into an equivalent electric signal and distribute the signal to the sink or base node via some reliable communication medium. Sensor nodes are spatially distributed in nature and the ambient conditions related to surrounding environment of the sensors are measured by sensing circuitries in sensor network [15]. Network Simulator (NS2) has been used for modeling the proposed the Routing Algorithms for Wireless Sensor Networks through Correlation Based Medium Access Control for better Energy Efficiency. Simulation parameters considered for this work are Network Area of Size 1500x1500 with 50 number of nodes and the packet length is 250 bytes. Initial energy consideration is 1000 joules, Bandwidth is 2MHz, Data rate is 1Mbps with Transmitting Power, Receiving Power and Ideal Power value of 1mW,Sleep power range is mw with the threshold value of 10. Distortion is one of the reliability constraint. It is observed that the distortion increases when the sensor nodes fail to report the event from within the defined correlation 295

3 region. The correlation region is changed dynamically according to the observed reliability. At sink node, the distortion is given by, D = E[d(S, Ŝ )] (1) In equation (1), D is the Distortion value and S is the event and S. Distance between the two nodes is calculated by the formula, Ŝ is the estimated value of d i,j = ρ si ρ sj (2) ρ is the Correlation coefficient located at the coordinates n i and n j. IV. COLLABORATIVE BASED MEDIUM ACCESS CONTROL (CC - MAC) The CC-MAC Protocol is used in the above model for providing Iterative Node Selection Algorithm. Primary components involved in CC - MAC protocol are, Iterative Node Selection Algorithm and E MAC and N MAC. E MAC is also known as Event MAC and N MAC is also known as Network MAC. The objective of the Event MAC is to filter out the correlated sensor records and the representative nodes in a correlated regions elected in a distributive manner [1]. E MAC forms the correlation based clusters. N MAC prioritizes the route through packets when the medium access is in usage. The Data Packet Structure of CC MAC protocol is given in Fig.1. Fig.1 Data Packet Structure CC MAC protocol has the capability to decrease redundant data and energy consumption. The representative node selection algorithm in CC-MAC protocol is random in nature. Estimate the signal parameters from the received samples such as σ s,ρ(i,j),ρ(s,i). Where σ s is the variance of event source and ρ(i,j),ρ(s,i) are the correlation coefficient and it is iteratively run until D max is met. INS estimates the variance σ s ² from collected data. The parameter Estimation part of INS includes the parameters such as Correlation coefficient and distance between the nodes and correlation parameter θ 1, the correlation parameter. Each observed sample, Xi of sensor node at time t is represented as X i = S i + N i, i = 1,..., N (3) where Si is the event information and N i is the observation noise. The sink is interested in reconstructing the source S according to observations of nodes n i which observe the spatially correlated version of S at (x i,y i ), i.e., S i. The physical phenomenon is modeled as joint Gaussian random variables (JGRVs) at each observation point as, The Correlation Coefficient is defined as ρ i,j. The covariance function is defined as ( ) k v. It is given by the expression, 296 (4)

4 θ ( / ) 2 PE d θ K ( ) 1 v d = e ; θ 1 > 0, θ 2 (0,2] (5) Correlation Region Fig.2 Representation of Correlation Region Fig.2 shows the representation of Correlation Region. The correlation region is defined as the region in which all the sensor nodes send the readings which are similar in nature, therefore it is enough to send a single report to represent the correlation region [3]. The objective of the spatial correlation is to prevent redundant data during transmission. V. ROUTING ALGORITHMS Two types of routing protocols are Proactive protocols and reactive protocols [14]. Proactive Routing Protocols maintain fresh list of destinations and their routes by periodically distributing routing tables throughout the network. The main disadvantages are respective amount of data for maintenance and slow reaction on restructuring and failures. Reactive Protocols are also known as On-demand protocols. This type of protocols finds a route on demand by flooding the network with route request packets. The main advantages of such algorithms are, High Latency time in route finding and Excessive flooding can lead to network clogging. Two different routing protocols are considered to analyze the behavior of CC MAC Protocol. They are AODV and DSR. Dynamic Source Routing is shortly known as DSR and Adhoc On Demand Distance Vector Routing is known as AODV. DSR uses source routing and AODV uses a table driven routing framework with destination numbers. Timer activities are not directly involved in DSR but AODV takes into account of timer activities. AODV uses traditional routing tables, one entry per destination. Whereas in DSR, certain multiple route cache entries per destination are used. Even through DSR and AODV share the on demand behavior, their routing mechanisms are different. The routing protocols AODV and DSR performance are compared based on the four different parameters. Such as End to End Delay, Packet Delivery Ratio, Packet Loss, Energy Consumption. A. Comparison of End to End delay with AODV and DSR End to End delay is defined as the ratio between sum of individual data packet delay to the total number of data packets delivered. 297

5 Fig.3 End to End Delay It is observed from Fig.3 that t as simulation time increases, the end to end delay with DSR is higher than that of AODV. This is because DSR uses source routing and AODV uses a table driven routing. B. Comparison Of Packet Delivery Ratio with AODV And DSR Packet Delivery ratio is the percentage of the ratio between total number of successfully delivered to the total number of data packets sent. data packets Fig.4 Packet Delivery Ratio It is observed from Fig.4 that as simulation time increases, the Packet delivery ratio with DSR is decreased 2% lesser than that of AODV. This is because DSR uses multiple route cache entries per destination and AODV uses traditional routing tables, one entry per destination. C. Comparison of Packet Loss with AODV and DSR Packet Loss is obtained by subtracting the number of packets sent by the source to the number of packets received by sink. 298

6 Fig.5 Packet Loss It is observed from Fig.5 that as simulation time increases, the Packet Losss with DSR is increased 38% than that of AODV. This is because AODV uses a broadcast route discovery algorithm and then Unicast route reply message. Also, AODV has the ability to provide Unicast, Multicast and broadcast communication. D. Comparison of Energy Consumption with AODV and DSR Energy Consumption is defined as the ratio between sum of energy expended by each node to the total number of data packets delivered. Fig.6 Energy Consumption It is observed from Fig.6 that as simulation time increases, the Energy Consumption of with AODV is less than that of DSR. This is because DSR needs support from MAC layer to identify the link failure. Also it is clear from Table V that there is a percentage decrease in energy consumption in WSN with AODV than with DSR protocol. VI. CONCLUSION AND FUTURE WORK The Routing Algorithms for Wireless Sensor Networks through Correlation Based Medium Access Control for better Energy Efficiency is proposed in this paper. The spatial correlation between the sensor nodes are considered in the proposed algorithm. Using NS2, Wireless Sensor Networks are simulated and the proposed algorithm is implemented with spatial correlation based CC MAC, AODV, DSR. The spatial resolution of nodes are controlled by deactivating the redundant nodes. The performance of CC MAC is compared with IEEE 299

7 by using Internode Selection algorithm. CC MAC yields better performance in energy efficiency. Parameters such as, End to End delay and Packet drop rate and Packet delivery ratio and Energy Consumption are compared. Among the routing protocols AODV gives better result than DSR. By reducing the redundant data from redundant nodes the spatial correlation proves that it is the better energy efficient method. In future work, field of grid and cluster head based algorithm will be considered. Also, CC MAC Protocol Can be modified for multiple correlation radius values and QOS requirement for various sensor node s information can also be considered. REFERENCES 1. Mehmet C.Vuran and Ian F.Akyildiz (2006), Spatial Correlation Based collaborative Medium Access Control Based Collaborative Medium Access control in Wireless Sensor Networks, IEEE / ACM Transaction on Networking, Vol.14,N Guoqiang Zheng and Shengyu Tang (2011) Spatial Correlation Based MAC Protocol for Event - Driven Wireless Sensor Networks, Journal of Networks, Vol.6, No Ghalib A.Shah and Muslim Bozyigit (2007), Exploiting Energy aware Spatial Correlation in Wireless Sensor Networks, 2nd International Workshop on software for Sensor Networks (SensorWave 2007). 4. Akyildiz. I.F.,et.al.(2006), Wireless Sensor Networks: A Survey Revisited, Computer Networks Journal,Elsevier. 5. SunHee Yoon and Cyrus Shahabi (2005), Exploring Spatial Correlation Towards an Energy Efficient Clustered Aggregation Technique (CAG), IEEE International Conference on Communication (ICC), pp Yingqi Xu and Wang-Chien Lee (2006), Exploring Spatial Correlation for Link Quality Estimation in wireless Sensor Networks, in Proc Fourth Annual IEEE International Conference on Pervasive Computing and Communications ( Percom 2006). 7. Ilker Demirkol et.al (2006), MAC Protocols for Wireless Sensor Networks : a Survey,.Communications Magazine, IEEE Volume 44, Issue 4, Page(s): Seema Bandyopadhyay and Edward Coyale. J,(2004), Spatio Temporal Sampling Rates and Energy Efficiency in wireless Sensor Networks, IEEE INFOCOM. 9. Y. Linde, A et.al., (1980), An algorithm for vector quantizer design, IEEE Trans. Commun., vol. COM-28, no. 1, pp The Network Simulator ns-2. [Online]. Available: 11.M. C. Vuran, et.al (2004), Spatio-temporal correlation: theory and applications for wireless sensor networks, Comput. Netw. J. (Elsevier), vol. 45, no. 3, pp Ye.W., et.al, (2004), Medium access control with coordinated adaptive sleeping for wireless sensor networks, IEEE/ACM Trans. Netw., vol. 12, no. 3, pp Akyildiz.I.F.,et.al (2001) Wireless sensor networks: a survey, Journal of Computer Networks, Elsevier. 14.Samir Das. R. et.al, (2000) Performance Comparison of Two On demand Routing Protocols for Adhoc Networks, Proceedings of the IEEE Conference on Computer Communications (INFOCOM), Tel Aviv, Israel, pp Rajeswari.A and Kalaivaani.P.T. (2011), A Bi-directional Energy Splitable Model For Energy Optimization In Wireless Sensor Networks, Proc of ICASEIT, Malaysia, pp