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International Journal of Electronics and Communication Engineering Research and Development
(IJECERD), ISSN 2248-9525(Prin...
International Journal of Electronics and Communication Engineering Research and Development
(IJECERD), ISSN 2248-9525(Prin...
International Journal of Electronics and Communication Engineering Research and Development
(IJECERD), ISSN 2248-9525(Prin...
International Journal of Electronics and Communication Engineering Research and Development
(IJECERD), ISSN 2248-9525(Prin...
International Journal of Electronics and Communication Engineering Research and Development
(IJECERD), ISSN 2248-9525(Prin...
International Journal of Electronics and Communication Engineering Research and Development
(IJECERD), ISSN 2248-9525(Prin...
International Journal of Electronics and Communication Engineering Research and Development
(IJECERD), ISSN 2248-9525(Prin...
International Journal of Electronics and Communication Engineering Research and Development
(IJECERD), ISSN 2248-9525(Prin...
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Implementation and analysis of multiple criteria decision routing algorithm for wireless sensor network

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Transcript of "Implementation and analysis of multiple criteria decision routing algorithm for wireless sensor network"

  1. 1. International Journal of Electronics and Communication Engineering Research and Development (IJECERD), ISSN 2248-9525(Print), ISSN- 2248-9533 (Online) Volume 4, Number 2, April-June (2014) 28 IMPLEMENTATION AND ANALYSIS OF MULTIPLE CRITERIA DECISION ROUTING ALGORITHM FOR WIRELESS SENSOR NETWORK [1] Shashank S. Bhagwat, [2] Mrs. Triveni C.L, [3] P C Srikanth [1] Department of ECE, Malnad College of Engineering Hassan, India [2] Department of ECE, Assistant Professor, Malnad College of Engineering, Hassan, India [3] Professor & Head, Department of ECE, Malnad College of Engineering, Hassan, India ABSTRACT Energy consumption is the key design criterion for routing data in wireless sensor network. However, some applications of wireless sensor network like disaster management, battlefield control etc. demand fast data delivery. In this paper, a data forwarding technique is proposed and its performance is compared with flooding algorithm. Here, the source nodes or intermediate nodes select a next node to forward the data to the destination based on different criteria. The process repeats until data reach the destination. In Multiple Criteria Decision Routing algorithm next node to forward the data is selected based on both its distance to the sink node and the remaining energy of the node. A comparative study of the performance of these techniques has been carried out and results are presented in this paper. In the simulation results show that Multiple Criteria Decision Routing algorithm performance is better than flooding algorithm. Keywords: Multiple Criteria Decision Routing (MCDR), Wireless Sensor Networks (WSN). I. INTRODUCTION With the advancement of wireless communication technologies, Wireless Sensor Networks (WSNs) have become useful for many applications. In particular, the monitoring applications covering large geographical areas are much benefited by the use of WSNs. However, in large-scale WSNs, data may need to follow a long multi-hop communication path and each intermediate node has to receive and transmit the packets. The residual energy of the intermediate nodes reduces due to such communication activities. Moreover, overall energy IJECERD © PRJ PUBLICATION International Journal of Electronics and Communication Engineering Research and Development (IJECERD) ISSN 2248– 9525 (Print) ISSN 2248 –9533 (Online) Volume 4, Number 2, April- June (2014), pp. 28-35 © PRJ Publication, http://www.prjpublication.com/IJECERD.asp
  2. 2. International Journal of Electronics and Communication Engineering Research and Development (IJECERD), ISSN 2248-9525(Print), ISSN- 2248-9533 (Online) Volume 4, Number 2, April-June (2014) 29 consumption for packet routing depends on the number of hops in the path. Therefore, it is important that the network must be designed in such a way that the average number of hops for routing the data from source to sink remains as minimum as possible. For many applications, timeliness of data delivery to the sink is also one of the essential requirements. An example of such applications is Disaster management. Although data transmission is fast when flooding routing protocol is used, energy consumption due to this protocol is high. In this paper a new routing algorithm for fast and energy efficient data delivery across WSN. In the MCDR routing algorithm the next node to forward the data is selected based on its remaining energy and its distance to the sink node. This technique ensures fast and energy efficient data delivery. The performance of MCDR algorithm is compared with flooding algorithm. II. RELATED WORK Extensive research work has been carried out on data routing techniques. In [1], the authors proposed Multiple Sink Dynamic Destination Geographic Routing (MSDDGR). It is based on greedy forwarding scheme. When a packet needs to be sent, the sender selects the nearest sink as the current destination. Also if the intermediate node sees another sink is nearer to it, then the current destination node is changed and the new sink node is selected as the destination. The authors in [2] address the problem of efficient routing the data from multiple sources to multiple sinks. The authors first define a mathematical model to derive an optimal solution. In the second phase, it is assumed that the initial state of the system is such that a tree exists for each sink connecting it to all the relevant sources. The path from source to sink is changed periodically adapting the different sink-rooted trees. The adaptation consists of selecting a different neighbor as the parent towards a given sink. In [3], the authors propose a protocol called MRMS (Multipath Routing in large scale sensor networks with Multiple Sink nodes) which incorporates multiple sink nodes. In MRMS, a primary path is created with minimum path cost. It also saves the other paths from different sinks. Thus, when the primary path is not reachable or if the residual energy of the sensors along the path falls below a certain threshold, another path is selected. A scalable multipath routing approach called Neighbor Sink Nexus (NSN) routing algorithm is presented in [4]. It is based on Set Cover problem. This paper claims that set cover method gives balanced and better performance in terms of energy efficiency and reduced latency for multiple sink WSNs. Malakooti et al. in [5] propose a Distributed Composite Multiple Criteria Routing approach. In this work two mechanisms, such as global multiple criteria routing and distributed multiple criteria routing are discussed. The multiple criteria like energy, delay and bit error rate are applied to the Distance Vector routing protocol to find the best possible path from any source to destination. Li et al. in [6] propose a data-centric multi-criteria routing algorithm called MCR. Here criteria like energy remaining at the sensor nodes, power consumption model, and group membership of each node are considered. Tabatabaei in [7] also uses the multiple criteria approach for mobile ad hoc networks with AODV routing protocol.
  3. 3. International Journal of Electronics and Communication Engineering Research and Development (IJECERD), ISSN 2248-9525(Print), ISSN- 2248-9533 (Online) Volume 4, Number 2, April-June (2014) 30 III. MUTIPLE CRITERIA DECISION ROUTING ALGORITHM In this section MCDR data routing algorithm designing is proposed. Before proceeding into the algorithm some assumptions common for all the three mentioned routing techniques are made. The assumptions are as follows. i. All the nodes in the network should know their position in that partition (X and Y coordinates). ii. All the nodes in the network should also know the position of the sink node in that network. The working of the MCDR algorithm is as follows. a. mcdr algorithm has two criteria, energy and distance to the sink. b. The Transmission Range For Each The Nodes In The Network Must Be Given. All The Nodes Are Present In The Transmission Range Of Source Node Is Determined source node broadcasts a request packet to all the nodes that are present in the transmission range. request packet includes the source id and sink node id and euclidian distance of the source node to the sink node. request packet structure shown in fig1. Fig 1: Request Packet Structure c. On receiving the request packet the nodes calculate their Euclidian distance to the sink node. Since every node in the partition know the position of the sink node and its position Euclidian distance can be calculated using the formula. (1) where x1 = x-coordinate of the sink node. y1 = y-coordinate of the sink node. x1 = x-coordinate of the sink node. y1 = y-coordinate of the sink node. d. Every node transmits the reply packet to the source node. The reply packet includes the id of the node, its distance with the sink node and the remaining energy of the node. Reply packet is shown in fig 2. Fig 2: Reply Packet Structure e. The utility factor is calculated for each node using the distance and the remaining energy in the reply packet. source id destination id dist source id node id destination id new_dist rem_energy
  4. 4. International Journal of Electronics and Communication Engineering Research and Development (IJECERD), ISSN 2248-9525(Print), ISSN- 2248-9533 (Online) Volume 4, Number 2, April-June (2014) 31 f. To calculate the utility factor weights are assigned to each alternative. If an alternative has more importance then it will be assigned with higher weightage. In the algorithm equal weight of 0.5 is assigned to both the alternatives. g. The distance value and the remaining energy must be normalized between 1 and 0. The normalization procedure is as follows. For energy the normalization is done using the formula (2) Normalization of the distance is done using the formula (3) a. The Utility Factor is calculated according to the equation where n = number of alternatives i.e., total number of nodes that comes under the transmission range of the source node. m = number of criteria. ZK(Oi) = normalized value of the criteria. WK = weights assigned to the criteria. b. Utility factor is calculated for each node in the transmission range of the source node. The source node or the intermediate node selects the next node to transmit the data with maximum utility factor. c. The above procedure is continued until the destination (sink) node is reached. IV. FLOODING In flooding [6], the source node floods all events to every node in the network. Whenever a sensor receives a data message, it keeps a copy of the message and forwards the message to every one of its neighboring sensors and the cycle repeats. It is an easy-to-implement routing scheme, and it is suitable for various network types, node distributions and environments. The main advantage of flooding is the increased reliability provided by this routing method. Since the message will be sent to at least once to every host it is almost guaranteed to reach its destination. But the unlimited broadcasting the packets in the flooding scheme will cause the broadcast storm. V. SIMULATION In the simulation 100 nodes are distributed over 100*100 area in random manner. Transmission range is 40 units. The simulation parameter are listed in table 1.
  5. 5. International Journal of Electronics and Communication Engineering Research and Development (IJECERD), ISSN 2248-9525(Print), ISSN- 2248-9533 (Online) Volume 4, Number 2, April-June (2014) 32 Table 1: Simulation Parameters Parameter Name Parameter Value No of Nodes 100 Environment Factor 0.5 Range 40 units Energy Amplicification 0.5mJ Energy Transmission 1mJ Attenuation Factor 0.02 Figure 3 shows the routing in flooding algorithm. It is clear that flooding takes multiple routes for data transmission and hence consumes more energy. Figure 3: Routing using Flooding algorithm Figure 4 shows the data rouintg in MCDR algorithm. Compared to figure 3 and figure 4 it is clear that MCDR performes better than flooding. Since MCDR routes the data to one of the neighbor nodes unlike flooding, this consumes lesser energy and its faster. Figure 4: Routing using MCDR algorithm Data routing will be faster if number of hops for data transmission is lesser and in the simulation it is shown that MCDR takes lesser number of hops than flooding and is shown in the figure 5.
  6. 6. International Journal of Electronics and Communication Engineering Research and Development (IJECERD), ISSN 2248-9525(Print), ISSN- 2248-9533 (Online) Volume 4, Number 2, April-June (2014) 33 Figure 5: Number of hops for Flooding and MCDR The performance of of the routing algorithm is better if it consumes the lesser energy. In the simulation it is clear that MCDR consumes lesser energy than the flooding and is shown in figure 6. Figure 6: Energy Consumed of FLOOD and MCDR In the simulation it is clear that MCDR takes lesser time to route the data than the flooding algorithm. Hence MCDR is faster compared to the flooding algorithm. Figure 7: Time Taken by Flooding and MCDR Power consumption of MCDR algorithm is also lesser compared to flooding. Power consumption of MCDR and flooding for 25 iterations is shown in figure 8.
  7. 7. International Journal of Electronics and Communication Engineering Research and Development (IJECERD), ISSN 2248-9525(Print), ISSN- 2248-9533 (Online) Volume 4, Number 2, April-June (2014) 34 Figure 8: Power Consumption of Flooding and MCDR The lifetime of WSN will be more if the routing algorithm manages to keep more nodes alive. In the figure 9 it is shown that MCDR manages to keep many nodes alive hence makes the network more reliable. Figure 9: Number of alive nodes using Flooding and MCDR VI. CONCLUSION AND FUTURE SCOPE It has been demonstrated that the proposed MCDR algorithm shows better performances over flooding routing protocol and using multiple criteria to choose the next node is the best choice in terms of increase in the life time of the network. Future work will focus on including additional criteria, fine-tuning of the weight values etc. The route hole problem will also be handled in future. REFERENCES [1] B. Elbhiri, S.EI. Fkihi, R. Saadane, D. Aboutajdine, Clustering in Wireless Sensor Networks based on Near Optimal Bi-partitions, in: Procs. of the Next Generation Internet (NGI), 2010. [2] B. Malakooti, I. Thomas, A Distributed Composite Multiple Criteria Routing Using Distance Vector, in: Procs. of the IEEE International Conference on Networking, Sensing and Control, Ft. Lauderdale, FL, 2006.
  8. 8. International Journal of Electronics and Communication Engineering Research and Development (IJECERD), ISSN 2248-9525(Print), ISSN- 2248-9533 (Online) Volume 4, Number 2, April-June (2014) 35 [3] D. Das, Z. Rehena, S. Roy, N. Mukherjee, Multiple-Sink Placement Strategies in Wireless Sensor Network, in: Procs. of the Fifth International conference on Communication Systems and Networks (COMSNETS 2013), Bangalore, India, 2013. [4] E.I. Oyman, C. Ersoy, Multiple Sink Network Design Problem in Large Scale Wireless Sensor Networks, in: Procs. of the International Conference on Communications (ICC), Paris, France, 2004. [5] I. Slama, B. Jouaber, D. Zeghlache, Energy Efficient Scheme for Large Scale Wireless Sensor Networks with Multiple Sinks, in: Procs. of the IEEE Wireless Communications and Networking Conference, WCNC, Las Vegas, USA, 2008. [6] K.N. Chaaran, M. Younus, M.Y. Javed, NSN based Multi-Sink Minimum Delay Energy Efficient Routing in Wireless Sensor Networks, European Journal of Scientific Research, 41, 2010, 399-411. [7] L. Cao, C. Xu, W. Shao, Multiple Sink Dynamic Destination Geographic Routing in Wireless Sensor Networks, in: Procs. of the International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), Huangshan, 2010. [8] P. Ciciriello, L. Mottola, G.P. Picco, Efficient Routing from Multiple Sources to Multiple Sinks in Wireless Sensor Networks, in: Procs. of the 4th European Conference on Wireless Sensor Networks (EWSN), 2007. [9] Q. Li, J. Beaver, A. Amer, P.K. Chrysanthis, Multi-Criteria Routing in Wireless Sensor-Based Pervasive Environments, Journal of Pervasive Computing and Communication, 1 (2005). [10] S. Tabatabaei, Multiple Criteria Routing Algorithms to Increase Durability Path in Mobile Ad hoc Networks, in: Procs. of the 4th International Conference for Internet Technology and Secured Transactions, ICITST, London, UK, 2009. [11] W.R. Heinzelman, A. Chandrakasan, H. Balakrishnan, Energy- Efficient Communication Protocol for Wireless Micro Sensor Networks, in: Procs. of the 33rd Hawaii International Conference on System Sciences, Hawaii, 2000.

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