A NOVEL ENERGY EFFICIENCY PROTOCOL FOR WSN BASED ON OPTIMAL CHAIN ROUTING

A NOVEL ENERGY EFFICIENCY PROTOCOL FOR WSN BASED ON OPTIMAL CHAIN ROUTING

• 1.
  Proceedings of the10th INDIACom; INDIACom-2016; IEEE Conference ID: 37465 2016 3rd International Conference on “Computing for Sustainable Global Development”, 16th - 18th March, 2016 Bharati Vidyapeeth's Institute of Computer Applications and Management (BVICAM), New Delhi (INDIA) Copy Right © INDIACom-2016; ISSN 0973-7529; ISBN 978-93-80544-20-5 488 A NOVEL ENERGY EFFICIENCY PROTOCOL FOR WSN BASED ON OPTIMAL CHAIN ROUTING Arun Agarwal PhD Research Scholar, GGSIPU New Delhi, India Email: [email protected] Khushboo Gupta PhD Research Scholar, UPTU Lucknow, India Email:[email protected] K P Yadav Director, KCCITM, Greater Noida|, India Email:[email protected] Abstract – Energy Efficiency in Wireless Sensor Network is one of the most significant aspects of routing in these networks. WSN consist of thousands of sensory nodes densely distributed over wide geographical network. As these nodes are deployed in remote areas where recharging is not possible, even if it is possible it will incur high cost. So there is a need of a protocol which facilitates less energy dissipation and thereby enhances the overall performance of the network. We surveyed several protocols such as LEACH, PEGASIS, ACT etc. and concluded that important performance measures are First Node Die (FND), Half Node Alive (HNA) and Last Node Alive (LNA). Values for above mentioned parameters vary for different protocols. In this paper we present a new protocol Energy Efficient Optimal Chain Protocol (EEOC) which outperforms all above mentioned protocols. We compared the results of all these protocols with EEOC and found that with respect to FND, HNA and LNA EEOC performs way better than others. Keywords – ACT, EEOC, Energy Efficiency, LEACH, PEGASIS, Routing Algorithm, Simulation, WSN Abbreviation: 1. ACT Arranging Cluster Range and Transmission Ranges 2. EEOC Energy Efficient Optimal Chain 3.LEACH Low energy adaptive clustering hierarchy 4.PEGASIS Power-Efficient GAthering in Sensor Information Systems 5. FND First Node Die 6. LNA Last Node Alive 7. HND Half Node Die 8. LNA Last Node Alive 9. SN Sensor Node 10. BS Base Station 11. MTE Multipath transmission energy efficient protocol 12. DCP Direct communication protocol 13. WSN Wireless Sensor Networks 14. CH Cluster Head I. INTRODUCTION Wireless Sensor Networks [1] consists of thousands of energy constrained Sensor Nodes (SNs) [2] which are spatially distributed over various geographical locations for performing their task that focus on simple data gathering applications. These SNs gathers data by sensing the environment and then send the same using wireless channel to remote Base Station (BS). SNs are self-configured but have constrained resources and therefore have limitations in computation and communication abilities. Moreover, these nodes are battery operated often limiting energy availability as battery may get depleted in the network. Although WSN are generally stationary after deployment but replacement of the battery may not be feasible due different geographical regions. Hence there is a requirement of effective and efficient solutions during various operations of data collection, processing, communication and storage. It has been studied in literature that in comparison to processing, communication from SNs to BS dissipates more energy as data transmission depends upon the area between source and destination. In recent years, various routing protocols [4] based upon clustering have been developed and deployed to reduce the network traffic from SNs to BS. Cluster-based routing protocols [3] are based upon hierarchical routing [17] in which nodes are arranged in different hierarchical levels sustained by parameters such as energy levels, processing capability etc. The low energy nodes can be used to observe and perform the sensing in the proximity of the target while the higher energy nodes can be used to process the data and send the information. Thus by creating cluster and assigning special tasks to CHs, the overall scalability, energy efficiency and network lifetime can be increased. These CHs can be chosen from the deployed SNs or can be more powerful than other SNs in terms of energy, processing power, bandwidth, and memory. In this paper, we have considered the node heterogeneity for hierarchical cluster based routing. The concept of advanced node has been introduced which are the nodes having large initial energy relative to other nodes in the network. The objective of this paper is to design and develop a novel energy efficient protocol for WSNs. The solution is
• 2.
  A NOVEL ENERGYEFFICIENCY PROTOCOL FOR WSN BASED ON OPTIMAL CHAIN ROUTING Copy Right © INDIACom-2016; ISSN 0973-7529; ISBN 978-93-80544-20-5 489 implemented and the performance of the protocol is evaluated by comparing it with the existing routing protocols like LEACH, PEGASIS and ACT. The organisation of the paper as follows: Section II addresses the work Literature Survey carried out in this field. Section III gives Problem Statement. Section IV gives a brief description on system design and implementation followed by Algorithms of routing protocols in Section V. Section V contains the Simulation Results. Section VI gives the conclusions and future works enhancement possible. II. LITERATURE SURVEY This section is the description of various routing algorithms [13] as proposed for WSN. It also includes brief comparison of each one of them with the advantages and disadvantage of the underlying mechanism (Table 1). Direct communication protocol (DCP) which involves direct communication of each node to the base station and thereby result in very large dissipation of energy in each round for those nodes which are very situated far away from the BS [7]. Because it follows radio model for energy dissipation where the dissipated energy is directly proportional to square of the distance between source and the sink. Thus depleting the area far away from the BS. Apart from these limitations DCP is still very simple to implement and it does not impart any complexities for calculation in each round and thereby decreasing the overall delay. Multipath transmission energy efficient protocol (MTE) underlying architecture relies on the basis of multipath propagation which involves relaying of data packets from farthest node to its neighbour node and repeating the same process until data arrives at the node very close to the BS whose responsibility is to transfer the collected data to the BS. This is a efficient protocol as it collects information from farthest nodes too and imparting the load on each node in between but imposing an additional work on nodes closer to BS thus resulting into a situation where nodes closer to BS dies very early thus the situation of first node die occurs very soon and performance degrades. Next protocol which removes all the disadvantages in DCP and MTE by applying a phenomenon of clusters and assigning a leader with each cluster designated as cluster head (CH). LEACH (Low energy adaptive clustering hierarchy) [5, 9] divides the entire area under consideration into number of clusters with each cluster having a CH which communicate directly to the BS. The remaining nodes will transmit data to the CH whose responsibility is to aggregate it and then transmit it directly to the BS. To avoid extra load on CH in every round LEACH proposed a mechanism for load sharing among all the nodes resides in the cluster itself by applying a probabilistic strategy which states that if a given node is CH in current round than it will not become CH in the coming rounds until and unless all the nodes remaining in the cluster will not become CH at least once. This approach will give fair chance to every node to become a CH. It achieves a factor of 8 reduction in energy dissipation as compared with conventional routing protocols. But as it applies that CH have to directly communicate with BS, clusters far away will die quickly as compared to the one situated closer to BS. PEGASIS (Power-Efficient GAthering in Sensor Information Systems) is another protocol used for routing in WSN which is based upon constructing a chain applying some greedy strategy for formation of the chain and transmitting data packets starting from the farthest node to the CH along the path as directed by the chain constructed. The main idea of this is to receive and transmit only to the close neighbors thus saving the overall energy dissipation [11]. This approach will distribute the energy node evenly among all the sensor nodes in the network which are organize to form a greedy chain. It performs the data fusion at every node except the farthest node in the chain. It will strictly allow a node to become the CH only when the entire remaining node will become the CH at least once. It will attain an improvement of two times the number of round compared to leach and will provide a near optimal performance. TABLE I. COMPARISION OF VARIOUS ROUTING PROTOCOL IN WSN Routing Protocol Advantage Disadvantage DCP [6] Simple & Easy to implement Overall Delay is reduced Large Energy dissipation Reduced Network Lifetime MTE[8] Multipath propagation Load Balancing Overload on nodes near to BS Delay Increases BCDCP[12] Centralized Control Reduced Energy Dissipation Overload on BS Bottleneck Congestion LEACH[6] Cluster Based Approach Load Sharing using probabilistic CH determination CH are heavily loaded Reduction in overall network lifetime PEGASIS[14] Uses greedy chain strategy for communication Near optimal performance Delay in determination of CH Heavy Load on CH ACT[15] Prolonged Network Lifetime Cross Level Transmission Complex to determine no. of clusters Overload on nodes near to BS At last we are describing the ACT (Arranging Cluster Range and Transmission Ranges) which will become a basis for our proposed work also which is the adaptive clustering protocol that reduces the load on the cluster near the base station by reducing the cluster size near to base station as they are the one which exhaust their power quickly because they are heavily loaded. ACT determines the cluster size and cluster radios and thus calculating total number of clusters in the area defined. Another approach that distinguishes it with LEACH is that there is a direct transmission from CH to BS in LEACH whereas ACT proposed that CH will transmit their available data up into the level where each cluster belongs to one or another level such as first level is the one nearest to the BS
• 3.
  Proceedings of the10th INDIACom; INDIACom-2016; IEEE Conference ID: 37465 2016 3rd International Conference on “Computing for Sustainable Global Development”, 16th - 18th March, 2016 Copy Right © INDIACom-2016; ISSN 0973-7529; ISBN 978-93-80544-20-5 490 [16].ACT also suggested cross level transmission to improve the overall quality of the network. ACT enhances the performances of entire network by increasing the overall network life time which may be stated as number of round before the first sensor node uses up its energy in the network. III. PROPOSED PROBLEM ACT assumes the following to build up its own system model: The positions of SNs and BS are fixed. The energy of all SNs kept initially the same. SNs are uniformly dispersed in the sensor field with density dn. In each round a SN transmits one unit of data to the CH A CH aggregates data and then forwards to BS. SNs can recognize their geographical position and the BS position via exchanging information SNs can adjust their energy levels. For transmitting data to BS directly, the maximum energy level can also be used. The covered area is a W X L rectangle, where W is the width and L is the length. It will assume that in each round every sensor node will transmit its data to CH, thus imposing an overhead on CH with each received data thus reducing the energy level of CH very soon and thereby reducing the overall network lifetime. Whereas if we use another data aggregation scheme like PEGASIS, it will create a dynamic chain in each round collecting information from each sensory node and forwarding the data up only to its neighbor up in the chain to the last node which directly transmit this aggregated data to the CH. This reduces the overall load on CH as it receives from only two nodes that are designated as last node in their own chain. But still there exists a problem with PEGASIS that CH has to transmit their collected data directly to the BS thus reducing the network lifetime. ACT deploys multi hop communications in which less energy is consumed from each sensor node. The load on each CH is balanced by considering cluster sizes and CH locations. Energy dissipation in ACT is very much lower than other available protocols but still does not enhances the overall network lifetime. Our research measures the network lifetime which may be defined as the number of rounds before the first sensor node avail up its energy in the network. We suggest that even though the CHs are selected and multi hop communication is there but the CH will be less utilized as it will dissipate less energy in receiving data from all nodes in its cluster. Rather it will be very much beneficial that we will create a chain as used in PEGASIS so that energy dissipation in CH is only due to two nodes not by all the nodes in its cluster. Thereby reducing the overall energy dissipation and enhances the overall network lifetime. IV. SYSTEM MODEL AND IMPLEMENTATION A. NETWORK MODEL Figure 1 shows the organization of SNs, CHs and BS at various levels in the network. CHs are chosen based upon some criteria for monitoring the SNs. Each CH sends the data to the BS. Fig 1. Network Model Our network model for simulation will assume the following: WSN include several static sensory nodes and a single base station. Sensor nodes are located uniformly in network field. Position of base station is fixed and it is centrally located. Sensory nodes are having limited battery power. B. ENERGY MODEL The energy model for our proposed protocol will consist of following energy components: Initial energy Ei Energy for data aggregation/sensing Ea Energy for data receiving Er Energy for data transmission Et Residual Energy Eres where Eres = Ei − (Ea+Er+Et) C. OUR APPROACH We have used this radio model for energy dissipation with respect to each round and iteration there is a reduction of energy that is being dissipated and for each node ordered from zero to n-1, total energy dissipation may be given by the following energy equations E0 = initial – ETx to E1 E1 = initial – ERx from E0 – aggregation – ETx to E2 E2 = initial – ERx from E1 – aggregation – ETx to E3 o o o En = initial – ERx from En-1 – aggregation – ETx to BS As it is being shown in the figure 2 that it comprises of a series of nodes connected with each other with a temporary chain. In our work we have divided our whole area under consideration into two equal halves. As node density is uniform we have just partitioned the given area into almost equal number of nodes where each node can have the location
• 4.
  A NOVEL ENERGYEFFICIENCY PROTOCOL FOR WSN BASED ON OPTIMAL CHAIN ROUTING Copy Right © INDIACom-2016; ISSN 0973-7529; ISBN 978-93-80544-20-5 491 of every other node in its area. Thus we have suggested to form an optimal chain based on inter distance between nodes where it is being advised that nodes communicate to only that node which is nearest from the given node constructing an optimal chain. This optimality brings a huge improvement in overall performance of the network as energy dissipated is directly proportional to square of the distance and we are using a chain where each node communicate to the one located at smallest distance. At each step the given node receives data from its predecessor node, aggregate it with its own data and then transmit it along the chain to the next node coming in the chain. This process starts from the two nodes situated at very far end in both the regions and progress along the connections given by that temporary chain up to the node nearest to the BS which is being designated at CH shown in figure 2. Fig 2. Temporary Chains in EEOC In every iteration the residual energy of each node is calculated and the determination of dynamic chain, two farthest nodes and the node nearest to BS i.e. CH is done. In each iteration the above said process is repeated calculating total number of dead nodes in each round. If the number of dead nodes is one i.e. if there exists a situation of first node die (FND) then the current round is stored as it indicates FND used for analyzing network performance. Similarly calculations is done for half node alive (HNA) and last node alive (LNA). The entire algorithm is executed multiple times so as to obtain average values to compare the network lifetime and efficiency of our proposed algorithm. V. EVALUTION PARAMETER PSEUDO CODE FOR EACH PROTOCOL A. LEACH PROTOCOL 1. Initialize system model parameters Base Station coordinates (50,300) Area under consideration (100,100) Number of nodes (n=100) Initial energy values (Eo=0.5J) Packet length (k=2000) 2. Create a random WSN consisting of n nodes randomly spaced in the area defined above. 3. Set probability value p=0.05 for determination of CH 4. For each round where maximum numbers of rounds are 9999 repeat steps 5 to 9 5. Determine the CH for each cluster using the given probability condition ' ) 1 mod(1 )( p rp p nT If n € G 6. Determine number of dead nodes If (dead==1) Output first_node_die If (dead==n) Output last_node_alive Exit from for loop step 4 7. Calculate the remaining energy values 8. Reassign probability values for determination of CH. 9. Plot x and y values to show no. of rounds and no. of nodes alive. 10. EXIT B. PEGASIS PROTOCOL 1. Initialize system model parameters Base Station coordinates (50,300) Area under consideration (100,100) Number of nodes (n=100) Initial energy values (Eo=0.5J) Packet length (k=2000) 2. Create a random WSN consisting of n nodes randomly spaced in the area defined above. 3. Repeat steps 4 to 11 till there exists a single node alive, 4. Determination of Greedy Chain and CH 5. Determine number of die nodes If (dead==1) Output first_node_die If (dead==n) Output last_node_alive Exit from for loop step 3 6. Calculation of energy dissipation using aggregation and data forwarding in each step. 7. Calculation of energy dissipation of CH. 8. Plot x and y values to show no. of rounds and no. of nodes alive. 9. EXIT C. ACT PROTOCOL 1. Initialize system model parameters Base Station coordinates (50,300) Area under consideration (100,100) Number of nodes (n=100) Initial energy values (Eo=0.5J) Packet length (k=2000)
• 5.
  Proceedings of the10th INDIACom; INDIACom-2016; IEEE Conference ID: 37465 2016 3rd International Conference on “Computing for Sustainable Global Development”, 16th - 18th March, 2016 Copy Right © INDIACom-2016; ISSN 0973-7529; ISBN 978-93-80544-20-5 492 2. Create a random WSN consisting of n nodes randomly spaced in the area defined above. 3. Determination of number of clusters and respective radius. 4. Determination of number of nodes in each cluster. 5. Assigning a random node as CH satisfying the probability condition as given in the proposed model. 6. Repeat steps 7 and 8 till total no. of died nodes is equals to n. 7. Calculate the following parameters for the proposed work in each iteration FND, HND & LNA C1, C2, C3 representing no. of nodes in b/w FND to HNA B1, B2, B3 representing no. of nodes in b/w HNA to LNA 8. Calculate the remaining energy values for each node in every round. 9. Form a table which shows the values as calculated in step 7. 10. EXIT. D. PROPOSED PROTOCOL (EEOC) 1. Initialize system model parameters Base Station coordinates (50,300) Area under consideration (100,100) Number of nodes (n=100) Initial energy values (Eo=0.5J) Packet length (k=2000) 2. Create a random WSN consisting of n nodes randomly spaced in the area defined above. 3. Repeat steps 4 to 11 till there exists a single node alive, 4. Determination of nearest node and two farthest nodes. 5. Determination of OPTIMAL Chain 1 in left of the area under consideration. 6. Determination of OPTIMAL Chain 2 in right of the area under consideration. 7. Determine number of die nodes If (dead==1) Output first_node_die If (dead==n) Output last_node_alive Exit from for loop step 3 8. Calculation of energy dissipation consumption for left area 9. Calculation of energy dissipation consumption for right area 10. Calculation of energy dissipation consumption for node nearest to Base Station i.e. CH 11. Plot x and y values to show no. of rounds and no. of nodes alive. 12. EXIT VI. SIMULATION AND RESULTS We have done simulations to evaluate the performance of the proposed EEOC protocol and the other three protocols, LEACH, PEGASIS and ACT. First of all, we have list the simulation parameters. Secondly, we present the simulation results, which show the performance results under different performance metrics. Finally, we discuss and analyses the simulation results. A. Simulation Parameters TABLE 2. COMPARISION OF VARIOUS ROUTING PROTOCOL IN WSN Parameter Value Network Size 100 X 100 Base Station Location 50 X 300 Number of Sensor Nodes 100 Density 1 node/ 100 m2 Initial Energy of each sensor nodes 0.5 J Energy Transmission 50 nJ Energy Reception 50 nJ Energy Data Aggreegation 5 nJ Packet Data Size 2000 bits Eelec 50 nJ/bit Єamp 100 pJ/bit/m2 B. Analysis Graph for FND Fig 3. Analysis Graph of FND 2. Analysis Graph for HNA Fig 4. Analysis Graph of HN
• 6.
  A NOVEL ENERGYEFFICIENCY PROTOCOL FOR WSN BASED ON OPTIMAL CHAIN ROUTING Copy Right © INDIACom-2016; ISSN 0973-7529; ISBN 978-93-80544-20-5 493 3. Analysis Graph for LND Fig 5. Analysis Graph of LND VII. CONCLUSION AND FUTURE SCOPE From the above simulation results it will be concluded that the said protocol i.e. EEOC routing protocol for WSN provides better results as compared from all other protocols such as LEACH, PEGASIS and ACT. The performance comparison of all these protocols are being shown in terms of simulation results as depicted in figure 3, 4 and 5. All these will reflect that in case of first node die, ACT will perform slightly better than our proposed approach EEOC but in all other performance criteria that is used to measure the performance and network lifetime our proposed approach will outperform than rest of the protocols analysed. EEOC will have drastic improvement over all other protocols in case of half node alive and last node alive. It will attain approximately 150 percent improvement as it is clear from the simulation results. Also it may be concluded from the study that these results can easily be used to decide for which application the given protocols may be used. It will be benefitted for application specific purposes where one have to choose from available options which protocol is very effective in the given scenario. Our study results in optimal coverage and prolonged network lifetime. In future we may also improve the first node die component of the EEOC protocol, so that it will be used in any specific application as the one which is optimal among all the available protocols designed. Another task which we will focus is of changing the cluster architecture hence to provide some better results and changing the entire procedure so as to suit heterogeneous environment also. REFERENCES [1] F. L. LEWIS, D.J. Cook and S.K. Das: Wireless Sensor Networks, in Wiley, New York, 2004 John Wiley & Sons, Inc., Publication. [2] Waltenegus Dargie and Christian Poellabauer: Fundamentals of Wireless Sensor Networks - Theory and Practices, Wiley series on wireless and mobile computing, 2010 John Wiley & Sons, Inc., Publication. [3] J. Hong, J. Kook, S. Lee, D. Kwon, S. Yi, T-LEACH: the method of threshold-based cluster head replacement for wireless sensor networks, Information Systems Frontiers (2009) 513–521. [4] J.N. Al-Karaki, A.E. Kamal, Routing techniques in in wireless sensor networks, IEEE Wireless Communication 11 (6) (2004) 6- 28. [5] Y.C. Chang, Z.S. Lin,J.L. Chen Cluster based self- organization management protocols for wireless sensor networks, IEEE Transaction on Consumer Electronics 52(1)(2006) 75-80. [6] W.R. Heinzelman, A. Chandrakasan, H. Balakrishnan, Energy-efficient communication protocol for wireless microsensor networks, in: Proceedings of Hawaii International Conference on System Sciences (HICSS), January 2000, pp. 1–10. [7] G. Xing, T. Wang, Z. Xie, and W. Jia, “Rendezvous Planning inWireless Sensor Networks with Mobile Elements,” IEEE Trans.Mobile Computing, vol. 7, no. 12, pp. 1430-1443, Dec. 2008. [8] Long Cheng, Sajal K. Das, Jiannong Cao, Canfeng Chen and Jian Ma, Minimum Transmission Multicast Routing Protocol for WSN : 2010 39th International Conference on Parallel Processing. [9] H.M. Abdulsalam, L.K. Kamel, W-LEACH: Weighted Low Energy Adaptive Clustering Hierarchy aggregation algorithm for data streams in wireless sensor networks, in: Proceedings of IEEE International Conference on Data Mining Workshops (ICDMW), 2010, pp. 1–8. [10] M.O. Farooq, A.B. Dogar, G.A. Shah, MR-LEACH: Multi- hop Routing with Low Energy Adaptive Clustering Hierarchy, in: Proceedings of IEEE International Conference on Sensor Technologies and Applications (SENSORCOMM), July 2010, pp. 262–268. [11] F. Bounbdallah, N. Bouabdallah, R. Boutaba, On Balancing energy consumption in wireless wireless sensor networks, IEEE Transaction on Vehicular Technology 58(6) (2009) 2909-2924. [12] Y. Liu, J. Gao, Y. Jia, L. Zhu, A cluster maintenance algorithm based on LEACH-DCHS protocol, in: Proceedings of IEEE International Conference on Networking, Architecture, and Storage, June 2008, pp. 165–166. [13] S.D. Muruganathan, D.C.F. Ma, R.I. Bhasin, A.O. Fapojuwo, A centralized energy-efficient routing protocol for wireless sensor networks, IEEE Communications Magazine 43 (3) (2005) 8–13. [14] S. Lindsey, C. Raghavendra, PEGASIS: power-efficient gathering in sensor information systems, in: Proceedings of IEEE Aerospace Conference, vol. 3, 2002, pp. 1125–1130. [15] HWei Kuang Lai , Chung Shuo Fan, Lin Yan Lin: Arranging cluster sizes and transmission ranges for wireless sensor networks, in: Elseviere Information Sciences 183 (2012) 117– 131. [16] M.B. Yassein, A. Al-zou’bi, Y. Khamayseh, W. Mardini, Improvement on leach protocol of wireless sensor network (vleach), International Journal of Digital Content Technology and its Applications 3 (2) (2009) 132–136. [17] C. Alippi, R. Camplani, M. Roveri, An adaptive LLC-based and hierarchical power –aware routing algorithm , IEEE Transaction on Instrumentation and Measurement 58(9)(2009) 3347-3357.