Analysis of Performance Improvement in Wireless Sensor Networks Based on Heuristic Algorithms Along with Soft Computing Approach

Volume 13, Issue 1, pp 47 - 67 http://dx.doi.org/10.22436/jmcs.013.01.05

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Authors

Morteza Kabiri - Department of Computer, Islamic Azad University Ayatollah Amoli Branch, Amol, Iran. Javad Vahidi - Department of Applied Mathematics, Iran University of Science and Technology, Tehran, Iran.

Abstract

The use of Wireless Sensor Networks (WSNs) has grown dramatically in recent decades, and the use of these networks in the areas of military, health, environment, business, etc. increases every day. A wireless sensor network consists of many tiny sensor nodes with wireless communications and work independently. In applications of such sensor nodes, hundreds or even thousands of low-cost sensor nodes are dispersed over the monitoring area, in which each sensor node periodically reports its sensed data to the base station (sink). Due to limitations in the communication range, sensor nodes transmit their sensed data through multiple hops. Each sensor node acts as a routing element for other nodes for transmitting data. One of the most important challenges in designing such networks is the management of energy consumption of nodes; because replacing or charging the batteries of these nodes are usually impossible. One of the main characteristics of these networks is that the network lifetime is highly related to the route selection. Unbalanced energy consumption is an inherent problem in WSNs characterized by the multi-hop routing and many-to-one traffic pattern. This uneven energy dissipation in many routing algorithms can cause network partition because some nodes that are part of the efficient path are drained from their battery energy quicker. To efficiently route data through transmission path from node to node and to prolong the overall lifetime of the network, In this thesis we proposed three new routing algorithms using a combination of both Fuzzy approach and A-star algorithm seeks to investigate the problems of balancing energy consumption and maximization of network lifetime for WSNs :A-Star with 3 parameters fuzzy system (A*3F), A-Star with 3 fuzzy system with 2 parameters using majority vote (A*3FMV) and A-Star with 3 fuzzy system with 2 parameters using simple additive weighting (A*3FSAW). The new methods is capable of selecting optimal routing path from the source node to the sink by favoring the highest remaining energy, minimum number of hops, lowest traffic load and energy consumption rate. We evaluate and compare the efficiency of the proposed algorithms with each other methods under the same criteria in four different topographical areas. Simulation results show that A*3PFSAW and A*3PFMV balances the energy consumption well among all sensor nodes and achieves an obvious improvement on the network lifetime that randomly scattered nodes and flat routing..

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Keywords

• Wireless Sensor Networks
• A-Star algorithm
• Fuzzy logic
• Network lifetime
• Multi-hop routing.

MSC

•  46H25
•  68M10

References

• [1] R. V. Kulkarni, A. Forster, G. K. Venayagamoorth, Computational intelligence in wireless sensor networks: A survey, IEEE Commun. Surveys Tutorials, 13 (2011), 68–96
  • View Article
  • Google Scholar


• [2] C. Hua, T. P. Yum, Optimal routing and data aggregation for maximizing lifetime of wireless sensor networks, IEEE ACM Trans. Netw., 16 (2008), 892–903
  • View Article
  • Google Scholar


• [3] H. Zhang, H. Shen, Balancing energy consumption to maximize network lifetime in data-gathering sensor networks, IEEE Trans. Par- allel Distrib. Syst., 20 (2009), 1526–1539
  • View Article
  • Google Scholar


• [4] J. N. Al-Karaki, A. E. Kamal, Routing techniques in wireless sensor networks: A survey, IEEE Wireless Commun., 11 (2004), 6–28
  • View Article
  • Google Scholar


• [5] H. R. Karkvandi, E. Pecht, O. Yadid, Effective lifetime-aware routing in wireless sensor networks, IEEE Sensors J., 11 (2011), 3359–3367
  • View Article
  • Google Scholar


• [6] K. Akkaya, M. Younis, A survey of routing protocols in wireless sensor networks, Ad Hoc Netw., 3 (2005), 325–349


• [7] F. Ren, J. Zhang, T. He, C. Lin, S. K. Das, EBRP: Energy-balanced routing protocol for data gathering in wireless sensor networks, IEEE Trans. Parallel Distrib. Syst., 22 (2011), 2108–2125
  • View Article
  • Google Scholar


• [8] M. J. Tsai, H. Y. Yang, W. Q. Huang, Axis-based virtual coordinate assignment protocol and delivery-guaranteed routing protocol in wireless sensor networks, in Proc. IEEE INFOCOM 26th Int. Conf. Comput. Commun., (2007), 2234–2242.
  • View Article
  • Google Scholar


• [9] J. Park, S. Sahni, An online heuristic for maximum lifetime routing in wireless sensor networks, IEEE Trans. Comput., 55 (2006), 1048–1056
  • View Article
  • Google Scholar


• [10] C. Wu, R. Yuan, H. Zhou, A novel load balanced and lifetime maximization routing protocol in wireless sensor networks, in Proc. IEEE Vehicular Technol. Conf. VTC Spring, (2008), 113–117.
  • View Article
  • Google Scholar


• [11] C. C. Hung, K. C. J. Lin, C.-C. Hsu, C.-F. Chou, C.-J. Tu, On enhancing network-lifetime using opportunistic routing in wireless sensor networks, in Proc. 19th Int. Conf. Comput. Commun. Netw, Aug., (2010), 1–6.
  • Google Scholar


• [12] R. Madan, S. Lall, Distributed algorithms for maximum lifetime routing in wireless sensor networks, IEEE Trans. Wireless Commun., 5 (2006), 2185–2193
  • View Article
  • Google Scholar


• [13] J. H. Chang, L. Tassiulas, Maximum lifetime routing in wireless sensor networks, IEEE/ACM Trans. Netw., 12 (2004), 609–619
  • View Article
  • Google Scholar


• [14] O. Zytoune, M. El-Aroussi, D. Aboutajdine, A uniform balancing energy routing protocol for wireless sensor networks, Wireless Personal Commun. , 55 (2010), 147–161
  • View Article
  • Google Scholar


• [15] Y. M. Lu, V. W. S. Wong, An energy-efficient multipath routing protocol for wireless sensor networks, in Proc. IEEE 64th Vehicular Technol. Conf., Sep. , (2006), 1–5.


• [16] C. Park, I. Jung, Traffic-aware routing protocol for wireless sensor networks, in Proc. IEEE Inform. Sci. Appl. Int. Conf., Apr., (2010), 1–8.
  • Google Scholar


• [17] M. R. Minhas, S. Gopalakrishnan, V. C. M. Leung, An online multipath routing algorithm for maximizing lifetime in wireless sensor networks, in Proc. IEEE Inform. Technol. New Generat. 6th Int. Conf., (2009), 581–586.
  • View Article
  • Google Scholar


• [18] M. A. Azim, A. Jamalipour, Performance evaluation of optimizedforwarding strategy for flat sensor networks, in Proc. IEEE GlobalTelecommun. Conf., (2007), 710–714.
  • View Article
  • Google Scholar


• [19] S. Y. Chiang, J. L. Wang, Routing analysis using fuzzy logicsystems in wireless sensor networks, Lecture Notes Comput. Sci., 5178 (2008), 966–973
  • View Article
  • Google Scholar


• [20] K. M. Rana, M. A. Zaveri, ASEER: A routing method to extend life of two-tiered wireless sensor network, Int. J. Adv. Smart Sensor Netw. Syst., 11 (2011), 1–16
  • Google Scholar


• [21] W. Dargie, C. Poellabauer, Network layer, in Fundamental of Wireless Sensor Networks Theory and Practice, New York: Wiley, (2011), 163–204.


• [22] L. A. Zadeh, Soft computing and fuzzy logic, IEEE Software, 11 (1994), 48–56
  • View Article
  • Google Scholar


• [23] K.-Y. Cai, L. Zhang, Fuzzy reasoning as a control problem, IEEETrans. Fuzzy Syst., 16 (2008), 600–614
  • View Article
  • Google Scholar


• [24] T. A. Runkler, Selection of appropriate defuzzification methods using application specific properties, IEEE Trans. Fuzzy Syst., 5 (1997), 72–79
  • View Article
  • Google Scholar


• [25] K. M. Passino, P. J. Antsaklis, A metric space approach to the specification of the heuristic function for the A* algorithm, IEEE Trans. Syst. Man Cybern., 24 (1994), 159–166
  • View Article
  • Google Scholar


• [26] X. H. Li, S. H. Hong, K. L. Fang, WSNHA-GAHR: A greedy and A* heuristic routing algorithm for wireless sensor networks in home automation, IET Commun, 5 (2011), 1797–1805
  • View Article
  • Google Scholar


• [27] J. Yao, C. Lin, X. Xie, A. J. Wang, C. C. Hung, Path planning forvirtual human motion using improved A* star algorithm, in Proc. IEEE Inform. Technol. New Generat. 7th Int. Conf., (2010), 1154–1158.
  • View Article
  • Google Scholar


• [28] W. Heinzelman, A. Chandrakasan, H. Balakrishnan, Energy-Efficient Communication Protocol for Wireless Mi- crosensor Networks, Proceedings of the 33rd Hawaii International Conference on System Sciences , (HICSS ’00) (2000)
  • View Article
  • Google Scholar


• [29] Deepak S. Gaikwad, Sampada Pimpale ,Routing Alternatives for Network Lifetime Maximization of WSNs Using Heuristic and Fuzzy Logic Approach , International Journal of Inventive Engineering and Sciences (IJIES) , ISSN: 2319–9598, Volume-1, Issue-6 (2013)
  • Google Scholar