Fuzzy PSO-based algorithm for controlling base station movements in a wireless sensor network
Fuzzy PSO-based algorithm for controlling base station movements in a wireless sensor network
There are strong limitations on the software, energy, and hardware capacities of a wireless sensor network (WSN) and therefore algorithms that increase the lifetime of a WSN are of great significance. In this paper, a mobile base station movement control strategy for WSNs is proposed. This strategy combines fuzzy logic node clustering, fuzzy cluster-head selection, and fuzzy logic control (FLC) of the base station movements. After determining cluster-heads, according to the distance and energy of the heads, the base station moves on a predefined square, triangle, circle, or hexagon shaped path. Direction and speed of the movements are controlled by FLC. In addition, a particle swarm optimization (PSO) algorithm is applied to optimally calculate the number of clusters, path shape and size, and the base station s speed vector amplitude and direction. The proposed strategy is numerically simulated for a WSN with randomly distributed nodes. The fuzzy clustering algorithm of this paper is compared with other conventional clustering methods. Moving the base station by the proposed FLC is also compared with a static base station. Results confirm substantial improvement in the lifetime of the moving base station WSN.
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- [1] Wang H, Yu K, Mao B. Self-localization and obstacle avoidance for a mobile robot. Neural Comput Appl 2009; 18: 495-506.
- [2] Kırba¸s ˙I, Bayılmı¸s B. HealthFace: A web-based remote monitoring interface for medical healthcare systems based on a wireless body area sensor networks. Turk J Elec Eng & Comp Sci 2012; 20: 629-663.
- [3] Tubaishat M, Zhuang P, Qi Q, Shang Y. Wireless sensor networks in intelligent transportation systems. Wirel Commun Mob Com 2009; 9: 287-302.
- [4] Basagni S, Naderi MY, Petrioli C, Spenza D. Wireless sensor networks with energy harvesting. In: Basagni S, Conti M, Giordano S, Stojmenovic I, editors. Mobile Ad Hoc Networking: Cutting Edge Directions. 2nd ed. Hoboken, NJ, USA: John Wiley & Sons Inc., 2013. pp. 703-736.
- [5] C¸ evik T, Zaim AH, Yılta¸s D. Localized power-aware routing with an energy-efficient pipelined wakeup schedule for wireless sensor networks. Turk J Elec Eng & Comp Sci 2012; 20: 964-987.
- [6] Abazari N, Akbarzadeh MR, Yaghmaee MH. Mobile base station management using fuzzy logic in wireless sensor networks. In: IEEE Second International Conference on Computer Engineering and Technology; 1618 April 2010; Chengdo, China. New York, NY, USA: IEEE. pp. 357-361.
- [7] Singh AK, Purohit N, Singh KP, Shukla M. A novel approach for lifetime analysis of sensor network using fuzzy logic. In: IEEE International Conference Proceeding India Conference; 1618 December 2011; Hyderabad, India. New York, NY, USA: IEEE. pp. 1-6.
- [8] Singh AK, Purohit N, Alkesh A. Minimization of energy consumption of wireless sensor networks using fuzzy logic. In: IEEE International Conference on Computational Intelligence and Communication System; 79 October 2011; Gowalior, India. New York, NY, USA: IEEE. pp. 519521.
- [9] Singh AK, Purohit N, Alkesh A. A moving base station strategy using fuzzy logic for lifetime enhancement in wireless sensor networks. In: IEEE International Conference on Communication System and Network Technologies. 35 June 2011; Katra, India. New York, NY, USA: IEEE. pp. 198-202.
- [10] Bagci H, Yazici A. An energy aware fuzzy approach to unequal clustering in wireless sensor networks. Appl Soft Comput 2013; 13: 1741-1749.
- [11] Hoang DC, Kumar R, Panda SK. Fuzzy C-means clustering protocol for wireless networks. In: IEEE International Symposium on Industrial Electronics; 47 July 2010; Bari, Italy. New York, NY, USA: IEEE. pp. 3477-3482.
- [12] Arabi Z. HERF: A hybrid energy efficient routing using a fuzzy method in wireless sensor networks. In: International Conference on Intelligent and Advanced Systems; 1517 June 2010; Kuala Lumpur, Malaysia. New York, NY, USA: IEEE. pp. 1-6.
- [13] Alshawi IS, Yan L, Pan W, Luo B. Fuzzy chessboard clustering and artificial bee colony routing method for energyefficient heterogeneous wireless sensor networks. Int J Commu Syst 2014; 27: 3581-3599.
- [14] Ortiz AM, Royo F, Olivares T, Castillo JC, Orozco-Barbosa L. On reactive routing protocols in ZigBee wireless sensor networks expert systems. Int J Commu Syst 2014; 31: 154-162.
- [15] C¸ evik T, Zaim AH. EETBR: Energy efficient token-based routing for wireless sensor networks. Turk J Elec Eng & Comp Sci 2013; 21: 513-526.
- [16] Bezdek JC. Pattern Recognition with Fuzzy Objective Function Algorithms. New York, NY, USA: Plenum Press, 1981.
- [17] Heinzelman W, Chandrakasan A, Balakrishnan H. Energy-efficient communication protocol for wireless micro sensor networks. In: Proceedings of the 33rd International Conference on System Sciences; 47 January 2000; Hawaii. New York, NY, USA: IEEE. pp. 1-10.
- [18] Heinzelman W, Sinha A, Wang A, Chandrakasan AP. Energy-scalable algorithms and protocols for wireless micro sensor networks. Proc. In: International Conference on Acoustics, Speech, and Signal Processing (ICASSP 00); 59 June 2000; ˙Istanbul, Turkey. New York, NY, USA: IEEE. pp. 3722-3725.
- [19] Handy MJ, Haase M, Timmermann D. Low energy adaptive clustering hierarchy with deterministic cluster-head selection. In: 4th International Workshop on Mobile and Wireless Communications Network; 911 September 2002; Stockholm, Sweden. New York, NY, USA: IEEE. pp. 368-372.
- [20] Bozdo˘gan AO, Yılmaz AE, Efe M. Performance analysis of swarm optimization approaches for the generalized ¨ assignment problem in multi-target tracking applications. Turk J Elec Eng & Comp Sci 2010; 18: 1059-1076.
- [21] Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks; 27 November1 December 1995; Perth, Australia. New York, NY, USA: IEEE. pp. 1942-1948.