Wireless sensor networks (WSNs) consist of sensor nodes with limited energy. Energy efficiency is an important issue in WSNs as the sensor nodes are deployed in rugged and non-care areas and consume a lot of energy to send data to the central station or sink if they want to communicate directly with the sink. Recently, the IEEE 802.15.4 protocol is employed as a low-power, low-cost, and low rate communication standard for WSNs, which ensures real-time applications via Guaranteed Time Slot (GTS). Accordingly, in this paper, a new protocol energy efficient bat algorithm and a mobile sink are presented. It can choose the optimal route based on: (1) distance of a sensor node to sink, (2), bat loudness, and (3) energy level of its battery. Using the OPNET simulator version 11.5, the proposed method was simulated by bat and NODIC protocols, and IEEE 802.15.4 and the results were considered in terms of energy consumption, end to end delay, signal to noise ratio, the success probability of sending data to sink and rate of passing data. The results of the simulation showed the use of the above-mentioned parameters in the proposed method leads to the improvement of the network throughput against using the IEEE 802.15.4 protocol, Bat algorithm, and NODIC protocol.
[1] E. László, K. Tornai, G. Treplán, and J. Levendovszky, "Novel load balancing scheduling algorithms for wireless sensor networks," in The Fourth Int. Conf. on Communication Theory, Reliability, and Quality of Service, Budapest, 2011, pp. 54-49.
[2] D.-R. Chen, "An energy-efficient QoS routing for wireless sensor networks using self-stabilizing algorithm," Ad Hoc Networks, vol. 37, pp. 240-255, 2016.
[3] V. Subrahmanyam, M. A. Zubair, A. Kumar, and P. Rajalakshmi, "A low power minimal error IEEE 802.15. 4 Transceiver for heart monitoring in IoT applications," Wireless Personal Communications, vol. 100, no. 2, pp. 611-629, 2018.
[4] S. Shriwastav and D. Ghose, "Round-table negotiation for fast restoration of connectivity in partitioned wireless sensor networks," Ad Hoc Networks, vol. 77, pp. 11-27, 2018.
[5] S. M. Bozorgi, A. S. Rostami, A. A. R. Hosseinabadi, and V. E. Balas, "A new clustering protocol for energy harvesting-wireless sensor networks," Computers & Electrical Engineering, vol. 64, pp. 233-247, 2017.
[6] J. Li and D. Liu, "DPSO-based clustering routing algorithm for energy harvesting wireless sensor networks," in 2015 International Conference on Wireless Communications & Signal Processing (WCSP), 2015, pp. 1-5: IEEE.
[7] S. Tabatabaei and A. M. Rigi, "Reliable routing algorithm based on clustering and mobile sink in wireless sensor networks," Wireless Personal Communications, vol. 108, no. 4, pp. 2541-2558, 2019.
[8] S. Tabatabaei, A. Rajaei, and A. M. Rigi, "A novel energy-aware clustering method via Lion Pride Optimizer Algorithm (LPO) and fuzzy logic in wireless sensor networks (WSNs)," Wireless Personal Communications, vol. 108, no. 3, pp. 1803-1825, 2019.
[9] A. Shelebaf and S. Tabatabaei, "A Novel Method for Clustering in WSNs via TOPSIS Multi-criteria Decision-Making Algorithm," Wireless Personal Communications, pp. 1-17, 2020.
[10] V. Rajasekar, K. Sathya, and J. Premalatha, "Energy Efficient Cluster Formation in Wireless Sensor Networks Based on Multi Objective Bat Algorithm," in 2018 International Conference on Intelligent Computing and Communication for Smart World (I2C2SW), 2018, pp. 116-120: IEEE.
[11] X.-S. Yang, "A new metaheuristic bat-inspired algorithm," in Nature inspired cooperative strategies for optimization (NICSO 2010): Springer, 2010, pp. 65-74.
[12] İ. Abasıkeleş-Turgut and O. G. Hafif, "NODIC: a novel distributed clustering routing protocol in WSNs by using a time-sharing approach for CH election," Wireless Networks, vol. 22, no. 3, pp. 1023-1034, 2016.
[13] M. Sharawi, E. Emary, I. A. Saroit, and H. El-Mahdy, "Bat swarm algorithm for wireless sensor networks lifetime optimization," Int. J, vol. 3, no. 5, pp. 654-664, 2014.
[14] Ebrahimi, S., & Tabatabaei, S. (2020). Using Clustering via Soccer League Competition Algorithm for Optimizing Power Consumption in WSNs (Wireless Sensor Networks). Wireless Personal Communications, 1-16.
[15] Maurya, S., Jain, V. K., & Chowdhury, D. R. (2019). Delay aware energy efficient reliable routing for data transmission in heterogeneous mobile sink wireless sensor network. Journal of Network and Computer Applications, 144, 118-137.
[16] Ragavan, P. S., & Ramasamy, K. (2020). Software defined networking approach based efficient routing in multihop and relay surveillance using Lion Optimization algorithm. Computer Communications, 150, 764-770.
[17] Yuvaraj, D., Sivaram, M., Ahamed, A. M. U., & Nageswari, S. (2019, October). An Efficient Lion Optimization Based Cluster Formation and Energy Management in WSN Based IoT. In International Conference on Intelligent Computing & Optimization (pp. 591-607). Springer, Cham.
[18] L. OPNET, "Specialized Model: http://www. opnet. com," ed: LTE.
Tabatabaei, S. (2021). An Energy Efficient Clustering Method using Bat Algorithm and Mobile Sink in Wireless Sensor Networks. Soft Computing Journal, 8(2), 102-115. doi: 10.22052/8.2.102
MLA
Shayesteh Tabatabaei. "An Energy Efficient Clustering Method using Bat Algorithm and Mobile Sink in Wireless Sensor Networks". Soft Computing Journal, 8, 2, 2021, 102-115. doi: 10.22052/8.2.102
HARVARD
Tabatabaei, S. (2021). 'An Energy Efficient Clustering Method using Bat Algorithm and Mobile Sink in Wireless Sensor Networks', Soft Computing Journal, 8(2), pp. 102-115. doi: 10.22052/8.2.102
VANCOUVER
Tabatabaei, S. An Energy Efficient Clustering Method using Bat Algorithm and Mobile Sink in Wireless Sensor Networks. Soft Computing Journal, 2021; 8(2): 102-115. doi: 10.22052/8.2.102
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