Optimization of Mobile Base Station Placement to Reduce Energy Consumption in Multi-hop Wireless Sensor Network
Subject Areas : Simulation Based OptimizationGholamreza Farahani 1 , Ameneh Farahani 2
1 - Department of Electrical Engineering and Information Technology, Iranian Research Organization for Science and Technology (IROST), Tehran, Iran
2 - Department of Industrial Engineering, Ooj Institute of Higher Education, Qazvin, Iran
Keywords: Floyd Warshall algorithm, Fuzzy logic, Genetic algorithm, Mobile base station, Wireless sensor networks,
Abstract :
Nowadays, wireless sensor networks (WSNs) are widely used in different sectors. The problem in these networks is the non-rechargeable batteries of these sensors, which limit the lifetime of the network. Therefore, the optimal energy consumption of sensors is an open research topic. In this paper, a new algorithm with the Development of Genetic Algorithm with the Floyd Warshall (DGAFW) has been proposed. Using the proposed DGAFW algorithm, the number of clusters and nodes assigned to each cluster is first determined with the Floyd Warshall algorithm and then the Cluster Head (CH) is selected using fuzzy logic. Finally, the optimal placement of the base station is specified by the combination of the Genetic Algorithm and the Floyd Warshall. The DGAFW algorithm is based on minimizing the distance of sending multi-hop messages. The simulation is carried out in MATLAB 2023a online software. The simulation results obtained from the DGAFW algorithm have been compared based on the distance, the amount of remaining energy in each round, and the number of rounds of network activity in the case where the location of the base station is fixed or randomly determined in each round. The results obtained show that the DGAFW algorithm compared to the case of random base station and fixed station respectively, has 12.7% and 14.3% shorter average message-sending distance in each round, 14.7% and 19.1% more residual energy and also 36% and 48% more rounds of network activity.
[1] Li, Y., Wang, Y., & He, G. (2012) “Clustering-based distributed support vector machine in wireless sensor networks,” Journal of Information & Computational Science, 9(4), 1083-1096.
[2] Vu, T. T., Nguyen, V. D., & Nguyen, H. M. (2014) “An energy-aware routing protocol for wireless sensor networks based on K-means clustering,” In AETA 2013: Recent Advances in Electrical Engineering and Related Sciences, (pp. 297-306), Springer, Berlin, Heidelberg.
[3] Loh, P. K., & Pan, Y. (2009) “An energy-aware clustering approach for wireless sensor networks,” International Journal of Communications, Network and System Sciences, 2(2), 131-141.
[4] Zahariadis, T., Leligou, H., Karkazis, P., Trakadas, P., Papaefstathiou, I., Vangelatos, C., & Besson, L. (2010) “Design and implementation of a trust-aware routing protocol for large WSNs,” International Journal of Network Security & Its Applications, 2(3), 52-68.
[5] Karthikeyan, V., Vinod, A., & Jeyakumar, P. (2014) “An energy efficient neighbour node discovery method for wireless sensor networks,” arXiv, abs/1402.3655.
[6] Hurni, P., & Braun, T. (2008) “Energy-efficient multi-path routing in wireless sensor networks,” In International Conference on Ad-Hoc Networks and Wireless, Springer, Berlin, Heidelberg, 72-85.
[7] Shin, D., Lee, J., Kim, J., & Song, J. (2009) “A2OMDV: An adaptive ad hoc on-demand multipath distance vector routing protocol using dynamic route switching,” Journal of Engineering Science and Technology, 4(2), 171-183.
[8] Vidhyapriya, R., & Vanathi, P. T. (2007) “Energy aware routing for wireless sensor networks,” In International IEEE Conference on Signal Processing, Communications and Networking, 545-550.
[9] Farahani, G. (2018) “Improvement of Multiple Routing Based on Fuzzy Clustering and PSO Algorithm in WSNs to Reduce Energy Consumption,” International Journal of Computer Networks and Communications, 10(6), 97-115.
[10] Kumar, M. A., Pullama, K. B., & Reddy, B. S. V. M. (2013) “Energy Efficient Routing in Wireless Sensor Networks,” International Journal of Emerging Technology and Advanced Engineering, 9(9), 172-176.
[11] Farahani, G. (2019) “Energy Consumption Reduction in Wireless Sensor Network Based on Clustering,” International Journal of Computer Networks and Communications, 11(2), 33-51.
[12] Samundiswary, P., & Anandkumar, S. R. (2012) “Throughput Analysis of Energy Aware Reactive Routing Protocol for Wireless Sensor Networks,” International Journal of Soft Computing and Engineering, 2(1), 497-501.
[13] Farahani, G. (2017) “Network Performance Enhancement with Optimization Sensor Placement in Wireless Sensor Network,” International Journal of Wireless & Mobile Networks, 9(1), 9-30.
[14] Makvandi, N., Hashemi, S. M., & Haghighat, P. (2014) “Detecting attacks in wireless sensor network using genetic algorithms,” In International Conference on Computing Technology and Information Management (ICCTIM), San Diego: Society of Digital Information and Wireless Communication.
[15] Farahani, G. (2020) “Feature Selection Based on Cross-Correlation for Intrusion Detection System,” Security and Communication Networks, 2020, 1-17.
[16] Farahani, G. (2021) “Black Hole Attack Detection Using K-Nearest Neighbor Algorithm and Reputation Calculation in Mobile Ad Hoc Networks,” Security and Communication Networks, 2021, 1-15.
[17] Singh, R., & Verma, A. K. (2017) “Energy efficient cross layer based adaptive threshold routing protocol for WSN,” AEU-International Journal of Electronics and Communications, 72, 166-173.
[18] Ke, W., Yangrui, O., Hong, J., Heli, Z., & Xi, L. (2016) “Energy aware hierarchical cluster-based routing protocol for WSNs,” The Journal of China Universities of Posts and Telecommunications, 23(4), 46-52.
[19] Yigit, M., Gungor, V. C., Fadel, E., Nassef, L., Akkari, N., & Akyildiz, I. F. (2016) “Channel-aware routing and priority-aware multi-channel scheduling for WSN-based smart grid applications,” Journal of Network and Computer Applications, 71, 50-58.
[20] Mohemed, R. E., Saleh, A. I., Abdelrazzak, M., & Samra, A. S. (2017) “Energy-efficient routing protocols for solving energy hole problem in wireless sensor networks,” Computer Networks, 114, 51-66.
[21] Ramanan, K., & Raj, E. B. (2017) “Derived Genetic Algorithm Optimizer for Energy Efficient Routing in Wireless Sensor Network,” Asian Journal of Research in Social Sciences and Humanities, 7(3), 217-233.
[22] Mansi, K. T., & Patel, M. M. (2018) “Energy efficient routing in wireless sensor network,” In International IEEE Conference on Inventive Research in Computing Applications (ICIRCA), 264-268.
[23] Raghavendra, Y. M., & Mahadevaswamy, U. B. (2020) “Energy efficient routing in wireless sensor network based on composite fuzzy methods,” Wireless Personal Communications, 114(3), 2569-2590.
[24] Sahu, M. K., & Patil, S. (2021) “Enhanced Double Cluster Head Selection using Ant-colony Optimization for Energy-efficient Routing in Wireless Sensor Network,” SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology, 13(1), 35-41.
[25] Tohidi, H., & Jabbari, M. M. (2012) “CRM in organizational structure design,” Procedia Technology, 1, 579-582.
[26] Tohidi, H., & Jabbari, M. M. (2012) “The necessity of using CRM,” Procedia Technology, 1, 514-516.
[27] Tohidi, H., Namdari, A., Keyser, T. K., & Drzymalski, J. (2017) “Information sharing systems and teamwork between sub-teams: a mathematical modeling perspective,” Journal of Industrial Engineering International, 13, 513-520.
[28] Mir, M., Yaghoobi, M., & Khairabadi, M. (2022) “A new approach to energy-aware routing in the Internet of Things using improved Grasshopper Metaheuristic Algorithm with Chaos theory and Fuzzy Logic,” Multimedia Tools and Applications, 82(4), 5133–5159.
[29] Haldurai, L., Madhubala, T., & Rajalakshmi, R. (2016) “A study on genetic algorithm and its applications,” International Journal of Computational Science and Engineering, 4(10), 139-143.
[30] Bierwirth, C., Mattfeld, D. C., & Kopfer, H. (1996) “On permutation representations for scheduling problems,” In International Conference on Evolutionary Computation—The 4th International Conference on Parallel Problem Solving, 310-318.