Energy-Efficient Topology to Enhance the Wireless Sensor Network Lifetime Using Connectivity Control

Main Article Content

Meysam Yari https://orcid.org/0000-0002-7407-0496
Parham Hadikhani
Zohreh Asgharzadeh

Keywords

Wireless Sensor Network, Connectivity Control, Lifetime, Meta-heuristic Algorithm, Energy Efficient

Abstract

Wireless sensor networks have attracted much attention because of many applications in the fields of industry, military, medicine, agriculture, and education. In addition, the vast majority of research has been done to expand their applications and improve their efficiency. However, there are still many challenges for increasing the efficiency in different parts of this network. One of the most important parts is to improve the network lifetime in the wireless sensor network. Since the sensor nodes are generally powered by batteries, the most important issue to consider in these types of networks is to reduce the power consumption of the nodes in such a way as to increase the network lifetime to an acceptable level. The contribution of this paper is using topology control, the threshold for the remaining energy in nodes, and two metaheuristic algorithms, namely SA (Simulated Annealing) and VNS (Variable Neighbourhood Search), to increase the energy remaining in the sensors. Moreover, using a low-cost spanning tree, an appropriate connectivity control among nodes is created in the network in order to increase the network lifetime. The results of simulations show that the proposed method improves the sensor lifetime and reduces the energy consumed.

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References

Bencan, G., Panpan, D., Peng, C., & Dong, R. (2020). Evolutionary game-based trajectory design algorithm for mobile sink in wireless sensor networks. International Journal of Distributed Sensor Networks, 16(3), 1550147720911000.
Gao, S., Zhang, H., & Das, S. K. (2010). Efficient data collection in wireless sensor networks with path-constrained mobile sinks. IEEE Transactions on Mobile Computing, 10(4), 592-608.
Hadikhani, P., Eslaminejad, M., Yari, M., & Mahani, E. A. (2020). An energy-aware and load balanced distributed geographic routing algorithm for wireless sensor networks with dynamic hole. Wireless Networks, 26, 507-519. https://doi.org/10.1007/s11276-019-02157-6
Hansen, P., Mladenovi?, N., Todosijevi?, R., & Hanafi, S. (2017). Variable neighborhood search: basics and variants. EURO Journal on Computational Optimization, 5(3), 423-454.
Hao, X., Wang, L., Yao, N., Geng, D., & Chen, B. (2018). Topology control game algorithm based on Markov lifetime prediction model for wireless sensor network. Ad Hoc Networks, 78, 13-23.
Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660-670.
Hou, J., & Zhang, Y. (2018). Mobile-Service Based Approach for Topology Control of Wireless Sensor Networks. Wireless Personal Communications, 102(2), 1839-1851.
Hua, C., & Yum, T.-S. P. (2008). Optimal routing and data aggregation for maximizing lifetime of wireless sensor networks. IEEE/ACM Transactions on Networking (TON), 16(4), 892-903.
Javadi, M., Mostafaei, H., Chowdhurry, M. U., & Abawajy, J. H. (2018). Learning automaton based topology control protocol for extending wireless sensor networks lifetime. Journal of Network and Computer Applications, 122, 128-136.
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680.
Liang, W., & Liu, Y. (2006). Online data gathering for maximizing network lifetime in sensor networks. IEEE Transactions on Mobile Computing, 6(1), 2-11.
Pandiyaraju, V., Logambigai, R., Ganapathy, S., & Kannan, A. (2020). An Energy Efficient Routing Algorithm for WSNs Using Intelligent Fuzzy Rules in Precision Agriculture. Wireless Personal Communications, 1-17.
Tan, H. Ö., & Körpeo?lu, I. (2003). Power efficient data gathering and aggregation in wireless sensor networks. ACM Sigmod Record, 32(4), 66-71.
Vecchio, M., & López-Valcarce, R. (2015). Improving area coverage of wireless sensor networks via controllable mobile nodes: A greedy approach. Journal of Network and Computer Applications, 48, 1-13.
Wang, J., Cao, Y., Li, B., Kim, H.-j., & Lee, S. (2017). Particle swarm optimization based clustering algorithm with mobile sink for WSNs. Future Generation Computer Systems, 76, 452-457.
Wang, J., Gao, Y., Liu, W., Sangaiah, A. K., & Kim, H.-J. (2019). Energy efficient routing algorithm with mobile sink support for wireless sensor networks. Sensors, 19(7), 1494.
Yarinezhad, R. (2019). Reducing delay and prolonging the lifetime of wireless sensor network using efficient routing protocol based on mobile sink and virtual infrastructure. Ad Hoc Networks, 84, 42-55.
Yetgin, H., Cheung, K. T. K., El-Hajjar, M., & Hanzo, L. H. (2017). A survey of network lifetime maximization techniques in wireless sensor networks, IEEE Communications Surveys & Tutorials, 19(2), 828-854.
Zhang, H., & Shen, H. (2008). Balancing energy consumption to maximize network lifetime in data-gathering sensor networks. IEEE Transactions on Parallel and Distributed Systems, 20(10), 1526-1539.
Zhang, H., Shen, H., & Tan, Y. (2007). Optimal energy balanced data gathering in wireless sensor networks. Paper presented at the 2007 IEEE International Parallel and Distributed Processing Symposium.
Zhao, H., Guo, S., Wang, X., & Wang, F. (2015). Energy-efficient topology control algorithm for maximizing network lifetime in wireless sensor networks with mobile sink. Applied Soft Computing, 34, 539-550.