WANG Lei1, HUO Jiuyuan1,2,3, Al-Neshmi Hamzah Murad Mohammed1
（1. School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China；2. Lanzhou Huahao Technology Co. Ltd, Lanzhou 730070, China； 3. National Cryosphere Desert Data Center (NCDC), Lanzhou 730070, China）
Abstract： The imbalance of energy consumption in wireless sensor networks (WSNs) easily results in the “hot spot” problem that the sensor nodes in a particular area die due to fast energy consumption. In order to solve the “hot spot” problem in WSNs, we propose an unequal clustering routing algorithm based on genetic algorithm (UCR-GA). In the cluster head election phase, the fitness function is constructed based on the residual energy, density and distance between nodes and base station, and the appropriate node is selected as the cluster head. In the data transmission phase, the cluster head selects single-hop or multi-hop communication mode according to the distance to the base station. After we comprehensively consider the residual energy of the cluster head and its communication energy consumption with the base station, an appropriate relay node is selected. The designed protocal is simulated under energy homogeneous and energy heterogeneity conditions, and the results show that the proposed routing protocal can effectively balance energy consumption, prolong the life cycle of network, and is appicable to heterogeneous networks.
Key words： wireless sensor networks (WSNs)； genetic algorithm (GA）； unequal clustering； multi-hop； life cycle of network; energy consumption
References
［1］SHARMA D, OJHA A, BHONDEKAR A P. Heterogeneity consideration in wireless sensor networks routing algorithms： a review. The Journal of Supercomputing, 2019, 75(5)： 2341-2394.
［2］MITTAL N, SINGH U, SOHI B S. A stable energy efficient clustering algorithm for wireless sensor networks. Wireless Networks, 2017, 23(6)： 1809-1821.
［3］BI X J, DIAO P F. Routing and clustering algorithm heterogeneous wireless sensor networks based on gravitational search algorithm. Control and Decision, 2017, 32(3)： 563-569.
［4］SUN Y Q, PENG J, LIU T, et al. Uneven clustering routing protocol based on dynamic partition for wireless sensor network. Journal on Communications, 2014, 35(1)： 198-206.
［5］KUILA P, GUPTA S K, JANA P K. A novel evolutionary approach for load balanced clustering problem for wireless sensor networks. Swarm & Evolutionary Computation, 2013, 12： 48-56.
［6］ATTEA B A, KHALIL E A. A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networksc. Applied Soft Computing Journal, 2012, 12(7)： 1950-1957.
［7］HEINZELMAN W R, CHANDRAKASAN A, BALAKRISHNAN H. Energy-efficient communication algorithm for wireless microsensor networks//The 33rd Annual Hawaii International Conference on System Sciences, Jan.7, 2000, Maui, Hi, USA. New York: IEEE, 2000, 2： 1-10.
［8］LIU T, LI F. Power-efficient clustering routing protocol based on applications in wireless sensor network//The 5th International Conference on Wireless Communications, Networking and Mobile Computing, Sept. 24-26, 2009, Beijing, China. New York: IEEE, 2009: 1-6.
［9］MALATHI L, GNANAMURTHY R K, CHANDRASEKARAN K. Energy efficient data collection through hybrid unequal clustering for wireless sensor networks. Computers & Electrical Engineering, 2015： 358-370.
［10］SMARAGDAKIS G, MATTA I, BESTAVROS A. SEP： A stable election protocol for clustered heterogeneous wireless sensor networks. Proceeding of International Workshop on Sensor & Actor Network Protocol & Applications, 2004.
［11］GU X, YU J, YU D, et al. ECDC： an energy and coverage-aware distributed clustering algorithm for wireless sensor networks. Computers & Electrical Engineering, 2014, 40(2)： 384-398.
［12］ZHANG Y, JI C G, LI J F. A routing algorithm based on distance for heterogeneous sensor networks with multilevel energies. Journal of Shanghai Jiaotong University, 2014, 48(7)： 953-958.
［13］WU X N, ZHANG C Y, ZHANG R L, et al. Clustering routing protocol based on improved PSO algorithm in WSN. Journal on Communications, 2019, 40(12)： 114-123.
［14］LATIFF N, TSIMENIDIS C C, SHARIF B S. Energy-aware clustering for wireless sensor networks using particle swarm optimization//IEEE 18th International Symposium on Personal, Sept. 3-7, 2007, Athens, Greece. New York: IEEE, 2007: 1-5.
［15］ANANDH S J, BABURAJ E. Energy efficient routing technique for wireless sensor networks using ant-colony optimization. Wireless Personal Communications, 2020, 114(4): 3419-3433.
［16］NAYAK P, SHREE P. Comparison of routing algorithms in WSN using NetSim simulator： LEACH vs LEACH-C. International Journal of Computer Applications, 2014, 106(11)： 975.
［17］SORO S, HEINZELMAN W B. Prolonging the lifetime of wireless sensor networks via unequal clustering//19th IEEE International Parallel and Distributed Processing Symposium, Apr. 4-8, 2005, Denver, Co, USA. New York: IEEE, 2005: 8.
［18］LI C F, CHEN G H, YE M, et al. An uneven cluster-based routing protocol for wireless sensor networks. Chinese Journal of Computers, 2007(1)： 27-36.
［19］LEE S, CHOE H, PARK B, et al. LUCA： an energy-efficient unequal clustering algorithm using location information for wireless sensor networks. Wireless Personal Communications An International Journal, 2011, 56(4)： 715-731.
［20］YU J G, QI Y Y, WANG G H. An energy-driven unequal clustering algorithm for heterogeneous wireless sensor networks. Journal of Control Theory and Applications, 2011, 9: 133-139.
［21］CHEN B C, YAO H Z, YANG M C, et al. A inter-cluster multi-hop routing protocol improved based on LEACH protocol. Chinese Journal of Sensors and Actuators, 2014, 27(3)： 373-377.
［22］BAYRAKL S, ERDOGAN S Z. Genetic algorithm based energy efficient clusters (GABEEC) in wireless sensor networks. Procedia Computer Science, 2012, 10： 247-254.
［23］MURUGANANTHAM N, EL-OCLA H. Routing using genetic algorithm in a wireless sensor network. Wireless Personal Communications, 2020, 111(4): 2703-2732.
［24］GENTA A, LOBIYAL D K, ABAWAJY J H. Energy efficient multipath routing algorithm for wireless multimedia sensor network. Sensors, 2019, 19(17): 1-21.
［25］WANG M, WANG S, ZHANG B. APTEEN routing protocol optimization in wireless sensor networks based on combination of genetic algorithms and fruit fly optimization algorithm. Ad Hoc Networks, 2020, 102： 102138.
［26］WANG T S, ZHANG G X, YANG X C, et al. Genetic algorithm for energy-efficient clustering and routing in wireless sensor networks. Journal of Systems and Software, 2018, 146： 196-214.
［27］BHATIA T, KANSAL S, GOEL S, et al. A genetic algorithm based distance-aware routing protocol for wireless sensor networks. Computers and Electrical Engineering, 2016, 56: 441-455.
［28］YUAN X H, ELHOSENY M, EL-MINIR H K, et al. A genetic algorithm-based, dynamic clustering method towards improved WSN longevity. Journal of Network and Systems Management, 2017: 25: 21-46.
［29］ELHOSENY M, YUAN X H, YU Z, et al. Balancing energy consumption in heterogeneous wireless sensor networks using genetic algorithm. IEEE Communications Letters, 2015, 19(12)： 2194-2197.
［30］ANAND V, PANDEY S. New approach of GA-PSO-based clustering and routing in wireless sensor networks. International Journal of Communication Systems, 2020, 33(16): 1-20.
［31］SHARMA D, OJHA A, BHONDEKAR A P. Heterogeneity consideration in wireless sensor networks routing algorithms： a review. The Journal of Supercomputing, 2019, 75(5)： 2341-2394.
基于遗传算法的无线传感器网络非均匀分簇路由协议
王磊1， 火久元1,2,3， Al-Neshmi Hamzah Murad Mohammed1
（1. 兰州交通大学 电子与信息工程学院， 甘肃 兰州 730070； 2. 兰州华浩科技有限公司， 甘肃 兰州 730070； 3. 国家冰川冻土沙漠科学数据中心， 甘肃 兰州 730070）
摘要:无线传感器网络能量消耗不均衡易造成网络中某一区域的传感器节点因能量过快消耗而死亡， 被称为“热点”问题。 为此， 提出了一种基于遗传算法的无线传感器网络非均匀分簇路由协议（Unequal clustering routing protocol based on genetic algorithm， UCR-GA）。 簇头选举阶段， 综合考虑节点的剩余能量、 密度和距离来构造适应度函数， 并选择合适的节点作为簇头； 数据传输阶段， 簇头根据与基站的距离选择通信方式为单跳或者多跳， 在综合考虑簇头剩余能量及其与基站的通信能耗的基础上， 选择出合适的中继节点。 对设计的协议在能量同构和能量异构的条件下分别进行仿真， 结果表明， 该协议能够有效均衡能量消耗， 延长网络生命周期， 并适用于异构网络。
关键词:无线传感器网络； 遗传算法； 非均匀分簇； 多跳； 网络生命周期； 能量消耗
引用格式：WANG Lei, HUO Jiuyuan, Al-Neshmi Hamzah Murad Mohammed． An unequal clustering routing protocal for wireless sensor networks based on genetic algorithm． Journal of Measurement Science and Instrumentation， 2022， 13（3）： 329-344. DOI： 10．3969／j．issn．1674-8042．2022．03．009
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