A novel topology optimization of coverage-oriented strategy for wireless sensor networks

Aiming at the key optimization problems of wireless sensor networks in complex industrial application environments, such as the optimum coverage and the reliability of the network, a novel topology optimization of coverage-oriented strategy for wireless sensor networks based on the wolf pack algorithm is proposed. Combining the characteristics of topology structure of wireless sensor networks and the optimization idea of the wolf pack algorithm redefines the group’s wandering and surprise behavior. A novel head wolf mutation strategy is proposed, which increases the neighborhood search range of the optimal solution, enhances the uniformity of wolf pack distribution and the ergodicity ability of the wolf pack search, and greatly improves the calculation speed and the accuracy of the wolf pack algorithm. With the same probability, the cluster heads are randomly selected periodically, and the overall energy consumption of wireless sensor networks is evenly distributed to the sensor node to realize the balanced distribution of the data of the member nodes in the cluster and complete the design of the topology optimization of wireless sensor networks. Through algorithm simulation and result analysis, compared with the particle swarm optimization algorithm and artificial fish swarm algorithm, the wolf swarm algorithm shows its advantages in terms of the residual energy of the sensor node, the average transmission delay, the average packet delivery rate, and the coverage of the network. Among them, compared with the particle swarm optimization algorithm and artificial fish swarm algorithm, the remaining energy of nodes has increased by 9.5% and 15.5% and the average coverage of the network has increased by 10.5% and 5.6%, respectively.

[1]  Ahmed M. Khedr,et al.  Coverage aware face topology structure for wireless sensor network applications , 2020, Wireless Networks.

[2]  Chia-Hung Lin,et al.  Synchronizing chaotification with support vector machine and wolf pack search algorithm for estimation of peripheral vascular occlusion in diabetes mellitus , 2014, Biomed. Signal Process. Control..

[3]  Abdelhamid Mellouk,et al.  A robust uncertainty-aware cluster-based deployment approach for WSNs: Coverage, connectivity, and lifespan , 2019, J. Netw. Comput. Appl..

[4]  Abdelhamid Mellouk,et al.  Static wireless sensor networks deployment using an improved binary PSO , 2016, Int. J. Commun. Syst..

[5]  Husheng Wu,et al.  Wolf Pack Algorithm for Unconstrained Global Optimization , 2014 .

[6]  Yingying Feng,et al.  Analysis of Network Coverage Optimization Based on Feedback K-Means Clustering and Artificial Fish Swarm Algorithm , 2020, IEEE Access.

[7]  P. He,et al.  Fault Detection for Systems With Model Uncertainty and Disturbance via Coprime Factorization and Gap Metric , 2021, IEEE Transactions on Cybernetics.

[8]  Yu Bai,et al.  Real-time online detection of trucks loading via genetic neural network , 2020 .

[9]  Ting Zhang,et al.  A new method of data missing estimation with FNN-based tensor heterogeneous ensemble learning for internet of vehicle , 2021, Neurocomputing.

[10]  Jesús Gutiérrez-Gutiérrez,et al.  On the topology design of large wireless sensor networks for distributed consensus with low power consumption , 2019, Int. J. Distributed Sens. Networks.

[11]  B. Nini,et al.  An improved fractal image compression using wolf pack algorithm , 2017, J. Exp. Theor. Artif. Intell..

[12]  Jong Hyuk Park,et al.  An improved ant colony optimization-based approach with mobile sink for wireless sensor networks , 2017, The Journal of Supercomputing.

[13]  Weihua Wu,et al.  Wireless sensor network coverage optimization based on whale group algorithm , 2018, Comput. Sci. Inf. Syst..

[14]  Karim Faez,et al.  AMOF: adaptive multi-objective optimization framework for coverage and topology control in heterogeneous wireless sensor networks , 2016, Telecommun. Syst..

[15]  Arun Kumar Sangaiah,et al.  Survey on clustering in heterogeneous and homogeneous wireless sensor networks , 2017, The Journal of Supercomputing.

[16]  Guang Li,et al.  An Energy-Balanced Routing Method Based on Forward-Aware Factor for Wireless Sensor Networks , 2014, IEEE Transactions on Industrial Informatics.

[17]  Jaime Lloret Mauri,et al.  CASMOC: a novel complex alliance strategy with multi-objective optimization of coverage in wireless sensor networks , 2017, Wirel. Networks.

[18]  Yu Zhao,et al.  A novel quantum-inspired binary wolf pack algorithm for difficult knapsack problem , 2019 .

[19]  Liu Yong,et al.  A clustering routing algorithm based on wolf pack algorithm for heterogeneous wireless sensor networks , 2020, Comput. Networks.

[20]  Jianqiao Yu,et al.  Three-dimensional unmanned aerial vehicle path planning using modified wolf pack search algorithm , 2017, Neurocomputing.

[21]  Abdul Hanan Abdullah,et al.  Coverage Enhancement Algorithms for Distributed Mobile Sensors Deployment in Wireless Sensor Networks , 2016, Int. J. Distributed Sens. Networks.

[22]  Ting Zhang,et al.  Novel unequal clustering routing protocol considering energy balancing based on network partition & distance for mobile education , 2017, J. Netw. Comput. Appl..

[23]  J. Lloret,et al.  Wireless Sensors Self-Location in an Indoor WLAN Environment , 2007, 2007 International Conference on Sensor Technologies and Applications (SENSORCOMM 2007).

[24]  Rastko R. Selmic,et al.  Topological and combinatorial coverage hole detection in coordinate-free wireless sensor networks , 2016, Int. J. Sens. Networks.

[25]  Jamal N. Al-Karaki,et al.  The Optimal Deployment, Coverage, and Connectivity Problems in Wireless Sensor Networks: Revisited , 2017, IEEE Access.

[26]  Fotis Foukalas,et al.  Topology control with coverage and lifetime optimization of wireless sensor networks with unequal energy distribution , 2017, Comput. Electr. Eng..

[27]  Ping He,et al.  A comprehensive survey on the reliability of mobile wireless sensor networks: Taxonomy, challenges, and future directions , 2018, Inf. Fusion.