An improved immune algorithm for node relocation to maximize confident information coverage in a hybrid sensor network

This paper studies the mobile node relocation problem to maximize the area confident information coverage while minimizing the movement cost in a hybrid sensor network consisting of both stationary and mobile nodes. For this multi-objective problem, we propose an improved immune algorithm by using an effective initial population generation and an efficient population attribute exchange during generation iteration. The initial population is generated based on the Denaunay triangulation for reducing coverage holes by stationary nodes; and a new soft-coded destination selection is applied based on the bipartite graph perfect matching. The simulation results show that the proposed algorithm outperforms the peer immune algorithms in terms of much higher coverage ratio, lower relocation cost and comparable computation complexity.

[1]  Dina S. Deif,et al.  Classification of Wireless Sensor Networks Deployment Techniques , 2014, IEEE Communications Surveys & Tutorials.

[2]  Bang Wang,et al.  Coverage problems in sensor networks: A survey , 2011, CSUR.

[3]  Baltasar Beferull-Lozano,et al.  Field estimation in wireless sensor networks using distributed kriging , 2012, 2012 IEEE International Conference on Communications (ICC).

[4]  Jiajun Zhu,et al.  Sensor Density for Confident Information Coverage in Randomly Deployed Sensor Networks , 2016, IEEE Transactions on Wireless Communications.

[5]  Athanasios V. Vasilakos,et al.  Genetic algorithms in wireless networking: techniques, applications, and issues , 2014, Soft Computing.

[6]  Mohammed Abo-Zahhad,et al.  Rearrangement of mobile wireless sensor nodes for coverage maximization based on immune node deployment algorithm , 2015, Comput. Electr. Eng..

[7]  Yongsheng Ding,et al.  Immune clonal selection algorithm for target coverage of wireless sensor networks , 2011, Int. J. Model. Identif. Control..

[8]  A. Halim Zaim,et al.  An Interactive Genetic Algorithm for Mobile Sensor Networks , 2013 .

[9]  Qingguo Zhang,et al.  A Two-Phase Coverage-Enhancing Algorithm for Hybrid Wireless Sensor Networks , 2017, Sensors.

[10]  T. Amudha,et al.  SOLVING JOB SHOP SCHEDULING PROBLEMS WITH CONSULTANT GUIDED SEARCH METAHEURISTICS , 2013 .

[11]  Mahmoud Reza Delavar,et al.  Wireless sensors deployment optimization using a constrained Pareto-based multi-objective evolutionary approach , 2016, Eng. Appl. Artif. Intell..

[12]  Yipeng Qu,et al.  Relocation of wireless sensor network nodes using a genetic algorithm , 2011, WAMICON 2011 Conference Proceedings.

[13]  Shigenobu Sasaki,et al.  A centralized immune-Voronoi deployment algorithm for coverage maximization and energy conservation in mobile wireless sensor networks , 2016, Inf. Fusion.

[14]  Alagan Anpalagan,et al.  Multi-objective optimization in sensor networks: Optimization classification, applications and solution approaches , 2016, Comput. Networks.

[15]  Jiajun Zhu,et al.  The Optimal Placement Pattern for Confident Information Coverage in Wireless Sensor Networks , 2016, IEEE Transactions on Mobile Computing.

[16]  Bang Wang,et al.  A novel node sinking algorithm for 3D coverage and connectivity in underwater sensor networks , 2017, Ad Hoc Networks.

[17]  Yan Huang,et al.  Energy-Efficient Map Interpolation for Sensor Fields Using Kriging , 2009, IEEE Transactions on Mobile Computing.

[18]  Pei-wei Tsai,et al.  Metaheuristics for the deployment problem of WSN: A review , 2015, Microprocess. Microsystems.

[19]  Di Ma,et al.  A survey of movement strategies for improving network coverage in wireless sensor networks , 2009, Comput. Commun..

[20]  Bang Wang,et al.  Sensor placement based on Delaunay triangulation for complete confident information coverage in an area with obstacles , 2015, 2015 IEEE 34th International Performance Computing and Communications Conference (IPCCC).

[21]  Ulrich Derigs.  A generalized Hungarian method for solving minimum weight perfect matching problems with algebraic objective , 1979, Discret. Appl. Math..

[22]  Laurence T. Yang,et al.  Confident information coverage in sensor networks for field reconstruction , 2013, IEEE Wireless Communications.

[23]  Naeim Rahmani,et al.  EAVD: An Evolutionary Approach Based on Voronoi Diagram for Node Deployment in Wireless Sensor Networks , 2011, SocProS.

[24]  Zhi Chen,et al.  Memetic Algorithm-Based Multi-Objective Coverage Optimization for Wireless Sensor Networks , 2014, Sensors.

[25]  Lizhong Jin,et al.  Node Distribution Optimization in Mobile Sensor Network Based on Multi-Objective Differential Evolution Algorithm , 2010, 2010 Fourth International Conference on Genetic and Evolutionary Computing.

[26]  Timothy C. Havens,et al.  Coverage optimization in a terrain-aware wireless sensor network , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[27]  Omar Banimelhem,et al.  Genetic Algorithm Based Node Deployment in Hybrid Wireless Sensor Networks , 2013 .

[28]  Abdelhamid Mellouk,et al.  Movement-Assisted Sensor Deployment Algorithms: A Survey and Taxonomy , 2015, IEEE Communications Surveys & Tutorials.