An Optimized Node Deployment Solution Based on a Virtual Spring Force Algorithm for Wireless Sensor Network Applications

How to effectively deploy all wireless sensors and save a system’s energy consumption is a key issue in current wireless sensor network (WSN) applications. Theoretical analysis has proven that a hexagonal structure is the best topology in the two-dimensional network, which can provide the maximum coverage area with the minimum number of sensor nodes and minimum energy consumption. Recently, many scientists presented their self-deployment strategies based on different virtual forces and discussed the corresponding efficiency via several case studies. However, according to our statistical analysis, some virtual force algorithms, e.g., virtual spring force, can still cause holes or twisted structure in a small region of the final network distribution, which cannot achieve the ideal network topology and will waste the system energy in real applications. In this paper, we first statistically analyzed the convergence and deployment effect of the virtual spring force algorithm to derive our question. Then we presented an optimized strategy that sensor deployment begins from the center of the target region by adding an external central force. At the early stage, the external force will be added to the most peripheral nodes to promote the formation of hexagonal topology and avoid covering holes or unusual structure. Finally, a series of independent simulation experiments and corresponding statistical results proved that our optimized deployment solution is very stable and effective, which can improve the energy consumption of the whole sensor network and be used in the application of a large scale WSN.

[1]  Sandra Sendra,et al.  Intelligent Wireless Sensor Network Deployment for Smart Communities , 2018, IEEE Communications Magazine.

[2]  Xin Liu,et al.  Differential Evolution-Based 3-D Directional Wireless Sensor Network Deployment Optimization , 2018, IEEE Internet of Things Journal.

[3]  Tao Zhang,et al.  A Node Deployment Algorithm Based on Van Der Waals Force in Wireless Sensor Networks , 2013, Int. J. Distributed Sens. Networks.

[4]  Xiaomin Zhao,et al.  3D Self-Deployment Algorithm in Mobile Wireless Sensor Networks , 2014, Int. J. Distributed Sens. Networks.

[5]  Tao Zhang,et al.  A Faster Convergence Artificial Bee Colony Algorithm in Sensor Deployment for Wireless Sensor Networks , 2013, Int. J. Distributed Sens. Networks.

[6]  Zhe Chen,et al.  Optimized Node Deployment Algorithm and Parameter Investigation in a Mobile Sensor Network for Robotic Systems , 2015 .

[7]  Gaige Wang,et al.  Dynamic Deployment of Wireless Sensor Networks by Biogeography Based Optimization Algorithm , 2012, J. Sens. Actuator Networks.

[8]  Manish Kumar,et al.  Benefits of using particle swarm optimization and Voronoi diagram for coverage in wireless sensor networks , 2017, 2017 International Conference on Emerging Trends in Computing and Communication Technologies (ICETCCT).

[9]  Xin Liu Differential Evolution-based 3D Directional Wireless Sensor Network Deployment Optimization , 2018 .

[10]  Dervis Karaboga,et al.  Probabilistic Dynamic Deployment of Wireless Sensor Networks by Artificial Bee Colony Algorithm , 2011, Sensors.

[11]  Michele Garetto,et al.  A Distributed Sensor Relocatlon Scheme for Environmental Control , 2007, 2007 IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems.

[12]  Chakchai So-In,et al.  Distributed Deployment Algorithm for Barrier Coverage in Mobile Sensor Networks , 2018, IEEE Access.

[13]  Kai Leung Yung,et al.  A Smart Bat Algorithm for Wireless Sensor Network Deployment in 3-D Environment , 2018, IEEE Communications Letters.

[14]  Yeh-Ching Chung,et al.  A Delaunay triangulation based method for wireless sensor network deployment , 2006, 12th International Conference on Parallel and Distributed Systems - (ICPADS'06).

[15]  R. Tang,et al.  Investigation of the Shielding Length on Yukawa System Crystallization in Mobile Sensor Network Applications , 2016, IEEE Transactions on Plasma Science.

[16]  Yu Zhang,et al.  Delaunay triangulation based localization scheme , 2017, 2017 29th Chinese Control And Decision Conference (CCDC).

[17]  Xin Liu,et al.  3-D Multiobjective Deployment of an Industrial Wireless Sensor Network for Maritime Applications Utilizing a Distributed Parallel Algorithm , 2018, IEEE Transactions on Industrial Informatics.

[18]  Amir G. Aghdam,et al.  Distributed Deployment Algorithms for Coverage Improvement in a Network of Wireless Mobile Sensors: Relocation by Virtual Force , 2017, IEEE Transactions on Control of Network Systems.

[19]  Tao Zhang,et al.  A Deployment Method Based on Spring Force in Wireless Robot Sensor Networks , 2014 .

[20]  Ammar W. Mohemmed,et al.  A wireless sensor network coverage optimization algorithm based on particle swarm optimization and Voronoi diagram , 2009, 2009 International Conference on Networking, Sensing and Control.

[21]  Tianjian Wang,et al.  A node localisation approach based on mobile beacon using particle swarm optimisation in wireless sensor networks , 2017, Int. J. Embed. Syst..

[22]  Anis Laouiti,et al.  Survey of deployment algorithms in wireless sensor networks: coverage and connectivity issues and challenges , 2017, Int. J. Auton. Adapt. Commun. Syst..