A Molecular Force Field-Based Optimal Deployment Algorithm for UAV Swarm Coverage Maximization in Mobile Wireless Sensor Network

In the mobile wireless sensor network (MWSN) field, there exists an important problem—how can we quickly form an MWSN to cover a designated working area on the ground using an unmanned aerial vehicle (UAV) swarm? This problem is of significance in many military and civilian applications. In this paper, inspired by intermolecular forces, a novel molecular force field-based optimal deployment algorithm for a UAV swarm is proposed to solve this problem. A multi-rotor UAV swarm is used to carry sensors and quickly build an MWSN in a designated working area. The necessary minimum number of UAVs is determined according to the principle that the coverage area of any three UAVs has the smallest overlap. Based on the geometric properties of a convex polygon, two initialization methods are proposed to make the initial deployment more uniform, following which, the positions of all UAVs are subsequently optimized by the proposed molecular force field-based deployment algorithm. Simulation experiment results show that the proposed algorithm, when compared with three existing algorithms, can obtain the maximum coverage ratio for the designated working area thanks to the proposed initialization methods. The probability of falling into a local optimum and the computational complexity are reduced, while the convergence rate is improved.

[1]  Pascal Bouvry,et al.  Area exploration with a swarm of UAVs combining deterministic chaotic ant colony mobility with position MPC , 2017, 2017 International Conference on Unmanned Aircraft Systems (ICUAS).

[2]  Youn-Hee Han,et al.  Centroid-Based Movement Assisted Sensor Deployment Schemes in Wireless Sensor Networks , 2009, 2009 IEEE 70th Vehicular Technology Conference Fall.

[3]  Ian Postlethwaite,et al.  UAV Path Following in Windy Urban Environments , 2014, J. Intell. Robotic Syst..

[4]  Mohammed Al-Shaboti,et al.  Multi-Robot Deployment Using a Virtual Force Approach: Challenges and Guidelines , 2016 .

[5]  Yipeng Qu.  Wireless Sensor Network Deployment , 2013 .

[6]  Steven Fortune,et al.  Voronoi Diagrams and Delaunay Triangulations , 2004, Handbook of Discrete and Computational Geometry, 2nd Ed..

[7]  Xi Wang,et al.  A Molecular Force-Based Deployment Algorithm for Flight Coverage Maximization of Multi-Rotor UAV , 2018, J. Intell. Robotic Syst..

[8]  Sarah K. Spurgeon,et al.  Fast Formation of Swarm of UAVs in Congested Urban Environment , 2017 .

[9]  Haiying Shen,et al.  A Delaunay-Based Coordinate-Free Mechanism for Full Coverage in Wireless Sensor Networks , 2012, 2012 41st International Conference on Parallel Processing.

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

[11]  Nathalie Mitton,et al.  Performance evaluation of novel distributed coverage techniques for swarms of flying robots , 2014, 2014 IEEE Wireless Communications and Networking Conference (WCNC).

[12]  Nor Azlina Ab Aziz,et al.  WIRELESS SENSOR NETWORKS COVERAGE-ENERGY ALGORITHMS BASED ON PARTICLE SWARM OPTIMIZATION , 2013 .

[13]  Dipankar Deb,et al.  Adaptive Failure Compensation of a Single Main-Rotor Helicopter , 2012 .

[14]  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.

[15]  Pascal Bouvry,et al.  Target Tracking Optimization of UAV Swarms Based on Dual-Pheromone Clustering , 2017, 2017 3rd IEEE International Conference on Cybernetics (CYBCON).

[16]  Krishnendu Chakrabarty,et al.  Sensor deployment and target localization based on virtual forces , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

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

[18]  Dipankar Deb,et al.  Adaptive failure compensation for coaxial rotor helicopter under propeller failure , 2012, 2012 American Control Conference (ACC).

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

[20]  Hamid Jafarkhani,et al.  Movement-Efficient Sensor Deployment in Wireless Sensor Networks , 2017, 2018 IEEE International Conference on Communications (ICC).

[21]  Andrey Koucheryavy,et al.  Swarm of Public Unmanned Aerial Vehicles as a Queuing Network , 2015 .

[22]  Thomas F. La Porta,et al.  Movement-assisted sensor deployment , 2004, IEEE INFOCOM 2004.

[23]  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.

[24]  Young-Sik Jeong,et al.  An energy-efficient self-deployment with the centroid-directed virtual force in mobile sensor networks , 2012, Simul..

[25]  Emilio Frazzoli,et al.  Distributed Algorithms for Environment Partitioning in Mobile Robotic Networks , 2011, IEEE Transactions on Automatic Control.

[26]  Krishnendu Chakrabarty,et al.  Sensor deployment and target localization in distributed sensor networks , 2004, TECS.

[27]  Ian Postlethwaite,et al.  A Probabilistically Robust Path Planning Algorithm for UAVs Using Rapidly-Exploring Random Trees , 2013, J. Intell. Robotic Syst..

[28]  Xiaojun Wu,et al.  Novel efficient deployment schemes for sensor coverage in mobile wireless sensor networks , 2018, Inf. Fusion.

[29]  Yun Liu,et al.  The Coverage Optimization for Wireless Sensor Networks Based on Quantum- Inspired Cultural Algorithm , 2013 .

[30]  Chu-Sing Yang,et al.  Voronoi-based coverage improvement approach for wireless directional sensor networks , 2014, J. Netw. Comput. Appl..

[31]  Haitham S. Hamza,et al.  Coverage in mobile wireless sensor networks (M-WSN): A survey , 2017, Comput. Commun..

[32]  Erdem Koyuncu,et al.  Deployment and Trajectory Optimization of UAVs: A Quantization Theory Approach , 2018, IEEE Transactions on Wireless Communications.

[33]  Jalal Habibi,et al.  A Gradient-Based Coverage Optimization Strategy for Mobile Sensor Networks , 2017, IEEE Transactions on Control of Network Systems.

[34]  Kamran Sayrafian-Pour,et al.  Distributed Deployment Algorithms for Improved Coverage in a Network of Wireless Mobile Sensors , 2014, IEEE Transactions on Industrial Informatics.

[35]  Mojtaba Vaezi,et al.  Mobile Sensors Deployment Subject to Location Estimation Error , 2017, IEEE Transactions on Vehicular Technology.

[36]  Thomas F. La Porta,et al.  Voronoi-based deployment of mobile sensors in the face of adversaries , 2014, 2014 IEEE International Conference on Communications (ICC).

[37]  M MohamedShaimaa,et al.  Coverage in mobile wireless sensor networks (M-WSN) , 2017 .

[38]  Ting-Yu Lin,et al.  Enhanced Deployment Algorithms for Heterogeneous Directional Mobile Sensors in a Bounded Monitoring Area , 2017, IEEE Transactions on Mobile Computing.

[39]  Jian Chen,et al.  Multi-objective optimization for coverage control in wireless sensor network with adjustable sensing radius , 2009, Comput. Math. Appl..

[40]  Mohammed Abo-Zahhad,et al.  Coverage maximization in mobile Wireless Sensor Networks utilizing immune node deployment algorithm , 2014, 2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE).