A Novel and Efficient Algorithm for three-dimensional Coverage and Deployment of Aerial Robots in Vector Spaces

The maximum coverage sensor deployment problem has attracted researchers of engineering sciences always as one of the fundamental phases in developing of communication and geospatial infrastructures. In this research, a novel strategy is proposed to tackle the maximum coverage robotic sensor deployment task in 3D vector spaces. For this purpose, first, a geometric algorithm is developed in order to detect the covered areas. The water cycle optimization algorithm is utilized to maximize the sensor coverage. Then, to avoid the problem of premature convergence to local optima and to improve the efficiency and searching potential on the problem, an improved water cycle algorithm with dynamic operations and fewer parameters is designed and developed. With regard to several scenarios with different spatial constraints, the efficiency of the proposed algorithm is compared to other methods based on robustness, running time, best and average of the coverage results, standard deviation, convergence speed, and wilcoxon statistical test. The assessment of the results reveals the superior performance of the proposed approach by success rate of 73% and coverage of 80% in a

[1]  Rajeev Shorey,et al.  Mobile, Wireless and Sensor Networks: Technology, Applications and Future Directions , 2005 .

[2]  Sonia Martínez,et al.  Coverage control for mobile sensing networks , 2002, IEEE Transactions on Robotics and Automation.

[3]  Yi Wang,et al.  On full-view coverage in camera sensor networks , 2011, 2011 Proceedings IEEE INFOCOM.

[4]  Okyay Kaynak,et al.  On Deployment of Wireless Sensors on 3-D Terrains to Maximize Sensing Coverage by Utilizing Cat Swarm Optimization With Wavelet Transform , 2014, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[5]  Tzung-Pei Hong,et al.  Metaheuristics for the Lifetime of WSN: A Review , 2016, IEEE Sensors Journal.

[6]  Taher Niknam,et al.  An efficient hybrid algorithm based on modified imperialist competitive algorithm and K-means for data clustering , 2011, Eng. Appl. Artif. Intell..

[7]  Feng Li,et al.  Autonomous deployment of wireless sensor networks for optimal coverage with directional sensing model , 2016, Comput. Networks.

[8]  Lars Imsland,et al.  Monitoring Moving Objects Using Aerial Mobile Sensors , 2016, IEEE Transactions on Control Systems Technology.

[9]  Yuhui Shi,et al.  Brain storm optimization algorithm for full area coverage of wireless sensor networks , 2016, 2016 Eighth International Conference on Advanced Computational Intelligence (ICACI).

[10]  Alexei A. Efros,et al.  Putting Objects in Perspective , 2006, CVPR.

[11]  Victor C. M. Leung,et al.  Key management issues in wireless sensor networks: current proposals and future developments , 2007, IEEE Wireless Communications.

[12]  Ganesh K. Venayagamoorthy,et al.  Bio-inspired Algorithms for Autonomous Deployment and Localization of Sensor Nodes , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[13]  Sheng Wang,et al.  Dynamic sensor nodes selection strategy for wireless sensor networks , 2007, 2007 International Symposium on Communications and Information Technologies.

[14]  M. Fesanghary,et al.  An improved harmony search algorithm for solving optimization problems , 2007, Appl. Math. Comput..

[15]  Roberto López-Valcarce,et al.  Improving area coverage of wireless sensor networks via controllable mobile nodes: A greedy approach , 2015, J. Netw. Comput. Appl..

[16]  Mohammad Bagher Menhaj,et al.  EXPLICA: An Explorative Imperialist Competitive Algorithm based on the notion of Explorers with an expansive retention policy , 2017, Applied Soft Computing.

[17]  M. Amaç Güvensan,et al.  On coverage issues in directional sensor networks: A survey , 2011, Ad Hoc Networks.

[18]  A. Rezaee Jordehi,et al.  An efficient chaotic water cycle algorithm for optimization tasks , 2015, Neural Computing and Applications.

[19]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

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

[21]  Ardeshir Bahreininejad,et al.  Water cycle algorithm - A novel metaheuristic optimization method for solving constrained engineering optimization problems , 2012 .

[22]  Kang Yen,et al.  Sensor network security: a survey , 2009, IEEE Communications Surveys & Tutorials.

[23]  Guangjie Han,et al.  A survey on coverage and connectivity issues in wireless sensor networks , 2012, J. Netw. Comput. Appl..

[24]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

[25]  Saeid Homayouni,et al.  AN EFFECTIVE HYBRID SUPPORT VECTOR REGRESSION WITH CHAOS- EMBEDDED BIOGEOGRAPHY-BASED OPTIMIZATION STRATEGY FOR PREDICTION OF EARTHQUAKE-TRIGGERED SLOPE DEFORMATIONS , 2015 .

[26]  Jorge Cortes,et al.  Distributed Control and Estimation of Robotic Vehicle Networks: An Overview of Part 2 , 2016, IEEE Control Systems.

[27]  Vijay Kumar,et al.  Swarm Distribution and Deployment for Cooperative Surveillance by Micro-Aerial Vehicles , 2016, J. Intell. Robotic Syst..

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

[29]  Mac Schwager,et al.  Adaptive Inter-Robot Trust for Robust Multi-Robot Sensor Coverage , 2013, ISRR.

[30]  Kevin Hammond,et al.  Research Directions in Parallel Functional Programming , 1999, Springer London.

[31]  Yong-Hyuk Kim,et al.  An Efficient Genetic Algorithm for Maximum Coverage Deployment in Wireless Sensor Networks , 2013, IEEE Transactions on Cybernetics.

[32]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[33]  Anlong Ming,et al.  A Coverage-Enhancing Method for 3D Directional Sensor Networks , 2009, IEEE INFOCOM 2009.

[34]  James D. Foley,et al.  Fundamentals of interactive computer graphics , 1982 .

[35]  Marc Parizeau,et al.  Black-box optimization of sensor placement with elevation maps and probabilistic sensing models , 2011, 2011 IEEE International Symposium on Robotic and Sensors Environments (ROSE).

[36]  Zygmunt J. Haas,et al.  Coverage and connectivity in three-dimensional networks with random node deployment , 2015, Ad Hoc Networks.

[37]  MostafaviMir Abolfazl,et al.  Impact of the Quality of Spatial 3D City Models on Sensor Networks Placement Optimization , 2012 .

[38]  Frank L. Lewis,et al.  Multi-Scale Adaptive Sampling with Mobile Agents for Mapping of Forest Fires , 2009, J. Intell. Robotic Syst..

[39]  James F. Blinn,et al.  Texture and reflection in computer generated images , 1998 .

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

[41]  Eva Besada Portas,et al.  Evolutionary trajectory planner for multiple UAVs in realistic scenarios , 2010 .

[42]  田口 玄一,et al.  Introduction to quality engineering : designing quality into products and processes , 1986 .

[43]  Hichem Snoussi,et al.  Sensor deployment optimization methods to achieve both coverage and connectivity in wireless sensor networks , 2015, Comput. Oper. Res..

[44]  M. R. Delavar,et al.  A MODIFIED GENETIC ALGORITHM FOR FINDING FUZZY SHORTEST PATHS IN UNCERTAIN NETWORKS , 2016 .

[45]  Christian Gagné,et al.  A GIS Based Wireless Sensor Network Coverage Estimation and Optimization: A Voronoi Approach , 2011, Trans. Comput. Sci..

[46]  A. Rezaee Jordehi,et al.  Gaussian bare-bones water cycle algorithm for optimal reactive power dispatch in electrical power systems , 2017, Appl. Soft Comput..

[47]  Jiming Chen,et al.  Stochastic Steepest-Descent Optimization of Multiple-Objective Mobile Sensor Coverage , 2010, 2010 IEEE 30th International Conference on Distributed Computing Systems.

[48]  Ashraf Hossain,et al.  Sensing Models and Its Impact on Network Coverage in Wireless Sensor Network , 2008, 2008 IEEE Region 10 and the Third international Conference on Industrial and Information Systems.

[49]  Gaurav S. Sukhatme,et al.  Autonomous deployment and repair of a sensor network using an unmanned aerial vehicle , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[50]  Ardeshir Bahreininejad,et al.  Water cycle algorithm with evaporation rate for solving constrained and unconstrained optimization problems , 2015, Appl. Soft Comput..

[51]  Marc Parizeau,et al.  Probabilistic Sensing Model for Sensor Placement Optimization Based on Line-of-Sight Coverage , 2013, IEEE Transactions on Instrumentation and Measurement.

[52]  Ardeshir Bahreininejad,et al.  Water cycle algorithm for solving multi-objective optimization problems , 2014, Soft Computing.

[53]  Ali Karci,et al.  Probabilistic dynamic distribution of wireless sensor networks with improved distribution method based on electromagnetism-like algorithm , 2016 .

[54]  Luis Felipe Gonzalez,et al.  An Overview of Small Unmanned Aerial Vehicles for Air Quality Measurements: Present Applications and Future Prospectives , 2016, Sensors.

[55]  Christian Gagné,et al.  Context-Aware Local Optimization of Sensor Network Deployment , 2015, J. Sens. Actuator Networks.

[56]  Matt Welsh,et al.  Monitoring volcanic eruptions with a wireless sensor network , 2005, Proceeedings of the Second European Workshop on Wireless Sensor Networks, 2005..

[57]  Parham Pahlavani,et al.  An efficient modified grey wolf optimizer with Lévy flight for optimization tasks , 2017, Appl. Soft Comput..

[58]  Changjun Jiang,et al.  Coverage Optimization in Wireless Mobile Sensor Networks , 2009, 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing.

[59]  Bruce F. Naylor Binary space partitioning trees as an alternative representation of polytopes , 1990, Comput. Aided Des..

[60]  Evsen Yanmaz,et al.  Survey on Unmanned Aerial Vehicle Networks for Civil Applications: A Communications Viewpoint , 2016, IEEE Communications Surveys & Tutorials.