Reliable multiple robot-assisted sensor relocation using multi-objective optimization

Wireless sensor networks provide a way to monitor a region of interest. Incorporating a robot into the sensor network provides a basis for other types of functionality to be added. One possibility is the replacement of damaged sensors with excess sensors within the wireless sensor network. This scenario has been defined as the “Robot-Assisted ¡Sensor Relocation” (RASR) problem and focused only on minimizing the length of the trajectory taken by the robot. RASR has been recently expanded on as a multi-objective optimization (MOO) problem to examine a more realistic scenario by considering the reliability and placement location of the passive sensors used for replacement; this new problem is termed “Reliable Robot-Assisted Sensor Relocation (RRASR). In this paper, the possibility of multiple robots servicing the sensor network is considered and the RRASR problem formulation is modified accordingly. In addition, load balancing of robots by adding an objective function to the MOO representation is included. We refer to this multi-robot version as Reliable Multiple Robot-Assisted Sensor Relocation. The performance of six state-of-the-art evolutionary MOO algorithms using sensor networks of varying sizes and inflicted damage levels is examined.

[1]  Barrie M. Baker,et al.  A genetic algorithm for the vehicle routing problem , 2003, Comput. Oper. Res..

[2]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[3]  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..

[4]  Biswanath Mukherjee,et al.  Wireless sensor network survey , 2008, Comput. Networks.

[5]  Bruno Petrato Bruck,et al.  Hybrid approach for the Multiple Vehicle Routing Problem with Deliveries and Selective Pickups , 2012, 2012 12th International Conference on Hybrid Intelligent Systems (HIS).

[6]  M. Golumbic.  Algorithmic graph theory and perfect graphs , 1980 .

[7]  Yu-Hsuan Huang,et al.  Genetic algorithm with path relinking for the multi-vehicle selective pickup and delivery problem , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[8]  Martin J. Oates,et al.  PESA-II: region-based selection in evolutionary multiobjective optimization , 2001 .

[9]  Falcon Martinez,et al.  Towards Fault Reactiveness in Wireless Sensor Networks with Mobile Carrier Robots , 2012 .

[10]  Emil M. Petriu,et al.  A multi-objective optimization approach to reliable Robot-Assisted Sensor Relocation , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[11]  Hokey Min,et al.  The multiple vehicle routing problem with simultaneous delivery and pick-up points , 1989 .

[12]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[13]  Voratas Kachitvichyanukul,et al.  Particle swarm optimization and two solution representations for solving the capacitated vehicle routing problem , 2009, Comput. Ind. Eng..

[14]  P. Parthiban,et al.  Optimization of Multiple Vehicle Routing Problems using Approximation Algorithms , 2010, ArXiv.

[15]  Beatrice M. Ombuki-Berman,et al.  Multi-Objective Genetic Algorithms for Vehicle Routing Problem with Time Windows , 2006, Applied Intelligence.

[16]  Markus Wagner,et al.  A fast approximation-guided evolutionary multi-objective algorithm , 2013, GECCO '13.

[17]  Chuan-Kang Ting,et al.  An evolutionary approach for the selective pickup and delivery problem , 2010, IEEE Congress on Evolutionary Computation.

[18]  Kalyanmoy Deb,et al.  An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints , 2014, IEEE Transactions on Evolutionary Computation.

[19]  David E. Goldberg,et al.  Alleles, loci and the traveling salesman problem , 1985 .

[20]  C. Coello,et al.  Improving PSO-based Multi-Objective Optimization using Crowding , Mutation and �-Dominance , 2005 .

[21]  Carlos A. Coello Coello,et al.  Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and epsilon-Dominance , 2005, EMO.

[22]  Ivan Stojmenovic,et al.  The one-commodity traveling salesman problem with selective pickup and delivery: An ant colony approach , 2010, IEEE Congress on Evolutionary Computation.

[23]  Christian Prins,et al.  A simple and effective evolutionary algorithm for the vehicle routing problem , 2004, Comput. Oper. Res..

[24]  David Simplot-Ryl,et al.  Wireless sensor and robot networks : from topology control to communication aspects , 2014 .

[25]  Jean Berger,et al.  A new hybrid genetic algorithm for the capacitated vehicle routing problem , 2003, J. Oper. Res. Soc..

[26]  Thiemo Krink,et al.  The LifeCycle Model: Combining Particle Swarm Optimisation, Genetic Algorithms and HillClimbers , 2002, PPSN.

[27]  Cheng Wang,et al.  Market-Based Sensor Relocation by Robot Team in Wireless Sensor Networks , 2014, Ad Hoc Sens. Wirel. Networks.

[28]  Ivan Stojmenovic,et al.  Generating random graphs for the simulation of wireless ad hoc, actuator, sensor, and internet networks , 2008, Pervasive Mob. Comput..

[29]  Marco Laumanns,et al.  Combining Convergence and Diversity in Evolutionary Multiobjective Optimization , 2002, Evolutionary Computation.

[30]  Ivan Stojmenovic,et al.  A harmony-seeking firefly swarm to the periodic replacement of damaged sensors by a team of mobile robots , 2012, 2012 IEEE International Conference on Communications (ICC).

[31]  David E. Goldberg,et al.  Genetic Algorithms, Tournament Selection, and the Effects of Noise , 1995, Complex Syst..

[32]  Markus Wagner,et al.  Approximation-Guided Evolutionary Multi-Objective Optimization , 2011, IJCAI.