TY - GEN
T1 - Optimized trajectories of multi-robot deploying wireless sensor nodes
AU - Khoufi, Ines
AU - Hadded, Mohamed
AU - Minet, Pascale
AU - Laouiti, Anis
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/4
Y1 - 2015/12/4
N2 - A main reason to the growth of wireless sensor networks deployed worldwide is their easy and fast deployment. In this paper we consider deployments assisted by mobile robots where static sensor nodes are deployed by mobile robots in a given area. Each robot must make a tour to place its sensor nodes. All sensor nodes must be placed at their precomputed positions. The Multi-Robot Deploying wireless Sensor nodes problem, called the MRDS problem, consists in minimizing the longest tour duration (i.e. the total deployment duration), the number of robots used and the standard deviation between duration of robots tours. After a formal definition of the MRDS problem, we show how to use a multi-objective version of genetic algorithms, more precisely the NSGA-II algorithm, to solve this multi-objective optimization problem. The solutions belonging to the best Pareto front are given to the designer in charge of selecting the best trade-off taking into account various criteria. We then show how to extend this method to take obstacles into account, which is more representative of real situations.
AB - A main reason to the growth of wireless sensor networks deployed worldwide is their easy and fast deployment. In this paper we consider deployments assisted by mobile robots where static sensor nodes are deployed by mobile robots in a given area. Each robot must make a tour to place its sensor nodes. All sensor nodes must be placed at their precomputed positions. The Multi-Robot Deploying wireless Sensor nodes problem, called the MRDS problem, consists in minimizing the longest tour duration (i.e. the total deployment duration), the number of robots used and the standard deviation between duration of robots tours. After a formal definition of the MRDS problem, we show how to use a multi-objective version of genetic algorithms, more precisely the NSGA-II algorithm, to solve this multi-objective optimization problem. The solutions belonging to the best Pareto front are given to the designer in charge of selecting the best trade-off taking into account various criteria. We then show how to extend this method to take obstacles into account, which is more representative of real situations.
U2 - 10.1109/WiMOB.2015.7348034
DO - 10.1109/WiMOB.2015.7348034
M3 - Conference contribution
AN - SCOPUS:84964285665
T3 - 2015 IEEE 11th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2015
SP - 724
EP - 731
BT - 2015 IEEE 11th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2015
Y2 - 19 October 2015 through 21 October 2015
ER -