TY - JOUR
T1 - Collective multi agent deployment for wireless sensor network maintenance
AU - Yedidsion, Harel
AU - Hermelin, Danny
AU - Segal, Michael
N1 - Funding Information:
The research was been supported by the following sources: Israel Science Foundation grant No. 1055/14 and grant No. 317/15 , the US Army Research Office under grant # W911NF-18-1-0399 , and the Helmsley Charitable Trust, USA through the Agricultural, Biological and Cognitive Robotics Initiative of Ben-Gurion University of the Negev.
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/6/1
Y1 - 2021/6/1
N2 - In this paper, we study the problem of wireless sensor network (WSN) maintenance using a team of physical autonomous mobile agents. The agents are deployed in the area of the WSN in such a way that would minimize the time it takes them to reach a failed sensor and repair it. The team must constantly optimize its collective deployment to account for occupied agents. The objective is to define the optimal deployment and task allocation strategy, that minimize the solution cost. The solution cost is a linear combination of the weighted sensors’ downtime, the agents’ traveling distance, and penalties incurred due to unrepaired sensors within a certain time limit. Our proposed solution algorithms are inspired by research in the field of computational geometry and the design of our algorithms is based on state of the art approximation algorithms for the classical problem of facility location. We empirically compare and analyze the performance of several proposed algorithms. The sensitivity of the algorithms’ performance to the following parameters is analyzed: agents to sensors ratio, sensors’ sparsity, frequency and distribution of failures, repair duration, repair capacity, and communication limitations. Our results demonstrate that: (i) cooperation enhances the team's performance by orders of magnitude, (ii) k-Median based deployment algorithm provides up to 30% improvement in downtime, (iii) k-Center based deployment incurs 10% fewest penalties, and (iv) k-Centroid based deployment is most efficient in terms of minimizing the overall costs, with up to 21% lower cost than the next best algorithm.
AB - In this paper, we study the problem of wireless sensor network (WSN) maintenance using a team of physical autonomous mobile agents. The agents are deployed in the area of the WSN in such a way that would minimize the time it takes them to reach a failed sensor and repair it. The team must constantly optimize its collective deployment to account for occupied agents. The objective is to define the optimal deployment and task allocation strategy, that minimize the solution cost. The solution cost is a linear combination of the weighted sensors’ downtime, the agents’ traveling distance, and penalties incurred due to unrepaired sensors within a certain time limit. Our proposed solution algorithms are inspired by research in the field of computational geometry and the design of our algorithms is based on state of the art approximation algorithms for the classical problem of facility location. We empirically compare and analyze the performance of several proposed algorithms. The sensitivity of the algorithms’ performance to the following parameters is analyzed: agents to sensors ratio, sensors’ sparsity, frequency and distribution of failures, repair duration, repair capacity, and communication limitations. Our results demonstrate that: (i) cooperation enhances the team's performance by orders of magnitude, (ii) k-Median based deployment algorithm provides up to 30% improvement in downtime, (iii) k-Center based deployment incurs 10% fewest penalties, and (iv) k-Centroid based deployment is most efficient in terms of minimizing the overall costs, with up to 21% lower cost than the next best algorithm.
KW - Algorithm design
KW - Mobile agents
KW - Multi agent coordination
KW - Wireless sensor networks
UR - http://www.scopus.com/inward/record.url?scp=85105348154&partnerID=8YFLogxK
U2 - 10.1016/j.engappai.2021.104265
DO - 10.1016/j.engappai.2021.104265
M3 - Article
AN - SCOPUS:85105348154
VL - 102
JO - Engineering Applications of Artificial Intelligence
JF - Engineering Applications of Artificial Intelligence
SN - 0952-1976
M1 - 104265
ER -