TY - GEN
T1 - Decentralized energy aware co-optimization of mobility and communication in multiagent systems
AU - Jaleel, Hassan
AU - Shamma, Jeff S.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/12/27
Y1 - 2016/12/27
N2 - Our goal is to design decentralized coordination strategies that enable agents to achieve global performance guarantees while minimizing the energy cost of their actions with an emphasis on feasibility for real-time implementation. As a motivating scenario that illustrates the importance of introducing energy awareness at the agent level, we consider a team of mobile nodes that are assigned the task of establishing a communication link between two base stations with minimum energy consumption. We formulate this problem as a dynamic program in which the total cost of each agent is the sum of both mobility and communication costs. To ensure that the solution is decentralized and real time implementable, we propose multiple suboptimal policies based on the concepts of approximate dynamic programming. To provide performance guarantees, we compute upper bounds on the performance gap between the proposed suboptimal policies and the global optimal policy. Finally, we discuss merits and demerits of the proposed policies and compare their performance using simulations.
AB - Our goal is to design decentralized coordination strategies that enable agents to achieve global performance guarantees while minimizing the energy cost of their actions with an emphasis on feasibility for real-time implementation. As a motivating scenario that illustrates the importance of introducing energy awareness at the agent level, we consider a team of mobile nodes that are assigned the task of establishing a communication link between two base stations with minimum energy consumption. We formulate this problem as a dynamic program in which the total cost of each agent is the sum of both mobility and communication costs. To ensure that the solution is decentralized and real time implementable, we propose multiple suboptimal policies based on the concepts of approximate dynamic programming. To provide performance guarantees, we compute upper bounds on the performance gap between the proposed suboptimal policies and the global optimal policy. Finally, we discuss merits and demerits of the proposed policies and compare their performance using simulations.
UR - http://www.scopus.com/inward/record.url?scp=85010788164&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85010788164&partnerID=8YFLogxK
U2 - 10.1109/CDC.2016.7798664
DO - 10.1109/CDC.2016.7798664
M3 - Conference contribution
AN - SCOPUS:85010788164
T3 - 2016 IEEE 55th Conference on Decision and Control, CDC 2016
SP - 2665
EP - 2670
BT - 2016 IEEE 55th Conference on Decision and Control, CDC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 55th IEEE Conference on Decision and Control, CDC 2016
Y2 - 12 December 2016 through 14 December 2016
ER -