TY - GEN
T1 - Multi-Agent Planning with Cardinality
T2 - 2018 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2018
AU - Abdul Careem, Maqsood Ahamed
AU - Dutta, Aveek
AU - Wang, Weifu
N1 - Publisher Copyright: © 2018 IEEE.
PY - 2019/1/11
Y1 - 2019/1/11
N2 - The distributed nature of policy violations in spectrum sharing necessitate the use of mobile autonomous agents (e.g., UAVs, self-driving cars, crowdsourcing) to implement cost-effective enforcement systems. We define this problem as Multi-agent Planning with Cardinality (MPC), where Cardinality represents multiple, unique agents visiting each infraction location to collectively improve the accuracy of the enforcement tasks. Designed as a practical and deployable system, our solution leverages crowdsourced information to determine the optimum Cardinality and provide a routing schedule for the agents to achieve the desired level of accuracy of detection and localization at minimum possible cost. We show that by estimating spatial orientation of the agents with single antenna, the accuracy is improved by 96% over crowdsourcing only. Using geographical maps as the basis, we solve the scheduling problem with a 3-approximation ratio in polynomial time that exhibits statistically similar performance under variety of urban locale across multiple continents. The longest path traversed by an agent on average is 1.2km per unit diagonal length of a rectangular geographic area, even when there are twice as many infractions as agents.
AB - The distributed nature of policy violations in spectrum sharing necessitate the use of mobile autonomous agents (e.g., UAVs, self-driving cars, crowdsourcing) to implement cost-effective enforcement systems. We define this problem as Multi-agent Planning with Cardinality (MPC), where Cardinality represents multiple, unique agents visiting each infraction location to collectively improve the accuracy of the enforcement tasks. Designed as a practical and deployable system, our solution leverages crowdsourced information to determine the optimum Cardinality and provide a routing schedule for the agents to achieve the desired level of accuracy of detection and localization at minimum possible cost. We show that by estimating spatial orientation of the agents with single antenna, the accuracy is improved by 96% over crowdsourcing only. Using geographical maps as the basis, we solve the scheduling problem with a 3-approximation ratio in polynomial time that exhibits statistically similar performance under variety of urban locale across multiple continents. The longest path traversed by an agent on average is 1.2km per unit diagonal length of a rectangular geographic area, even when there are twice as many infractions as agents.
UR - https://www.scopus.com/pages/publications/85061930347
U2 - 10.1109/DySPAN.2018.8610414
DO - 10.1109/DySPAN.2018.8610414
M3 - Conference contribution
T3 - 2018 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2018
BT - 2018 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 22 October 2018 through 25 October 2018
ER -