posted on 2018-01-05, 10:52authored byMason Thammawichai, Sujit Baliyarasimhuni, Eric C. Kerrigan, Joao Borges de Sousa
Typical mobile agent networks, such as multi-UAV systems, are constrained by limited resources: energy, computing power, memory and communication bandwidth. In particular,
limited energy affects system performance directly, such as system lifetime. Moreover, it has been demonstrated experimentally in the wireless sensor network literature that the total energy consumption is often dominated by the communication cost, i.e. the computational and the sensing energy are small compared to the
communication energy consumption. For this reason, the lifetime of the network can be extended significantly by minimizing the
communication distance as well as the amount of communication data, at the expense of increasing computational cost. In this work, we aim at attaining an optimal trade-off between the communication and the computational energy. Specifically, we propose a mixed-integer optimization formulation for a multihop
hierarchical clustering-based self-organizing UAV network incorporating data aggregation, to obtain an energy-efficient information routing scheme. The proposed framework is tested on two applications, namely target tracking and area mapping. Based on simulation results, our method can significantly save energy compared to a baseline strategy, where there is no data aggregation and clustering scheme.
History
School
Aeronautical, Automotive, Chemical and Materials Engineering
Department
Aeronautical and Automotive Engineering
Published in
IEEE Transactions on Aerospace and Electronic Systems
Citation
THAMMAWICHAI, M., 2017. Optimizing communication and computation for multi-UAV information gathering applications. IEEE Transactions on Aerospace and Electronic Systems, 54(2), pp. 601-615.
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