Optimizing Communication and Computation for Multi-UAV Information Gathering Applications

Typical mobile agent networks, such as multi-unmanned aerial vehicle (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 dem...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on aerospace and electronic systems Jg. 54; H. 2; S. 601 - 615
Hauptverfasser: Thammawichai, Mason, Baliyarasimhuni, Sujit P., Kerrigan, Eric C., Sousa, Joao B.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.04.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Agents (artificial intelligence)
Agglomeration
Base stations
Cluster analysis
Clustering
Communication
Communications systems
Computer memory
Computer simulation
Cooperative systems
Data aggregation
Data management
Energy conservation
Energy consumption
multiagent systems
optimal control
Optimization
Remote sensors
Routing
Sensors
Target tracking
Tracking
Unmanned aerial vehicles
unmanned aerial vehicles (UAVs)
Wireless sensor networks
ISSN:0018-9251, 1557-9603
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Typical mobile agent networks, such as multi-unmanned aerial vehicle (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 paper, we aim at attaining an optimal tradeoff 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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2017.2761139