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• W2329441459 abstract "Unattended ground sensors (UGS) in long-term distributed sensing deployments benefit greatly from the incorporation of unmanned aerial systems (UAS). For instance, the mobility of data-ferrying UAS may be leveraged to reduce the cost of communication between UGS, as well as extend the effective coverage and endurance of the distributed UGS network. Since the UAS are also limited in endurance, a UAS may only ferry data between a subset of the UGS during each sortie. This is particularly problematic for extended operations in nonstationary spatio-temporal domains, as the model obtained from the set of UGS may rapidly lose relevance. Moreover, the informativeness ofor the Value-of-Information (VoI) available at each UGS may not be equal. Our approach, termed Exploitation by Informed Exploration between Isolated Operatives (EIEIO), learns a generative spatiotemporal model for the arrival of VoI at each UGS. Through EIEIO, we anticipate and prioritize the subset of UGS with the highest VoI for each data ferrying sortie. Furthermore, a lower bound on the requisite sampling time for homogeneous Poisson processes is leveraged to provide a bound on how many times the UAS must visit each UGS in order to learn a spatio-temporal VoI model." @default.
• W2329441459 created "2016-06-24" @default.
• W2329441459 creator A5024922650 @default.
• W2329441459 creator A5073739058 @default.
• W2329441459 date "2015-01-02" @default.
• W2329441459 modified "2023-09-23" @default.
• W2329441459 title "Adaptive Algorithms for Autonomous Data-Ferrying in Nonstationary Environments" @default.
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• W2329441459 doi "https://doi.org/10.2514/6.2015-0117" @default.
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