PROJECT TITLE :
Path Finding for Maximum Value of Information in Multi-Modal Underwater Wireless Sensor Networks - 2018
We contemplate underwater multi-modal wireless sensor networks (UWSNs) appropriate for applications on submarine surveillance and monitoring, where nodes offload information to a mobile autonomous underwater vehicle (AUV) via optical technology, and coordinate using acoustic communication. Sensed knowledge are associated with a value, decaying in time. In this scenario, we address the problem of finding the path of the AUV therefore that the Price of Information (VoI) of the data delivered to a sink on the surface is maximized. We have a tendency to define a Greedy and Adaptive AUV Path-finding (GAAP) heuristic that drives the AUV to gather data from nodes depending on the VoI of their knowledge. For benchmarking the performance of AUV path-finding heuristics, we tend to outline an integer linear programming (ILP) formulation that accurately models the considered situation, deriving a path that drives the AUV to gather and deliver information with the most VoI. In our experiments GAAP consistently delivers additional than 80 p.c of the theoretical most VoI determined by the ILP model. We tend to additionally compare the performance of GAAP with that of alternative ways for driving the AUV among sensing nodes, specifically, random methods, TSP-based mostly methods and a “lawn mower”-like strategy. Our results show that GAAP continually outperforms each other heuristic in terms of delivered VoI, conjointly obtaining higher energy efficiency.
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