Approximation Algorithms for Reliability-Aware Maximum VoI on AUV-Aided Data Collections

Hao Guo, Xiaohui Wei, Xingwang Wang, Xiaonan Wang, Chenghao Ren, Meikang Qiu

Abstract: Underwater Wireless Sensor Networks (UWSNs) show great potential in ocean exploration on data collection. Recently, with the increasing amount of underwater sensed data, the Autonomous Underwater Vehicle (AUV) is introduced as a mobile sink to collect data from sensors. Existing research mainly regards the Value of Information (VoI) as the metric of real-time value such as data importance and timeliness, and they are committed to finding a path with maximum VoI for efficient collection. However, due to the limitation of AUV energy, partial sensors and their data may be omitted by the optimal path. From the perspective of the integrality dimension, data in the areas not covered by the path are indispensable for UWSN applications, which eventually reduces the reliability of the collection. To maximum VoI and improve reliability simultaneously, in this work, we propose approximate algorithms for reliability-aware AUV-aided data collection framework that extends the definition of VoI as the combination of the real-time VoI and reliability VoI. To find the optimal path under the framework, we first propose a dynamic priority strategy to re-quantify VoI on each sensor. Then we utilize an existing \((2+\epsilon )\) approximation algorithm to find the optimal path without considering data timeliness which is formulated as the Orienteering Problem (OP). After that, we propose a novel polynomial-factor approximation algorithm to consider the decay of real-time VoI by reducing such variant OP into k-TSP. Finally, simulation results validate the effectiveness of the proposed approximation algorithms.