Issue No. 02 - Feb. (2018 vol. 17)
Petrika Gjanci , Department of Computer Science of the UniversitÓ di Roma ?La Sapienza?, Roma, Italy
Chiara Petrioli , Department of Computer Science of the UniversitÓ di Roma ?La Sapienza?, Roma, Italy
Stefano Basagni , ECE Department of Northeastern University, Boston, MA
Cynthia A. Phillips , Center for Computing Research of Sandia National Laboratories, Albuquerque, NM
Ladislau Boloni , EECS Department, University of Central Florida, Orlando, FL
Damla Turgut , EECS Department, University of Central Florida, Orlando, FL
We consider underwater multi-modal wireless sensor networks (UWSNs) suitable for applications on submarine surveillance and monitoring, where nodes offload data to a mobile autonomous underwater vehicle (AUV) via optical technology, and coordinate using acoustic communication. Sensed data are associated with a value, decaying in time. In this scenario, we address the problem of finding the path of the AUV so that the
Value of Information (VoI) of the data delivered to a sink on the surface is maximized. We define a Greedy and Adaptive AUV Path-finding (GAAP) heuristic that drives the AUV to collect data from nodes depending on the VoI of their data. For benchmarking the performance of AUV path-finding heuristics, we define an integer linear programming (ILP) formulation that accurately models the considered scenario, deriving a path that drives the AUV to collect and deliver data with the maximum VoI. In our experiments GAAP consistently delivers more than 80 percent of the theoretical maximum VoI determined by the ILP model. We also compare the performance of GAAP with that of other strategies for driving the AUV among sensing nodes, namely, random paths, TSP-based paths and a “lawn mower”-like strategy. Our results show that GAAP always outperforms every other heuristic in terms of delivered VoI, also obtaining higher energy efficiency.
Monitoring, Optical sensors, Wireless sensor networks, Acoustics, Mobile computing, Optimized production technology
P. Gjanci, C. Petrioli, S. Basagni, C. A. Phillips, L. Boloni and D. Turgut, "Path Finding for Maximum Value of Information in Multi-Modal Underwater Wireless Sensor Networks," in IEEE Transactions on Mobile Computing, vol. 17, no. 2, pp. 404-418, 2018.