Introduction to Parallel Computing
Introduction to Parallel Computing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Active learning for adaptive mobile sensing networks
Proceedings of the 5th international conference on Information processing in sensor networks
International Journal of Intelligent Systems Technologies and Applications
ACE in the Hole: Adaptive Contour Estimation Using Collaborating Mobile Sensors
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
Accurate localization of low-level radioactive source under noise and measurement errors
Proceedings of the 6th ACM conference on Embedded network sensor systems
RTSS '09 Proceedings of the 2009 30th IEEE Real-Time Systems Symposium
Exploiting Reactive Mobility for Collaborative Target Detection in Wireless Sensor Networks
IEEE Transactions on Mobile Computing
Mobile Scheduling for Spatiotemporal Detection in Wireless Sensor Networks
IEEE Transactions on Parallel and Distributed Systems
Maximum Likelihood Localization of a Diffusive Point Source Using Binary Observations
IEEE Transactions on Signal Processing
Detection and localization of vapor-emitting sources
IEEE Transactions on Signal Processing
Source localization by spatially distributed electronic noses for advection and diffusion
IEEE Transactions on Signal Processing
Distributed Sequential Bayesian Estimation of a Diffusive Source in Wireless Sensor Networks
IEEE Transactions on Signal Processing
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Water resources and aquatic ecosystems are facing increasing threats from climate change, improper waste disposal, and oil spill incidents. It is of great interest to deploy mobile sensors to detect and monitor certain diffusion processes (e.g., chemical pollutants) that are harmful to aquatic environments. In this paper, we propose an accuracy-aware diffusion process profiling approach using smart aquatic mobile sensors such as robotic fish. In our approach, the robotic sensors collaboratively profile the characteristics of a diffusion process including source location, discharged substance amount, and its evolution over time. In particular, the robotic sensors reposition themselves to progressively improve the profiling accuracy. We formulate a novel movement scheduling problem that aims to maximize the profiling accuracy subject to limited sensor mobility and energy budget. We develop an efficient greedy algorithm and a more complex near-optimal radial algorithm to solve the problem. We conduct extensive simulations based on real data traces of robotic fish movement and wireless communication. The results show that our approach can accurately profile dynamic diffusion processes under tight energy budgets. Moreover, a preliminary evaluation based on the implementation on TelosB motes validates the feasibility of deploying our movement scheduling algorithms on mote-class robotic sensor platforms.