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 the 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 GPS localization errors, 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 profiling algorithms on mote-class robotic sensor platforms.
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