Accuracy-aware aquatic diffusion process profiling using robotic sensor networks

Abstract

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.

Evolutionary multiobjective design of a flexible caudal fin for robotic fish

“…Third, with fin/body movements occurring at relatively low frequencies (typically only a few Hz), these systems are less likely to harm aquatic animals or become jammed with foreign objects. Given these characteristics, robotic fish are anticipated to play an important role in environmental monitoring [1], inspection of underwater structures [3], tracking of hazardous wastes and oil spills [4], and the study of natural systems [5][6][7][8]. However, while investigations of robotic fish have produced many advances over the past two decades [9][10][11][12][13][14][15][16], robotic fish still do not rival their biological counterparts in terms of swimming abilities.…”

Spatiotemporal Aquatic Field Reconstruction Using Cyber-Physical Robotic Sensor Systems

“…According to our experimental results presented in Figure 4, the link PRR in the swarm drops at most 15% when the distance is increased by 2. Note that, based on the measurements in our previous work [Wang et al 2012], the closed-loop motion control algorithms usually introduce small position errors (in the order of 10 cm) and the GPS localization errors are generally around 2 m in an outdoor environment.…”

“…Another study [Singh et al 2006] develops active learning schemes for mobile sensor networks, which plan the movements of mobile sensors based on the feedback of previous measurements. In our previous work [Wang et al 2012], we develop movement scheduling algorithms for a school of robotic fish to profile aquatic diffusion processes. Several recent studies focus on leveraging sensors' mobility to reconstruct physical fields that follow the Gaussian process.…”

Enhancing a Model-Free Adaptive Controller through Evolutionary Computation

“…As an emerging class of embedded computing systems, robotic fish are anticipated to play an important role in environmental monitoring [15], inspection of underwater structures [5], and tracking of hazardous wastes and oil spills [22]. Similar to live fish, robotic fish accomplish swimming by deforming their bodies or fin-like appendages.…”