Adaptive decentralized control of underwater sensor networks for modeling underwater phenomena

Detweiler, Carrick; Doniec, Marek; Jiang, Mingshun; Schwager, Mac; Chen, Robert; Rus, Daniela

doi:10.1145/1869983.1870008

Cited by 20 publications

(14 citation statements)

References 33 publications

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“…Each mooring was equipped with a Turner Designs CDOM fluorometer. This high resolution profiling experiment in conjunction with Carrick Detweiler, MIT, (Detweiler et al, 2010) used our Neponset CDOM Observatory as a testbed for computer sensor network software aimed to maximize observations of high spatial gradient oceanic features.…”

Section: Resultsmentioning

confidence: 99%

A Chromophoric Dissolved Organic Matter (CDOM) Observatory

Chen¹,

Gardner²,

Tian³

2011

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Grant Number: N000140910346 http://www.cesn.org/projects/beacon.php LONG-TERM GOALSThe long-term goal of this project is to develop an understanding of coastal systems such that optical properties of complex coastal waters can be retrieved and predicted from remote sensing and modeling efforts. OBJECTIVESThis project focuses on establishing a moderate-scale observatory (Neponset) to develop watershed and coastal ocean models and remote sensing algorithms and extending this knowledge to a larger system (Hudson).Specific Objectives: 1) Design and deploy CDOM observatory components in the Neponset Estuary and Boston Harbor.

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Section: Resultsmentioning

confidence: 99%

A Chromophoric Dissolved Organic Matter (CDOM) Observatory

Chen¹,

Gardner²,

Tian³

2011

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“…In this article we explore an important problem in aquatic monitoring, namely, reconstruction of spatiotemporal aquatic process. Many physical and biological phenomena in an aquatic environment, including Harmful Algal Blooms (HABs) [Dolan et al 2007], lake surface temperature [Xu et al 2011], and plume concentration of chemical substance [Detweiler et al 2010], can be modeled as spatiotemporal aquatic fields that usually follow certain distributions such as the spatiotemporal Gaussian process. For instance, Figure 1(a) shows the HABs on two inland lakes in Wisconsin, 1999.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Aquatic Field Reconstruction Using Cyber-Physical Robotic Sensor Systems

Wang

Tan

Xing

et al. 2014

ACM Trans. Sen. Netw.

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Monitoring important aquatic processes like harmful algal blooms is of increasing interest to public health, ecosystem sustainability, marine biology, and aquaculture industry. This article presents a novel approach to spatiotemporal aquatic field reconstruction using inexpensive, low-power mobile sensing platforms called robotic fish. Robotic fish networks are a typical example of cyber-physical systems where the design of cyber components (sensing, communication, and information processing) must account for inherent physical dynamics of the robots and the aquatic environment. Our approach features a rendezvous-based mobility control scheme where robotic fish collaborate in the form of a swarm to sense the aquatic environment in a series of carefully chosen rendezvous regions. We design a novel feedback control algorithm that maintains the desirable level of wireless connectivity for a sensor swarm in the presence of significant environment and system dynamics. Information-theoretic analysis is used to guide the selection of rendezvous regions so that the spatiotemporal field reconstruction accuracy is maximized subject to the limited sensor mobility. The effectiveness of our approach is validated via implementation on sensor hardware and extensive simulations based on real data traces of water surface temperature field and on-water ZigBee wireless communication.

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“…This sensor then communicates information on the position of the next sensor to the robot and the process continues in this manner. The third algorithm, AdaptivePath, is based on our prior work in developing adaptive decentralized control algorithms that optimizes the depths of underwater sensors for sensing [5] and for determining the path of an underwater robot in networks of underwater sensors while also constraining the length of the path [4]. In this work we develop and analyze these algorithms in simulation.…”

Section: Introductionmentioning

confidence: 99%

“…Detweiler et al presented a decentralized controller optimization by adjusting depths of underwater sensors in [5]. In [14], Liu et al introduced a method of joint optimization for minimizing communication cost and maximizing information gainand Stranders et al [18] presented an on-line, decentralized coordination algorithm for a team of mobile sensors.…”

Section: Introductionmentioning

confidence: 99%

Path Planning Algorithms for Robotic Underwater Sensing in a Network of Sensors

Banerjee

Detweiler

2014

Proceedings of the International Conference on Underwater Networks %Systems - WUWNET '14

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Monitoring lakes, rivers, and oceans is critical to improving our understanding of complex large-scale ecosystems. In this work, we develop and analyze three path planning algorithms for underwater robots to optimize sensing in conjunction with networks of underwater sensors. The algorithms require different levels of knowledge about the environment: global, local, and decentralized control of the robot by the sensor network. We find our global Voronoi approach produces paths that are typically best for sensing, but are longer, which can be problematic if the robot has limited endurance. The local algorithm, inspired by Tangent Bug, produces paths that are usually shorter while still having good sensing. The decentralized controller also has good sensing and short paths and has the advantage that it can also adapt the depths of the underwater sensors to jointly optimize the sensor network and robot sensing and the robot path length. The drawback is the somewhat higher communication and processing requirements. For each of these algorithms we perform a detailed analysis and comparison in simulation. We identify limitations of each and provide framework for future improvements.

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Adaptive decentralized control of underwater sensor networks for modeling underwater phenomena

Cited by 20 publications

References 33 publications

A Chromophoric Dissolved Organic Matter (CDOM) Observatory

A Chromophoric Dissolved Organic Matter (CDOM) Observatory

Spatiotemporal Aquatic Field Reconstruction Using Cyber-Physical Robotic Sensor Systems

Path Planning Algorithms for Robotic Underwater Sensing in a Network of Sensors

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