Control of Mobile Sensor Networks: A State-of-the-Art Review

Author(s):  
J. Karl Hedrick ◽  
Brandon Basso ◽  
Joshua Love ◽  
Anouck R. Girard ◽  
Andrew T. Klesh

This paper presents a state-of-the-art survey in the broad area of Mobile Sensor Networks (MSNs). There is currently a great deal of interest in having autonomous vehicles carrying sensors and communication devices that can conduct ISR (intelligence, surveillance and reconnaissance) operations. Although this paper will discuss issues common to mobile sensor networks, the applications will generally be associated with autonomous vehicles. Areas that are addressed are: 1. Mission definition languages that allow the human to compose a mission defined in terms of tasks; 2. Communication issues including hardware, software, and network connectivity; 3. Task allocation among the assets generally by a market-based approach; 4. Path planning for individual agents; and 5. Platform motion control using autopilots with and without GPS signals and including collision avoidance.

Author(s):  
Derek A. Paley ◽  
Artur Wolek

The control of mobile sensor networks uses sensor measurements to update a model of an unknown or estimated process, which in turn guides the collection of subsequent measurements—a feedback control framework called adaptive sampling. Applications for adaptive sampling exist in a wide range of settings, especially for unmanned or autonomous vehicles that can be deployed cheaply and in cooperative groups. The dynamics of mobile sensor platforms are often simplified to planar self-propelled particles subject to the ambient flow of the surrounding fluid. Sensor measurements are assimilated into continuous or discrete models of the process of interest, which in general can vary in space and time. The variability of the estimated process is one metric to score future candidate sampling trajectories, along with information- and uncertainty-based metrics. Sampling tasks are allocated to the network using centralized or decentralized optimization, in order to avoid redundant measurements and observational gaps.


2015 ◽  
Vol 26 (7) ◽  
pp. 1971-1983 ◽  
Author(s):  
Zhuofan Liao ◽  
Jianxin Wang ◽  
Shigeng Zhang ◽  
Jiannong Cao ◽  
Geyong Min

Author(s):  
J. Karl Hedrick ◽  
Brandon Basso ◽  
Joshua Love ◽  
Benjamin M. Lavis

This paper compares some of the common tools and techniques that enable state-of-the-art systems to provide high-level control of mobile sensor networks. There is currently a great deal of interest in employing unmanned and autonomous vehicles in intelligence, surveillance, and reconnaissance operations. Although this paper addresses issues common to all mobile sensor networks, the applications presented are typically associated with autonomous vehicles. We focus specifically on three high-level areas: 1. mission definition languages that allow human users to compose missions defined in terms of tasks, 2. communication-addressing degradation and loss and relationship to underlying system architecture design, and 3. task allocation among the assets.


2010 ◽  
Vol 21 (3) ◽  
pp. 490-504 ◽  
Author(s):  
Fu-Long XU ◽  
Ming LIU ◽  
Hai-Gang GONG ◽  
Gui-Hai CHEN ◽  
Jian-Ping LI ◽  
...  

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