Decentralized Environmental Modeling by Mobile Sensor Networks

By Kevin M. Lynch, Ira B. Schwartz, Penghui Yang, Randy A. Freeman. 2008

285 citations recorded by the source corpus.

Subfields: Computer Networks and Communications

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Wei Ren
2. Wenwu Yu
3. Guanrong Chen
4. Yongcan Cao
5. Randy A. Freeman
6. Kevin M. Lynch
7. Z. Duan
8. Penghui Yang

Citations in the corpus

• An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination (2012, background): “Main research on this topic has been reported in [118]–[121], where the joint estimation and control problem was considered subject to disturbances [118], [119] or without disturbances [120], [121].”
• Analysis of consensus protocols with bounded measurement errors (2011, background): “The ability of a team of interacting agents to reach an agreement on some quantity of interest is often a key issue for the solution of many problems in different application domains, like distributed sensing [1], cooperative control of autonomous vehicles [2], rendezvous algorithms for autonomous a”
• Decentralized estimation and control of graph connectivity in mobile sensor networks (2008, background): “Applications for mobile sensor networks include target tracking [15], [25], [22], [12], formation and coverage control [1], [2], [4], [6], environmental monitoring [10], [ 11 ], [17], [20], and several others.”
• Multi-Agent Coordination by Decentralized Estimation and Control (2008, methodology): “Dynamic consensus filters have also been utilized to construct decentralized Kalman filters [23], [30], [41], [47].”
• Efficient Distributed Reinforcement Learning through Agreement (2008, background): “, [8] and citations therein); we cannot do it justice in this space.”
• Coherent Pattern Prediction in Swarms of Delay-Coupled Agents (2012, methodology): “e used in conjunction with simple scalable algorithms to achieve multivehicle cooperative motion [14]. Specific goals include: ob - stacle avoidance [11], boundary tracking [15], environmental sensing [13, 16] and decentralized target tracking [17]. An important problem is that of environmental conse”