Information-driven dynamic sensor collaboration

By Feng Zhao, Jae-Hyuck Shin, J. Reich. IEEE Signal Processing Magazine, 2002

822 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/6013e9e4c0b5f3e498b92989859f16db6ba50dea

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In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

On the Convergence of Decentralized Gradient Descent (2013, background): “These properties are important for applications where data are collected by distributed agents, communication to a fusion center is expensive or impossible, and/or agents tend to keep their raw data private; such applications arise in wireless sensor networks [16, 24, 27, 38], multivehicle and multi”
Balancing Communication and Computation in Distributed Optimization (2017, background): “This is motivated by its wide applicability to many important engineering and scientific domains, such as wireless sensor networks [1]–[4], multivehicle and multirobot networks [5]–[7], smart grids [8], [9], and machine learning [10], [11].”
Mobile Sensor Network Control Using Mutual Information Methods and Particle Filters (2010, methodology): “Information theoretic costs metrics have been used to manage sensors [19], and led to algorithms to control sensor networks for information gathering over an area by parameterizing the motion of collectives of vehicles [20].”
Collaborative signal and information processing: an information-directed approach (2003, background): “IDSQ [26], [5] formulates the tracking problem as a more general distributed constrained optimization that maximizes information gain of sensors while minimizing communication and resource usage.”
Distributed target classification and tracking in sensor networks (2003, background): “Recent work on a related topic is reported in [22].”
Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey (2016, background): “The simulation results showed that the lifetime in terms of the number of time steps at which the network becomes non-functional of the proposed approach can achieve up to 25 time steps while that of the popular strategy for sensor selection based on the FIM (Fisher Information Matrix) [104] is arou”
On a stochastic sensor selection algorithm with applications in sensor scheduling and sensor coverage (2006, methodology): “…dealt with robust sensor scheduling (A. Savkin and R. Evans and E. Ska(cid:12)das 2000), a greedy algorithm with an information based cost measure (Zhao et al. 2002), a numerical method for obtaining sub-optimal schedules with error bounds (Alriksson and Rantzer 2005) and include the works of…”
Compressed Distributed Gradient Descent: Communication-Efficient Consensus over Networks (2018, background): “, distributed sensing in wireless sensor networks [1]–[4], decentralized machine learning [5], [6], multi-agent robotic systems [7]–[9], smart grids [10], [11], to name just a few.”
A Tutorial on Bernoulli Filters: Theory, Implementation and Applications (2013, background): “This problem has been studied by many authors, see for example [37], [64], [65].”