Distributed compression in a dense microsensor network

By S. Pradhan, Julius Kusuma, K. Ramchandran. IEEE Signal Processing Magazine, 2002

591 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9a646c792ad5072fe693b9e5cab5b02cee2b0ece

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Citations in the corpus

Non-parametric distributed quantization-estimation using wireless sensor networks (2005, background): “However, except for recent works where spatial correlation is exploited to reduce the amount of information exchanged [1, 2, 7, 8], use of WSNs for the equally important problem of distributed parameter estimation remains largely uncharted.”
Distributed Video Coding (2005, background): “Their later work [4]–[7] considers the symmetric case where and are encoded with the same rate.”
Distributed source coding for sensor networks (2004, methodology): “start with the same example given in [5] and [6] and extend it to more general cases later.”
Spatio-temporal correlation: theory and applications for wireless sensor networks (2004, background): “On the other hand, there exist some proposals which attempt to exploit spatial correlation in WSN [13,14].”
On energy provisioning and relay node placement for wireless sensor networks (2005, background): “The two-tiered network architecture is motivated by recent advances in distributed source coding (DSC) [5], [ 17 ], [21], which can exploit redundancy in information collected among neighboring sensors without intersensor communications (Slepian‐Wolf [23] and Wyner‐Ziv [26] theorems).”
Data compression algorithms for energy-constrained devices in delay tolerant networks (2006, background): “One group of prior work focuses on exploiting high spatial correlatio n in data from fixed sensors in dense networks [10][14][26].”
A survey on data compression in wireless sensor networks (2005, background): “Since the current video encoding technologies are mostly designed on utilizing the motion estimation and compensation, it will require a high computation power, which sensor nodes are not usually equipped with.”
Bandwidth-constrained distributed estimation for wireless sensor networks-part II: unknown probability density function (2006, background): “, for recent works where spatial correlation is exploited to reduce the amount of information exchanged among nodes [2], [3], [6], [7], [11], [16], [19], [20], use of WSNs for the equally important problem of distributed parameter estimation remains a largely”
Bandwidth-constrained distributed estimation for wireless sensor Networks-part I: Gaussian case (2006, background): “More recently, a great deal of attention has focused on decentralized algorithms exploiting spatial correlation to reduce transmission requirements [3], [4], [6], [14], [17], [19].”
Gossip Algorithms for Distributed Signal Processing (2010, methodology): “Distributed source coding approaches attempt to reduce the total number of bits transmitted by leveraging the celebrated results of Slepian and Wolf [114] to code with side information [115], [116].”
Dimensions: why do we need a new data handling architecture for sensor networks? (2003, unknown): “In building this system, we draw from significant research in information theory [5, 6, 17 ], databases [7, 14], spatio-temporal data mining [18, 19], and previous work in sensor networks [11, 20].”
Compressive wireless sensing (2006, background): “Moreover, data collected at nearby nodes in a dense sensor network is expected to be highly correlated [14].”
Growth codes: maximizing sensor network data persistence (2006, background): “, they can compress or recode data to increase delivery efficiency [34, 33].”
Spatial correlation-based collaborative medium access control in wireless sensor networks (2006, background): “There exists some research to study the correlation in WSN [6], [13], [17] in recent years.”
Optimal energy management policies for energy harvesting sensor nodes (2008, background): “, reducing the number of bits to transmit ([2], [3]), by choosing the best modulation strategy ([4]), by using efficient transmission scheduling to take advantage of charge recovery phenomenon ([5]), by exploiting power saving modes (sleep/listen) periodically ([6]), using energy efficient routing (”