CStorage: Distributed Data Storage in Wireless Sensor Networks Employing Compressive Sensing

Talari, Ali; Rahnavard, Nazanin

doi:10.1109/glocom.2011.6134318

Cited by 30 publications

(31 citation statements)

References 15 publications

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“…A distributed way of compressive sensing data scattering was considered by A. Talari and N. Rahnavard [11]. Each node disseminates its data throughout the network to make the data stored in the network.…”

Section: Related Workmentioning

confidence: 99%

Distributed compressive data gathering in low duty cycled wireless sensor networks

Wang

Zhu

Jiang

et al. 2014

2014 IEEE 33rd International Performance Computing and Communications Conference (IPCCC)

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Wireless sensor networks (WSNs) are gaining popularity in practical monitoring and surveillance applications. Because of the limited energy of sensor nodes, many WSNs work in a low duty cycle mode to effectively extend their network lifetime. However, low duty cycling also decreases transmission efficiency and makes data gathering more challenging. By exploiting the redundancy of in real sensing data, we propose a novel and distributed approach for data gathering in wireless sensor networks, employing the compressed sensing theory. Instead of selecting a fixed sink, all data can be retrieved from an arbitrary node within the network. Moreover, we use sequential observations to dynamically fit the sparsity of various data sets. With extensive simulations, we show that our approach is efficient with tunable accuracy in different node duty cycles.

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Section: Related Workmentioning

confidence: 99%

Distributed compressive data gathering in low duty cycled wireless sensor networks

Wang

Zhu

Jiang

et al. 2014

2014 IEEE 33rd International Performance Computing and Communications Conference (IPCCC)

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“…For instance, CS found great utilization in MIMO broadcast network [3], orthogonal This frequency division multiplexing (OFDM) system [4] [5], heterogeneous cellular networks (HetNets) [6], distributed data storage [7], multiple description coding (MDC) [8], multimedia communication [9], [10], network fault identification [11], [12], and time series analysis [13].…”

Section: Introductionmentioning

confidence: 99%

A Framework for Compressive Sensing of Asymmetric Signals Using Normal and Skew-Normal Mixture Prior

Wang

Rahnavard

2015

IEEE Trans. Commun.

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In this work, we are interested in the compressive sensing of sparse signals whose significant coefficients are distributed asymmetrically with respect to zero. To properly address this problem, we develop a framework utilizing a two-state normal and skew normal mixture density as the prior distribution of the signal. The significant and insignificant coefficients of the signal are represented by skew normal and normal distributions, respectively. A novel approximate message passing based algorithm is developed to estimate the signal from its compressed measurements. A fast gradient-based estimator is designed to infer the density of each state. Experiment results on simulated data and two real-world tests, i.e., multi-input multi-output (MIMO) communication system and weather sensor network, confirm that our proposed technique is powerful in exploiting asymmetrical feature, and outperforms many sophisticated methods.

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“…We will show both CStorage-P and CStorage-B reduce the total number of transmissions compared to existing algorithms for data storage in WSNs without routing tables, while CStorage-B surpasses CStorage-P in the number of transmissions. The initial results of this paper on CStorage-P have appeared in [8]. In this paper, we design AB and introduce CStorage-B.…”

Section: Introductionmentioning

confidence: 99%

CStorage: Decentralized compressive data storage in wireless sensor networks

Talari

Rahnavard

2016

Ad Hoc Networks

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In this paper, we employ compressive sensing (CS) to design a distributed compressive data storage (CStorage) algorithm for wireless sensor networks (WSNs). First, we assume no neighbor information or routing table is available at nodes and employ the well-known probabilistic broadcasting (PB) to disseminate sensors reading throughout the network to form compressed samples (measurements) of the network readings at each node. After the dissemination phase, a data collector may query any arbitrary set of M ≪ N nodes for their measurement and reconstruct all N readings using CS. We refer to the first implementation of CStorage by CStorage-P. Next, we assume that nodes collect two-hop neighbor information and design a novel parameterless and scalable data dissemination algorithm referred to by alternating branches (AB), and design CStorage-B. We discuss the advantages of CStorage-P and CStorage-B and show that they consider

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CStorage: Distributed Data Storage in Wireless Sensor Networks Employing Compressive Sensing

Cited by 30 publications

References 15 publications

Distributed compressive data gathering in low duty cycled wireless sensor networks

Distributed compressive data gathering in low duty cycled wireless sensor networks

A Framework for Compressive Sensing of Asymmetric Signals Using Normal and Skew-Normal Mixture Prior

CStorage: Decentralized compressive data storage in wireless sensor networks

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