CStorage: Decentralized compressive data storage in wireless sensor networks

Abstract

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

A Survey on Data Storage and Information Discovery in the WSANs-Based Edge Computing Systems

“…In WSNs, the problem of data storage and information discovery has been studied for many years. In a nutshell, data are stored in WSNs mainly based on the following three models: (1) data are stored among the sensor nodes in a distributed manner [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ]; (2) data are stored at the static Sink node/nodes intensively [ 29 , 30 , 31 , 32 , 33 ]; (3) data are collected and stored at some mobile elements [ 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ], such as mobile Sinks. For the first model, queries should be launched across the sensor nodes to search for and discover the needed information; for the second and third models, the needed information can be discovered directly at the static Sink node/nodes or the mobile elements.…”

Sparse Signal Acquisition via Compressed Sensing and Principal Component Analysis

“…Since its introduction, CS was tested with WSNs performing temperature [5], EM emission [6], and ECG [7] monitoring, video streaming [8], etc. Other applications of the CS theory include WSN decentralized data storage [9], accelerated MRI [10], or digital modulation recognition [11].…”

A Data-Gathering Scheme with Joint Routing and Compressive Sensing Based on Modified Diffusion Wavelets in Wireless Sensor Networks

“…In recent years, a large number of CS-based techniques have been investigated to gather data on WSNs [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. However, most of the current CS-based methods neglect the topology structure or property of sensor node readings, such as spatial characteristics.…”

“…So far, various CS-based data-gathering schemes have been presented to promote the networks’ efficiency for data gathering in WSNs [ 12 , 13 , 14 , 15 ]. (See Section 2 for more details.)…”

“…For instance, [ 12 ] proposes a data-gathering scheme that diminishes the bottleneck of the sink in WSNs. In [ 13 ], the spatial correlation of sensor node readings is leveraged to improve the performance of WSNs. Wu et al propose a sparsest representation basis, which makes full use of the spatial property of sensor node readings [ 14 ].…”