Distributed Storage Schemes for Controlling Data Availability in Wireless Sensor Networks

Shen, Yulong; Xi, Ning; Pei, Qingqi; Ma, Jianfeng; Xu, Qian; Zuo-shun, WU

doi:10.1109/cis.2011.126

Cited by 4 publications

(5 citation statements)

References 13 publications

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“…TinyDSM [11] and DSforCDA [42] focus on reliability, providing robust distributed storage so that data can always be recovered despite possible node failures. TinyDSM ensures data availability by introducing data redundancy.…”

Section: A Fully Distributed Data Storagementioning

confidence: 99%

Mist and Edge Storage: Fair Storage Distribution in Sensor Networks

2019

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Sensor/Actuator devices are currently being massively adopted, often as nodes of larger sensor networks. These sensor networks are typically dedicated to context acquisition (e.g., get temperature) as well as providing acting services (e.g., open the blinds). However, regarding their own data storage, data is usually sent to Fog/Cloud servers. Fog/Cloud storage solutions provide several advantages over sensor network storage solutions, but also some drawbacks. For instance, in Cloud environments, privacy and legal issues may appear, while in Fog, additional costly hardware must be purchased and maintained, at least a server with redundant storage or many servers when distributed data storage is required. Nowadays, sensor nodes count in thousands around us, and they have significantly increased their storage and computational capabilities over the past few years. Therefore, traditional Fog/Cloud storage solutions could be combined or even replaced by Mist/Edge storage solutions for many use cases. A principal contribution of this paper is a novel data distribution and replication storage solution for wireless sensor networks, the first to consider sensor node heterogeneity to find the optimal storage replication according to node capabilities. The solution has been carefully planned and implemented to run even in very low-end microcontrollers, that lives in many of our surrounding smart devices. Other contributions include data comparing Mist/Edge and Amazon S3 regular storage, showing that there remains plenty of room for research into Mist/Edge storage, as well as into the industry itself.INDEX TERMS Mist computing, edge computing, distributed storage, sensor networks.

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Section: A Fully Distributed Data Storagementioning

confidence: 99%

Mist and Edge Storage: Fair Storage Distribution in Sensor Networks

2019

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“…When some nodes fail, data collector can recover the sensory data by visiting other survived nodes. Currently, there are two typical redundant methods: one is based on replicas [10][11] and the other one is based on network coding [12][13][14][15].…”

Section: Related Workmentioning

confidence: 99%

“…Such networks are usually unattended, which means that sink nodes may appear periodically to collect sensory data, or fixed sink nodes are vulnerable to natural disasters and artificial attacks. In order to prevent data loss caused by node failures, redundant fault-tolerant measures [10][11][12][13][14][15] must be taken to store the sensory data throughout the network. For the purpose of realizing efficient storage and fast collection of sensory data, an energy-efficient redundant data storage algorithm QRNCDS based on MST (Minimum Spanning Tree) and quasi-randomized network coding is proposed in this paper.…”

Section: Introductionmentioning

confidence: 99%

A Novel Redundant Data Storage Algorithm Based on Minimum Spanning Tree and Quasi-randomized Matrix

2018

KSII TIIS

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For intermittently connected wireless sensor networks deployed in hash environments, sensor nodes may fail due to internal or external reasons at any time. In the process of data collection and recovery, we need to speed up as much as possible so that all the sensory data can be restored by accessing as few survivors as possible. In this paper a novel redundant data storage algorithm based on minimum spanning tree and quasi-randomized matrix-QRNCDS is proposed. QRNCDS disseminates k source data packets to n sensor nodes in the network (n>k) according to the minimum spanning tree traversal mechanism. Every node stores only one encoded data packet in its storage which is the XOR result of the received source data packets in accordance with the quasi-randomized matrix theory. The algorithm adopts the minimum spanning tree traversal rule to reduce the complexity of the traversal message of the source packets. In order to solve the problem that some source packets cannot be restored if the random matrix is not full column rank, the semi-randomized network coding method is used in QRNCDS. Each source node only needs to store its own source data packet, and the storage nodes choose to receive or not. In the decoding phase, Gaussian Elimination and Belief Propagation are combined to improve the probability and efficiency of data decoding. As a result, part of the source data can be recovered in the case of semi-random matrix without full column rank. The simulation results show that QRNCDS has lower energy consumption, higher data collection efficiency, higher decoding efficiency, smaller data storage redundancy and larger network fault tolerance.

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“…In [74] the authors adopt a replication solution. However, their proposal is based on the hypothesis of the random nature of failures in wireless sensor networks.…”

Section: Approximation Based Techniquesmentioning

confidence: 99%

“…T a the complexities of or to efficiently extract th is performed by minimizing the activities of o eployment and the resource limitation, sensor nodes are always subject to random failures [38]. For these reasons, in [74] the authors propose a distributed energy-efficient replica placement for increasing data availability and prolonging the network lifetime. The basic idea is, taking into account probabilistic node failures, they compute (based on probabilistic equations) sufficient data replica of nodes with minimal communication cost between nodes such that the whole network data could be reached after a failure.…”

Section: Approximation Based Techniquesmentioning

confidence: 99%

Distributed Database Management Techniques for Wireless Sensor Networks

Diallo

Rodrigues

Sène

et al. 2015

IEEE Trans. Parallel Distrib. Syst.

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Abstract-In sensor networks, the large amount of data nsor reWireless Se composed of a large nu activities are sometimes not negligible in energy consumption generated by sensors greatly influences the lifetime of the network. In order to manage this amount of sensed data in an energy-efficient way, new methods of storage and data query are needed. In this way, the distributed database approach for sensor networks is proved as one of the most energy-efficient data storage and query techniques. This paper surveys the state of the art of the techniques used to manage data and queries in wireless sensor networks based on the distributed paradigm. A classification of these techniques is also proposed. The goal of this work is not only to present how data and query management techniques have advanced nowadays, but also show their benefits and drawbacks, and to identify open issues providing guidelines for further contributions in this type of distributed architectures.

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Distributed Storage Schemes for Controlling Data Availability in Wireless Sensor Networks

Cited by 4 publications

References 13 publications

Mist and Edge Storage: Fair Storage Distribution in Sensor Networks

Mist and Edge Storage: Fair Storage Distribution in Sensor Networks

A Novel Redundant Data Storage Algorithm Based on Minimum Spanning Tree and Quasi-randomized Matrix

Distributed Database Management Techniques for Wireless Sensor Networks

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