Replication vs erasure coding in data centric storage for wireless sensor networks

Albano, Michele; Chessa, Stefano

doi:10.1016/j.comnet.2014.11.018

Cited by 16 publications

(3 citation statements)

References 29 publications

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“…There is no protection against any unauthorized data collection and data corruption in this work. Albano et al [19] used erasure coding with probabilistic data distribution to ensure data confidentiality. But the method has low coverage of survivability.…”

Section: Related Workmentioning

confidence: 99%

Energy Aware Compressive Sensing Assisted Secure Data Survivability and Data Collection in Unattended Wireless Sensor Networks

2022

IJIES

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Mobile sink-based sensor networks deployed in unattended environment are characterized by infrequent sink visits. In these applications, ensuring the survivability and confidentiality of data is important till it is collected by the sink. The data must also be prevented from collection by agents other than sink and leakage by neighbours. Data collection in unattended environment must also be energy efficient to prolong the life time of the network. This work proposes a novel Energy Aware Adaptive Compressive Sensing (EA-ACS) assisted secure data survivability scheme which is able to optimize energy consumption at cost of data accuracy level desired by applications. The solution introduces three novelties: adaptive compressive sensing for reducing the foot print of data and energy consumption during transmission, a novel bi party authentication scheme for secure data collection by sink and low energy overhead piggy bagging for data replication. Through performance in NS2, the proposed solution is found to provide at least 6% higher data survivability and 20% lower energy consumption compared to existing works.

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Section: Related Workmentioning

confidence: 99%

Energy Aware Compressive Sensing Assisted Secure Data Survivability and Data Collection in Unattended Wireless Sensor Networks

2022

IJIES

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“…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.…”

Section: Analyzing Whether the Data-storage And Information-discovmentioning

confidence: 99%

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

Liang

Liu

et al. 2018

Sensors

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In the post-Cloud era, the proliferation of Internet of Things (IoT) has pushed the horizon of Edge computing, which is a new computing paradigm with data processed at the edge of the network. As the important systems of Edge computing, wireless sensor and actuator networks (WSANs) play an important role in collecting and processing the sensing data from the surrounding environment as well as taking actions on the events happening in the environment. In WSANs, in-network data storage and information discovery schemes with high energy efficiency, high load balance and low latency are needed because of the limited resources of the sensor nodes and the real-time requirement of some specific applications, such as putting out a big fire in a forest. In this article, the existing schemes of WSANs on data storage and information discovery are surveyed with detailed analysis on their advancements and shortcomings, and possible solutions are proposed on how to achieve high efficiency, good load balance, and perfect real-time performances at the same time, hoping that it can provide a good reference for the future research of the WSANs-based Edge computing systems.

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“…The Sinbad method [9] adjusts the layout of the replica periodically based on the network balance. Albano [10] reduces the network transmission during encoding and decoding by caching the network storage. Binary regenerating codes [11] reduce the reconstruction bandwidth consumption by simplifying the decoding process.…”

Section: Erasure Coding In Distribution Storage Cluster Systemmentioning

confidence: 99%

Rack Aware Data Placement for Network Consumption in Erasure-Coded Clustered Storage Systems

Shao¹,

Song²,

Bian³

et al. 2018

Information

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Abstract:The amount of encoded data replication in an erasure-coded clustered storage system has a great impact on the bandwidth consumption and network latency, mostly during data reconstruction. Aimed at the reasons that lead to the excess data transmission between racks, a rack aware data block placement method is proposed. In order to ensure rack-level fault tolerance and reduce the frequency and amount of the cross-rack data transmission during data reconstruction, the method deploys partial data block concentration to store the data blocks of a file in fewer racks. Theoretical analysis and simulation results show that our proposed strategy greatly reduces the frequency and data volume of the cross-rack transmission during data reconstruction. At the same time, it has better performance than the typical random distribution method in terms of network usage and data reconstruction efficiency.

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Replication vs erasure coding in data centric storage for wireless sensor networks

Cited by 16 publications

References 29 publications

Energy Aware Compressive Sensing Assisted Secure Data Survivability and Data Collection in Unattended Wireless Sensor Networks

Energy Aware Compressive Sensing Assisted Secure Data Survivability and Data Collection in Unattended Wireless Sensor Networks

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

Rack Aware Data Placement for Network Consumption in Erasure-Coded Clustered Storage Systems

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