Cluster‐Based Arithmetic Coding for Data Provenance Compression in Wireless Sensor Networks

Xu, Qinbao; Akhtar, Rizwan; Zhang, Xing; Wang, Changda

doi:10.1155/2018/9576978

Cited by 11 publications

(6 citation statements)

References 20 publications

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“…lossless [14], [15] Use Arithmetic Coding to encode provenance as a coding interval. [14] use global probabilities to encode provenance while [15] use local probabilities to encode provenance.…”

Section: Methodsmentioning

confidence: 99%

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Provenance-enabled packet path tracing in the RPL-based internet of things

et al. 2020

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The interconnection of resource-constrained and globally accessible things with untrusted and unreliable Internet make them vulnerable to attacks including data forging, false data injection, and packet drop that affects applications with critical decisionmaking processes. For data trustworthiness, reliance on provenance is considered to be an effective mechanism that tracks both data acquisition and data transmission. However, provenance management for sensor networks introduces several challenges, such as low energy, bandwidth consumption, and efficient storage. This paper attempts to identify packet drop (either maliciously or due to network disruptions) and detect faulty or misbehaving nodes in the Routing Protocol for Low-Power and Lossy Networks (RPL) by following a bi-fold provenance-enabled packed path tracing (PPPT) approach. Firstly, a system-level ordered-provenance information encapsulates the data generating nodes and the forwarding nodes in the data packet. Secondly, to closely monitor the dropped packets, a node-level provenance in the form of the packet sequence number is enclosed as a routing entry in the routing table of each participating node. Lossless in nature, both approaches conserve the provenance size satisfying processing and storage requirements of IoT devices. Finally, we evaluate the efficacy of the proposed scheme with respect to provenance size, provenance generation time, and energy consumption.

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Section: Methodsmentioning

confidence: 99%

“…This scheme assigns each node a global cumulative probability according to a node occurrence probability among all the used packet paths. [14] presents an extension of [15]. In this scheme, authors proposed an incremental clusterbased arithmetic coding scheme for encoding provenance in packet.…”

Section: Related Workmentioning

confidence: 99%

Provenance-enabled packet path tracing in the RPL-based internet of things

et al. 2020

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“…The last aggregation method is a gradient deployment algorithm which aims to deal with the energy hole problem in IoT applications. Some works such in [13][14][15][16] use data compression techniques in order to reduce the packet size sent to the sink. The authors of [13] propose a prioritybased compressed data aggregation (PCDA) technique in order to reduce the amount of heath data transmitted.…”

Section: Related Workmentioning

confidence: 99%

“…S-LEC codeword consists of two parts: the entropy code specifying the group and the binary code representing the index in the group. In [15], a coding provenance scheme (CPS) has been proposed. Compared to traditional compression techniques, CPS ensures a high provenance compression rate as well as it encodes and decodes incrementally the compression ratio at the base station depending on the condition observed.…”

Section: Related Workmentioning

confidence: 99%

All-in-one: Toward hybrid data collection and energy saving mechanism in sensing-based IoT applications

Ibrahim

Harb

Mansour

et al. 2021

Peer-to-Peer Netw. Appl.

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Big data collection and storage have become one of the most obvious challenge in this era. Indeed, much of that data is collected thanky to a huge number of connected devices in sensing-based IoT applications. Thus, in order to deal with data growth in such applications, researchers have focused on data reduction approach as an efficient solution for minimizing the amount of data collection and saving the limited sensor energy in such networks. Mainly, data reduction approach relies on various kinds of data processing techniques such that aggregation, compression, prediction, clustering, sensing frequency adaptation and spatial-temporal correlation. However, each of those techniques has its own advantages and disadvantages regarding sensor energy saving, data reduction ratio, data accuracy, complexity, etc. In this paper, we propose a hybrid data collection and energy saving mechanism, called All-in-One, for sensing-based IoT applications. The proposed mechanism takes advantages from existing data reduction techniques while optimizing various performance metrics. All-in-One relies on the cluster network architecture and works on three main phases: on-period, in-period and in-node. The first phase, e.g. on-period, allows each sensor node to search the similarity among its periodic collected data then to reduce its data transmission to the Cluster-Head (CH) by applying either data aggregation, compression or prediction technique. The second phase, e.g. in-period, allows each sensor to study the variation of the monitored condition then to reduce its data collection according to two techniques, on-off transmission or adapting sensing frequency. The last phase, e.g. in-node, is applied at the CH level and aims to remove the re-

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“…According to the received quota and its remaining energy, the children node selects its suitable compression method and its sensing rate during the next period. In [10], a coding provenance scheme (CBP) has been proposed. Compared to traditional compression techniques, CBP ensures a high provenance compression rate as well as it encodes and decodes incrementally the compression ratio at the base station depending on the condition observed.…”

Section: Related Workmentioning

confidence: 99%

An energy-efficient data prediction and processing approach for the internet of things and sensing based applications

Harb

Jaoude

Makhoul

2019

Peer-to-Peer Netw. Appl.

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The Internet of Things (IoT) is a vision in which billions of smart objects are linked together. In the IoT, "things" are expected to become active and enabled to interact and communicate among themselves and with the environment by exchanging data and information sensed about the environment. In this future interconnected world, multiple sensors join the internet dynamically and use it to exchange information all over the world in semantically interoperable ways. Therefore, huge amounts of data are generated and transmitted over the network. Thus, these applications require massive storage, huge computation power to enable real-time processing, and high-speed network. In this paper, we propose a data prediction and processing approach aiming to reduce the size of data collected and transmitted over the network while guaranteeing data integrity. This approach is dedicated to devices/sensors with low energy and computing resources. Our proposed technique is composed of two stages: on-node prediction model and in-network aggregation algorithm. The first stage uses the Lagrange interpolation polynomial model to reduce the amount of data generated by sensor nodes while, the second stage uses a statistical test, i.e. Kolmogorov-Smirnov, and aims to reduce the redundancy between data generated by neighbouring nodes. Simulation on real sensed data reveals that the proposed approach significantly reduces the amount of data generated and transmitted over the network thus, conserving sensors' energies and extending the network lifetime.

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Cluster‐Based Arithmetic Coding for Data Provenance Compression in Wireless Sensor Networks

Cited by 11 publications

References 20 publications

Provenance-enabled packet path tracing in the RPL-based internet of things

Provenance-enabled packet path tracing in the RPL-based internet of things

All-in-one: Toward hybrid data collection and energy saving mechanism in sensing-based IoT applications

An energy-efficient data prediction and processing approach for the internet of things and sensing based applications

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