CS‐PLM: Compressive Sensing Data Gathering Algorithm Based on Packet Loss Matching in Sensor Networks

Sun, Zeyu; Tao, Rong; Xiong, Naixue

doi:10.1155/2018/5131949

Cited by 16 publications

(10 citation statements)

References 21 publications

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“…In Formulation (10), N A (dγ ) is the quantity of range nodes whose its own area is dAγ from the convergence node. P{N A (dγ ) = 1} is expressed as the probability of having a node.…”

Section: Reliability Data Reconsruction a Data Retransmission Mementioning

confidence: 99%

Edge Computing in Internet of Things: A Novel Sensing-Data Reconstruction Algorithm Under Intelligent-Migratoin Stragegy

2020

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“…In Formulation (10), N A (dγ ) is the quantity of range nodes whose its own area is dAγ from the convergence node. P{N A (dγ ) = 1} is expressed as the probability of having a node.…”

Section: Reliability Data Reconsruction a Data Retransmission Mementioning

confidence: 99%

Edge Computing in Internet of Things: A Novel Sensing-Data Reconstruction Algorithm Under Intelligent-Migratoin Stragegy

2020

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“…In order to tackle the issue of errors and packet losses during data transmission in WSNs, Sun et al proposed a data gathering algorithm based on Packet Loss Matching to do just that [44]. Based on simulation result, it reveals that the algorithm can help in alleviating packet losses during data gathering.…”

Section: A Data Collection In Wireless Sensor Network (Wsns)mentioning

confidence: 99%

Data Collection in Sensor Cloud: Recent Advances, Taxonomy, Use Cases and Open Challenges

Ali¹

2020

Preprint

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<div>In recent years, Wireless Sensor Networks (WSNs) technologies are used in many important</div><div>areas, including weather forecasting, security, environmental monitoring, health care, and industry.</div><div>Nonetheless, due to the constraints of WSNs in terms of energy, processing, connectivity, computation and</div><div>data integrity. An important issue in this research field is the efficient management of the large number</div><div>of WSN’s data. Therefore, there is a need for strong and scalable high-performance computing and large</div><div>storage infrastructure for real-time processing and storage of WSN’s data. This article attempts to discuss</div><div>data collection methods in WSNs, sensor cloud, and IoT. Also, in this context, a general overview of</div><div>WSNs and sensor cloud platforms, including their definitions, architectures, and applications are given.</div><div>We investigate, highlight and report recent advances in data collection technology by categorizing and</div><div>classifying data collection methods in WSNs, sensor cloud, IoT and developing a taxonomy based on</div><div>these classifications. Furthermore, the analysis and synthesis of existing works in this research domain</div><div>is highlighted. The identification of key research challenges for future work and use case in this research</div><div>domain were also highlighted and discussed.</div>

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“…Then three different schemes were used to choose the parent nodes and further construct the data aggregation tree. For the wireless sensor network with different initial node energy, paper [26] investigated on the problem of finding a data aggregation tree among the minimum-distance trees to maximize the network lifetime. This problem was equivalent to finding a minimum distance tree with a minimum load and it was solved by the transformation into the generalized semi-matching subproblem.…”

Section: Related Workmentioning

confidence: 99%

An Optimized Clustering Communication Protocol Based on Intelligent Computing in Information-Centric Internet of Things

et al. 2019

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A narrow bandwidth may lead to a large amount of redundant data, which further causes the interruptions of the communication network. In order to address this problem, an optimized clustering communication protocol based on intelligent computing (CCP-IC) is proposed in this paper. First, we adopt the intelligent algorithm to perform the optimization of the clustering in the sensor network. The adaptation function and the heuristic function are introduced to make a targeted choice on the cluster head for the next hop of the nodes in the network. Second, the controllable threshold parameter and variation coefficient are employed to optimize the shortest path chosen by the network routing. Therefore, the node energy consumption is lowered when the minimum network delay is guaranteed and the transmission efficiency is improved. Finally, it is verified via the simulation results and compared with other algorithms; the proposed protocol reduces the network energy consumption by 15.3% and prolongs the network lifetime by 18.72%, which proves the validity and effectiveness of the proposed protocol. INDEX TERMS Internet of Things, clustering communication protocol, intelligent computing, network lifetime.

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CS‐PLM: Compressive Sensing Data Gathering Algorithm Based on Packet Loss Matching in Sensor Networks

Cited by 16 publications

References 21 publications

Edge Computing in Internet of Things: A Novel Sensing-Data Reconstruction Algorithm Under Intelligent-Migratoin Stragegy

Edge Computing in Internet of Things: A Novel Sensing-Data Reconstruction Algorithm Under Intelligent-Migratoin Stragegy

Data Collection in Sensor Cloud: Recent Advances, Taxonomy, Use Cases and Open Challenges

An Optimized Clustering Communication Protocol Based on Intelligent Computing in Information-Centric Internet of Things

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