Deterministic binary matrix based compressive data aggregation in big data WSNs

Liu, Cuiye; Guo, Songtao; Shi, Ye; Yang, Yuanyuan

doi:10.1007/s11235-017-0294-3

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…11, the line with black arrow is the direction − − → D sink and the green squares are the grids which are located inside the activity area S s and on the direction − − → D sink simultaneously. As a result, there are four grids meet the above conditions, which have the center coordinates (9,9), (10,8), (11,7), and (12, 6), respectively. For each possible optimal location obtained above, the sink node moves and generates the topology structure based on BMST algorithm, which is shown in Fig.…”

Section: A Location Update Strategy Of the Sink Nodementioning

confidence: 99%

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Location Updating Scheme of Sink Node Based on Topology Balance and Reinforcement Learning in WSN

Wang

Zhou

et al. 2019

IEEE Access

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This paper proposes a scheme for updating the location of the sink node to balance the network topology when a wireless sensor network (WSN) is scaled up. We divide the proposed location update scheme into two steps, namely, searching the optimal location and designing the pathfinding algorithm. For the former, to find the optimal location of the sink node simply and efficiently, we only consider the information of the expanded longer paths and some key nodes instead of the global information of the entire network, which is easy to implement with a low-computational load. Then, considering the general unattended application scenario, we propose an improved reinforcement learning (RL) algorithm for the sink node to calculate a feasible efficient path, and then the sink node follows the path to reach the optimal location. Finally, through simulations, we demonstrate the optimal position of the sink node in expanded scenarios and successfully let the sink node learn the effective pathfinding method to reach the target position. A large number of simulation results verify the efficiency and effectiveness of our proposed scheme from the perspective of the efficiency of the pathfinding algorithm.

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Section: A Location Update Strategy Of the Sink Nodementioning

confidence: 99%

“…FIGURE 12.Updated topology structure generated by the sink node on coordinates (a) (9, 9), (b) (10, 8), (c)(11,7), and (d)(12,6).…”

mentioning

confidence: 99%

Location Updating Scheme of Sink Node Based on Topology Balance and Reinforcement Learning in WSN

Wang

Zhou

et al. 2019

IEEE Access

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“…When the network is partitioned into some clusters, data transmission can be classified into two stages: intra-cluster transmission and inter-cluster transmission. One of disadvantages of existing clustering algorithms [9][10][11][12][13][14][15][16][17][18][19] is that thet do not give the energy consumption model and do not study what the optimal number of clusters should be in a network. Moreover, most of these existing algorithms do not fit well into the big data environment.…”

Section: Introductionmentioning

confidence: 99%

Spectral partitioning and fuzzy C-means based clustering algorithm for big data wireless sensor networks

Wang

Guo

et al. 2018

J Wireless Com Network

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In wireless sensor networks, sensor nodes are usually powered by battery and thus have very limited energy. Saving energy is an important goal in designing a WSN. It is known that clustering is an effective method to prolong network lifetime. Due to the development of big data, there are more sensor nodes and data needed to process. So how to cluster sensor nodes cooperatively and achieve an optimal number of clusters in a big data WSN is an open issue. In this paper, we first propose an analytical model to give the optimal number of clusters in a wireless sensor network. We then propose a centralized cluster algorithm based on spectral partitioning method. After that, we present a distributed implementation of the clustering algorithm based on fuzzy C-means method. Finally, we conduct extensive simulations, and the results show that the proposed algorithms outperform the hybrid energy-efficient distributed (HEED) clustering algorithm in terms of energy cost and network lifetime.

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An Efficient Approach for Big Data Aggregation Mechanism in Heterogeneous Wireless Connected Sensor Networks

Boubiche

Bilami

Boubiche

2021

Wireless Pers Commun

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Deterministic binary matrix based compressive data aggregation in big data WSNs

Cited by 6 publications

References 26 publications

Location Updating Scheme of Sink Node Based on Topology Balance and Reinforcement Learning in WSN

Location Updating Scheme of Sink Node Based on Topology Balance and Reinforcement Learning in WSN

Spectral partitioning and fuzzy C-means based clustering algorithm for big data wireless sensor networks

An Efficient Approach for Big Data Aggregation Mechanism in Heterogeneous Wireless Connected Sensor Networks

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