Compressive Sensing-Based Data Aggregation Approaches for Dynamic WSNs

Wang, Xindi; Zhou, Qingfeng; Gu, Yuantao; Tong, Jun

doi:10.1109/lcomm.2019.2909861

Cited by 9 publications

(11 citation statements)

References 9 publications

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“…An isolated area is randomly selected, and the location, where the local density of the monitoring target is relatively large, is deployed to deploy the first sensor node. The greedy method based on the local density of the monitoring target is used for tackling the coverage problem of other locations in this area [48], [49]. Specifically, within the maximum communication range R max of the currently deployed sensor node locations x i , the location x i+1 with the highest local density of the monitoring target is selected as the deployment location of the next sensor node, that is,…”

Section: Implementation Of Im-csr Algorithm a Level Determinatiomentioning

confidence: 99%

CSR-IM: Compressed Sensing Routing-Control- Method With Intelligent Migration-Mechanism Based on Sensing Cloud-Computing

Sun

Liu

et al. 2020

IEEE Access

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Considering the event-based WSNs routing, the frequent changes of topology may result to the large energy costs in the whole network. Therefore, this paper proposes a Compressed Sensing Routingcontrol-method with Intelligent Migration-mechanism based on Sensing Cloud-computing (CSR-IM). First, the method gives a determining the moving speed and position of the target node through compressed sensing theory, and at the same time, it gives the lower bound calculation process of the target node state estimation value at k + 1 time by the probability knowledge. Second, with the purpose of reducing the network load, the routing tree with the center of fog nodes is established to obtain the data in the route effectively and optimize the data aggregation routing process, and then energy cost of the whole network is balanced. Finally, the simulation experiments show that method of this paper (CSR-IM) and other algorithms have improved the average data aggregation rate by 8.19%, and the average network coverage has increased by 12.65%, which proves that the proposed algorithm is effective and practical. INDEX TERMS Intelligent migration-mechanism, sensing cloud-computing, routing-control-method, compressed sensing.

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Section: Implementation Of Im-csr Algorithm a Level Determinatiomentioning

confidence: 99%

CSR-IM: Compressed Sensing Routing-Control- Method With Intelligent Migration-Mechanism Based on Sensing Cloud-Computing

Sun

Liu

et al. 2020

IEEE Access

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show abstract

“…If we directly adopt the action selection mechanism as described in Algorithm.3 during each action selection round, the sink will move down with probability 8 15 and move right with probability 7 15 . As shown in the figure above, the sink node may move into the area which is called a ''trap''.…”

Section: ) Risk Avoidance Mechanismmentioning

confidence: 99%

“…Initially, the maximum communication radius of each sensor node is set to 2.4, which can also be measured as the distance of a single hop. The initial location of the sink node is given by (8,10) and the balanced path length L b is given by 8+5 2 . The coordinates of the leaf nodes which are on the paths with length no less than L b are (19,2), (19,3), and the corresponding path length are 8 and 8, respectively.…”

Section: A Location Update Strategy Of the Sink Nodementioning

confidence: 99%

“…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%

“…Taking into account the practical application of WSN, more and more studies focus on the actual implementation of the WSN protocols [6], [7], which brings tough challenges in protocol design, such as the dynamic characteristic of the WSN scenario. In a typical WSN, the scale of the network is often dynamically adjusted according to changes in monitoring objectives, that is, when the monitoring area is scaled-up or increasing the monitoring quantity in some local areas [8], the number of nodes and the corresponding topology structure in the network may have to change synchronously.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Wang

Zhou

et al. 2019

IEEE Access

Self Cite

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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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Compressive sensing and paillier cryptosystem based secure data collection in WSN

Ifzarne¹,

Hafidi²,

Idrissi³

2021

J Ambient Intell Human Comput

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Compressive Sensing-Based Data Aggregation Approaches for Dynamic WSNs

Cited by 9 publications

References 9 publications

CSR-IM: Compressed Sensing Routing-Control- Method With Intelligent Migration-Mechanism Based on Sensing Cloud-Computing

CSR-IM: Compressed Sensing Routing-Control- Method With Intelligent Migration-Mechanism Based on Sensing Cloud-Computing

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

Compressive sensing and paillier cryptosystem based secure data collection in WSN

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