Location Updating Scheme of Sink Node Based on Topology Balance and Reinforcement Learning in WSN
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...
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doaj-9dc4542cb27e4a50bc2d5606a3908bfb2021-04-05T17:26:09ZengIEEEIEEE Access2169-35362019-01-01710006610008010.1109/ACCESS.2019.29297568766792Location Updating Scheme of Sink Node Based on Topology Balance and Reinforcement Learning in WSNXindi Wang0https://orcid.org/0000-0002-3726-3467Qingfeng Zhou1https://orcid.org/0000-0002-5015-7335Chunxiao Qu2Gao Chen3Junjuan Xia4https://orcid.org/0000-0003-2787-6582School of Electrical Engineering and Intelligentization, Dongguan University of Technology, Dongguan, ChinaSchool of Electrical Engineering and Intelligentization, Dongguan University of Technology, Dongguan, ChinaSchool of Electrical Engineering and Intelligentization, Dongguan University of Technology, Dongguan, ChinaSchool of Electrical Engineering and Intelligentization, Dongguan University of Technology, Dongguan, ChinaSchool of Computer Science, Guangzhou University, Guangzhou, ChinaThis 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.https://ieeexplore.ieee.org/document/8766792/Network scalinglocation updatepathfindingreinforcement learningwireless sensor network |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xindi Wang Qingfeng Zhou Chunxiao Qu Gao Chen Junjuan Xia |
spellingShingle |
Xindi Wang Qingfeng Zhou Chunxiao Qu Gao Chen Junjuan Xia Location Updating Scheme of Sink Node Based on Topology Balance and Reinforcement Learning in WSN IEEE Access Network scaling location update pathfinding reinforcement learning wireless sensor network |
author_facet |
Xindi Wang Qingfeng Zhou Chunxiao Qu Gao Chen Junjuan Xia |
author_sort |
Xindi Wang |
title |
Location Updating Scheme of Sink Node Based on Topology Balance and Reinforcement Learning in WSN |
title_short |
Location Updating Scheme of Sink Node Based on Topology Balance and Reinforcement Learning in WSN |
title_full |
Location Updating Scheme of Sink Node Based on Topology Balance and Reinforcement Learning in WSN |
title_fullStr |
Location Updating Scheme of Sink Node Based on Topology Balance and Reinforcement Learning in WSN |
title_full_unstemmed |
Location Updating Scheme of Sink Node Based on Topology Balance and Reinforcement Learning in WSN |
title_sort |
location updating scheme of sink node based on topology balance and reinforcement learning in wsn |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
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. |
topic |
Network scaling location update pathfinding reinforcement learning wireless sensor network |
url |
https://ieeexplore.ieee.org/document/8766792/ |
work_keys_str_mv |
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