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An adaptive link-level recovery mechanism for electric power iot based on lorawan


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Wang, Yuqi, Shao, Sujie, Guo, Shaoyong, Chai, Ruijun, Qi, Feng and Kadoch, Michel. 2021. « An adaptive link-level recovery mechanism for electric power iot based on lorawan ». Intelligent Automation and Soft Computing, vol. 27, nº 1. pp. 287-298.

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Electric power Internet of Things (IoT) is a network system that can meet multiple requirements of the power grid, such as infrastructure, environment recognition, interconnection, perception and control. Long Range Radio Wide Area Network (LoRaWAN) with the advantages of ultra-long transmission and ultra-low power consumption, becomes the most widely used protocol in the electric power IoT. However, its extremely simple star topology also leads to several problems. When most of terminals depend on one or several gateways for communication, the gateways with heavier communication tasks have poorer communication quality. The load of each gateway is unbalanced, which is hardly conducive to a long-term network operation. At the same time, in the electric power IoT environment, there are some features such as complex terminal deployment environment, wide coverage, and large interference. These characteristics can lead to more vulnerable links and even interruptions in communication services. Therefore, this paper proposes an adaptive link-level recovery mechanism based on link adjustment for LoRaWAN. When a communication link fails, multiple candidate links are selected based on Quality of Service (QoS) requirements, the distribution of LoRaWAN gateways and repeaters. The final adopted link is selected from multiple candidate links using the following method. Considering network load balancing, a Link Recovery Adaptive algorithm based on the Kuhn-Munkras algorithm (LRAKM) is designed from the perspective of fault tolerance. This method is to adaptively adjust some communication tasks to the sub-optimal communication link. One or more gateways on the optimal communication link of these communication tasks are overloaded. This adaptive adjustment can make the network load more balanced. The simulation result shows that LRAKM has a higher link recovery rate. It also shows that the whole network is more balanced in both sparse and dense environments. Furthermore, when the network load is heavier, LRAKM also has a better effect on balancing the network load and improving the link recovery rate.

Item Type: Peer reviewed article published in a journal
Kadoch, Michel
Affiliation: Génie électrique
Date Deposited: 09 Feb 2021 16:13
Last Modified: 17 Dec 2021 15:56

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