Open Access

ARTICLE

Self-Adaptive Fault Recovery Mechanism Based on Task Migration Negotiation

by Ruijun Chai¹, Sujie Shao^1,*, Shaoyong Guo¹, Yuqi Wang¹, Xuesong Qiu¹, Linna Ruan²

1 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China
2 The Cloud Computing and Distributed Systems Laboratory, School of Computing and Information Systems, The University of Melbourne, VIC3010, Australia

* Corresponding Author: Sujie Shao. Email:

Intelligent Automation & Soft Computing 2021, 27(2), 471-482. https://doi.org/10.32604/iasc.2021.013373

Received 30 August 2020; Accepted 19 November 2020; Issue published 18 January 2021

Abstract

Long Range Radio (LoRa) has become one of the widely adopted Low-Power Wide Area Network (LPWAN) technologies in power Internet of Things (PIoT). Its major advantages include long-distance, large links and low power consumption. However, in LoRa-based PIoT, terminals are often deployed in the wild place and are easily affected by bad weather or disaster, which could easily lead to large-scale operation faults and could seriously affect the normal operation of the network. Simultaneously, the distribution characteristics of outdoor terminals with wide coverage and large links lead to a sharp increase in the difficulty and cost of fault recovery. Given this background, this paper proposes a self-adaptive fault recovery mechanism for PIoT terminals based on task migration negotiation. Firstly, based on the terminal fault type and service category assessment, a selection strategy of a candidate neighbor terminal or a terminal set is studied to deal with the fault recovery problem among two scenarios: the same rate and the boundary of the rate change, while considering the adaptive characteristics of the LoRa data rate. Secondly, the adaptive terminal task migration negotiation mechanism is discussed. Then, a novel Terminal Fault Self-Adaptive Recovery (TFSR) algorithm is proposed. Simulation results show that, compared with the Genetic Algorithm (GA) and Discrete Particle Swarm Optimization (DPSO) Algorithm, our proposed algorithm can maintain a higher fault recovery rate and a lower task recovery cost in the case of frequent faults.

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Keywords

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