Reliable Fault Tolerant-Based Multipath Routing Model for Industrial Wireless Control Systems

Abdulrab, Hakim; Hussin, Fawnizu Azmadi; Aziz, Azrina Abd; Awang, Azlan; Ismail, Idris; Devan, P. Arun Mozhi

doi:10.3390/app12020544

Cited by 26 publications

(14 citation statements)

References 56 publications

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“…The multipath routing (MPR) algorithm is proposed in [ 20 ] that provides the industrial wireless mesh networks with low-cost planning, high reliability, and low-level latency. The algorithm builds three primary paths, each consisting of multiple nodes with different hops’ numbers.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Enhancing Graph Routing Algorithm of Industrial Wireless Sensor Networks Using the Covariance-Matrix Adaptation Evolution Strategy

Alharbi

Mackenzie

Pezaros

2022

Sensors

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The emergence of the Industrial Internet of Things (IIoT) has accelerated the adoption of Industrial Wireless Sensor Networks (IWSNs) for numerous applications. Effective communication in such applications requires reduced end-to-end transmission time, balanced energy consumption and increased communication reliability. Graph routing, the main routing method in IWSNs, has a significant impact on achieving effective communication in terms of satisfying these requirements. Graph routing algorithms involve applying the first-path available approach and using path redundancy to transmit data packets from a source sensor node to the gateway. However, this approach can affect end-to-end transmission time by creating conflicts among transmissions involving a common sensor node and promoting imbalanced energy consumption due to centralised management. The characteristics and requirements of these networks encounter further complications due to the need to find the best path on the basis of the requirements of IWSNs to overcome these challenges rather than using the available first-path. Such a requirement affects the network performance and prolongs the network lifetime. To address this problem, we adopt a Covariance-Matrix Adaptation Evolution Strategy (CMA-ES) to create and select the graph paths. Firstly, this article proposes three best single-objective graph routing paths according to the IWSN requirements that this research focused on. The sensor nodes select best paths based on three objective functions of CMA-ES: the best Path based on Distance (PODis), the best Path based on residual Energy (POEng) and the best Path based on End-to-End transmission time (POE2E). Secondly, to enhance energy consumption balance and achieve a balance among IWSN requirements, we adapt the CMA-ES to select the best path with multiple-objectives, otherwise known as the Best Path of Graph Routing with a CMA-ES (BPGR-ES). A simulation using MATALB with different configurations and parameters is applied to evaluate the enhanced graph routing algorithms. Furthermore, the performance of PODis, POEng, POE2E and BPGR-ES is compared with existing state-of-the-art graph routing algorithms. The simulation results reveal that the BPGR-ES algorithm achieved 87.53% more balanced energy consumption among sensor nodes in the network compared to other algorithms, and the delivery of data packets of BPGR-ES reached 99.86%, indicating more reliable communication.

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Section: Background and Literature Reviewmentioning

confidence: 99%

Enhancing Graph Routing Algorithm of Industrial Wireless Sensor Networks Using the Covariance-Matrix Adaptation Evolution Strategy

Alharbi

Mackenzie

Pezaros

2022

Sensors

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“…2) Prediction for errors in tasks. Frequent task migration causes additional energy consumption and influences the system performance [138]. Therefore, potential faults can be predicted based on the characteristics of tasks executed in the long term.…”

Section: Opportunities In Service Faulttolerant Schedulingmentioning

confidence: 99%

Intelligent Identification over Power Big Data: Opportunities, Solutions, and Challenges

Luo¹,

Li²,

Yang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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The emergence of power dispatching automation systems has greatly improved the efficiency of power industry operations and promoted the rapid development of the power industry. However, with the convergence and increase in power data flow, the data dispatching network and the main station dispatching automation system have encountered substantial pressure. Therefore, the method of online data resolution and rapid problem identification of dispatching automation systems has been widely investigated. In this paper, we perform a comprehensive review of automated dispatching of massive dispatching data from the perspective of intelligent identification, discuss unresolved research issues and outline future directions in this area. In particular, we divide intelligent identification over power big data into data acquisition and storage processes, anomaly detection and fault discrimination processes, and fault tracing for dispatching operations during communication. A detailed survey of the solutions to the challenges in intelligent identification over power big data is then presented. Moreover, opportunities and future directions are outlined.

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“…These faults can cause failure to occur in the network. Different type of fault is identified by distinct mechanisms that achieve network reliability [30]. Reliability is correlated with maintainability and availability [31], [32].…”

Section: Related Workmentioning

confidence: 99%

MD-MARS: Maintainability Framework Based on Data Flow Prediction Using Multivariate Adaptive Regression Splines Algorithm in Wireless Sensor Network

et al. 2023

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The demand for Wireless Sensor Networks is increasing day by day because of their diverse nature. Due to the limited energy, it is a complex task to retract the sensor node after deployment. So, there is a requirement for network maintainability before the deployment phase for its smooth working. It is achieved in three phases: hardware of the sensor node, communication and external environmental phase. This paper focuses on network maintainability in the communication phase. A novel framework MD-MARS is presented to enhance the network maintainability. This framework is classified into three phases namely analysis of performance parameters, data flow optimization and maintainability evaluation. In the initial phase, the performance parameter is analyzed using NS2 simulator. The next phase deals with data flow optimization using a machine learning algorithm. It reduces congestion and enhances network performance. The proposed algorithm is finely tuned to different degrees using the Grid Search approach to achieve the highest accuracy. The best model is selected based on accuracy and minimizes the prediction error. This algorithm predicts with the highest accuracy of 99.83%, lowest being 21.17%. Maintainability is achieved in the last phase using the total time taken to optimize the data flow. Several observations of repair time are determined for the best-tune model during the prediction of optimized data flow. These observations are used to calculate the mean time to repair, standard deviation, probability density function, maintainability and repair rate. The maximum maintainability achieved in this paper is 97.67% at a repair time of 26.07 milliseconds.INDEX TERMS Data flow prediction, maintainability, multivariate adaptive regression splines (MARS), Quality of Service, repair time, wireless sensor network.

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Reliable Fault Tolerant-Based Multipath Routing Model for Industrial Wireless Control Systems

Cited by 26 publications

References 56 publications

Enhancing Graph Routing Algorithm of Industrial Wireless Sensor Networks Using the Covariance-Matrix Adaptation Evolution Strategy

Enhancing Graph Routing Algorithm of Industrial Wireless Sensor Networks Using the Covariance-Matrix Adaptation Evolution Strategy

Intelligent Identification over Power Big Data: Opportunities, Solutions, and Challenges

MD-MARS: Maintainability Framework Based on Data Flow Prediction Using Multivariate Adaptive Regression Splines Algorithm in Wireless Sensor Network

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