A Mobility-Supported Routing Mechanism in Industrial IoT Networks

Cao, Jian; Wang, Xingwei; Huang, Min; Zhou, Xinhao

doi:10.1109/access.2019.2900289

Cited by 9 publications

(2 citation statements)

References 44 publications

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“…Eventually, for addressing this problem of selfish nodes, a dynamic revenue control process was presented for promoting co-operation amongst nodes. In [16], a new routing process dependent upon probability computation and segment routing was presented for solving the mobility and scalability problems in IIoT routing. Although, probability computation addresses the unbalanced load state affected by mobility by forwarding packets for distinct routings with particular probability.…”

Section: Related Workmentioning

confidence: 99%

Trust Aware Multi-Objective Metaheuristic Optimization Based Secure Route Planning Technique for Cluster Based IIoT Environment

2022

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Industrial Internet of Things (IIoT) finds use in several industrial applications like robots, medical devices, and software-defined manufacturing processes. Apart from the promising benefits of the IIoT networks, several challenging issues need to be resolved, such as network connectivity, security, privacy, heterogeneity, scheduling, and energy efficiency. Due to the large-scale deployment and heterogeneity of the nodes in IIoT networks, some energy-limited nodes have existed in the IIoT networks, resulting in reduced network lifetime. At the same time, security and privacy are also considered as the major issues that exist in the design of IIoT, which can be addressed using secure routing techniques. In this view, this study develops a trust-aware multiobjective metaheuristic optimization-based secure clustering with route planning (TAMOMO-SCRP) technique for cluster-based IIoT environment. The presented TAMOMO-SCRP technique mainly focuses on the design of bald eagle search (BES) algorithm for clustering and routing processes. The proposed TAMOMO-SCRP model derives a fitness function for accomplishing maximum energy efficiency and security. For an effective clustering process, the TAMOMO-SCRP model designs an objective function involving four parameters such as trust level (TL), communication cost (CC), residual energy (RE), and node degree (ND). Besides, the route selection process is based on the fitness function with two variables, namely, queue length and link quality. For assessing the enhanced performance of the TAMOMO-SCRP model, a wide range of experiments were carried out to get the outcomes of network life time(NLT) as 39451,half network die (HND) as 25950 and Stability period (SP) 8000 time calculated no. of alive nodes. The achieved outcomes make sure the better performance of the TAMOMO-SCRP technique against the other recent approaches.INDEX TERMS Industrial Internet of Things, clustering, bald eagle search algorithm, trust aware protocols, security.

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Section: Related Workmentioning

confidence: 99%

Trust Aware Multi-Objective Metaheuristic Optimization Based Secure Route Planning Technique for Cluster Based IIoT Environment

2022

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“…In [61], a distributed processing model based on Network Programming is proposed that considers a single logical machine deployment. In [62], SRv6 is used to circumvent overloaded connections in industrial IoT networks to obtain better performance between the IoT network and the core. However, SRv6 characteristics are not taken into account in this study.…”

Section: Related Workmentioning

confidence: 99%

QoS Performance Measurement Through SRv6 Network Programming for Smart City IoT Traffic

Kuri¹

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Segment Routing over IPv6, also known as SRv6, is a modern networking solution that aims to improve the current Internet of Things (IoT) network's reliability, availability, and scalability. Performance measures are required to evaluate SRv6 behaviors or functions. The proposed work aims to provide real IoT traffic profiles to assess the performance of SRv6 behaviors. In particular, a three-module SRv6 programming model is proposed to measure the performance of SRv6 policy headend and endpoint behavior and ensure reliability and quality of service (QoS). Moreover, a novel finder algorithm for maximum receive rate (MRR) benchmarking is proposed, which can outperform existing techniques in terms of throughput/bandwidth performance while maintaining the same computational resources. Finally, implementation results provide insights into forwarding different IoT use-cases traffic based on the functional service requirements. That also ensures a higher usage level of existing IoT networks, minimizing the need for additional capacity and lowering network costs.

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