Towards efficient and energy-aware query processing for industrial internet of things

Liu, Liang; Wang, Yiting; Meng, Weizhi; Xu, Zhe; Gao, Wenzhao; Ma, Zuchao

doi:10.1007/s12083-021-01163-w

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…Eventually, it can be responsible for the performance and maintenance of Distance oriented directed acyclic graph (DODAG). Liu et al [8] presented an efficient technique named Trust and Energy-aware based Holistic optimizing Algorithm for Spatial window query (TEHAS) that enhances the efficacy of query processing, decreases the energy utilization, and avoids the loss of important data. Besides the theoretical analysis, the experimental outcomes represent that the technique is carried out superior to one of the present techniques considering efficacy, communication security, and energy utilization.…”

Section: Related Workmentioning

confidence: 99%

“…However, a single centralized trust management entity is unable to constantly handle the trust in the plant IIoT system comprised of a massive amount of sensitive and heterogeneous devices. Indeed, distributed trust management system is extensively employed in the composition of services in IoT, however, they are not suitable for trust management in industrial environment [8].…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%

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

2022

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“…In the past decade, there has been a significant expansion in computer networks and networked digital devices, such as Internet of Things (IoT) [7]. While how to preserve the security of information and communication has become a critical issue.…”

Section: Introductionmentioning

confidence: 99%

Towards Artificial Neural Network Based Intrusion Detection with Enhanced Hyperparameter Tuning

Calugar

Meng

Zhang

2022

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

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Due to the development of complex communication paradigms and the rise in the number of inter-connected digital devices, intrusion detection system (IDS) has become one basic and important security mechanism to identify cyber intrusions and protect computer networks. Currently, various deep learning algorithms have been studied in intrusion detection to achieve a high detection rate, whereas the detection performance may be still dependent on specific datasets. To maintain the detection performance, parameter optimization is believed as an effective solution. Motivated by this observation, in this work, we propose a concise but effective hyperparameter tuning process to enhance the artificial neural network (ANN) based IDS. In the evaluation, we consider three ANN variants and four datasets. The experimental results indicate that our approach can outperform similar studies and typical learning algorithms.

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Improved trustworthy, speed, and energy-efficient GPSR routing algorithm in large-scale WSN

Kumar

Hariharan

2022

Measurement: Sensors

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Towards efficient and energy-aware query processing for industrial internet of things

Cited by 5 publications

References 24 publications

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

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

Towards Artificial Neural Network Based Intrusion Detection with Enhanced Hyperparameter Tuning

Improved trustworthy, speed, and energy-efficient GPSR routing algorithm in large-scale WSN

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