Dragonfly-based swarm system model for node identification in ultra-reliable low-latency communication

Bhardwaj, Sanjay; Kim, Dong-Seong

doi:10.1007/s00521-020-05056-6

Cited by 15 publications

(2 citation statements)

References 48 publications

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“…An opportunistic network is one that is made up of linked nodes in wireless environment [7]. Walking distance is the maximum communication range between two linked nodes.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Clustering Algorithm for IIoT Based Mobile Opportunistic Networks

Zhao

Akter

2022

Security and Communication Networks

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The clustering algorithms play a crucial role for energy saving solutions in mobile opportunistic networks. If the selection of cluster head is made appropriately, then the energy can be consumed optimally. The existing clustering algorithms do not consider the optimal selection of the cluster head resulting in low survival rates and high energy consumption rates in nodes. The adaptive clustering is required in Industrial Internet of Things (IIoT) based sophisticated networks where seamless connectivity is imperative for rapid communication. In order to meet this research gap, an adaptive clustering algorithm for mobile opportunistic networks is proposed within military bases that uses a heuristic algorithm (GA) for adaptive clustering. An analysis of the opportunity network at the connected military base is carried out and the mobile opportunity model is constructed using the adaptive clustering for the similar traffic. In a mobile machine network, the next hop node is determined by the node clustering principle. A LEACH clustering protocol enables communication between cluster heads and base stations based on single-hop and multihop cluster nodes. In order to perform adaptive clustering of mobile network nodes based on network partitioning and scheduling of clusters, genetic algorithms are used. The proposed approach can be applied to the IIoT systems in places where adaptive clustering is required to optimize energy consumption, to reduce latency rates, and to enhance the throughput of mobile networks. The experimental findings suggest that the proposed adaptive strategy is capable of optimizing energy consumption rates, reducing network latency, and boosting efficiency by increasing throughput in mobile opportunistic networks.

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“…An opportunistic network is one that is made up of linked nodes in wireless environment [7]. Walking distance is the maximum communication range between two linked nodes.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Clustering Algorithm for IIoT Based Mobile Opportunistic Networks

Zhao

Akter

2022

Security and Communication Networks

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“…Bhardwaj and Kim proposed 121 the Dragonfly Node Identification Algorithm (DNIA) to optimize criteria for URLLC systems. They evaluated the suggested approach using several benchmark functions and found that DNIA produced an optimal global solution with improved convergence behavior compared to other optimization algorithms.…”

Section: Urllc Technique In Iotmentioning

confidence: 99%

Ultra‐reliability and low‐latency communications on the internet of things based on5Gnetwork: Literature review, classification, and future research view

Sefati

Halunga

2023

Trans Emerging Tel Tech

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A new technology known as the Internet of Things (IoT) uses several sensor devices and communication protocols. By implementing cutting‐edge and modern equipment, people use IoT to make their lives easier. Home automation is one of them, and it works with actuators and sensors. However, increasing the number of devices in the IoT network could degrade the Quality of Service (QoS). Therefore, an appropriate framework in software and hardware can improve the Quality of Experience (QoE) and QoS for all users. One of the critical QoS measures in IoT is called Ultra Reliability and Low Latency Communication (URLLC). URLLC is essential in the IoT network released from the third Generation Partnership Project (3GPP) cellular. However, a systematic and comprehensive investigation of the practical procedures for URLLC in IoT needs to be done. This paper comprehensively investigates the existing methodologies in this subject. All the chosen techniques are separated into four categories to obtain a complete picture of the topic: structure‐based, diversity‐based, metaheuristic algorithm‐based, and channel state information. In this paper, we also investigate more benefits and drawbacks of other QoS when URLLC is applied in the IoT network. This paper highlights the challenges of URLLC in IoT networks and describes future open issues in detail to provide an efficient way for researchers in this field.

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Meta-heuristic Algorithms in IoT-Based Application: A Systematic Review

Hassan

2023

Lecture Notes in Networks and Systems

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Dragonfly-based swarm system model for node identification in ultra-reliable low-latency communication

Cited by 15 publications

References 48 publications

Adaptive Clustering Algorithm for IIoT Based Mobile Opportunistic Networks

Adaptive Clustering Algorithm for IIoT Based Mobile Opportunistic Networks

Ultra‐reliability and low‐latency communications on the internet of things based on5Gnetwork: Literature review, classification, and future research view

Meta-heuristic Algorithms in IoT-Based Application: A Systematic Review

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