Modified Grey Wolf Optimization(GWO) based Accident Deterrence in Internet of Things (IoT) enabled Mining Industry

Recent technological improvements have revolutionized the wireless sensor network–based industrial sector with the emergence of Internet of Things. Internet of Drones, a branch of Internet of Things, is used for the communication among drones. As drones are mobile in nature, they cause frequent topological changes. This changing topology causes scalability, stability, and route selection issues in Internet of Drones. To handle these issues, we propose a bio-inspired clustering scheme using dragonfly algorithm for cluster formation and management. In this article, we propose cluster head election based on the connectivity with the base station along with the fitness function which consists of residual energy and position of the drones. Furthermore, for route selection we propose an optimal path selection based on the residual energy and position of drone for efficient communication. The proposed scheme shows better results as compared to other bio-inspired clustering algorithms on the basis of evaluation benchmarks such as cluster building time, network energy consumption, cluster lifetime, and probability of successful delivery. The results indicate that the proposed scheme has improved 60% and 38% with respect to ant colony optimization and grey wolf optimization, respectively, in terms of average cluster building time while average energy consumption has improved 23% and 33% when compared to the ant colony optimization and grey wolf optimization, respectively.

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Comparative study between metaheuristic algorithms for internet of things wireless nodes localization

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v12i1.pp660-668 ◽

2022 ◽

Vol 12 (1) ◽

pp. 660

Author(s):

Rana Jassim Mohammed ◽

Enas Abbas Abed ◽

Mostafa Mahmoud El-gayar

Keyword(s):

Wireless Networks ◽

Internet Of Things ◽

Heuristic Algorithms ◽

Narrow Range ◽

Low Cost ◽

Grey Wolf ◽

Grey Wolf Optimization ◽

Wide Range ◽

Limited Power ◽

Metaheuristic Optimization Algorithms

<p>Wireless networks are currently used in a wide range of healthcare, military, or environmental applications. Wireless networks contain many nodes and sensors that have many limitations, including limited power, limited processing, and narrow range. Therefore, determining the coordinates of the location of a node of the unknown location at a low cost and a limited treatment is one of the most important challenges facing this field. There are many meta-heuristic algorithms that help in identifying unknown nodes for some known nodes. In this manuscript, hybrid metaheuristic optimization algorithms such as grey wolf optimization and salp swarm algorithm are used to solve localization problem of internet of things (IoT) sensors. Several experiments are conducted on every meta-heuristic optimization algorithm to compare them with the proposed method. The proposed algorithm achieved high accuracy with low error rate (0.001) and low power <br />consumption.</p>

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A new fuzzy-based method for load balancing in the cloud-based Internet of things using a grey wolf optimization algorithm

International Journal of Communication Systems ◽

10.1002/dac.4370 ◽

2020 ◽

Vol 33 (8) ◽

pp. e4370 ◽

Cited By ~ 3

Author(s):

Li Xingjun ◽

Shao Zhiwei ◽

Cheng Hongping ◽

Bayan Omar Mohammed

Keyword(s):

Internet Of Things ◽

Load Balancing ◽

Optimization Algorithm ◽

Grey Wolf ◽

Grey Wolf Optimization

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Grey wolf optimization with fuzzy logic for energy‐efficient communication in wireless sensor network‐based Internet of Things scenario

International Journal of Communication Systems ◽

10.1002/dac.4981 ◽

2021 ◽

Author(s):

Kritika Verma ◽

Niyati Baliyan

Keyword(s):

Fuzzy Logic ◽

Wireless Sensor Network ◽

Internet Of Things ◽

Sensor Network ◽

Energy Efficient ◽

Wireless Sensor ◽

Grey Wolf ◽

Grey Wolf Optimization ◽

Energy Efficient Communication ◽

Efficient Communication

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Meta-heuristic-based offloading task optimization in mobile edge computing

International Journal of Distributed Sensor Networks ◽

10.1177/15501477211023021 ◽

2021 ◽

Vol 17 (6) ◽

pp. 155014772110230

Author(s):

Aamir Abbas ◽

Ali Raza ◽

Farhan Aadil ◽

Muazzam Maqsood

Keyword(s):

Energy Consumption ◽

Internet Of Things ◽

Response Time ◽

Mobile Devices ◽

Optimization Algorithm ◽

Edge Computing ◽

Optimal Selection ◽

Mobile Edge Computing ◽

Grey Wolf ◽

Grey Wolf Optimization

With the recent advancements in communication technologies, the realization of computation-intensive applications like virtual/augmented reality, face recognition, and real-time video processing becomes possible at mobile devices. These applications require intensive computations for real-time decision-making and better user experience. However, mobile devices and Internet of things have limited energy and computational power. Executing such computationally intensive tasks on edge devices either leads to high computation latency or high energy consumption. Recently, mobile edge computing has been evolved and used for offloading these complex tasks. In mobile edge computing, Internet of things devices send their tasks to edge servers, which in turn perform fast computation. However, many Internet of things devices and edge server put an upper limit on concurrent task execution. Moreover, executing a very small size task (1 KB) over an edge server causes increased energy consumption due to communication. Therefore, it is required to have an optimal selection for tasks offloading such that the response time and energy consumption will become minimum. In this article, we proposed an optimal selection of offloading tasks using well-known metaheuristics, ant colony optimization algorithm, whale optimization algorithm, and Grey wolf optimization algorithm using variant design of these algorithms according to our problem through mathematical modeling. Executing multiple tasks at the server tends to provide high response time that leads to overloading and put additional latency at task computation. We also graphically represent the tradeoff between energy and delay that, how both parameters are inversely proportional to each other, using values from simulation. Results show that Grey wolf optimization outperforms the others in terms of optimizing energy consumption and execution latency while selected optimal set of offloading tasks.

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Evaluation of Grey Wolf Optimization Algorithm on Rigid and Flexible Receptor Docking

10.26434/chemrxiv.11733393 ◽

2020 ◽

Author(s):

Kin Meng Wong ◽

Shirley Siu

Keyword(s):

Scoring Function ◽

Ligand Docking ◽

Receptor Protein ◽

Pose Prediction ◽

Grey Wolf ◽

Success Rates ◽

Autodock Vina ◽

Grey Wolf Optimization ◽

Structure Based Drug Design ◽

Docking Program

Protein-ligand docking programs are indispensable tools for predicting the binding pose of a ligand to the receptor protein in current structure-based drug design. In this paper, we evaluate the performance of grey wolf optimization (GWO) in protein-ligand docking. Two versions of the GWO docking program – the original GWO and the modified one with random walk – were implemented based on AutoDock Vina. Our rigid docking experiments show that the GWO programs have enhanced exploration capability leading to significant speedup in the search while maintaining comparable binding pose prediction accuracy to AutoDock Vina. For flexible receptor docking, the GWO methods are competitive in pose ranking but lower in success rates than AutoDockFR. Successful redocking of all the flexible cases to their holo structures reveals that inaccurate scoring function and lack of proper treatment of backbone are the major causes of docking failures.

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