Grey wolf optimization based clustering algorithm for vehicular ad-hoc networks

Fahad, Muhammad; Aadil, Farhan; Rehman, Zahoor Ur; Khan, Salabat; Shah, Peer Azmat; Muhammad, Khan; Lloret, Jaime; Haoxiang, Wang; Lee, Jong Weon; Mehmood, Irfan

doi:10.1016/j.compeleceng.2018.01.002

Cited by 161 publications

(70 citation statements)

References 14 publications

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“…In the proposed method, the grey wolf behavioral method is used. The grey wolf optimization algorithm can reveal an efficient performance compared to other well‐established optimizers . The hunting steps of wolves are the same as load balancing.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…The grey wolf optimization algorithm can reveal an efficient performance compared to other well-established optimizers. 47,48 The hunting steps of wolves are the same as load balancing. In the proposed method, the tasks of wolves and virtual machines are the prey, and these tasks (wolves) should be assigned to virtual machines (prey).…”

Section: Proposed Algorithmmentioning

confidence: 99%

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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

Li¹,

Zhiwei²,

Cheng³

et al. 2020

Int J Communication

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Summary Cloud computing provides high accessibility, scalability, and flexibility in the era of computing for different practical applications. Internet of things (IoT) is a new technology that connects the devices and things to provide user required services. Due to data and information upsurge on IoT, cloud computing is usually used for managing these data, which is known as cloud‐based IoT. Due to the high volume of requirements, service diversity is one of the critical challenges in cloud‐based IoT. Since the load balancing issue is one of the NP‐hard problems in heterogeneous environments, this article provides a new method for response time reduction using a well‐known grey wolf optimization algorithm. In this paper, we supposed that the response time is the same as the execution time of all the tasks that this parameter must be minimized. The way is determining the status of virtual machines based on the current load. Then the tasks will be removed from the machine with the additional load depending on the condition of the virtual machine and will be transferred to the appropriate virtual machine, which is the criterion for assigning the task to the virtual machine based on the least distance. The results of the CloudSim simulation environment showed that the response time is developed in compared to the HBB‐LB and EBCA‐LB algorithm. Also, the load imbalancing degree is improved in comparison to TSLBACO and HJSA.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Proposed Algorithmmentioning

confidence: 99%

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

Li¹,

Zhiwei²,

Cheng³

et al. 2020

Int J Communication

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“…Some hybrid routing schemes used in MANETs, VANETs, and FANETs are ant colony optimization [119], grey wolf optimization [120,121], moth flame optimization [122,123], and energy aware link based clustering [102].…”

Section: Routing Frameworkmentioning

confidence: 99%

Routing Schemes in FANETs: A Survey

Khan

Yau

Noor

et al. 2019

Sensors

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Flying ad hoc network (FANET) is a self-organizing wireless network that enables inexpensive, flexible, and easy-to-deploy flying nodes, such as unmanned aerial vehicles (UAVs), to communicate among themselves in the absence of fixed network infrastructure. FANET is one of the emerging networks that has an extensive range of next-generation applications. Hence, FANET plays a significant role in achieving application-based goals. Routing enables the flying nodes to collaborate and coordinate among themselves and to establish routes to radio access infrastructure, particularly FANET base station (BS). With a longer route lifetime, the effects of link disconnections and network partitions reduce. Routing must cater to two main characteristics of FANETs that reduce the route lifetime. Firstly, the collaboration nature requires the flying nodes to exchange messages and to coordinate among themselves, causing high energy consumption. Secondly, the mobility pattern of the flying nodes is highly dynamic in a three-dimensional space and they may be spaced far apart, causing link disconnection. In this paper, we present a comprehensive survey of the limited research work of routing schemes in FANETs. Different aspects, including objectives, challenges, routing metrics, characteristics, and performance measures, are covered. Furthermore, we present open issues.

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“…This factor can be changed in a range of [1-MaxIteration ants ]. By our experiment, if the DP CS value has changed in the range of [1][2][3][4][5][6][7][8][9][10], the convergence speed of the algorithm for finding the best solution becomes quicker. On the other hand, increasing this value by more than 10 reduces accuracy for finding the best solution by ants.…”

Section: (I) Dynamic Pheromone Effectiveness (Dpe)mentioning

confidence: 99%

“…Such principles can be deduced from bird behavior, genes, and insect behavior. In this context, we can name some well-known algorithm approaches for VANET clustering, such as ACO-based [7,8], the Grey Wolf Optimizer (GWO) [9], and Particle Swarm Optimization (PSO) [10]. To be precise, most of the research in this area is done based on a single-objective problem, learning weights and static ranges of transmission, except for Dragon Fly Algorithm [11].This study focuses on answering some important questions about routing in a wireless mesh network that are listed below.…”

mentioning

confidence: 99%

Clustering-Based Modified Ant Colony Optimizer for Internet of Vehicles (CACOIOV)

2019

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The Internet of Vehicles (IoV) has recently become an emerging promising field of research due to the increasing number of vehicles each day. IoV is vehicle communications, which is also a part of the Internet of Things (IoT). Continuous topological changes of vehicular communications are a significant issue in IoV that can affect the change in network scalability, and the shortest routing path. Therefore, organizing efficient and reliable intercommunication routes between vehicular nodes, based on conditions of traffic density is an increasingly challenging issue. For such issues, clustering is one of the solutions, among other routing protocols, such as geocast, topology, and position-based routing. This paper focuses mainly on the scalability and the stability of the topology of IoV. In this study, a novel intelligent system-based algorithm is proposed (CACOIOV), which stabilizes topology by using a metaheuristic clustering algorithm based on the enhancement of Ant Colony Optimization (ACO) in two distinct stages for packet route optimization. Another algorithm, called mobility Dynamic Aware Transmission Range on Local traffic Density (DA-TRLD), is employed together with CACOIOV for the adaptation of transmission range regarding of density in local traffic. The results presented through NS-2 simulations show that the new protocol is superior to both Ad hoc On-demand Distance Vector (AODV) routing and (ACO) protocols based on evaluating routing performance in terms of throughput, packet delivery, and drop ratio, cluster numbers, and average end-to-end delay.

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Grey wolf optimization based clustering algorithm for vehicular ad-hoc networks

Cited by 161 publications

References 14 publications

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

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

Routing Schemes in FANETs: A Survey

Clustering-Based Modified Ant Colony Optimizer for Internet of Vehicles (CACOIOV)

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