TD‐PSO: Task distribution approach based on particle swarm optimization for vehicular ad hoc network

Taha, Mohammad Bany; Talhi, Chamseddine; Ould-Slimane, Hakima; Alrabaee, Saed

doi:10.1002/ett.3860

Cited by 18 publications

(9 citation statements)

References 53 publications

(74 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Results have demonstrated that PSO can achieve nearoptimal beamforming solutions. Considering vehicular adhoc networks, a task-distribution PSO is proposed to efficiently distribute tasks among vehicles that belong to the same cluster [228]. The results shows that the proposed PSO scheme outperforms GA in terms of overhead reduction while its overhead performance is comparable with linear programing.…”

Section: B Wireless Communicationsmentioning

confidence: 99%

Particle Swarm Optimization: A Comprehensive Survey

et al. 2022

View full text Add to dashboard Cite

Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Although the original PSO has shown good optimization performance, it still severely suffers from premature convergence. As a result, many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance. Mainly, the standard PSO has been modified by four main strategies: modification of the PSO controlling parameters, hybridizing PSO with other well-known meta-heuristic algorithms such as genetic algorithm (GA) and differential evolution (DE), cooperation and multi-swarm techniques. This paper attempts to provide a comprehensive review of PSO, including the basic concepts of PSO, binary PSO, neighborhood topologies in PSO, recent and historical PSO variants, remarkable engineering applications of PSO, and its drawbacks. Moreover, this paper reviews recent studies that utilize PSO to solve feature selection problems. Finally, eight potential research directions that can help researchers further enhance the performance of PSO are provided.

show abstract

Section: B Wireless Communicationsmentioning

confidence: 99%

Particle Swarm Optimization: A Comprehensive Survey

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Using real-world traces to model the mobility of edge servers, the PSO is shown to have lower service latency than the linear programming method when the quality is relaxed, but it is not preferred to use when the quality requirement becomes stricter. The computation offloading problem is also investigated in many other studies [123]- [125]. For example, to support latency-sensitive applications on the Internet of vehicles (IoV), the work in [124] considers combining software-defined networking (SDN) and edge computing, and proposes a holistic architecture.…”

Section: B Computation Offloadingmentioning

confidence: 99%

Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications

Pham¹,

Nguyen²,

Mirjalili³

et al. 2020

Preprint

View full text Add to dashboard Cite

Due to the proliferation of smart devices and emerging applications, many next-generation technologies have been paid for the development of wireless networks. Even though commercial 5G has just been widely deployed in some countries, there have been initial efforts from academia and industrial communities for 6G systems. In such a network, a very large number of devices and applications are emerged, along with heterogeneity of technologies, architectures, mobile data, etc., and optimizing such a network is of utmost importance. Besides convex optimization and game theory, swarm intelligence (SI) has recently appeared as a promising optimization tool for wireless networks. As a new subdivision of artificial intelligence, SI is inspired by the collective behaviors of societies of biological species. In SI, simple agents with limited capabilities would achieve intelligent strategies for high-dimensional and challenging problems, so it has recently found many applications in nextgeneration wireless networks (NGN). However, researchers may not be completely aware of the full potential of SI techniques. In this work, our primary focus will be the integration of these two domains: NGN and SI. Firstly, we provide an overview of SI techniques from fundamental concepts to well-known optimizers. Secondly, we review the applications of SI to settle emerging issues in NGN, including spectrum management and resource allocation, wireless caching and edge computing, network security, and several other miscellaneous issues. Finally, we highlight open challenges and issues in the literature, and introduce some interesting directions for future research.

show abstract

“…In partial encryption IoT devices encrypts the data with only dummy attribute and delegate most of CP-ABE cryptography operations to EPS server. Several schemes proposed this idea such as the scheme proposed in [12,24,25]. The details of full and partial encryption will discuss later in this section.…”

Section: The Proposed Schemementioning

confidence: 99%

Adaptive Ciphertext Policy Attribute Based Encryption Scheme for Internet of Things Devices Using Decision Tree

Taha¹,

Suwi²,

Khaswneh³

et al. 2020

RIA

View full text Add to dashboard Cite

The Internet of Things (IoT) has recently become a hot spot for researchers and its industrial importance is growing exponentially day after day. Statistics show that the number of IoT devices will reach fifty billion by 2020. IoT applications are backed through clouds where data is stored and processed by gigantic processing systems and accessed only by authorized users. In the present work, we outsource the encryption and decryption of ABE operations to proxy servers in order to reduce the computation overhead of encryption and decryption. However, in some cases performing full encryption is more worthy than partial encryption if IoT resources are fit to perform this encryption thus, our scheme allows to adaptively switch from partial encryption to full encryption based on the resources that available and the number of attributes in access tree. The decision maker component (manager) uses decision tree to select the appropriate scheme based on the context at encryption time. Finally, we evaluate the performance of our scheme with other scheme proposed for constrained devices.

show abstract

TD‐PSO: Task distribution approach based on particle swarm optimization for vehicular ad hoc network

Cited by 18 publications

References 53 publications

Particle Swarm Optimization: A Comprehensive Survey

Particle Swarm Optimization: A Comprehensive Survey

Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications

Adaptive Ciphertext Policy Attribute Based Encryption Scheme for Internet of Things Devices Using Decision Tree

Contact Info

Product

Resources

About