Cooperative coding and caching scheduling via binary particle swarm optimization in software-defined vehicular networks

Xiao, Ke; Li, Kai; Xu, Xincao; Feng, Liang; Wu, Zhou; Zhao, Qiangwei

doi:10.1007/s00521-020-04978-5

Cited by 8 publications

(4 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It cannot process in the case of a single chromosome; it starts from many points, having a higher fitness value. GA cannot search from a particular area, so it takes much time to explore the whole search space [14]. The time complexity of GA is O (n 3/2 log n) when a gene has the same length.…”

Section: Motivation and Gap Analysismentioning

confidence: 99%

“…Study [14] focuses on enhancing the bandwidth efficiency of I2V communication considering software-define vehicular networks (SDVN). Where, RSUs are connected with the controller, to take the scheduling decisions based on received requests from vehicles.…”

Section: Related Workmentioning

confidence: 99%

“…As compared to ACO, the CS has less parameters; there is no dependency to take th next step and brood parasitism is the best parameter to find better output; moreover, will not stick into local optima. The objective function is shown in Equation (14), where new iteration is taken by cuckoo p, using current iteration xp t , Levy flight distributio function is used to update the solution Levy (λ), while α is the step size. As compared to ACO, the CS has less parameters; there is no dependency to take the next step and brood parasitism is the best parameter to find better output; moreover, it will not stick into local optima.…”

Section: Optimisation Using Cuckoo Searchmentioning

confidence: 99%

See 2 more Smart Citations

Cooperative Content Caching Framework Using Cuckoo Search Optimization in Vehicular Edge Networks

et al. 2023

View full text Add to dashboard Cite

Vehicular edge networks (VENs) connect vehicles to share data and infotainment content collaboratively to improve network performance. Due to technological advancements, data growth is accelerating, making it difficult to always connect mobile devices and locations. For vehicle-to-vehicle (V2V) communication, vehicles are equipped with onboard units (OBU) and roadside units (RSU). Through back-haul, all user-uploaded data is cached in the cloud server’s main database. Caching stores and delivers database data on demand. Pre-caching the data on the upcoming predicted server, closest to the user, before receiving the request will improve the system’s performance. OBUs, RSUs, and base stations (BS) cache data in VENs to fulfill user requests rapidly. Pre-caching reduces data retrieval costs and times. Due to storage and computing expenses, complete data cannot be stored on a single device for vehicle caching. We reduce content delivery delays by using the cuckoo search optimization algorithm with cooperative content caching. Cooperation among end users in terms of data sharing with neighbors will positively affect delivery delays. The proposed model considers cooperative content caching based on popularity and accurate vehicle position prediction using K-means clustering. Performance is measured by caching cost, delivery cost, response time, and cache hit ratio. Regarding parameters, the new algorithm outperforms the alternative.

show abstract

Section: Motivation and Gap Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Optimisation Using Cuckoo Searchmentioning

confidence: 99%

See 1 more Smart Citation

Cooperative Content Caching Framework Using Cuckoo Search Optimization in Vehicular Edge Networks

et al. 2023

View full text Add to dashboard Cite

show abstract

“…On the other hand, great efforts have been devoted to vehicular data dissemination, such as end-edge-cloud cooperative data dissemination architecture [9] and intent-based network control framework [10]. To improve caching efficiency, some other researchers proposed vehicular content caching frameworks, such as blockchain-empowered content caching [11], cooperative coding and caching scheduling [12], and edgecooperative content caching [13]. Some researchers studied vehicular task offloading mechanisms, such as deep-learningbased energy-efficient task offloading [14], real-time multiperiod task offloading [15], alternating direction method of multipliers (ADMMs) and particle swarm optimization (PSO) combined task offloading [16].…”

Section: Introductionmentioning

confidence: 99%

Enabling Digital Twin in Vehicular Edge Computing: A Multi-Agent Multi-Objective Deep Reinforcement Learning Solution

Xu¹,

Li²,

Dai³

et al. 2022

Preprint

View full text Add to dashboard Cite

With recent advances in sensing technologies, wireless communications, and computing paradigms, traditional vehicles are evolving to electronic consumer products, driving the research on digital twins in vehicular edge computing (DT-VEC). This paper makes the first attempt to achieve the quality-cost tradeoff in DT-VEC. First, a DT-VEC architecture is presented, where the heterogeneous information can be sensed by vehicles and uploaded to the edge node via vehicle-to-infrastructure (V2I) communications. The DT-VEC are modeled at the edge node, forming a logical view to reflect the physical vehicular environment. Second, we model the DT-VEC by deriving an ISAC (integrated sensing and communication)-assisted sensing model and a reliability-guaranteed uploading model. Third, we define the quality of DT-VEC by considering the timeliness and consistency, and define the cost of DT-VEC by considering the redundancy, sensing cost, and transmission cost. Then, a biobjective problem is formulated to maximize the quality and minimize the cost. Fourth, we propose a multi-agent multiobjective (MAMO) deep reinforcement learning solution implemented distributedly in the vehicles and the edge nodes. Specifically, a dueling critic network is proposed to evaluate the advantage of action over the average of random actions. Finally, we give a comprehensive performance evaluation, demonstrating the superiority of the proposed MAMO.

show abstract

Towards Efficient Federated Learning via Vehicle Selection and Resource Optimization in IoV

Gong,

Yan,

Zhang

et al. 2024

Communications in Computer and Information Science

View full text Add to dashboard Cite

Cooperative coding and caching scheduling via binary particle swarm optimization in software-defined vehicular networks

Cited by 8 publications

References 26 publications

Cooperative Content Caching Framework Using Cuckoo Search Optimization in Vehicular Edge Networks

Cooperative Content Caching Framework Using Cuckoo Search Optimization in Vehicular Edge Networks

Enabling Digital Twin in Vehicular Edge Computing: A Multi-Agent Multi-Objective Deep Reinforcement Learning Solution

Towards Efficient Federated Learning via Vehicle Selection and Resource Optimization in IoV

Contact Info

Product

Resources

About