Dimensioning and Layout Planning of 5G-Based Vehicular Edge Computing Networks Towards Intelligent Transportation

Lin, Bin; Zhou, Xian; Duan, Jianli

doi:10.1109/ojvt.2020.2988645

Cited by 12 publications

(6 citation statements)

References 29 publications

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“…However, the energy consumption is also generated to maintain basic work, which can be given by e basic x = p basic t mec x (6) where p basic is the power of the onboard devices to maintain basic operation. The energy consumption that subtask x offloads to the MEC server during transmission is e trans x = p up t trans x (7) where p up denotes the communication power when the onboard device is uploading.…”

Section: Mec Computingmentioning

confidence: 99%

“…By providing essential computing resources at the network edge, MEC alleviates both vehicles and the core network from computational burdens. Therefore, MEC technology and offloading methods have emerged as a new paradigm for providing energy-efficient and rapid computing services, which foster enhanced cooperation between vehicles and roads, such as addressing the uncertainty and latency in IoT-eHealth systems using fog computing [6], optimizing resource placement in 5G vehicular networks for intelligent transportation [7], and developing intelligent offloading strategies in high-mobility vehicular environments [8]. In early studies, Moubayed et al focused on service deployment in Intelligent Transportation Systems using mobile edge computing, introducing an edge-based model to optimize vehicular network services, aiming to minimize latency and maximize availability [9].…”

Section: Introductionmentioning

confidence: 99%

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ST-HO: Symmetry-Enhanced Energy-Efficient DAG Task Offloading Algorithm in Intelligent Transport System

Gao,

Luo,

Zhan

et al. 2024

Symmetry

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In Intelligent Transport Systems (ITSs), Internet of Vehicles (IoV) communications and computation offloading technology have been introduced to assist with the burdensome sensing task processing, thus prompting a new design paradigm called mobile sensing–communication–computation (MSCC) synergy. Most researchers have focused on offloading strategy design to reduce energy consumption or execution costs, but ignore the intrinsic characteristics of tasks, which may lead to poor performance. This paper studies the offloading strategy of vehicle MSCC tasks represented by a Directed Acyclic Graph (DAG) structure. According to the DAG dependency of the subtasks, this paper proposes a computation offloading strategy to optimize energy consumption under time constraints. An energy consumption model for task execution is established. Then, the Simulated Annealing and Tabu Search hybrid optimization algorithm (ST-HO) is designed to solve the problem of minimizing the energy consumption. Crucially, this research integrates the concept of symmetry into the typical DAG structure of MSCC tasks, ensuring the integrity and efficiency of task execution in ITS. The simulation results show that ST-HO reduces energy consumption by at least 5.58% compared to the conventional algorithm. Particularly, the convergence speed of ST-HO is improved by 52.63% when the replication strategy of symmetric task is considered.

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Section: Mec Computingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ST-HO: Symmetry-Enhanced Energy-Efficient DAG Task Offloading Algorithm in Intelligent Transport System

Gao,

Luo,

Zhan

et al. 2024

Symmetry

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show abstract

“…To be specific, it deals with the dimensioning and layout planning of VFC architectural components and generates a topology that optimizes parameters such as the response time, energy consumption, and cost of infrastructure, etc. [ 13 ]. Often, these parameters are conflicting, so the resulting topology must give an optimal balance (trade-off) between the network and equipment power consumption, interference and performance (e.g., in terms of throughput or latency).…”

Section: Motivationmentioning

confidence: 99%

“…and dynamic (heavy vehicles, taxis and buses) deployed at appropriate locations to provide computing services to the ITS users [ 12 ]. Although VFC architecture can provide ultra-low latency communications and a very low response time, deploying the architecture in the real world is a complex task [ 13 ]. The naive strategy to blindly place more and more resources is not practical because the corresponding cost will be very high.…”

Section: Introductionmentioning

confidence: 99%

SONG: A Multi-Objective Evolutionary Algorithm for Delay and Energy Aware Facility Location in Vehicular Fog Networks

Hussain

Azar

Ahmed

et al. 2023

Sensors

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With the emergence of delay- and energy-critical vehicular applications, forwarding sense-actuate data from vehicles to the cloud became practically infeasible. Therefore, a new computational model called Vehicular Fog Computing (VFC) was proposed. It offloads the computation workload from passenger devices (PDs) to transportation infrastructures such as roadside units (RSUs) and base stations (BSs), called static fog nodes. It can also exploit the underutilized computation resources of nearby vehicles that can act as vehicular fog nodes (VFNs) and provide delay- and energy-aware computing services. However, the capacity planning and dimensioning of VFC, which come under a class of facility location problems (FLPs), is a challenging issue. The complexity arises from the spatio-temporal dynamics of vehicular traffic, varying resource demand from PD applications, and the mobility of VFNs. This paper proposes a multi-objective optimization model to investigate the facility location in VFC networks. The solutions to this model generate optimal VFC topologies pertaining to an optimized trade-off (Pareto front) between the service delay and energy consumption. Thus, to solve this model, we propose a hybrid Evolutionary Multi-Objective (EMO) algorithm called Swarm Optimized Non-dominated sorting Genetic algorithm (SONG). It combines the convergence and search efficiency of two popular EMO algorithms: the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Speed-constrained Particle Swarm Optimization (SMPSO). First, we solve an example problem using the SONG algorithm to illustrate the delay–energy solution frontiers and plotted the corresponding layout topology. Subsequently, we evaluate the evolutionary performance of the SONG algorithm on real-world vehicular traces against three quality indicators: Hyper-Volume (HV), Inverted Generational Distance (IGD) and CPU delay gap. The empirical results show that SONG exhibits improved solution quality over the NSGA-II and SMPSO algorithms and hence can be utilized as a potential tool by the service providers for the planning and design of VFC networks.

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“…A task scheduling pricing model in a collaborative MEC framework based on the application of contract theory and prospect theory is developed in [10]. Finally, a complex techno-economic cost analysis of MEC deployment aiming to provide full network coverage for CVs is presented in [11]. All these papers demonstrate the effectiveness of MEC for CVs.…”

Section: A Mec In a Vehicular Environmentmentioning

confidence: 99%

Blockchain-based Route Selection with Allocation of Radio and Computing Resources for Connected Vehicles

Gazda¹,

Maksymyuk²,

Bečvář³

et al. 2023

Preprint

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<p>Most of the existing solutions for communication of connected vehicles (CVs) are focused on the optimization of resource allocation, however, not taking the driving routes of the CVs into account. In addition, no existing work considers the offloading of computing tasks from CVs to edge computing servers. In this paper, we introduce joint vehicular route selection and radio and computing resource allocation for CVs. The proposed algorithm minimizes the ratio of failed tasks along the entire vehicular route considering the availability of both radio and computing resources. To manage the allocation of resources among multiple CVs for each vehicular route, we develop a blockchain-based framework allowing resource reservation by means of nonfungible tokens (NFTs). Each NFT represents an exclusive right to the required amount of radio and computing resources for the given road segment and defined time interval. Simulation results show that the proposed vehicular route selec-<br> tion algorithm reduces the ratio of tasks not completed before deadline by up to 69% compared to the existing state-of-the-art algorithms.</p>

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Dimensioning and Layout Planning of 5G-Based Vehicular Edge Computing Networks Towards Intelligent Transportation

Cited by 12 publications

References 29 publications

ST-HO: Symmetry-Enhanced Energy-Efficient DAG Task Offloading Algorithm in Intelligent Transport System

ST-HO: Symmetry-Enhanced Energy-Efficient DAG Task Offloading Algorithm in Intelligent Transport System

SONG: A Multi-Objective Evolutionary Algorithm for Delay and Energy Aware Facility Location in Vehicular Fog Networks

Blockchain-based Route Selection with Allocation of Radio and Computing Resources for Connected Vehicles

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