Energy-aware resource management in Internet of vehicles using machine learning algorithms

2.1. Resource management strategies

Resource management strategy is a popular and interesting portion of cloud-edge computing in IoT environments [28]. There are enormous contributions from researchers towards resource management strategies. There are two main categorizations for resource management strategies in the IoV including application-based resource management and hardware-based resource management. Application-based resource management strategies consist of resource allocation, task scheduling, resource selection and composition, resource provisioning, resource discovery and computation offloading. In IoV environment, all the cloud-edge computing resources as well as the intelligent Road Unit Systems (RUS) [11] and smart applications can be obtained from the cloud-edge providers without huge investment by the user in a vehicular communication. The intelligent RUS environment has three main layers for navigating data as transmission layer, management of services as applications layer and finally improvement of services as gradient layer. It is a critical point for interconnection of vehicles, which need low computing power and energy consumption thus keep away from purchasing costly servers. According to Fig. 1, the resource management platform includes cloud data centers, fog nodes, base stations and sensors as IoT devices, smart vehicles and user applications. Communication between fog nodes, cloud data centers and smart applications is considered as application-based resource management strategies. On the other hand, collaboration between smart devices, vehicles and the RUS is considered as hardware-based resource management strategies. In this content, device assignment and inter-connected vehicles are important methods for managing hardware-based resource management strategies.

OPEN IN VIEWER

Fig. 1.

Resource management platform for the IoV environment [24].

Vehicular communication case studies should be categorized to different types of technical communication strategies according to main road subsides scenarios. The main communication strategies in the IoV environment is categorized into five classes as follows: Vehicle to Vehicle (V2V), Vehicle to Cloud (V2C), Vehicle to Application (V2A), Vehicle to Road unit (V2R) and Vehicle to Infrastructure (V2I) strategy. For example, in a smart city with multiple IoT devices, vehicles, user applications, and RUS stations create a smart IoV platform form a 2-dimensional V2V, V2A and V2I communication methods that navigates a smart vehicular connection between existing attributes. As shown in Fig. 2, existing communication methods is shown based on each vehicular connectivity with cloud-edge servers, user applications and RUS stations. Designing a comfortable communication method for the IoV platform is based on vehicle density, number of RUS stations, type of roadway and multiline highway mode.

OPEN IN VIEWER

Fig. 2.

IoV communication types [9].

OPEN IN VIEWER

Fig. 3.

Categorization of AI-based algorithms for energy-aware resource management strategies in IoV.

In this section, applied machine learning and deep learning algorithms are described to optimize energy-aware resource allocation approaches in the IoV.

Wang et al. [30] presented a new resource allocation approach that describes to IoV enable inter connect vehicles method Used to support existing services as well as increase the quality and experience of users. The computational tasks generated by these applications on vehicles limit the load on resources, allowing edge-to-board tasks (EI) to be loaded onto other servers. Excessive workload can lead to fierce competition for computational resources between vehicles and ultimately increase energy and delay in processing, consumption and system costs to solve this problem by managing the knowledgeable uplink resource for transfer, we have used it as a Markov decision-making process with variable formulation time. Total latency, energy consumption, and cost of quantum-inspired reinforcement learning (QRL) represent an evolving intelligence-driven edge evacuation strategy the simulation result, presents above algorithm can significantly reduce the transmission delay and computational delay and purpose of this joint optimization method is to maintain a balance between the two items.

Zhao et al. [36] proposed a new to vehicle fog computing (VFC) approach and demonstrates a new incentive resource allocation mechanism that combines resource segments and resource benefits. Authors presented distribute profound to use reinforcement learning is used to allocate resources and reduce system torsion. Task offloading approach based on the queue model; several vehicles are presented to avoid the collision of the decision to unload tasks. The simulation result, show the numerical experiment approach illustrates to propose scheme had achieved a significant resource allocation performance betterment in task offloading.

He et al. [13] proposed a new Demonstrates a multi-layered resource management optimization algorithm to improve the Quality of Experience (QoE) in IoV. In this paper, the communication downlink transmission system is designed for input and output rate factors in a random queue model. The proposed optimization algorithm transfers a the exchange problem between queue stability and QoE can be decomposed through Lyapunov optimization algorithm and a series of online modes that include joint optimization of rate control, power allocation and mobile relay selection. They solved this problem by Lagrangian method independently. The simulation results, show of the allocation method increase the optimization power and the optimization factors of mobile relay selection to achieve low complexity.

Muhammad et al. [23] proposed a new resource allocation efficient method to increasing communication of vehicles in IoV. One of the new goals in vehicle technology is the rapid jump in the green energy revolution in electric vehicles. Other reason is reduced carbon footprints and use the high energy IoE to reduction energy consumption. Authors applied simulation provide, an efficient and active resource organization for IoV services by cloud-edge infrastructure using machine learning for resource management and virtual network function in the primary resource.

Liu et al. [20] proposed a new resource allocation approach in IoV. This method increases the energy efficiency of the system according to the fog computing vehicular (FCV) network and non-orthogonal multiple access (NOMA). By considering resource management approach, the energy consumption is solved by the power allocation, Reel encrypted Chemical reaction optimization Algorithm (RCCRO) is the Chemical Reaction Optimization algorithm (CRO). In simulation results, the fog computing technology is applied to Improves local storage computing capabilities in IoV.

Lee et al. [17] presented a new road side unit (RSU) method for a trust-aware resource allocation in the fog-based IoV. The RSU suggests the fog services for vehicles. In this paper, services allocation uses a Vickery-Clarke-Groves (VCG) auction mechanism. Also, RSU has offered a bid as a seller that sells computing resources for buyers’ computing resources services.

To secure transactions, the China Blockchain system is distributed and implemented among RSUs based on the Fabric Hyperlieger Framework for transaction verification. The simulation result, shows that the presented system provides service stability while providing trustworthy service.

LiWang et al. [21] presented a new for vehicular clouds computing framework to match resource allocation in the IoV. This framework presents two stages for allocated scheme that splits template searching from power energy. The framework uses tree-based random subgraph isomorphism mechanism to increase yield. The result simulation The method of power allocation based on achieving an exchange between completion time and energy consumption has been presented in this research.

Cesarano et al. [4] offered an energy aware prototype for IoV systems, where roadside units require to be continually allocated and re-allocated to the operational vehicles. The advantage of this study is offering a quick heuristic-based method. Moreover, the suggested algorithm can effectively operate in real-time the roadside units, determining at each term those to be started and those to be changed off. Experimental results confirmed an appreciable growth in general cost with a reasonable delay in discovering the explanation.

3.2. Resource scheduling approach

This subsection shows existing AI-based resource scheduling approaches for optimizing energy consumption in the IoV environments.

Xin et al. [31] presented a new AI-based resource scheduling method to revolutionize the IoV environment for wireless and portable devices that became essential for various consumer activities to transfer multimedia materials. The proposed method presents two new algorithms called EQOA and QQOA as an alternative approach to the proposed problems, to provide a system for multimedia communication as well as multimedia IoV transmission via mobile devices, which provides the QoE optimization method the maximum amount of QoE increases the maximum quality, has increased the enjoyment of users of smartphones. Finally, the proposed algorithms for implementing IoV are generated from the baseline, and transitions are considered as promising competitors during multimedia transitions.

Ejaz et al. [8] presented a new IoV framework that Includes the deployment of scheduling methods for mobile charging infrastructure. This framework formulates an optimization obstacle to decreases the final cost of mobile charging substructure placement as long as consider constraints on the number of electric vehicles. The above IoV-based scheduling framework proposes to minimize the charging distance of electric vehicles (EVs). An important strength of this article is the disregard for traffic sensors in the proposed IoV scheduling method to enable EV users. The experimental results, show that the Impact of fixed charging optimization on charging infrastructure and schedule have efficient performance for EVs.

Finally, Yaghoob et al. [33] provided energy-efficient scheduling dissemination (E2SD) as a fog-based method in terms of decreasing the density of the messages in the queue. This method is for IoV which is modernized of VANET. They benefit E2SD to notify traffic status and if an emergency occurred, it will integrate the whole system for help to gather a rescue team and provide the required securities. Compared with resembling schemes; E2SD is capable of reducing delivery expenses, bandwidth consumption, and lagging time in addition to the model’s main purpose which is preventing message congestion and accelerating message delivery. The future goal is to study the effect on the diversity of queue extent interface in emergency and message aggregating situations.

Shen et al. [27] offered an energy-aware reasonable edge scheduling method for energy optimization of the IoV in smart cities for vehicle movement discharging with the calculated quantity of time with smaller energy usage. Moreover, an optimized energy-aware scheduling method is developed to decrease the entire communication delay and decrease the energy usage during discharge. The experimental results showed that the proposed method improved the performance rate with less communication delay, smaller energy usage for vehicle movement systems in smart cities.

3.3. Resource offloading approach

In this subsection, we have categorized resource offloading approaches for energy-aware resource management strategies in the IoV.

Lin et al. [19] presented a new Peer-to-Peer (P2P) computing resource offloading system approach to balance the dynamic spatial and temporal needs of IoV computing resources in smart city. From one side, guarantee transaction security and privacy-preserving in the system. On the one side, ensuring transaction security and privacy in the system is important they have used the China Consortium blockchain approach and demonstrated the process of trading secure computing resources without regard to trusted third parties to encourage unique smart vehicles to participate in the system. They co-build a two-stage Stackelberg game, extracting buyer and seller optimization tools, as well as computational pricing strategies and optimal trading volume in the proposed game. At the end simulations result, Security analysis shows that system security efficiently and numerically indicates the encouragement of strategic cooperation between the buyer and smart vehicles.

Sharma et al. [26] presented a new mechanism method for Manage vehicles by following the principles of the Chinese blockchain to connect to each other causes need for serious issues to update the total number of transactions that may lead to energy consumption for vehicles. Solve and provide an effective way to optimally control the number of transactions through distributed clustering can manage the Internet energy needs of Chinese blockchain vehicles. The simulation results, show that the approach presented in this paper is better than blockchain in terms of the number of transactions required and energy saving to share the total amount of blockchain data.

Bahreini et al. [2] presented a new energy-based resource management model in Vehicular Edge Computing systems. This model includes an energy manager algorithm to handle vehicular resources and resource selector algorithm for managing participating vehicles in Vehicular Edge Computing systems. The simulation results show that the proposed model minimizes energy consumption of vehicles’ computational resources and execution time with respect to Workload Types Data Size. The main weakness of this research is ignoring data transmission size for computational energy consumption of vehicles.

Zhai et al. [34] proposed a new resource offloading method Examines the discharge problem in SDN and IoV systems based on fog calculations. They tried to be aware on energy dynamic offloading scheme and it has been suggested that IoV runtime may increase battery life to increase performance in applications. The existing battery power cost implementation model is explained with the aim of improving the dynamic weight optimization and the relationship between them is considered in the cost model. Problems to be solved by heuristic optimization algorithm so that the simulation result, the discharge plan can manage more applications with available battery power under application dependency constraints.

Zhang et al. [35] presented a new of changes the fast growing of vehicle applications model and a IoV will be a practical architecture for dealing with big data and an important link for smart cities in the future. Fault control is cited as the main data: cloud and cloud computing, which is responsible for computing to local fog servers (LFSs). By consider some agents likes: mobility, latency, localization and scalability. The above paper introduces the concept of using services for regional cooperative architecture (CFC-IoV) to deal with large IoV data in the smart city. The simulation result, the model is hierarchical in fog and also focuses on optimizing the energy efficiency and loss rate of LFSs in CFC-IoV.

Hameed et al. [12] proposed a new load-balancing approach is based on the capacity to process the IoT environment to perform distributed fog calculations with energy awareness and high vehicle performance. This approach suggests position, speed, and communication of exciting clusters which allows node position prediction to manage dynamic networks. The simulation result show that the proposed approach decreases network delay and increases network utilization balanced network with respect to energy consumption. The main weakness of this research is Includes simultaneous improvement of energy consumption and management of network resource performance.

Moreover, Sulistyo et al. [29] presented a power offloading control for the challenges that were found during the implementation of VANET, similar to SINR of vehicles, in addition to its safety and intelligence. The authors provided power control by using a fuzzy system with the usage of the FPC algorithm, which could adjust transmitter power based on the vehicle’s SINR differences. They evaluated the method by a program using C++ language and simulated the environment on a road 5 kilometers long and with 3 lanes, withstanding between 200 and 500 vehicles. The results of this experiment show the performance of FPC, not only in the population of vehicles but also in the system throughput. As could be observed, it can increase the system throughput and the SINR of vehicles in the vehicular network.

Collotta et al. [7] proposed a useful fuzzy-based offloading management method to handle power usage and grade of services of IoV applications. The advantage of this study is presenting two fuzzy switches to improve the battery life while maintaining an adequate throughput to workload ratio founded on the theoretical example of battery usage. The simulation results indicated that this method is able to reach lower power usage, resulting in the most extended battery lifetime.

3.4. Power-based resource selection approach

In this subsection, there are just four research studies to evaluate power-based resource selection approach in the IoV using machine learning methodologies.

Yang et al. [32] presented a new method to policy jointly communication selection on transfer resource block and power control D2D that enabled V2V based in IoV connection network. The other side low delay communication for V2V Links That Include the Total Capacity of Large V2I Links. Due to unfamiliar environments, a decentralized reinforcement learning model with a new reward function is used for political communication with the environment. The main goal is (ETAC) approach to improve the learning convergence speed support reliable and delay-sensitive vehicular services in IoV networks. The simulation result, demonstrates that the ETAC approach can effectively reduce interference in IoV networks and ensures the need for delay and reliability of the V2V link to achieve fast convergence speeds and high stabilization, compared to other available methods.

Also, Bahreini et al. [1] proposed a new connection management algorithms to reduce the energy consumption of computational nodes in electric vehicles. This method can Achieving energy minimization in electric vehicles is connected to energy saving for electric vehicles for different levels by taking advantage of computing power settings. The evaluation of the above algorithm shows that the computing energy consumption of vehicles is significantly reduced with advanced baselines. Finally, this simulation results, algorithm average energy savings compared to baselines that run locally are more energy efficient than baselines that run only with RSUs.

In the other work, Khan et al. [16] offered an energy-aware resource selection design to save energy for the IoV Networks. Moreover, this study assumed a real 6G vehicular design where numerous Road-Side Units transmit with their associated IoV via a downlink protocol. Simulation outcomes indicated that the proposed optimization design with ambient backscatter transmissions is better than the other methods. Finally, Chaudhary et al. [5] presented a collaborative power-saving and resource selection method in IoV. This procedure has an effective energy usage algorithm using concurrent wireless data and power transition to amplify energy performance. Besides, to decrease different security attacks, a lightweight security organization protocol is developed that simply trusted vehicles can intercommunicate with each other and with the closest base stations. The experimental results demonstrated that the suggested procedure achieved more useful energy efficiency and more increased throughput.