Optimization of Software Component Allocation for Autonomous Driving in Cloud–Vehicular Edge

ABSTRACTThe increasing processing demands of vehicle applications pose a major challenge for the Internet of Vehicles (IoVs). Multi‐access edge computing (MEC) offloads computation‐intensive activities to edge servers, significantly enhancing computing capacity and extending vehicle battery life. Nevertheless, traditional offloading strategies fail to effectively balance computation tasks among vehicles and edge servers, resulting in suboptimal resource utilization and increased energy consumption. Therefore, an Energy‐Efficient Computation Offloading Scheme for Vehicular Edge Computing utilizing Improved Golden Eagle Optimization Algorithm (EECO‐VEC‐IGEOA) is proposed. An Improved Golden Eagle Optimization Algorithm (IGEOA) is designed to optimize energy‐efficient computation offloading in vehicular edge computing networks (VECN). By dynamically allocating computation tasks between vehicles and edge servers depending on real‐time conditions, the EECO‐VEC‐IGEOA model aims to improve overall network performance and energy efficiency. The EECO‐VEC‐IGEOA method reduces energy consumption by 14.62%, 16.84%, and 19.16%, and task completion time by 15.84%, 18.92%, and 20.69% compared to the existing approaches.

Multi-access edge computing (MEC) is a distributed computing framework that provides computation capability and data storage at the edge of the network to save bandwidth and reduce latency. However, the computing capacity of the MEC system could be insufficient when an excessive number of tasks are offloaded for execution. To alleviate the excessive burden on MECSs and tap the underutilized resources of WDs, we investigate the computation offloading and shunting problem in the hybrid edge computing (HEC) network. In this paper, we formulate an optimization problem to minimize the average weighted sum of total time delay and energy consumption. Due to the high computational complexity and dimensionality, we propose the deep reinforcement learning-based computation offloading and shunting (DCOS) algorithm to solve this problem. Finally, we validate the convergence property and evaluate the time complexity of the DCOS algorithm. Compared with the other algorithms, the simulation results show that the DCOS algorithm can reduce the average weighted cost significantly.

SummaryIn 5G cloud computing, the most notable and considered design issues are the energy efficiency and delay. The majority of the recent studies were dedicated to optimizing the delay issue by leveraging the edge computing concept, while other studies directed its efforts towards realizing a green cloud by minimizing the energy consumption in the cloud. Active queue management‐based green cloud model (AGCM) as one of the recent green cloud models reduced the delay and energy consumption while maintaining a reliable throughput. Multiaccess edge computing (MEC) was established as a model for the edge computing concept and achieved remarkable enhancement to the delay issue. In this paper, we present a handoff scenario between the two cloud models, AGCM and MEC, to acquire the potential gain of such collaboration and investigate its impact on the cloud fundamental constraints; energy consumption, delay, and throughput. We examined our proposed model with simulation showing great enhancement for the delay, energy consumption, and throughput over either model when employed separately.

ABSTRACT6G technology is expected to transform communications by allowing the Internet of Everything, representing a huge advance in 2030. While B5G has not yet been established, various nations are actively working on 5G, but certain research groups are presently devoting their attention to the creation of 6G technologies. The upcoming 6G networks promise higher quality of service (QoS) features, including virtual reality and holographic communications. Multi‐Access Edge Computing (MEC) and, in particular, the offloading idea are key components of 6G innovation that enable resource‐intensive application design. If the wireless link used for computational offloading is inefficient, MEC's true potential can be impeded. Intelligent beamforming and MEC have garnered attention recently. Systematically optimizing the wireless communication environment, these developments improve connectivity between user equipment (UE) and base station (BS). By increasing the electrical charge of the reflectable signal, intelligent beamforming increases the range and efficacy of Back Communication. Particularly, this study assesses how well the MEC structure performs in urban microcellular settings when intelligent beamforming is used in communications. It has been demonstrated that the use of Intelligent Reflecting Surfaces (IRS) greatly reduces spectrum and energy usage. This study's results are implemented in Python software. Our suggested strategy shows better latency compared to other optimization methods and shorter task completion times than traditional methods. When compared to conventional techniques, the suggested MEC‐enabled network showcasing lower latency (4.9 ms) and efficient network congestion management and task completion time (30 ms) demonstrates a significant performance. These results highlight how intelligent beamforming and MEC have a lot of potential to shape the architecture of 6G networks in the future.

Sparse code multiple access (SCMA) is a kind of code-domain non-orthogonal multiple access (NOMA) scheme, which can support the increasing requirements for high spectral efficiency and massive connections. Meanwhile, multi-access edge computing (MEC) is a promising technology for providing resource-constrained users with computing resources. The integration of these two technologies can efficiently improve the computation service. In this paper, we propose a novel optimization scheme in the SCMA-based MEC network from the perspective of energy and latency for the Internet of Things (IoT) devices. To minimize the total overhead of devices, the communication and computation resources allocation, as well as computation offloading are jointly considered. Primarily, a joint SCMA codebook assignment based on the bidirectional matching principle and optimal power allocation algorithm is proposed to maximize the uplink transmit rate. Moreover, we put forward CPU frequency scheduling strategies utilizing convex optimization to optimize the computing resources allocation of local devices and the MEC server. Finally, a low-complexity task offloading policy based on simulated annealing is presented. Numerical results show that our proposed joint optimization algorithm for resource allocation and computation offloading can achieve a good compromise between time delay and energy consumption for IoT devices.

The rapid development of 5G technology and edge computing prompts emerging edge applications with heterogeneous requirements for network services with respect to latency, reliability and throughput. Although content distribution networks (CDN) and edge computing proposals such as multiaccess edge computing (MEC) improve quality of service by deploying services close to end users at the edge, the session efficiency of service still is constrained by host name resolution which also leads to quality degradation of service in the case of user mobility. In this article, we proposed a novel network architecture, Named Service Edge Network (NSEN), on the basis of DNS protocol. We enabled NSEN functionality in the MEC reference architecture and presented high-efficiency session models to satisfy the requirements of various edge applications and enhance the mobility support in distributed MEC environment. The simulation and experiment results demonstrate the NSENenabled MEC architecture can improve session efficiency of MEC services in 5G cellular networks in terms of latency and throughput.

Multi-access edge computing (MEC) has emerged as a promising technology to facilitate efficient vehicular applications, such as autonomous driving, path planning and navigation. By offloading tasks from vehicles to MEC servers (MECSs), the MEC system can facilitate computation-intensive applications with hard latency constraints in vehicles with limited computing resources. However, owing to the mobility of vehicles, the vehicles are not evenly distributed across the MEC system. Therefore, some MECSs are heavily congested, whereas others are lightly loaded. If a task is offloaded to a congested MECS, it can be blocked or have high latency. Moreover, service interruption would occur because of the high mobility and limited coverage of the MECS. In this paper, we assume that the task can be divided into a set of subtasks and computed by multiple MECSs in parallel. Therefore, we propose a method of task migration with partitioning. To balance loads, the MEC system migrates the set of subtasks of tasks in an overloaded MECS to one or more underloaded MECSs according to the load difference. Simulations have indicated that, compared with conventional methods, the proposed method can increase the satisfaction of quality-of-service requirements, such as low latency, service reliability, and MEC system throughput by optimizing load balancing and task partitioning.

Efficient administration of computing resources in Multi-access Edge Computing (MEC) networks is a very active research topic. Task sharing in particular, is one of the capital problems with respect to MEC architectures although is mostly addressed from the end-user standpoint. Moreover, standard MEC frameworks do not consider task sharing schemes for computing servers at the edge level even though mechanisms of this kind could increase the utilization of resources in MEC setups. The integration of blockchain technologies into multi server cloud and edge computing solutions has started to gain steam in recent times, largely in part for its potential to enhance the functionality, security, and privacy of cloud-based architectures. In this context, this paper presents a task sharing mechanism for MEC servers with precedent-dependent tasks based on the Hyperledger Fabric framework. Hyperledger Fabric is a blockchain platform that provides a light-weight and distributed interaction playing field for the servers that mitigates the security and privacy concerns related to the collaboration scheme. For sharing tasks among the servers, the call dependencies of the tasks for a user application are captured by a control-flow graph and an optimization problem is formulated to obtain the allocation of tasks. Numerical results on the performance of the proposed task sharing mechanism are presented.

Distributed edge computing paradigms such as edge computing, fog computing, cloudlets, edge clouds, multi-access edge computing, etc., have emerged as promising solutions to fulfill the IoT applications' context-aware and latency requirements. In these computing paradigms, computational and storage resources are present near the IoT devices in the network between the IoT device and cloud data centers. There has been tremendous growth in machine learning research and its applications in many domains in recent times. This work investigates machine learning applications in edge environments. Then a classifier is proposed to classify edge applications in edge computing. At last, a detailed discussion on issues and open challenges is provided to explore future possibilities.

Optimizing the energy consumption of an MEC (Multi-Access Edge Computing) system is a crucial challenge for operation cost reduction and environmental conservation. In this paper, we address an MECS (MEC Server) sleep control problem that aims to reduce the energy consumption of the system while providing users with a reasonable service delay by adjusting the number of active MECSs according to the load imposed on the system. To tackle the problem, we identify two crucial issues that influence the design of an effective sleep control technique and propose methods to address each of these issues. The first issue is accurately predicting the system load. Changes in system load are spatio-temporally correlated among MECSs. By leveraging such correlation information with STGCN (Spatio-Temporal Graph Convolutional Network), we enhance the prediction accuracy of task arrival rates for each MECS. The second issue is rapidly selecting MECSs to sleep when the load distribution over an MEC system is given. The problem of choosing sleep MECS is a combinatorial optimization problem with high time complexity. To address the issue, we employ a genetic algorithm and quickly determine the optimal sleep MECS with the predicted load information for each MECS. Through simulation studies, we verify that compared to the LSTM (Long Short-Term Memory)-based method, our method increases the energy efficiency of an MEC system while providing a compatible service delay.

In recent years, there is development in the field of computing devices; Internet of Things (IoT) becomes the latest trend. IoT comprises ubiquitous things that are associated with day-to-day life of individuals like smartphones, smart TV, laptops, and now vehicles too. Internet of Vehicles (IoV) has become the latest area of research used to develop applications in the field of traffic management and road safety. A collaborative approach of cloud and edge computing is termed cloud-edge computing. To manage the enormous amount of IoT devices and the coordination among IoT, the cloud and edge concept of computation offloading is required. In the process of computation offloading, tasks are computationally offloaded to the cloud data center that enhances the resource utilization of the cloud server and minimizes the energy utilization for the tasks. This paper represents the literature review related to various computational offloading schemes in cloud-edge computing proposed as part of the study. The resources comprise of related book chapters and research papers from different publishers of international and national reputes. The study is carried out with the analysis of various computation offloading schemes in cloud-edge computing for the Internet of Vehicles. In addition, computing technologies like cloud computing, edge computing, and computation offloading for the Internet of Vehicles (IoV) were also discussed.KeywordsInternet of Things (IoT)Edge computingCloud computingComputation offloadingInternet of Vehicles (IoV)

The extensive growth in Industrial Internet of Things (IIoT) applications has tremendously increased the demands for low latency and resource-sensitive computing to accomplish critical industrial automations. This has leveraged the use of some proficient computing paradigms such as multiaccess edge computing (MEC), which facilitates a low latency and scalable solution for the execution of industrial workloads. However, continual generation of industrial data has imposed a substantial amount of stress on the resource-constrained MEC systems. In this perspective, our study proposes a consolidated stochastic computation offloading (CSCO) framework to address the increasing computational demands of MEC-based IIoT systems. The proposed framework efficiently handles industrial workloads by modeling them as stochastic processes to observe the number of data packets denied service due to the finite number of busy MEC servers. We provide an analytical solution corresponding to the loss probability of data packets denied service at the MEC servers. This leads to the development of a computation offloading mechanism for time-critical tasks. Furthermore, we provide the expression for conditional waiting time (CWT) and unconditional waiting time (UWT) of the data packets waiting to be offloaded to the remote cloud servers. Through extensive numerical simulations, it is inferred that the proposed CSCO framework provides promising results in characterizing the stochastic behavior of MEC-based IIoT systems, thereby providing a low latency, and resource sensitive solution for the considered system.

Computation offloading is a main issue in multi-access edge computing (MEC). In particular, in aerial MEC (AMEC) relying on UAVs/ drones, due to limited resources of single devices in providing services to end users, we always have to offload tasks to different devices within the network. However, in wireless communications, there are obstacles in transmission distance between devices sending out offloading requests and others with idle resources. Furthermore, UAVs in AMEC also have to face the challenges of limited energy supply from batteries. As a result, in this article, we design a Monte Carlo tree search enhanced hybrid strategy in achieving time- and energy-aware computation offloading for AMEC. The simulation results show that the proposed solutions can greatly reduce time cost and energy consumption while using UAVs as flying offloaders.

The rapid development of new information and communication technologies (ICTs) and the deployment of advanced Internet of Things (IoT)-based devices has led to the study and implementation of edge computing technologies in smart grid (SG) systems. In addition, substantial work has been expended in the literature to incorporate artificial intelligence (AI) techniques into edge computing, resulting in the promising concept of edge intelligence (EI). Consequently, in this article, we provide an overview of the current state-of-the-art in terms of EI-based SG adoption from a range of angles, including architectures, computation offloading, and cybersecurity concerns. The basic objectives of this article are fourfold. To begin, we discuss EI and SGs separately. Then we highlight contemporary concepts closely related to edge computing, fundamental characteristics, and essential enabling technologies from an EI perspective. Additionally, we discuss how the use of AI has aided in optimizing the performance of edge computing. We have emphasized the important enabling technologies and applications of SGs from the perspective of EI-based SGs. Second, we explore both general edge computing and architectures based on EI from the perspective of SGs. Thirdly, two basic questions about computation offloading are discussed: what is computation offloading and why do we need it? Additionally, we divided the primary articles into two categories based on the number of users included in the model, either a single user or a multiple user instance. Finally, we review the cybersecurity threats with edge computing and the methods used to mitigate them in SGs. Therefore, this survey comes to the conclusion that most of the viable architectures for EI in smart grids often consist of three layers: device, edge, and cloud. In addition, it is crucial that computation offloading techniques must be framed as optimization problems and addressed effectively in order to increase system performance. This article typically intends to serve as a primer for emerging and interested scholars concerned with the study of EI in SGs.

In recent years, AIoT (Artificial Intelligence of Things) applications have received lots of attention since it exploits widely deployed IoT devices to collect data and perform an action while leveraging AI to obtain knowledge and insights. When deploying AIoT in 5G networks, Multi-access Edge Computing (MEC) is appropriate for enabling local management of IoT devices and computation of machine learning (ML) algorithms. Facing the multitude of threats in the ML data layer, IoT service layer, and 5G communications layer, MEC should enable corresponding detection and mitigation schemes to protect AIoT applications. In this article, we examine the existing security solutions located in each layer and discuss the interrelated challenges. For example, a network-traffic-based IDS solution in MEC might capture IoT malware but fail to identify a growing file-less attack. We suggest that firmware emulation in IoT endpoint should be included to provide system-level behaviors to network-based detectors in MEC so that file-less attacks can be distinguished. The potential of a backdoor attack aiming to poison data or corrupt ML models cannot be ignored in AIoT applications, and a corresponding detector should be implemented in MEC. Due to the rise of low-cost Software-Defined Radio (SDR), malicious attacks using rogue base stations (BSs) have become more popular. It implies that security protection at MEC in the communications layer is necessary. This article, therefore, proposes a novel platform, M3Inspector, where inspectors located in mobiles and AIoT machines collect information from surrounding BSs and provide them to MEC. MEC determines the rogue BS and makes a notification to users subscribing to the local service. A realistic 5G experimental platform with rogue BSs is developed. The results demonstrate that attack detection and mitigation can be implemented in the MEC paradigm to significantly improve the security protection of AIoT from the perspectives of rogue BS attacks and file-less attacks.