Towards reliable and low-latency vehicular edge computing networks

Abstract. To enable autonomous driving in intelligent transportation systems, vehicular communication is one of the promising approaches to ensure safe, efficient, and comfortable travel. However, to this end, there is a huge amount of application data that needs to be exchanged and processed which makes satisfying the critical requirement in vehicular communication, i.e., low latency and ultra-reliability, challenging. In particular, the processing is executed at the vehicle user equipment (VUE) locally. To alleviate the VUE’s computation capability limitations, mobile edge computing (MEC), which pushes the computational and storage resources from the network core towards the edge, has been incorporated with vehicular communication recently. To ensure low latency and high reliability, jointly allocating resources for communication and computation is a challenging problem in highly dynamics and dense environments such as urban areas. Motivated by these critical issues, we aim to minimize the higher-order statistics of the end-to-end (E2E) delay while jointly allocating the communication and computation resources in a vehicular edge computing scenario. A novel risk-sensitive distributed learning algorithm is proposed with minimum knowledge and no information exchange among VUEs, where each VUE learns the best decision policy to achieve low latency and high reliability. Compared with the average-based approach, simulation results show that our proposed approach has the better network-wide standard deviation of E2E delay and comparable average E2E delay performance

URLLC-Awared Resource Allocation for Heterogeneous Vehicular Edge Computing

Vehicular edge computing (VEC) is a promising technology to support real-time vehicular applications, where vehicles offload intensive computation tasks to the nearby VEC server for processing. However, the traditional VEC that relies on single communication technology cannot well meet the communication requirement for task offloading, thus the heterogeneous VEC integrating the advantages of dedicated short-range communications (DSRC), millimeter-wave (mmWave) and cellular-based vehicle to infrastructure (C-V2I) is introduced to enhance the communication capacity. The communication resource allocation and computation resource allocation may significantly impact on the ultra-reliable low-latency communication (URLLC) performance and the VEC system utility, in this case, how to do the resource allocations is becoming necessary. In this paper, we consider a heterogeneous VEC with multiple communication technologies and various types of tasks, and propose an effective resource allocation policy to minimize the system utility while satisfying the URLLC requirement. We first formulate an optimization problem to minimize the system utility under the URLLC constraint which modeled by the moment generating function (MGF)-based stochastic network calculus (SNC), then we present a Lyapunov-guided deep reinforcement learning (DRL) method to convert and solve the optimization problem. Extensive simulation experiments illustrate that the proposed resource allocation approach is effective.Comment: 29 pages, 14 figure

Secure Data Transactions in Mobile Cloud Computing using FAAS

In recent times, security breaches have come to light in mobile cloud transactions, raising concerns about the vulnerability of data stored in mobile clouds. This data is at risk of tampering or unauthorized modification by external users, especially because it resides within a public cloud infrastructure managed by organizations. Such breaches can significantly impact the authenticity and integrity of the stored data. Mobile cloud computing (MCC) is a technology designed to facilitate the transfer of data and communication with end-users over the internet through a mobile cloud infrastructure. To address the urgent need to secure and protect data stored in mobile clouds, we propose the implementation of the Mobile Cloud-Security Model (MCSM). This innovative model is poised to provide an elevated level of data security and integrity for user data by harnessing the power of Federated Learning (FL) and Federation as a Service (FaaS). Federated Learning (FL) seamlessly integrates into the data training process, culminating in the generation of a model using the data hosted in the mobile cloud. This pioneering approach enables collaborative model training while steadfastly upholding data privacy and security. Federation as a Service (FaaS) represents a cloud-based solution that streamlines collaboration and data sharing among diverse organizations or entities. It simplifies the complex processes of configuring trust relationships, managing identities, and establishing data exchange agreements among federated entities, all made possible through the provision of Service Level Agreements (SLAs) for data stored in the mobile cloud. The user data stored in the mobile cloud will be retrieved using Machine Learning (ML) algorithms that learn from user data. Subsequently, this data is offloaded from the edge devices. The outcome of this research is to maintain user data within the FAAS cloud service with higher-level of confidentiality, security and integrity of user’s data

Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services

Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This survey paper focuses on the deployment of AIGC applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile AIGC networks, that provide personalized and customized AIGC services in real time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, finetuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGCdriven creative applications and use cases for mobile AIGC networks. Additionally, we discuss the implementation, security, and privacy challenges of deploying mobile AIGC networks. Finally, we highlight some future research directions and open issues for the full realization of mobile AIGC networks

Service Provisioning in Edge-Cloud Continuum Emerging Applications for Mobile Devices

Disruptive applications for mobile devices can be enhanced by Edge computing facilities. In this context, Edge Computing (EC) is a proposed architecture to meet the mobility requirements imposed by these applications in a wide range of domains, such as the Internet of Things, Immersive Media, and Connected and Autonomous Vehicles. EC architecture aims to introduce computing capabilities in the path between the user and the Cloud to execute tasks closer to where they are consumed, thus mitigating issues related to latency, context awareness, and mobility support. In this survey, we describe which are the leading technologies to support the deployment of EC infrastructure. Thereafter, we discuss the applications that can take advantage of EC and how they were proposed in the literature. Finally, after examining enabling technologies and related applications, we identify some open challenges to fully achieve the potential of EC, and also research opportunities on upcoming paradigms for service provisioning. This survey is a guide to comprehend the recent advances on the provisioning of mobile applications, as well as foresee the expected next stages of evolution for these applications