In this paper, we explore the feasibility of solar-powered road-side unit (SRSU)-assisted vehicular edge computing (VEC) system, where SRSU is equipped with small cell base station (SBS) and VEC server, both of which are powered solely by solar energy. However, the limited capacity of solar energy, VEC server's computing, and SBS's bandwidth resources may prohibit vehicle users (VUs) from offloading their vehicular applications to VEC server for better service quality. We address this challenge by dynamically determining vehicular task partitioning and offloading, VEC server's system configuration, and vehicular application level adjustment decisions. We aim at minimizing the end-to-end delay of vehicular applications while maximizing their application level performance (e.g., accuracy). We also implement an object detection vehicular application on an edge computing platform and measure the corresponding energy consumption, computation delay, and detection accuracy performance to establish empirical models for the SRSU-assisted VEC system. We then propose a dynamic programming-based heuristic algorithm which jointly makes the task partitioning and offloading, as well as system and application-level adaption decisions in real-time. We build a simulation framework with the above empirical models to evaluate the proposed algorithm. The simulation results show that our proposed approach can significantly reduce the end-to-end delay while maximizing the detection accuracy compared to existing techniques.

During the past few years, serverless computing has changed the paradigm of application development and deployment in the cloud and edge due to its unique advantages, including easy administration, automatic scaling, built-in fault tolerance, etc. Nevertheless, serverless computing is also facing challenges such as long latency due to the cold start. In this paper, we present an in-depth performance analysis of cold start in the serverless framework and propose HotC, a container-based runtime management framework that leverages the lightweight containers to mitigate the cold start and improve the network performance of serverless applications. HotC maintains a live container runtime pool, analyzes the user input or configuration file, and provides available runtime for immediate reuse. To precisely predict the request and efficiently manage the hot containers, we design an adaptive live container control algorithm combining the exponential smoothing model and Markov chain method. Our evaluation results show that HotC introduces negligible overhead and can efficiently improve the performance of various applications with different network traffic patterns in both cloud servers and edge devices.

Multi-vehicle perception fusion is an emerging advanced vehicular application providing Vehicle Users (VUs) with comprehensive driving assistance. This involves each VU's individual vehicular perception tasks and additional fusion tasks at the end. However, performing these perception fusion applications on some VUs may not be feasible due to the applications' high computing complexity. Vehicular Edge Computing (VEC) server, which is collocated with a Road-side Unit, together with VUs' Vehicular Local Computing (VLC) units can be used to support these perception fusion applications through task offloading. Achieving this edge-collaborated perception fusion with task offloading involves multiple challenges: (i) frequently varying uplink channel conditions, (ii) sequential and parallel task dependencies, and (iii) uncertain task composition induced by unknown object detection outputs in the dynamic driving environment. In this paper, a real-time mechanism is proposed to minimize the end-to-end delay of an edge-collaborated multi-vehicle perception fusion application by addressing these challenges. Based on the real-time channel conditions, VEC and VLC server capacities, and number of detected objects, this algorithm jointly determines the bandwidth allocation, task partitioning, offloading, and execution scheduling for all the involved tasks of the multi-vehicle perception fusion application. Numerical results show that the proposed approach can significantly reduce the end-to-end fusion application latency compared to existing techniques.

Vehicular networking has seen continued evolution over decades with the recently emerging paradigm of Cellular Vehicle-to-Everything (C-V2X) communications beginning to pick up momentum for adoption on today's roadways. Initial iterations of C-V2X grew from the LTE Device-to-Device framework and targeted application use cases that required the exchange of small packets of information: where a vehicle is, what it is doing, etc. Many of the next generation of use cases require the transfer of sensory data from vehicles to the edge, e.g., tele-driving, cooperative perception, computation offloading, etc. This work evaluates whether today's commercially available vehicular networking solutions can support the higher data rates required to carry this sensory data from vehicles to a Roadside Unit using a C-V2X testbed based on C-V2X Mode 4, which operates autonomously in shared spectrum. It is experimentally shown that C-V2X is capable of carrying the most common form of vehicle sensor data, images, with a frame latency of approximately 50 ms; however, these transmissions are often unreliable due to C-V2X Mode 4's lack of adaptation capability. To mitigate this, a Reinforcement Learning (RL) problem is proposed that can adapt the transmission parameters of image frames using readily available out of band information in order to achieve a 23.3% relative improvement in effective throughput. Extensions to this RL problem are developed that allow explicit control over a desired risk tolerance, such as the probability of transmitting an image but not receiving it. Using this extension, the developed RL solution learns an adaptive transmission policy that successfully delivers 87.1% of the image frames which are transmitted (a 33.6% absolute improvement) while still maintaining the throughput advantage. Ultimately this work finds that today's commercial vehicular networking solutions are capable of supporting applications that require sensor datac sharing by using RL to overcome the limitations of C-V2X Mode 4.

With a diverse range of applications in both private and public sector, the market value of Unmanned Aerial Vehicles (UAVs) skyrocketed to 17.31 billion USD in 2024 and is expected to reach 32.95 billion USD by 2030. 3GPP considered UAVs in Release 15 and will include them in Release 18 as part of the 5G-Advanced technology. To provide smooth and safe inclusion in the current airspace, fast and reliable communications between UAVs and between ground base stations and UAVs are needed. Several research studies have been conducted, but there is no comprehensive picture of their advancements in terms of wireless communications and networking for UAVs. This survey paper provides detailed information on the current status of UAV communications and networking, including important aspects such as architectural solutions, protocols and design options related to spectrum management, resource optimization and security requirements. Efforts related to standardization and integration with different applications are also covered. The survey will help researchers and practitioners in the field of wireless communications, UAV service provision and telecommunications learn about the current state of the art and open the door to future research and development avenues in their fields.