The current Cellular Vehicle-to-Everything (C-V2X) Sidelink communication protocol provides a low latency interface for sharing short safety messages among Road-Side Units and vehicles. However, while its packets are broadcasted in the channel, the throughput is vulnerable to channel conditions and cannot meet the needs of the emerging connected and autonomous vehicles applications (e.g. vehicular fusion tasks), which require multimodal and multi-source sensor data sharing. In this work, we establish a C-V2X testbed on the campus of the University of California, San Diego, to study the feasibility of using C-V2X Sidelink communications for transmitting sensor data in real-time. We implement an end-to-end RGB sensor data (i.e. camera image frames) transmission mechanism on the C-V2X Sidelink testbed and explore the corresponding Quality of Service (QoS) characteristics under two configurable link-level parameters, the Modulation and Coding Scheme (MCS) and packet size. We then propose a cross-layer predictive and adaptive framework which adjusts, in real-time, the MCS and packet size settings based on side-channel information to optimize the QoS for image frame transmission. The real-world trace-driven emulation shows that the proposed policy improves the average frame goodput performance by 28% compared to fixed configuration policies that are used in current Sidelink communications.

Emerging connected and autonomous vehicles involve complex applications requiring not only optimal computing resource allocations but also efficient computing architectures. In this paper, we unfold the critical performance metrics required for emerging vehicular computing applications and show with preliminary experimental results, how optimal choices can be made to satisfy the static and dynamic computing requirements in terms of the performance metrics. We also discuss the feasibility of edge computing architectures for vehicular computing and show tradeoffs for different offloading strategies. The paper shows directions for light weight, high performance and low power computing paradigms, architectures and design-space exploration tools to satisfy evolving applications and requirements for connected and autonomous vehicles.

In this paper, we explore a sustainable solution to growing vehicular computing and communication needs by utilizing edge computing and communication resources of a network of Solar- powered Roadside Units (SRSUs), along with any vehicular computing resources available. The solution ensures no additional grid energy expended while minimizing any QoS loss for the vehicle users (VUs). An SRSU consists of a small cell base station (SBS) and a road-edge computing (REC) node, is powered by a low-cost solar system. VUs can offload their vehicular application tasks to SRSUs to receive high throughput and low latency services. However, the limited capacity of solar energy, REC computing, and bandwidth resources may cause service disruption and affect the Quality of Service (QoS) that VUs receive. To minimize such QoS loss, we formulate a dynamic offloading QoS loss minimization problem, where the different subtasks of a VU application are optimally executed either locally using the VU computing resource or remotely using the REC resource, considering the energy, computing and bandwidth constraints of the SRSU network. We then propose to solve it by a heuristic algorithm which jointly makes the optimal user association, subtask offloading, and SRSU resource allocation decisions. To evaluate the proposed algorithm, we build a simulation framework consisting of a dense SRSU network using real-world solar energy generation and urban vehicular traffic data. The simulation results show that our proposed approach can significantly reduce QoS loss compared to other best effort strategies.

This paper shows the viability of Solar-powered Road Side Units (SRSU), consisting of small cell base stations and Mobile Edge Computing (MEC) servers, and powered solely by solar panels with battery, to provide connected vehicles with a low- latency, easy-to-deploy and energy-efficient communication and edge computing infrastructure. However, SRSU may entail a high risk of power deficiency, leading to severe Quality of Service (QoS) loss due to spatial and temporal fluctuation of solar power generation. Meanwhile, the data traffic demand also varies with space and time. The mismatch between solar power generation and SRSU power consumption makes optimal use of solar power challenging. In this paper, we model the above problem with three sub-problems, the SRSU power consumption minimization problem, the temporal energy balancing problem and spatial energy balancing problem. Three algorithms are proposed to solve the above sub-problems, and they together provide a complete joint battery charging and user association control algorithm to minimize the QoS loss under delay constraint of the computing tasks. Results with a simulated urban environment using actual solar irradiance and vehicular traffic data demonstrates that the proposed solution reduces the QoS loss significantly compared to greedy approaches.

A novel Fog Radio Access Network architecture based on General Purpose Processor(GPP) Platform is proposed. Traditionally, people consider fog computing service as an independent computing unit in the Radio Access Network(RAN). Our design, on the contrary, considers both the computing resource for RAN and for fog computing application as an integrated computing resource pool. Based on the observation of the CPU load measurement on the Baseband Unit(BBU) testbed, we further scrutinize our proposed model and prove that it is feasible in the realistic RAN system.