Remote driving, orteleoperating AutonomousVehicles (AVs), is a key application that emerging 5G networks aim to support. In this paper, we conduct a systematic feasibility study of AV teleoperation over commercial 5G networks from bothcross-layerandend-to-end (E2E)perspectives. Given the critical importance oftimely delivery of sensor data, such as camera and LiDAR data, for AV teleoperation, we focus in particular on the performance of uplink sensor data delivery. We analyze the impact of Physical Layer (PHY layer) 5G radio network factors, including channel conditions, radio resource allocation, and Handovers (HOs), on E2E latency performance. We also examine the impact of 5G networks on the performance of upper-layer protocols and E2E application Quality-of-Experience (QoE) of the adaptation mechanisms used for real-time sensor data delivery, such as Real-Time Streaming Protocol (RTSP) and Web Real Time Communication (WebRTC). Our study reveals the challenges posed by today’s 5G networks and the limitations of existing sensor data streaming mechanisms. The insights gained will help inform the co-design of future-generation wireless networks, edge cloud systems, and applications to overcome the low-latency barriers in AV teleoperation.

As the plethora of Internet of Things (IoT) devices gradually make their way into our lives, several Cloud Service Providers (CSPs) have developed IoT gateway platforms (SDKs) that solely connects IoT devices to their respective cloud. Such gateways have 1) cumbersome IoT device configuration; 2) inflexible IoT data managements; and 3) support no/little cross-vendor edge computation and cloud analytics. We term these commercial gateway SDKs as cloud-centric. In this paper, we study the state-of-the-art vendor-locked IoT Gateway solutions and approaches and propose an edge-centric paradigm through an evolutionary framework, dubbed VeerEdge for developing IoT gateways. We leverage computing and storage capabilities at the network edge for edge-based device & IoT service management and data processing. We exploit availability of multiple cloud services for "best" IoT data analytics. Evaluation results show that VeerEdge achieves this with negligible overhead in terms of latency, CPU and RAM usage when compared to state-of-the-art industrial IoT gateways.