Navigating through roadworks represents one of the main sources of safety risk for Connected and Autonomous Vehicles (CAVs) due to the altered road layouts. The built-in base maps do not normally reflect these changes, causing CAVs to experience difficulties in sensing and trajectory generation. Therefore, the objective of this paper is to evaluate different collision-free trajectory generation for CAVs at roadworks to improve safety and traffic performance. Trajectory generation algorithms using lane-level dynamic maps were examined for: 1) CAVs rely on data from in-vehicle sensor only; and 2) CAVs receive additional information via a Smart Traffic Cone (STC) in advance regarding roadwork configurations. Experiments were conducted at a controlled motorway facility operated by National Highways (England) using a vehicle instrumented with a suite of sensors. Schematics of the roadworks scenario were translated into an integrated simulation platform consisting of a traffic microsimulation (VISSIM) to simulate traffic dynamics and a sub-microscopic simulator (PreScan) capable of simulating vehicle autonomy and connectivity. Results indicate that traffic conflicts and delays decrease by 40% and 3% respectively when CAVs receive additional information in advance (i.e., Scenario 2) compared to the other scenario. These findings would assist road network operators in developing ‘CAV-enabled roadworks’ and vehicle manufacturers in designing a vehicle-based ‘roadworks assist’ system.

In today's moving world to ambitious 5G, 3GPP has defined three fundamental and promising services, namely enhanced Mobile Broadband (eMBB), ultra Reliable Low Latency Communication (uRLLC), and massive Machine Type Communication (mMTC) by tuning user's needs on these services to network slicing. While the Network Function Virtualization (NFV) and Software Defined Networking (SDN) are used to enable the network slicing in the mobile network, an effective end-to-end slice management in 5G system is still a challenge to improve the network performance in terms of throughput, latency, and connectivity for each of these envisioned services. In this paper, we focus on the end-to-end network slice life cycle management of network slices on different sites using a single management and orchestration entity with a coherent proof of concept. We propose algorithms for efficiently activating, deactivating, and decommissioning the network slices, using real time status information of network slices from Network Slice Management Function (NSMF). Our results show that by adopting better strategy in these algorithms and considering learned user traffic from the past, in controlling various phases of slice life cycle, we can reduce the response time for a user request by 50%.

Recent advances in LTE operating in unlicensed spectrum (LTE-U) has grabbed a lot of attention from industry and academia. The duty cycled LTE-U is shown to be fair with Wi-Fi technology by following an ON-OFF cycle for its transmission. However, the effect of LTE-U on the video quality of Wi-Fi users has not been studied in the literature. In this work, we study the video quality performance of a Wi-Fi user in the presence of LTE-U, in a testbed system. Our results show that the parameters that contribute to the video QoE (Quality of Experience) of Wi-Fi users get adversely affected as the fraction of channel utilized by LTE-U increases, but the same is not shown to be true with another Wi-Fi. We found that poor video QoE of Wi-Fi users in the presence of LTE-U is because of a large number of packet collisions and less channel access time due to ON cycle of LTE-U.