Reconfigurable Intelligent Surfaces for the Connectivity of Autonomous Vehicles

The use of real-time software-controlled reconfigurable intelligent surface (RIS) units is proposed to increase the reliability of vehicle-to-everything (V2X) communications. The optimum placement problem of the RIS units is formulated by considering their sizes and operating modes. The solution of the problem is given, where it is shown that the placement of the RIS depends on the locations of the transmitter and the receiver. The proposed RIS-supported highway deployment can combat the high path loss experienced by the use of higher frequency bands, including the millimeter-wave and the terahertz bands, that are expected to be used in the next-generation wireless networks, enabling the use of the existing base station deployment plans to remain operational, while providing reliable and energy-efficient connectivity for autonomous vehicles.Comment: 5 pages, 4 figures

Scaling and Placing Distributed Services on Vehicle Clusters in Urban Environments

Many vehicles spend a significant amount of time in urban traffic congestion. Due to the evolution of autonomous vehicles, driver assistance systems, and in-vehicle entertainment, these vehicles have plentiful computational and communication capacity. How can we deploy data collection and processing tasks on these (slowly) moving vehicles to productively use any spare resources? To answer this question, we study the efficient placement of distributed services on a moving vehicle cluster. We present a macroscopic flow model for an intersection in Dublin, Ireland, using real vehicle density data. We show that such aggregate flows are highly predictable (even though the paths of individual vehicles are not known in advance), making it viable to deploy services harnessing vehicles’ sensing capabilities. After studying the feasibility of using these vehicle clusters as infrastructure, we introduce a detailed mathematical specification for a task-based, distributed service placement model. The distributed service scales according to the resource requirements and is robust to the changes caused by the mobility of the cluster. We formulate this as a constrained optimization problem, with the objective of minimizing overall processing and communication costs. Our results show that jointly scaling tasks and finding a mobility-aware, optimal placement results in reduced processing and communication costs compared to the two schemes in the literature. We compare our approach to an autonomous vehicular edge computing-based naive solution and a clustering-based solution

Graph-based Heuristic Solution for Placing Distributed Video Processing Applications on Moving Vehicle Clusters

Vehicular fog computing (VFC) is envisioned as an extension of cloud and mobile edge computing to utilize the rich sensing and processing resources available in vehicles. We focus on slow-moving cars that spend a significant time in urban traffic congestion as a potential pool of onboard sensors, video cameras, and processing capacity. For leveraging the dynamic network and processing resources, we utilize a stochastic mobility model to select nodes with similar mobility patterns. We then design two distributed applications that are scaled in real-time and placed as multiple instances on selected vehicular fog nodes. We handle the unstable vehicular environment by a), Using real vehicle density data to build a realistic mobility model that helps in selecting nodes for service deployment b), Using communitydetection algorithms for selecting a robust vehicular cluster using the predicted mobility behavior of vehicles. The stability of the chosen cluster is validated using a graph centrality measure, and c), Graph-based placement heuristics is developed to find the optimal placement of service graphs based on a multi-objective constrained optimization problem with the objective of efficient resource utilization. The heuristic solves an important problem of processing data generated from distributed devices by balancing the trade-off between increasing the number of service instances to have enough redundancy of processing instances to increase resilience in the service in case of node or link failure, versus reducing their number to minimize resource usage. We compare our heuristic to a mixed integer program (MIP) solution and a first-fit heuristic. Our approach performs better than these comparable schemes in terms of resource utilization and/or has a lesser service latency when compared to an edge computingbased service placement scheme

Density/Flow reconstruction via heterogeneous sources and Optimal Sensor Placement in road networks

International audienceThis paper addresses the two problems of flow and density reconstruction in Road Transportation Networks with heterogeneous information sources and cost effective sensor placement. Following a standard modeling approach, the network is partitioned in cells, whose vehicle densities change dynamically in time according to first order conservation laws. The first problem is to estimate flow and the density of vehicles using as sources of information standard fixed sensors, precise but expensive, and Floating Car Data, less precise due to low penetration rates, but already available on most of main roads. A data fusion algorithm is proposed to merge the two sources of information to estimate the network state. The second problem is to place sensors by trading off between cost and performance. A relaxation of the problem, based on the concept of Virtual Variances, is proposed and solved using convex optimization tools. The efficiency of the designed strategies is shown on a regular grid and in the real world scenario of Rocade Sud in Grenoble, France, a ring road 10.5 km long

The Modelling Of Tyre Rotation Behaviour With Tyre Pressure Monitoring System

The number of motorized vehicles is rapidly increasing in the technology driven countries, and led to the dramatic increase in road accident. The causes of accidents can be categorized into three major factors which are road environmental condition, human behaviour, and vehicle defects. The vehicle defects are the only parameter that is controllable when compared with to other two factors. Statistics show that the tyre and wheels-related from motorcycles is the critical reason and major contributor to road death accident. Therefore, there is the necessity to build a system that is able to monitor the on-road tyre condition. Several existing monitoring systems are available, but each has its own advantages and disadvantages based on the application’s limitation. For example, the important parameter such as pneumatic pressure captured from the tyre is not in real-time, thus it may become worst when there is air leakage. Besides that, tyre rotation behaviour such as acceleration, deceleration and sharp brake condition is not considered which may tend to build up heat. Especially in the countries on the equator which have warm road pavement throughout the daytime. In addition, the placement of transceiver for wireless communication need to determine in order to avoid misinterpretation on the wrong/delayed result captured. The research objective is to develop a monitoring system that combines the advantages of direct and indirect measurement system in order to overcome the problem as discussed. The system needs to capture the real-time pressure level on running tyre and provide calculations on the total distance travelled by the vehicle through algorithms from investigation of tyre rotation behaviour. Apart from that, the power level parameter was studied through the received signal strength index (RSSI) calibration for transmission quality purposes. The system consist of two parts which are the transmitter module and receiver module. The transmitter module is built from combination of hardware such as microcontroller, bluetooth module and sensing devices which sat on the tyre rim to acquire tyre condition. Whereas, the receiver module is responsible to collect and analyze information from the transmitter module and provide a feedback whenever an abnormal tyre condition occurred. Several experiments were conducted, the result shows that the placement of transceiver can be justified with consistent RSSI at -70 dBm from different tyre rotation speed and different transmitter’s directions with the same displacement. The result also shows that the performance of tyre rotation behaviour is able to identify and provide the estimation of distance travelled by the vehicle with evidence support from distance travel calculation. Lastly, the pneumatic pressure level inside the tyre was captured and the result accuracy is further ensured with reversed engineering method with ± 20 kpa from project tolerance. Overall, the research work is able to capture the real-time pressure level on running tyre, provide calculation on total distance travelled based on tyre rotation cycle and position the transceiver based on the power level parameter to ensure the transmission quality

Electric Autonomous Mobility-on-Demand: Joint Optimization of Routing and Charging Infrastructure Siting

The advent of vehicle autonomy, connectivity and electric powertrains is expected to enable the deployment of Autonomous Mobility-on-Demand systems. Crucially, the routing and charging activities of these fleets are impacted by the design of the individual vehicles and the surrounding charging infrastructure which, in turn, should be designed to account for the intended fleet operation. This paper presents a modeling and optimization framework where we optimize the activities of the fleet jointly with the placement of the charging infrastructure. We adopt a mesoscopic planning perspective and devise a time-invariant model of the fleet activities in terms of routes and charging patterns, explicitly capturing the state of charge of the vehicles by resampling the road network as a digraph with iso-energy arcs. Then, we cast the problem as a mixed-integer linear program that guarantees global optimality and can be solved in less than 10 min. Finally, we showcase two case studies with real-world taxi data in Manhattan, NYC: The first one captures the optimal trade-off between charging infrastructure prevalence and the empty-mileage driven by the fleet. We observe that jointly optimizing the infrastructure siting significantly outperforms heuristic placement policies, and that increasing the number of stations is beneficial only up to a certain point. The second case focuses on vehicle design and shows that deploying vehicles equipped with a smaller battery results in the lowest energy consumption: Although necessitating more trips to the charging stations, such fleets require about 12% less energy than the vehicles with a larger battery capacity