Autonomous Vehicle Coordination with Wireless Sensor and Actuator Networks

A coordinated team of mobile wireless sensor and actuator nodes can bring numerous benefits for various applications in the field of cooperative surveillance, mapping unknown areas, disaster management, automated highway and space exploration. This article explores the idea of mobile nodes using vehicles on wheels, augmented with wireless, sensing, and control capabilities. One of the vehicles acts as a leader, being remotely driven by the user, the others represent the followers. Each vehicle has a low-power wireless sensor node attached, featuring a 3D accelerometer and a magnetic compass. Speed and orientation are computed in real time using inertial navigation techniques. The leader periodically transmits these measures to the followers, which implement a lightweight fuzzy logic controller for imitating the leader's movement pattern. We report in detail on all development phases, covering design, simulation, controller tuning, inertial sensor evaluation, calibration, scheduling, fixed-point computation, debugging, benchmarking, field experiments, and lessons learned

ICT Integration for Electric Vehicles as Data Collector and Distributor of Data Services

At present, automotive companies are very interested in information communication technology (ICT), electric vehicle sensors, and their associated intelligent transport systems (ITS) applications. The production of in-vehicle sensors is developing continuously because of their proven benefits in preventing accidents, improving driving e?ciency, and collecting data for sensor-based services. These advantages are not only limited to the vehicle’s driver but also to the drivers of other vehicles and web database server as third parties. In this paper, we present Vehicle as a Data Collector and Distributor (VADCD), a concept that explains how a sensor-equipped vehicle can be considered as a pivotal, mobile source of sensory data and sensor-related applications and services

Car-to-Cloud Communication Traffic Analysis Based on the Common Vehicle Information Model

Although connectivity services have been introduced already today in many of the most recent car models, the potential of vehicles serving as highly mobile sensor platform in the Internet of Things (IoT) has not been sufficiently exploited yet. The European AutoMat project has therefore defined an open Common Vehicle Information Model (CVIM) in combination with a cross-industry, cloud-based big data marketplace. Thereby, vehicle sensor data can be leveraged for the design of entirely new services even beyond traffic-related applications (such as localized weather forecasts). This paper focuses on the prediction of the achievable data rate making use of an analytical model based on empirical measurements. For an in-depth analysis, the CVIM has been integrated in a vehicle traffic simulator to produce CVIM-complaint data streams as a result of the individual behavior of each vehicle (speed, brake activity, steering activity, etc.). In a next step, a simulation of vehicle traffic in a realistically modeled, large-area street network has been used in combination with a cellular Long Term Evolution (LTE) network to determine the cumulated amount of data produced within each network cell. As a result, a new car-to-cloud communication traffic model has been derived, which quantifies the data rate of aggregated car-to-cloud data producible by vehicles depending on the current traffic situations (free flow and traffic jam). The results provide a reference for network planning and resource scheduling for car-to-cloud type services in the context of smart cities

The AutoMat CVIM - A Scalable Data Model for Automotive Big Data Marketplaces

In the past years, connectivity has been introduced in automotive production series, enabling vehicles as highly mobile Internet of Things sensors and participants. The Horizon 2020 research project AutoMat addressed this scenario by building a vehicle big data marketplace in order to leverage and exploit crowd-sourced sensor data, a so far unexcavated treasure. As part of this project the Common Vehicle Information Model (CVIM) as harmonized data model has been developed. The CVIM allows a common understanding and generic representation, brand-independent throughout the whole data value and processing chain. The demonstrator consists of CVIM vehicle sensor data, which runs through the different components of the whole AutoMat vehicle big data processing pipeline. Finally, at the example of a traffic measurement service the data of a whole vehicle fleet is aggregated and evaluated

Mobile air quality studies (MAQS) in inner cities: particulate matter PM10 levels related to different vehicle driving modes and integration of data into a geographical information program

ABSTRACT: BACKGROUND: Particulate matter (PM) is assumed to exert a major burden on public health. Most studies that address levels of PM use stationary measure systems. By contrast, only few studies measure PM concentrations under mobile conditions to analyze individual exposure situations. METHODS: By combining spatial-temporal analysis with a novel vehicle-mounted sensor system, the present Mobile Air Quality Study (MAQS) aimed to analyse effects of different driving conditions in a convertible vehicle. PM10 was continuously monitored in a convertible car, driven with roof open, roof closed, but windows open, or windows closed. RESULTS: PM10 values inside the car were nearly always higher with open roof than with roof and windows closed, whereas no difference was seen with open or closed windows. During the day PM10 values varied with high values before noon, and occasional high median values or standard deviation values due to individual factors. Vehicle speed in itself did not influence the mean value of PM10; however, at traffic speed (10 -- 50 km/h) the standard deviation was large. No systematic difference was seen between PM10 values in stationary and mobile cars, nor was any PM10 difference observed between driving within or outside an environmental (low emission) zone. CONCLUSIONS: he present study has shown the feasibility of mobile PM analysis in vehicles. Individual exposure of the occupants varies depending on factors like time of day as well as ventilation of the car; other specific factors are clearly identifiably and may relate to specific PM10 sources. This system may be used to monitor individual exposure ranges and provide recommendations for preventive measurements. Although differences in PM10 levels were found under certain ventilation conditions, these differences likely are not of concern for the safety and health of passengers

Deploying Wireless Sensor Devices in Intelligent Transportation System Applications

As future intelligent infrastructure will bring together and connect individuals, vehicles and infrastructure through wireless communications, it is critical that robust communication technologies are developed. Mobile wireless sensor networks are self-organising mobile networks where nodes exchange data without the need for an underlying infrastructure. In the road transport domain, schemes which are fully infrastructure-less and those which use a combination of fixed (infrastructure) devices and mobile devices fitted to vehicles and other moving objects are of significant interest to the ITS community as they have the potential to deliver a ‘connected environment’ where individuals, vehicles and infrastructure can co-exist and cooperate, thus delivering more knowledge about the transport environment, the state of the network and who indeed is travelling or wishes to travel. This may offer benefits in terms of real-time management, optimisation of transportation systems, intelligent design and the use of such systems for innovative road charging and possibly carbon trading schemes as well as through the CVHS (Cooperative Vehicle and Highway Systems) for safety and control applications. As the wireless sensor networks technology is still relatively new and very little is known about its real application in the transport domain. Our involvement in the transport-related projects provides us with an opportunity to carry out research and development of wireless sensor network applications in transport systems. This chapter outlines our experience in the ASTRA (ASTRA, 2005), TRACKSS (TRACKSS, 2007) and EMMA (EMMA, 2007) projects and provides an illustration of the important role that the wireless sensor technology can play in future ITS. This chapter also presents encouraging results obtained from the experiments in investigating the feasibility of utilising wireless sensor networks in vehicle and vehicle to infrastructure communication in real ITS applications

Design of a multiple bloom filter for distributed navigation routing

Unmanned navigation of vehicles and mobile robots can be greatly simplified by providing environmental intelligence with dispersed wireless sensors. The wireless sensors can work as active landmarks for vehicle localization and routing. However, wireless sensors are often resource scarce and require a resource-saving design. In this paper, a multiple Bloom-filter scheme is proposed to compress a global routing table for a wireless sensor. It is used as a lookup table for routing a vehicle to any destination but requires significantly less memory space and search effort. An error-expectation-based design for a multiple Bloom filter is proposed as an improvement to the conventional false-positive-rate-based design. The new design is shown to provide an equal relative error expectation for all branched paths, which ensures a better network load balance and uses less memory space. The scheme is implemented in a project for wheelchair navigation using wireless camera motes. © 2013 IEEE

IoT system for anytime/anywhere monitoring and control of vehicles’ parameters

This paper presents an IoT (Internet of Things) system designed to allow the monitoring and control of parameters of the users’ vehicles, anytime and anywhere in the world, through the Internet. The system prototype was developed and tested using an electric vehicle (EV) and the respective sensor systems. The main components of the proposed IoT system are: a Bluetooth Low Energy (BLE) intra-vehicular wireless sensor network (IVWSN); a mobile device that acts both as the vehicle’s gateway, connecting the IVWSN to the Internet, and as the vehicle’s human machine interface (HMI); an online server/database, based on Firebase; a client, which can be either a mobile device or a personal computer; and a residential wireless sensor network (WSN). The use of a wireless network to collect sensor data inside of the vehicle introduces some advantages when compared with conventional wired networks, whereas the inclusion of a residential WSNs in the proposed IoT architecture allows the provision of additional features, such as automatic control of the EV battery charging process. Experimental results are provided to assess the performance of the developed IVWSN and HMI.This work has been supported by COMPETE: POCI-01-0145- FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio