Internet of Things (IoT) devices play a crucial role in the overall development of IoT in providing countless applications in various areas. Due to the increasing interest and rapid technological growth of sensor technology, which have certainly revolutionized the way we live today, a need to provide a detailed analysis of the embedded platforms and boards is consequential. This paper presents a comprehensive survey of the recent and most-widely used commercial and research embedded systems and boards in different classification emphasizing their key attributes including processing and memory capabilities, security features, connectivity and communication interfaces, size, cost and appearance, operating system support, power specifications, and battery life and listing some interesting projects for each device. Through this exploration and discussion, readers can have an overall understanding on this area and foster more subsequent studies.

A Review of Low-End, Middle-End, and High-End Iot Devices

Vehicular congestion is directly impacting the efficiency of the transport sector. A wireless sensor network for vehicular clients is used in Internet of Vehicles based solutions for traffic management applications. It was found that vehicular congestion detection by using Internet of Vehicles based connected vehicles technology are practically feasible for congestion handling. It was found that by using Fog Computing based principles in the vehicular wireless sensor network, communication in the system can be improved to support larger number of nodes without impacting performance. In this paper, connected vehicles technology based vehicular congestion identification techniques are studied. Computing paradigms that can be used for the vehicular network are studied to develop a practically feasible vehicular congestion detection system that performs accurately for a large coverage area and multiple scenarios. The designed system is expected to detect congestion to meet traffic management goals that are of primary importance in intelligent transportation systems.

Fog Computing for Detecting Vehicular Congestion, an Internet of Vehicles Based Approach: A Review

With the large use of wireless sensor devices, the interest in positioning and tracking by means of wireless sensor networks is expected to grow further. Particularly, accurate localization of a moving target is a fundamental requirement in several Machine to Machine monitoring applications. Tracking using Received Signal Strength Indicator (RSSI) has been frequently adopted thanks to the availability and the low cost of this parameter. In this paper, we propose an innovative target tracking algorithm which combines learning regression tree approach and filtering methods using RSSI metric. Regression Tree algorithm is investigated in order to estimate the position using the RSSI. This method is combined to filtering approaches yielding to more refined results. The suggested approach is evaluated through simulations and experiments. We also compare our method to existing algorithms available in the literature. The numerical and experimental results show the relevance and the efficiency of our method.

An accurate prediction method for moving target localization and tracking in wireless sensor networks

Intelligent vehicles use advanced driver assistance systems (ADASs) to mitigate driving risks. There is increasing demand for an ADAS framework that can increase driving safety by detecting dangerous driving behavior from driver, vehicle, and lane attributes. However, because dangerous driving behavior in real-world driving scenarios can be caused by any or a combination of driver, vehicle, and lane attributes, the detection of dangerous driving behavior using conventional approaches that focus on only one type of attribute may not be sufficient to improve driving safety in realistic situations. To facilitate driving safety improvements, the concept of dangerous driving intensity (DDI) is introduced in this paper, and the objective of dangerous driving behavior detection is converted into DDI estimation based on the three attribute types. To this end, we propose a framework, wherein fuzzy sets are optimized using particle swarm optimization for modeling driver, vehicle, and lane attributes and then used to accurately estimate the DDI. The mean opinion scores of experienced drivers are employed to label DDI for a fair comparison with the results of our framework. The experimental results demonstrate that the driver, vehicle, and lane attributes defined in this paper provide useful cues for DDI analysis; furthermore, the results obtained using the framework are in favorable agreement with those obtained in the perception study. The proposed framework can greatly increase driving safety in intelligent vehicles, where most of the driving risk is within the control of the driver.

Automatic Dangerous Driving Intensity Analysis for Advanced Driver Assistance Systems From Multimodal Driving Signals

In the last decade, the growth of the automotive market with the aid of technologies has been notable for the economic, automotive and technological sectors. Alongside this growing recognition, the so called Internet of Intelligent Vehicles (IoIV) emerges as an evolution of the Internet of Things (IoT) applied to the automotive sector. Closely related to IoIV, emerges the concept of Industrial Internet of Things (IIoT), which is the current revolution seen in industrial automation. IIoT, in its turn, relates to the concept of Industry 4.0, that is used to represent the current Industrial Revolution. This revolution, however, involves different areas: from manufacturing to healthcare. The Industry 4.0 can create value during the entire product lifecycle, promoting customer feedback, that is, having information about the product history throughout it is life. In this way, the automatic communication between vehicle and factory was facilitated, allowing the accomplishment of different analysis regarding vehicles, such as the identification of a behavioral pattern through historical driver usage, fuel consumption, maintenance indicators, so on. Thus, allowing the prevention of critical issues and undesired behaviors, since the automakers lose contact with the vehicle after the purchase. Therefore, this paper aims to propose a customer feedback platform for vehicle manufacturing in Industry 4.0 context, capable of collecting and analyzing, through an OBD-II (On-Board Diagnostics) scanner, the sensors available by vehicles, with the purpose of assisting in the management, prevention, and mitigation of different vehicular problems. An intercontinental evaluation conducted between Brazil and Italy locations shown the feasibility of platform and the potential to use in order to improve the vehicle manufacturing process.

A Customer Feedback Platform for Vehicle Manufacturing Compliant with Industry 4.0 Vision.

This paper describes the work that has been done in the design and development of a wireless on-board diagnostic system (OBD II) fleet management system. The system aims to measure speed, distance, and fuel consumption of vehicles for tracking and analysis purposes. An OBD II reader is designed to measure speed and mass air flow, from which the distance and fuel consumption are also computed. This data is then transmitted via WiFi to a remote server. The system also implements global positioning system tracking to determine the location of the vehicle. A database management system is implemented at the remote server for the storage and management of transmitted data and a graphical user interface is developed for analysing the transmitted data. Various qualification tests are conducted to verify the functionality of the system. The results demonstrate that the system is capable of reading the various parameters, and can successfully process, transmit, and display the readings.

Design and Implementation of a Wireless OBD II Fleet Management System

Machine to machine communication in intelligent transportation is a technology that aims to interconnect various components such as sensors, vehicles, road infrastructures and wireless networks. The significance thereof is to solve problems such as road congestion, road accidents and high vehicle fuel consumption. This paper gives an overview of how Machine-to-machine (M2M) communication can be used in intelligent transportation systems (ITS) to improve road safety and efficiency, where Vehicular ad-hoc networks (VANETs) play a major role. These applications include traffic light control, fleet management and smart grid systems. Some of M2M architectures that have been devised are also discussed.

Ntefeng Ruth Moloisane

Papers

Design and Implementation of a Wireless OBD II Fleet Management System

Wireless machine-to-machine communication for intelligent transportation systems: Internet of vehicles and vehicle to grid