Edge/Fog Computing Technologies for IoT Infrastructure II.

The prevalence of smart devices and cloud computing has led to an explosion in the amount of data generated by IoT devices [...].

Edge/Fog Computing Technologies for IoT Infrastructure.

The prevalence of smart devices and cloud computing has led to an explosion in the amount of data generated by IoT devices [...].

Industrial Wireless Sensor Networks: Protocols and Applications.

Wireless sensor networks are penetrating our daily lives, and they are starting to be deployed even in an industrial environment. The research on such industrial wireless sensor networks (IWSNs) considers more stringent requirements of robustness, reliability, and timeliness in each network layer. This Special Issue presents the recent research result on industrial wireless sensor networks. Each paper in the special issue has unique contributions in the advancements of industrial wireless sensor network research and we expect each paper to promote the relevant research and the deployment of IWSNs.

SoEasy: A Software Framework for Easy Hardware Control Programming for Diverse IoT Platforms.

Many Internet of Things (IoT) applications are emerging and evolving rapidly thanks to widespread open-source hardware platforms. Most of the high-end open-source IoT platforms include built-in peripherals, such as the universal asynchronous receiver and transmitter (UART), pulse width modulation (PWM), general purpose input output (GPIO) ports and timers, and have enough computation power to run embedded operating systems such as Linux. However, each IoT platform has its own way of configuring peripherals, and it is difficult for programmers or users to configure the same peripheral on a different platform. Although diverse open-source IoT platforms are widespread, the difficulty in programming those platforms hinders the growth of IoT applications. Therefore, we propose an easy and convenient way to program and configure the operation of each peripheral using a user-friendly Web-based software framework. Through the implementation of the software framework and the real mobile robot application development along with it, we show the feasibility of the proposed software framework, named SoEasy.

A wireless sensor network-based portable vehicle detector evaluation system.

In an upcoming smart transportation environment, performance evaluations of existing Vehicle Detection Systems are crucial to maintain their accuracy. The existing evaluation method for Vehicle Detection Systems is based on a wired Vehicle Detection System reference and a video recorder, which must be operated and analyzed by capable traffic experts. However, this conventional evaluation system has many disadvantages. It is inconvenient to deploy, the evaluation takes a long time, and it lacks scalability and objectivity. To improve the evaluation procedure, this paper proposes a Portable Vehicle Detector Evaluation System based on wireless sensor networks. We describe both the architecture and design of a Vehicle Detector Evaluation System and the implementation results, focusing on the wireless sensor networks and methods for traffic information measurement. With the help of wireless sensor networks and automated analysis, our Vehicle Detector Evaluation System can evaluate a Vehicle Detection System conveniently and objectively. The extensive evaluations of our Vehicle Detector Evaluation System show that it can measure the traffic information such as volume counts and speed with over 98% accuracy.

S3: school zone safety system based on wireless sensor network.

School zones are areas near schools that have lower speed limits and where illegally parked vehicles pose a threat to school children by obstructing them from the view of drivers. However, these laws are regularly flouted. Thus, we propose a novel wireless sensor network application called School zone Safety System (S3) to help regulate the speed limit and to prevent illegal parking in school zones. S3 detects illegally parked vehicles, and warns the driver and records the license plate number. To reduce the traveling speed of vehicles in a school zone, S3 measures the speed of vehicles and displays the speed to the driver via an LED display, and also captures the image of the speeding vehicle with a speed camera. We developed a state machine based vehicle detection algorithm for S3. From extensive experiments in our testbeds and data from a real school zone, it is shown that the system can detect all kinds of vehicles, and has an accuracy of over 95% for speed measurement. We modeled the battery life time of a sensor node and validated the model with a downscaled measurement; we estimate the battery life time to be over 2 years. We have deployed S3 in 15 school zones in 2007, and we have demonstrated the robustness of S3 by operating them for over 1 year.