An Intelligent IoT Sensing System for Rail Vehicle Running States Based on TinyML

Real-time identification of the running state is one of the key technologies for a smart rail vehicle. However, it is a challenge to accurately real-time sense the complex running states of the rail vehicle on an Internet-of-Things (IoT) edge device. Traditional systems usually upload a large amount of real-time data from the vehicle to the cloud for identification, which is laborious and inefficient. In this paper, an intelligent identification method for rail vehicle running state is proposed based on Tiny Machine Learning (TinyML) technology, and an IoT system is developed with small size and low energy consumption. The system uses a Micro-Electro-Mechanical System (MEMS) sensor to collect acceleration data for machine learning training. A neural network model for recognizing the running state of rail vehicles is built and trained by defining a machine learning running state classification model. The trained recognition model is deployed to the IoT edge device at the vehicle side, and an offset time window method is utilized for real-time state sensing. In addition, the sensing results are uploaded to the IoT server for visualization. The experiments on the subway vehicle showed that the system could identify six complex running states in real-time with over 99% accuracy using only one IoT microcontroller. The model with three axes converges faster than the model with one. The model recognition accuracy remained above 98% and 95%, under different installation positions on the rail vehicle and the zero-drift phenomenon of the MEMS acceleration sensor, respectively. The presented method and system can also be extended to edge-aware applications of equipment such as automobiles and ships.

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Security and Privacy in Smart Farming: Challenges and Opportunities

Internet of Things (IoT) and smart computing technologies have revolutionized every sphere of 21 st century humans. IoT technologies and the data driven services they offer were beyond imagination just a decade ago. Now, they surround us and influence a variety of domains such as automobile, smart home, healthcare, etc. In particular, the Agriculture and Farming industries have also embraced this technological intervention. Smart devices are widely used by a range of people from farmers to entrepreneurs. These technologies are used in a variety of ways, from finding real-time status of crops and soil moisture content to deploying drones to assist with tasks such as applying pesticide spray. However, the use of IoT and smart communication technologies introduce a vast exposure to cybersecurity threats and vulnerabilities in smart farming environments. Such cyber attacks have the potential to disrupt the economies of countries that are widely dependent on agriculture. In this paper, we present a holistic study on security and privacy in a smart farming ecosystem. The paper outlines a multi layered architecture relevant to the precision agriculture domain and discusses the security and privacy issues in this dynamic and distributed cyber physical environment. Further more, the paper elaborates on potential cyber attack scenarios and highlights open research challenges and future directions.

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An Optimal Home Energy Management Paradigm With an Adaptive Neuro-Fuzzy Regulation

In the smart grid paradigm, residential consumers should participate actively in the energy exchange mechanisms by adjusting their consumption and generation. To this end, a proper home energy management system (HEMS), in addition to achieving a high level of comfort for the consumers, should handle the practical difficulties due to the uncertainty and technical limits. With this aim, in this paper, a new HEMS is proposed to carry out day-ahead management and real-time regulation. While an optimal scheduling solution based on some forecasted values of uncertain parameters is achieved for day ahead management, real-time regulation is accomplished by an adaptive neuro-fuzzy inference system, which can regulate the gaps between the forecasted and real values. Investigated case studies indicate that the proposed HEMS can find an optimal operating scenario with an acceptable success rate for real-time regulation.

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Multi-Level Time-Sensitive Networking (TSN) Using the Data Distribution Services (DDS) for Synchronized Three-Phase Measurement Data Transfer

This paper presents the design and implementation of a Multi-level Time Sensitive Networking (TSN) protocol based on a real-time communication platform utilizing Data Distribution Service (DDS) middleware for data transfer of synchronous three phase measurement data. To transfer ultra-high three phase measurement samples, the DDS open-source protocol is exploited to shape the network’s data traffic according to specific Quality of Service (QoS) profiles, leading to low packet loss and low latency by synchronizing and prioritizing the data in the network. Meanwhile the TSN protocol enables time-synchronization of the measured data by providing a common time reference to all the measurement devices in the network, making the system less expensive, more secure and enabling time-synchronization where acquiring GPS signals is a challenge. A software library was developed and used as a central Quality of Service (QoS) profile for the TSN implementation. The proposed design and implemented real-time simulation prototype presented in this paper takes in consideration diverse scenarios at multiple levels of prioritization including publishers, subscribers, and data packets. This allows granular control and monitoring of the data for traffic shaping, scheduling, and prioritization. The major strength of this protocol lies in the fact that it’s not only in real time but it’s time-critical too. The simulation prototype implementation was performed using the Real Time Innovation (RTI) Connext connectivity framework, custom-built MATLAB classes and DDS Simulink blocks. Simulation results show that the proposed protocol achieves low latency and high throughput, which makes it a desired option for various communication systems involved in microgrids, smart cities, military applications and potentially other time-critical applications, where GPS signals become vulnerable and data transfer needs to be prioritized.

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Traffic Signal Phase Scheduling Based on Device-to-Device Communication

Device-to-device (D2D) communications enable direct communications among mobile entities, which brings new revolutions to existing cellular networks. Many use cases which can benefit from D2D are introduced such as vehicles-to-vehicles communication, vehicles-to-infrastructure communication, machine-to-machine communication, and so on. With the help of these information communication techniques, we propose a real-time traffic signal control approach to relieve traffic problems in this paper. Currently, a series of traffic problems, such as traffic congestion, traffic accidents, and vehicle exhaust emission, are increasingly inconveniencing city residents, especially in rush hours. One of the most dominating approaches to relieve the traffic congest is to determine the phase timing of traffic signals. However, a major shortcoming of the existing phase timing related control strategies is of highly computational complexity, which causes, to some extent, a response delay. The approach based on D2D communication, in this paper, on one hand can collect data of various types via sensors and actuators and on the other hand can reduce the response time as much as possible. Specifically, considering an intersection with four legs, we encoded the corresponding set of signal lights of each leg using a genetic algorithm. To evaluate the efficiency of phase timing plan in this paper, we have conducted extensive simulations, and the results show that our approach can respond to the considered traffic flow within one second. Compared with other traffic signal control systems, the performance is improved almost by 67% with regards to the queue length waiting at the intersections during traffic signal light cycle(s).

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