Implement DNN technology by using wireless sensor network system based on IOT applications

The smart Internet of Things-based system suggested in this research intends to increase network and application accuracy by controlling and monitoring the network. This is a deep learning network. The invisible layer's structure permits it to learn more. Improved quality of service supplied by each sensor node thanks to element-modified deep learning and network buffer capacity management. A customized deep learning technique can be used to train a system that can focus better on tasks. The researchers were able to implement wireless sensor calculations with 98.68 percent precision and the fastest execution time. With a sensor-based system and a short execution time, this article detects and classifies the proxy with 99.21 percent accuracy. However, we were able to accurately detect and classify intrusions and real-time proxy types in this study, which is a significant improvement over previous research

Sustainable Practices of Smart Mobility Service (SMS): A Case Study of Public Transportation System in Kuala Lumpur, Malaysia

The public transportation system contributing immensely to national economic progress. Regardless of various obstacles, the public transportation system has achieved growth. This study aimed to examine the influences of sustainable practices of Smart Mobility Service (SMS) of public transportation system in Kuala Lumpur, Malaysia. The key objective this study to justify the relationship between sustainable practices such as Smart Governance (SG), Smart Infrastructure (SI), Smart Technology (ST), Smart Citizen (SC) are proposed to justify is there any significant relationships with Smart Mobility Services (SMS) in public transportation system in Kuala Lumpur. This research believes that the basis to determine the Smart Mobility Services (SMS) in public transportation system. Limitations of the study are discussed and directions for future research are suggested.

Traffic Accidents Analysis & Prediction in UAE

UAE has state-of-the-art roads and traffic infrastructure, and yet there has been a rapid increase in the number of traffic accidents. As per National Agenda 2021, the UAE traffic departments are working to minimize the rate of road traffic death per 100 thousand people. The Traffic and Patrols Directorate in Abu Dhabi launched a road safety management plan. This aims at decreasing the fatalities from traffic accidents to 3 per 100,000 inhabitants by 2021. According to police, distracted driving, sudden swerving, entering a road without ensuring that it is clear, tailgating and speeding without considering the road conditions has caused serious accidents so far. The disturbing figure has led to the amendment of the federal traffic law which now imposes hefty penalties. This study aims to identify the trends and evaluate the information available on possible causes. In the due course of the report, I would like to understand these trends around road accidents and their inherent causes. This would help the government bolster road safety measures in order to reduce the number or even avoid accidents on the road. There has been much research work done on the above to determine causes and driving factors of accidents on the road

Estimation computational models of the cyber-physical systems functioning

This paper reviews the use of computational models to support the functioning of cyber-physical systems (CPS) in the parallel world of the Internet of Things (IoT). Existing models, methods, techniques and their implementation in this direction are studied. The necessity of using machine learning methods due to inaccuracy, fuzziness, incompleteness of the transmitted data from sensors of physical systems is substantiated. The task is to make informed decisions in a timely manner to support the functioning of real objects of a particular cyber-physical system in real time conditions

Wireless networks for traffic light control on urban and aerotropolis roads

This paper presents a traffic lights system based on wireless communication, providing a support infrastructure for intelligent control in smart cities and aerotropolis scope. An aerotropolis is a metropolitan subregion which infrastructure is centered around an airport [1]. Traffic intensity is increasing all over the world. Intelligent dynamic traffic lights system control are sought for replacing classic conventional manual and time based systems. In this work a wireless sensors network is designed and implemented to feed real time data to the intelligent traffic lights systems control. A physical prototype is implemented for experimental validation outside laboratory environment. The physical prototype shows robustness against unexpected issues or local failures. Results are positive in the scope of the experiences made and promising in terms of extending the tests to larger areas

Real-Time Detection of Vine Trunk for Robot Localization Using Deep Learning Models Developed for Edge TPU Devices

The concept of the Internet of Things (IoT) in agriculture is associated with the use of high-tech devices such as robots and sensors that are interconnected to assess or monitor conditions on a particular plot of land and then deploy the various factors of production such as seeds, fertilizer, water, etc., accordingly. Vine trunk detection can help create an accurate map of the vineyard that the agricultural robot can rely on to safely navigate and perform a variety of agricultural tasks such as harvesting, pruning, etc. In this work, the state-of-the-art single-shot multibox detector (SSD) with MobileDet Edge TPU and MobileNet Edge TPU models as the backbone was used to detect the tree trunks in the vineyard. Compared to the SSD with MobileNet-V1, MobileNet-V2, and MobileDet as backbone, the SSD with MobileNet Edge TPU was more accurate in inference on the Raspberrypi, with almost the same inference time on the TPU. The SSD with MobileDet Edge TPU achieved the second-best accurate model. Additionally, this work examines the effects of some features, including the size of the input model, the quantity of training data, and the diversity of the training dataset. Increasing the size of the input model and the training dataset increased the performance of the model.info:eu-repo/semantics/publishedVersio

Recent Advances in Graph-based Machine Learning for Applications in Smart Urban Transportation Systems

The Intelligent Transportation System (ITS) is an important part of modern transportation infrastructure, employing a combination of communication technology, information processing and control systems to manage transportation networks. This integration of various components such as roads, vehicles, and communication systems, is expected to improve efficiency and safety by providing better information, services, and coordination of transportation modes. In recent years, graph-based machine learning has become an increasingly important research focus in the field of ITS aiming at the development of complex, data-driven solutions to address various ITS-related challenges. This chapter presents background information on the key technical challenges for ITS design, along with a review of research methods ranging from classic statistical approaches to modern machine learning and deep learning-based approaches. Specifically, we provide an in-depth review of graph-based machine learning methods, including basic concepts of graphs, graph data representation, graph neural network architectures and their relation to ITS applications. Additionally, two case studies of graph-based ITS applications proposed in our recent work are presented in detail to demonstrate the potential of graph-based machine learning in the ITS domain

Train me if you can: decentralized learning on the deep edge

The end of Moore’s Law aligned with data privacy concerns is forcing machine learning (ML) to shift from the cloud to the deep edge. In the next-generation ML systems, the inference and part of the training process will perform at the edge, while the cloud stays responsible for major updates. This new computing paradigm, called federated learning (FL), alleviates the cloud and network infrastructure while increasing data privacy. Recent advances empowered the inference pass of quantized artificial neural networks (ANNs) on Arm Cortex-M and RISC-V microcontroller units (MCUs). Nevertheless, the training remains confined to the cloud, imposing the transaction of high volumes of private data over a network and leading to unpredictable delays when ML applications attempt to adapt to adversarial environments. To fill this gap, we make the first attempt to evaluate the feasibility of ANN training in Arm Cortex-M MCUs. From the available optimization algorithms, stochastic gradient descent (SGD) has the best trade-off between accuracy, memory footprint, and latency. However, its original form and the variants available in the literature still do not fit the stringent requirements of Arm Cortex-M MCUs. We propose L-SGD, a lightweight implementation of SGD optimized for maximum speed and minimal memory footprint in this class of MCUs. We developed a floating-point version and another that operates over quantized weights. For a fully-connected ANN trained on the MNIST dataset, L-SGD (float-32) is 4.20× faster than the SGD while requiring only 2.80% of the memory with negligible accuracy loss. Results also show that quantized training is still unfeasible to train an ANN from the scratch but is a lightweight solution to perform minor model fixes and counteract the fairness problem in typical FL systems.This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. This work has also been supported by FCT within the PhD Scholarship Project Scope: SFRH/BD/146780/2019

Addressing Transportation Equity by Comparing In-Service Performance of Roadside Safety Devices through Machine Learning Modeling

Transportation equity plays an important role in modern communities, and a fair distribution of transportation infrastructures is vital as an integral part of transportation planning process. The In-Service Performance Evaluation (ISPE) satisfies transportation safety requirements by identifying the problems of roadside safety devices during installation and maintenance process with proper solutions, and the performance results reveal the current statue of target devices in specific areas. Although several studies have been conducted to emphasize transportation equity, there is still a lack of equity research specifically focusing on the deploying of roadside safety devices associated with ISPE results. With proper comparison of in-service performance results in different areas, the importance of ensuring transportation equity of all communities and areas in the decision-making process is able to be demonstrated. This thesis utilizes Machine Learning models to analyze linked crash and roadway data related to major roadside safety devices implemented in Texas. Three typical roadside safety devices are selected to be assessed, including: (1) guardrail, (2) median barrier, and (3) bridge rail. By comparing both statistical and Machine Learning based modeling analysis with rural and metropolitan areas in specific counties, it is demonstrated that distributions of crashes that end up causing heavy property damage or serious injuries is higher in rural communities regardless of its lower crash frequency. The data analysis result suggests that parameters related to roadway conditions and transportation infrastructures tend to have higher influence over the performances of rural safety devices. Additional one year of crash data analysis also addresses the importance of transportation equity under the COVID-19 pandemic period. Recommendations on improving overall equity and Environmental Justice (EJ) within all regions are conducted with stated findings

Internet of Things security with machine learning techniques:a systematic literature review

Abstract. The Internet of Things (IoT) technologies are beneficial for both private and businesses. The growth of the technology and its rapid introduction to target fast-growing markets faces security challenges. Machine learning techniques have been recently used in research studies as a solution in securing IoT devices. These machine learning techniques have been implemented successfully in other fields. The objective of this thesis is to identify and analyze existing scientific literature published recently regarding the use of machine learning techniques in securing IoT devices. In this thesis, a systematic literature review was conducted to explore the previous research on the use of machine learning in IoT security. The review was conducted by following a procedure developed in the review protocol. The data for the study was collected from three databases i.e. IEEE Xplore, Scopus and Web of Science. From a total of 855 identified papers, 20 relevant primary studies were selected to answer the research question. The study identified 7 machine learning techniques used in IoT security, additionally, several attack models were identified and classified into 5 categories. The results show that the use of machine learning techniques in IoT security is a promising solution to the challenges facing security. Supervised machine learning techniques have better performance in comparison to unsupervised and reinforced learning. The findings also identified that data types and the learning method affects the performance of machine learning techniques. Furthermore, the results show that machine learning approach is mostly used in securing the network