Smart cities are being developed worldwide with the use of technology to improve the quality of life of citizens and enhance their safety. Video surveillance is a key component of smart city infrastructure, as it involves the installation of cameras at strategic locations throughout the city for monitoring public spaces and providing real-time surveillance footage to law enforcement and other city representatives. Video surveillance systems have evolved rapidly in recent years, and are now integrated with advanced technologies like deep learning, blockchain, edge computing, and cloud computing. This study provides a comprehensive overview of video surveillance systems in smart cities, as well as the functions and challenges of those systems. The aim of this paper is to highlight the importance of video surveillance systems in smart cities and to provide insights into how they could be used to enhance safety, security, and the overall quality of life for citizens.

A Survey of Video Surveillance Systems in Smart City

Vehicle-to-everything (V2X) in networks is a communication technology that allows vehicles to communicate with their surroundings. Traffic congestion and unawareness of the travel of emergency vehicles (EVs) lead to delays in reaching the destination of the EV. In order to overcome this time delay, we propose a jitter-to-highly reliable (J2H) approach of customizing the traffic signals and an alert passer mechanism to alert other vehicles. Once an EV is started, its source, destination and level of emergency will be updated on the network, and based on the traffic density, the fastest route to reach the destination is determined. The V2X system in the J2H approach passes an alert to all the traffic signals on that route. The traffic signals will continuously monitor the position of the vehicle by using the Global Positioning System (GPS). Based on the position of the vehicle, the distance between the vehicle and the traffic signal on that route is periodically updated. Once the vehicle comes within the range of the closest traffic signal, based on constraints such as number of lanes, emergency level, types of roads, traffic density, number of EVs approaching, and time of arrival of the vehicles, the traffic signal will be customized. The V2X then passes the information to all the traffic signals that are available in the route of the EV. The alert passer mechanism warns about the approaching EV to other vehicles on that route. Thus, by adapting the J2H technique, EVs can overcome the time delay to reach the destination. Traffic congestion is overcome by customizing the traffic signals. Path blockage can be cleared by vehicle-to-vehicle (V2V) communication.

https://www.mdpi.com/2076-3417/13/3/1950/pdf?version=1675335238

V2X-Based Highly Reliable Warning System for Emergency Vehicles

Connected and autonomous vehicles (CAVs) have witnessed significant attention from industries, and academia for research and developments towards the on-road realisation of the technology. State-of-the-art CAVs utilise existing navigation systems for mobility and travel path planning. However, reliable connectivity to navigation systems is not guaranteed, particularly in urban road traffic environments with high-rise buildings, nearby roads and multi-level flyovers. In this connection, this paper presents TAKEN-Traffic Knowledge-based Navigation for enabling CAVs in urban road traffic environments. A traffic analysis model is proposed for mining the sensor-oriented traffic data to generate a precise navigation path for the vehicle. A knowledge-sharing method is developed for collecting and generating new traffic knowledge from on-road vehicles. CAVs navigation is executed using the information enabled by traffic knowledge and analysis. The experimental performance evaluation results attest to the benefits of TAKEN in the precise navigation of CAVs in urban traffic environments.

/pdf/taken-a-traffic-knowledge-based-navigation-system-for-20ip2s2a.pdf

TAKEN: A Traffic Knowledge-Based Navigation System for Connected and Autonomous Vehicles

Plate-type hollow black TiO2 (HL/BT) with a high NIR reflectance was fabricated for the first time as a LiDAR-detectable black material. A TiO2 layer was formed on commercial-grade glass by using the sol-gel method to obtain a plate-type structure. The glass template was then etched with hydrofluoric acid to form a hollow structure, and blackness was further achieved through NaBH4 reduction, which altered the oxidation state of TiO2 to black TixO2x-1 or Ti4+ to Ti3+ and Ti2+. The blackness of the HL/BT material was maintained by a novel approach that involved etching prior to reduction. The thickness of the TiO2 layer was controlled to maximize the NIR reflectance when applied as paint. The HL/BT material with a thickness of 140 nm (HL/BT140) showed a blackness (L*) of 13.3 and high NIR reflectance of 23.6% at a wavelength of 905 nm. This is attributed to the effective light reflection at the interface created by the TiO2 layer and the hollow structure. Plate-type HL/BT140 provides excellent spreadability, durability, and thermal stability in practical paint applications compared with sphere-type materials due to the higher contacting area to the applied surface, making it suitable for use as a LiDAR-detectable inorganic black pigment in autonomous environments.

Designing Novel LiDAR-Detectable Plate-Type Materials: Synthesis, Chemistry, and Practical Application for Autonomous Working Environment.



Recently, researchers have proposed a lot of methods to boost the performance of convolutional neural networks (CNNs) for classifying remote sensing images (RSI). However, the methods' performance improvements were insignificant, while time and hardware costs increased dramatically due to re-modeling. To tackle this problem, this study sought a simple, lightweight, yet more accurate solution for RSI semantic classification (RSI-SC). At first, we proposed a set of mathematical derivations to analyze and identify the best way among different technical roadmaps. Afterward, we selected a simple route that can significantly boost a single CNN's performance while maintaining simplicity and reducing costs in time and hardware. The proposed method, called RE-EfficientNet, only consists of a lightweight EfficientNet-B3 and a concise training algorithm named RE-CNN. The novelty of RE-EfficientNet and RE-CNN includes the following: First, EfficientNet-B3 employs transfer learning from ImageNet-1K and excludes any complicated re-modeling. It can adequately utilize the easily accessible pre-trained weights for time savings and avoid the pre-training effect being weakened due to re-modeling. Second, RE-CNN includes an effective combination of data augmentation (DA) transformations and two modified training tricks (TTs). It can alleviate the data distribution shift from DA-processed training sets and make the TTs more effective through modification according to the inherent nature of RSI. Extensive experimental results on two RSI sets prove that RE-EfficientNet can surpass all 30 cutting-edge methods published before 2023. It gives a remarkable improvement of 0.50% to 0.75% in overall accuracy (OA) and a 75% or more reduction in parameters. The ablation experiment also reveals that RE-CNN can improve CNN OA by 0.55% to 1.10%. All the results indicate that RE-EfficientNet is a simple, lightweight and more accurate solution for RSI-SC. In addition, we argue that the ideas proposed in this work about how to choose an appropriate model and training algorithm can help us find more efficient approaches in the future.



Simple is best: A single-CNN method for classifying remote sensing images

The rapid development of Autonomous Vehicles (AVs) increases the requirement for the accurate prediction of objects in the vicinity to guarantee safer journeys. For effectively predicting objects, sensors such as Three-Dimensional Light Detection and Ranging (3D LiDAR) and cameras can be used. The 3D LiDAR sensor captures the 3D shape of the object and produces point cloud data that describes the geometrical structure of the object. The LiDAR-only detectors may be subject to false detection or even non-detection over objects located at high distances. The camera sensor captures RGB images with sufficient attributes that describe the distinct identification of the object. The high-resolution images produced by the camera sensor benefit the precise classification of the objects. However, hindrances such as the absence of depth information from the images, unstructured point clouds, and cross modalities affect assertion and boil down the environmental perception. To this end, this paper proposes an object detection mechanism that fuses the data received from the camera sensor and the 3D LiDAR sensor (OD-C3DL). The 3D LiDAR sensor obtains point clouds of the object such as distance, position, and geometric shape. The OD-C3DL employs Convolutional Neural Networks (CNN) for further processing point clouds obtained from the 3D LiDAR sensor and the camera sensor to recognize the objects effectively. The point cloud of the LiDAR is enhanced and fused with the image space on the Regions of Interest (ROI) for easy recognition of the objects. The evaluation results show that the OD-C3DL can provide an average of 89 real-time objects for a frame and reduces the extraction time by a recall rate of 94%. The average processing time is 65ms, which makes the OD-C3DL model incredibly suitable for the AVs perception. Furthermore, OD-C3DL provides mean accuracy for identifying automobiles and pedestrians at a moderate degree of difficulty is higher than that of the previous models at 79.13% and 88.76%.

/pdf/real-time-3d-object-detection-and-classification-in-20q29o8y.pdf

Real-Time 3D Object Detection and Classification in Autonomous Driving Environment Using 3D LiDAR and Camera Sensors

Enhancing the Security of WPA2/PSK Authentication Protocol in Wi-Fi Networks

The innovative idea enhances the functionality of the healthcare network. It swiftly examines the narrow band Internet of Things (i-NB-IoT) systems in wireless networks, concentrating on several fundamental characteristics for medical network descriptions. A new networking protocol called the Internet of Things narrowband (i-NB) were created for the IoT with a low bandwidth. The critical importance is given to monitoring the patients’ heart and breathing rates while this sensor for the parameter exchangers manages their body temperature, which is observed using a straightforward gadget. The doctor is in charge of monitoring patient data, but our system is also offered to present the same information alongside the identities of adjacent patients for self-defense. With the help of standard measures like i-DD, i-ONC and i-RTT. These connected measures are able to send and receive large amounts of data, frequently over great distances and with very high latency. For i-NB, the novel physical-layer control channel operation is employed to transmit the scheduling opinion in the downlink and provide feedback data in the uplink.

Wireless Medical Sensor Network based Healthcare Monitoring System in Narrow Band IoT

The recent advancements in various communication technologies have paved the way for sensor devices to connect with the internet. It can be expected that the quantity of internet-connected devices can reach a maximum of 75.44 billion by 2025. The quantities of such devices directly influence the amount of data to be transferred. The Internet of Things (IoT) is one of the technologies that provide connectivity among such devices through the existing internet. In addition to this, the IoT generates big data with the characteristics such as time-dependent, location-dependent, and multi-modal data quality. This paper presents a detailed survey about various machine learning techniques that treat the issues of the data generated by IoT devices. Further, the applications of various machine learning algorithms to the IoT data for extracting higher-level information have been presented.

The Role of Machine Learning in IoT: A Survey

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Real-Time 3D Object Detection and Classification in Autonomous Driving Environment Using 3D LiDAR and Camera Sensors

Enhancing the Security of WPA2/PSK Authentication Protocol in Wi-Fi Networks

Wireless Medical Sensor Network based Healthcare Monitoring System in Narrow Band IoT

The Role of Machine Learning in IoT: A Survey