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This paper explores the concept of smart cities and the role of the Internet of Things (IoT) and machine learning (ML) in realizing a data-centric smart environment. Smart cities leverage technology and data to improve the quality of life for citizens and enhance the efficiency of urban services. IoT and machine learning have emerged as key technologies for enabling smart city solutions that rely on large-scale data collection, analysis, and decision-making. This paper presents an overview of smart cities' various applications and discusses the challenges associated with implementing IoT and machine learning in urban environments. The paper also compares different case studies of successful smart city implementations utilizing IoT and machine learning technologies. The findings suggest that these technologies have the potential to transform urban environments and enable the creation of more livable, sustainable, and efficient cities. However, significant challenges remain regarding data privacy, security, and ethical considerations, which must be addressed to realize the full potential of smart cities. [ABSTRACT FROM AUTHOR]

Accidents often occur in the earth—typhoons, floods, earthquakes, traffic accidents and so on. Whether these accidents can be timely and effectively responded to has been an important indicator to judge whether a region is advanced or not. IoT provide a possibility to solve such emergent problems by intelligent monitoring, diagnosis and repair. For example, coastal cities are often attacked by typhoons, if typhoon meteorological identification and early warning can be effectively carried out, many unnecessary property and personnel losses can be reduced. Accurate typhoon prediction has very important practical significance. However, current typhoon monitoring and prediction are mainly based on simulation with meteorological data; the accuracy still needs to be improved. Nowadays, the technology of Internet of Things (IoT) and remote sensing technology become more and more closely linked; many IoT systems in smart cities' can obtain high-resolution remote sensing image data. Therefore, it is possible to use urban IoT system to realize the early warning of typhoon. In this paper, we propose a deep learning method for typhoon cloud recognition and typhoon center location, and design a general algorithm framework, including data preprocessing, model training and parameter selection, test and result analysis. Besides, we implement a typhoon early warning demo system. The experimental results show that our algorithm is better than the traditional methods in recognition accuracy. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) paves the way for the modern smart industrial applications and cities. Trusted Authority acts as a sole control in monitoring and maintaining the communications between the IoT devices and the infrastructure. The communication between the IoT devices happens from one trusted entity of an area to the other by way of generating security certificates. Establishing trust by way of generating security certificates for the IoT devices in a smart city application can be of high cost and expensive. In order to facilitate this, a secure group authentication scheme that creates trust amongst a group of IoT devices owned by several entities has been proposed. The majority of proposed authentication techniques are made for individual device authentication and are also utilized for group authentication; nevertheless, a unique solution for group authentication is the Dickson polynomial based secure group authentication scheme. The secret keys used in our proposed authentication technique are generated using the Dickson polynomial, which enables the group to authenticate without generating an excessive amount of network traffic overhead. IoT devices' group authentication has made use of the Dickson polynomial. Blockchain technology is employed to enable secure, efficient, and fast data transfer among the unique IoT devices of each group deployed at different places. Also, the proposed secure group authentication scheme developed based on Dickson polynomials is resistant to replay, man-in-the-middle, tampering, side channel and signature forgeries, impersonation, and ephemeral key secret leakage attacks. In order to accomplish this, we have implemented a hardware-based physically unclonable function. Implementation has been carried using python language and deployed and tested on Blockchain using Ethereum Goerli's Testnet framework. Performance analysis has been carried out by choosing various benchmarks and found that the proposed framework outperforms its counterparts through various metrics. Different parameters are also utilized to assess the performance of the proposed blockchain framework and shows that it has better performance in terms of computation, communication, storage and latency. [ABSTRACT FROM AUTHOR]