A smart city is an urbanization region that collects data using several digital and physical devices. The information collected from such devices is used efficiently to manage revenues, resources, and assets, etc., while the information obtained from such devices is utilized to boost performance throughout the city. Cloud-based Internet of Things (IoT) applications could help smart cities that contain information gathered from citizens, devices, homes, and other things. This information is processed and analyzed to monitor and manage transportation networks, electric utilities, resources management, water supply systems, waste management, crime detection, security mechanisms, proficiency, digital library, healthcare facilities, and other opportunities. A cloud service provider offers public cloud services that can update the IoT environment, enabling third-party activities to embed IoT data within electronic devices executing on the IoT. In this paper, the author explored cloud-based IoT applications and their roles in smart cities.

The concept of Cloud Computing has been distinguished as one of the major computing models in recent years. Cloud computing has become a great innovation that has important consequences not just for services on the internet but also for the entire Information technology (IT) market. Its emergence aims to optimize on-demand technology, hardware and information provisioning as a service, reaching the economy of scale in the distribution and operation of IT strategies. A great deal of cloud computing research has been concerned over some obstacles and challenges that rely upon behind the lure of cloud computing. Security has been always raised as one of the most critical issues of cloud computing where resolving such an issue would result in constant growth in the use and popularity of the cloud. Security requirements represent a major issue that has to be met in order of easing some of these obstacles. This article presents the role of cloud computing in the IT sectors.

Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as adaptive technology to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively manage the searching system strategy. This algorithm is derived from natural selection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the improved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation process to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natural selection or genetics principles. For random testing, historical information is provided with intelligent enslavement to continue moving the search out from the area of improved features for processing of the outcomes. It is a category of heuristics of evolutionary history using behavioral science-influenced methods like an annuity, gene, preference, or combination (sometimes refers to as hybridization). This method seemed to be a valuable tool to find solutions for problems optimization. In this paper, the author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation.

Federated learning (FL) is a cutting-edge artificial intelligence approach. It is a decentralized problem-solving technique that allows users to train using massive data. Unprocessed information is stored in advanced technology by a secret confidentiality service, which incorporates machine learning (ML) training while removing data connections. As researchers in the field promote ML configurations containing a large amount of private data, systems and infrastructure must be developed to improve the effectiveness of advanced learning systems. This study examines FL in-depth, focusing on application and system platforms, mechanisms, real-world applications, and process contexts. FL creates robust classifiers without requiring information disclosure, resulting in highly secure privacy policies and access control privileges. The article begins with an overview of FL. Then, we examine technical data in FL, enabling innovation, contracts, and software. Compared with other review articles, our goal is to provide a more comprehensive explanation of the best procedure systems and authentic FL software to enable scientists to create the best privacy preservation solutions for IoT devices. We also provide an overview of similar scientific papers and a detailed analysis of the significant difficulties encountered in recent publications. Furthermore, we investigate the benefits and drawbacks of FL and highlight comprehensive distribution scenarios to demonstrate how specific FL models could be implemented to achieve the desired results.

Blockchain (BC) in the Internet of Things (IoT) is a novel technology that acts with decentralized, distributed, public and real-time ledger to store transactions among IoT nodes. A blockchain is a series of blocks, each block is linked to its previous blocks. Every block has the cryptographic hash code, previous block hash, and its data. The transactions in BC are the basic units that are used to transfer data between IoT nodes. The IoT nodes are different kind of physical but smart devices with embedded sensors, actuators, programs and able to communicate with other IoT nodes. The role of BC in IoT is to provide a procedure to process secured records of data through IoT nodes. BC is a secured technology that can be used publicly and openly. IoT requires this kind of technology to allow secure communication among IoT nodes in heterogeneous environment. The transactions in BC could be traced and explored through anyone who are authenticated to communicate within the IoT. The BC in IoT may help to improve the communication security. In this paper, I explored this approach, its opportunities and challenges.

A self-driving vehicle can navigate autonomously in smart cities without the need for human intervention. The emergence of Autonomous Connected Vehicles (ACVs) poses a substantial threat to public and passenger safety due to the possibility of cyber-attacks, which encompass remote hacking, manipulation of sensor data, and probable disablement or accidents. The sensors collect data to facilitate the network’s recognition of local landmarks, such as trees, curbs, pedestrians, signs, and traffic lights. ACVs gather vast amounts of data, encompassing the exact geographical coordinates of the vehicle, captured images, and signals received from various sensors. To create a fully autonomous system, it is imperative to intelligently integrate several technologies, such as sensors, communication, computation, machine learning (ML), data analytics, and other technologies. The primary issues in ACVs involve data privacy and security when instantaneously exchanging substantial volumes of data. This study investigates related data security and privacy research in ACVs using the Blockchain-enabled Federated Reinforcement Learning (BFRL) framework. This paper provides a literature review examining data security and privacy in ACVs and the BFRL framework that can be used to protect ACVs. This study presents the integration of FRL and Blockchain (BC) in the context of smart cities. Furthermore, the challenges and opportunities for future research on ACVs utilising BFRL frameworks are discussed.