The emergence of Internet of Things (IoT) technologies has led to the development of smart cities, potentially improving urban life through efficient and effective use of resources. The success of IoT-based applications in smart cities is contingent on several factors, including network power challenges. Deploying IoT-based applications in smart cities requires a reliable and efficient network infrastructure to ensure smooth and uninterrupted data communication. One of the significant challenges faced in this regard is low network power, which can significantly impact the performance of IoT devices and, consequently, the entire smart city system. This article addresses the issue of low network power in IoT-based applications in smart cities. It investigates the causes of this problem and the potential solutions that can be adopted to mitigate its impact. The article also highlights the importance of network power management in the context of IoT-based applications in smart cities. By implementing effective network power management techniques, it is possible to optimize the performance of IoT devices and extend their battery life, thus ensuring the overall efficiency and sustainability of the smart city system. The article concludes by emphasizing the need for continuous research and development to overcome the challenges of low network power and further enhance IoT technology's capabilities in smart cities.

Prior studies on behaviour-based threat detection on Internet of Things (IoT) device networks have generated machine-learning models with a limited and frequently unproven capacity to learn from unseen data. In this study, we provide a generalizability-focused modelling technique for IoT network assaults that also improves detection and performance. Firstly, we develop a multi-step feature selection technique that minimizes overfitting and provides an enhanced rolling window strategy for feature extraction. Second, in order to prevent frequent data leaks that have restricted the generalizability of earlier models, we develop and test our models using separate train and test datasets. Third, we employ a wide range of machine learning models, assessment measures, and datasets to assess our approach thoroughly. Lastly, we employ explainable AI approaches to strengthen the models' confidence, enabling us to pinpoint the characteristics that support precise attack detection. Models are updated gradually by use of algorithms such as Online Naive Bayes and Stochastic Gradient Descent (SGD).

The DDoS (Distributed Denial of Service) attack is a type of Cyberattack in which multiple attackers aim to attack different network resources like a server or a website. Although many statistical methods have already been designed for DDoS attack detection, designing a real-time detector with low computational overhead is still one of the main concerns. The already existing datasets are highly important and can be used for constructing and checking new solutions. It is the most dangerous attack against IPv6 networks today. The attack uses Internet Control Message Protocol version 6 (ICMPv6) messages. DDoS attack can be detected in various ways like a sudden fluctuation in the traffic of a website or unreal raise in the requests to resources. DDoS attacks are among the four most malicious attacks, like social engineering, ransomware, and supply chain attacks. It’s relatively easy to confuse DDoS attacks with other cyber threats. As for now most of our application and infrastructure resides on the cloud. As for cloud providers, the services provider must facilitate some tools to prevent the attack on their services and their user. Some of the major cloud providers give us this type of facility (AWS, Azure, and GCPThis cloud service provider offers cloud DDoS mitigation and prevention that operates entirely outside of your current network, inside the Internet cloud, and can identify and stop DDoS attacks before they even get to you. For bigger installations, routing is utilised to ensure that all network traffic, regardless of type, is filtered before delivery via a clean pipe. Domain name system (DNS) is used to direct inbound traffic through a scrubbing centre before delivery to the server. DDoS mitigation and prevention in the cloud is not only speedy, but also incredibly effective at stopping DDoS attacks.

Blockchain is a system recording data that makes it difficult or impossible to alter, hack, or defraud the system. A blockchain is a digital ledger of transactions replicated and distributed over computer servers. Blockchain technology is a significant new financial innovation radically reshaping corporate transactions. It is a decentralized network that supports and employs multiple encryption algorithms. These safe, robust, and adaptable transactions are incorporated into another significant computer paradigm, cloud computing. This paper implements a blockchain-based secure method to make the cloud secure. Initially, the transactions are recorded in the blockchain using the SHA-256 cryptographic algorithm. After that, transactions are serialized, and block features are cached. Thirdly, the blockchain created new blocks after fulfilling desired requirements and performing transactions. Furthermore, the transaction is monitored by using the Merkle tree method in each step of the transaction to make it more secure and trustworthy. However, the research also highlighted the gaps and the impact of the gaps in the current work.