The prompt expansion of Internet of Things (IoT) devices necessitates advanced security frameworks to protect data integrity, confidentiality, and availability in resource-constrained environments. Traditional security solutions are often resource-intensive for IoT devices with limited computational power and energy resources. This study addresses these inadequacies by proposing a novel approach formulated to such constraints. This study propose the trust-enhanced lightweight security framework (TELSF), integrating two novel components: 1) the adaptive lightweight encryption algorithm (ALEA) and 2) the trust-aware data protection model (TADPM). ALEA employs dynamic key generation through a lightweight hash function, ensuring unique and regularly updated encryption keys based on device context and behavior. TADPM enhances this framework by continuously assessing device trustworthiness through direct interactions, aggregated feedback from neighboring devices, and contextual parameters, such as location and device capabilities. Performance evaluations demonstrate that TELSF significantly enhances security and operational efficiency, reducing computational overhead by 18%, improving energy efficiency by 20%, and increasing data transmission security by 10% compared to existing solutions.

The Web of Things (WoT) is a network that facilitates the formation and distribution of information its users make. Young people nowadays, digital natives, have no trouble relating to others or joining groups online since they have grown up in a world where new technology has pushed communications to a nearly real-time level. Shared private messages, rumours, and sexual comments are all examples of online harassment that have led to several recent cases worldwide. Therefore, academics have been more interested in finding ways to recognise bullying conduct on these platforms. The effects of cyberbullying, a terrible form of online misbehaviour, are distressing. It takes several documents, but the text is predominant on social networks. Intelligent systems are required for the automatic detection of such occurrences. Most previous research has used standard machine-learning techniques to tackle this issue. The increasing pervasiveness of cyberbullying in WoT and other social media platforms is a significant cause for worry that calls for robust responses to prevent further harm. This study offers a unique method of leveraging the deep learning (DL) model binary coyote optimization-based Convolutional Neural Network (BCNN) in social networks to identify and classify cyberbullying. An essential part of this method is the combination of DL-based abuse detection and feature subset selection. To efficiently detect and address cases of cyberbullying via social media, the proposed system incorporates many crucial steps, including preprocessing, feature selection, and classification. A binary coyote optimization (BCO)-based feature subset selection method is presented to enhance classification efficiency. To improve the accuracy of cyberbullying categorization, the BCO algorithm efficiently chooses a selection of key characteristics. Cyberbullying must be tracked and classified across all internet channels, and Convolutional Neural Network (CNN) is constructed. With a best-case accuracy of 99.5% on Formspring, 99.7% on Twitter, and 99.3% on Wikipedia, the suggested algorithm successfully identified the vast majority of cyberbullying content.

By exploring the future network, smart technologies promote the development of cutting-edge industrial applications. Internet of Things (IoT) systems use sensing approaches to acquire data and control real-time processing and complex tasks. Several techniques have been proposed for coping with environmental behavior in industrial management and reducing the response in crucial circumstances. However, due to the unique and limited constraints of the industrial environment, managing data routing and sustainable development are recent research concerns. In addition, security is essential for industrial communication systems due to the probability of unauthorized access, thus trust level must be improved. The framework addresses real-world challenges in industrial networks by incorporating a lightweight data verification algorithm designed for green communication, reducing energy consumption while maintaining data integrity. First, predictive computing is implemented using ant colony optimization (ACO) based on real-time requirements and selects the dynamic and communication channels for data transmission across the industrial platform. Second, mobile sinks offer more authentic techniques for verifying sensor data and delivering it securely to the cloud servers. The framework was evaluated and validated in a simulation-based environment, revealing a considerable improvement in terms of network throughput, packet drop ratio, connectivity ratio, and network overhead over the existing approaches.