IoT is one of the fastest-growing technologies and it is estimated that more than a billion devices would be utilised across the globe by the end of 2030. To maximise the capability of these connected entities, trust and reputation among IoT entities is essential. Several trust management models have been proposed in the IoT environment; however, these schemes have not fully addressed the IoT devices’ features, such as device’s role, device type and its dynamic behavior in a smart environment. As a result, traditional trust and reputation models are insufficient to tackle these characteristics and uncertainty risks while connecting nodes to the network. Whilst continuous study has been carried out and various articles suggest promising solutions in constrained environments, research on trust and reputation is still at its infancy. In this paper, we carry out a comprehensive literature review on state-of-the-art research on the trust and reputation of IoT devices and systems. Specifically, we first propose a new structure, namely a new taxonomy, to organise the trust and reputation models based on the ways trust is managed. The proposed taxonomy comprises of traditional trust management-based systems and artificial intelligence-based systems, and combine both the classes which encourage the existing schemes to adapt these emerging concepts. This collaboration between the conventional mathematical and the advanced ML models result in design schemes that are more robust and efficient. Then we drill down to compare and analyse the methods and applications of these systems based on community-accepted performance metrics,e.g. scalability, delay, cooperativeness and efficiency. Finally, built upon the findings of the analysis, we identify and discuss open research issues and challenges, and further speculate and point out future research directions.

Correlated quality metrics extracted from a source code repository can be utilized to design a model to automatically predict defects in a software system. It is obvious that the extracted metrics will result in a highly unbalanced data, since the number of defects in a good quality software system should be far less than the number of normal instances. It is also a fact that the selection of the best discriminating features significantly improves the robustness and accuracy of a prediction model. Therefore, the contribution of this paper is twofold, first it selects the best discriminating features that help in accurately predicting a defect in a software component. Secondly, a cost-sensitive logistic regression and decision tree ensemble-based prediction models are applied to the best discriminating features for precisely predicting a defect in a software component. The proposed models are compared with the most recent schemes in the literature in terms of accuracy, area under the curve, and recall. The models are evaluated using 11 datasets and it is evident from the results and analysis that the performance of the proposed prediction models outperforms the schemes in the literature.

This study examines how user-generated content, social interactions, and virtual economies impact the Metaverse's changing environment. It offers crucial knowledge for policymakers, stakeholders, and developers of the Metaverse in building interesting and long-lasting virtual worlds. The study surveyed 385 respondents from the South Asian demographic using a Likert rating scale through an online survey. Demographic variables were analyzed using SPSS version 29, focusing on frequency and percentage distribution. Means and standard deviations were calculated for each variable and survey item. The main analysis included multiple regression to evaluate hypotheses regarding the interaction between user-generated content, social interaction, and the virtual economy. User-generated content significantly improves Metaverse environments (= 0.22, p<0.01**), highlighting its crucial part in creating immersive virtual spaces. Metaverse environments bene t from social interactions (=0.16, p<0.01**), fostering involvement and community. In the growth of the Metaverse, virtual economies are extremely important (=0.501, p<0.001***), encouraging user participation and innovation. This research, which uses rigorous statistical analysis and focuses on South Asian demographics, offers novel insights into the metaverse dynamics. It serves as a crucial resource for the Metaverse's future growth and development by guiding developers, stakeholders, and policymakers as they navigate the Metaverse's seemingly limitless potential.

The emerging wireless body area networks (WBANs) have a great potential for the growth and development of future ubiquitous healthcare systems. However, due to the use of unreliable wireless media, WBANs are exposed to a variety of attacks. The prevention of these attacks depends upon the cryptographic techniques. The strength of cryptography is based on the keys used for encryption and decryption in the communication process. Security is still an alarming challenge for WBANs and needs attention of the research community. The proposed work introduces a hybrid key management scheme for both intra-WBAN and inter-WBAN communications. The proposed technique is based on preloaded keys as well as keys automatically generated from biometrics of the human body. The biometric-based calculations are of linear time complexity to cater the strict resource constraints and security requirements of WBANs. The proposed security mechanism provides an efficient solution for the security of both intra-WBAN and inter-WBAN communications. The results of the proposed technique are compared with an existing key management technique known as BARI+. The results show significant improvement over the results produced by BARI+ in terms of storage, communication, energy overhead, and security.

The Internet of Things (IoT) is a significant technological advancement that allows for seamless device integration and data flow. The development of the IoT has led to the emergence of several solutions in various sectors. However, rapid popularization also has its challenges, and one of the most serious challenges is the security of the IoT. Security is a major concern, particularly routing attacks in the core network, which may cause severe damage due to information loss. Routing Protocol for Low-Power and Lossy Networks (RPL), a routing protocol used for IoT devices, is faced with selective forwarding attacks. In this paper, we present a federated learning-based detection technique for detecting selective forwarding attacks, termed FL-DSFA. A lightweight model involving the IoT Routing Attack Dataset (IRAD), which comprises Hello Flood (HF), Decreased Rank (DR), and Version Number (VN), is used in this technique to increase the detection efficiency. The attacks on IoT threaten the security of the IoT system since they mainly focus on essential elements of RPL. The components include control messages, routing topologies, repair procedures, and resources within sensor networks. Binary classification approaches have been used to assess the training efficiency of the proposed model. The training step includes the implementation of machine learning algorithms, including logistic regression (LR), K-nearest neighbors (KNN), support vector machine (SVM), and naive Bayes (NB). The comparative analysis illustrates that this study, with SVM and KNN classifiers, exhibits the highest accuracy during training and achieves the most efficient runtime performance. The proposed system demonstrates exceptional performance, achieving a prediction precision of 97.50%, an accuracy of 95%, a recall rate of 98.33%, and an F1 score of 97.01%. It outperforms the current leading research in this field, with its classification results, scalability, and enhanced privacy.