Recently, Wireless Sensor Network (WSN) technology has emerged extensively. This began with the deployment of small-scale WSNs and progressed to that of larger-scale and Internet of Things-based WSNs, focusing more on energy conservation. Network clustering is one of the ways to improve the energy efficiency of WSNs. Network clustering is a process of partitioning nodes into several clusters before selecting some nodes, which are called the Cluster Heads (CHs). The role of the regular nodes in a clustered WSN is to sense the environment and transmit the sensed data to the selected head node; this CH gathers the data for onward forwarding to the Base Station. Advantages of clustering nodes in WSNs include high callability, reduced routing delay, and increased energy efficiency. This article presents a state-of-the-art review of the available optimization techniques, beginning with the fundamentals of clustering and followed by clustering process optimization, to classifying the existing clustering protocols in WSNs. The current clustering approaches are categorized into meta-heuristic, fuzzy logic, and hybrid based on the network organization and adopted clustering management techniques. To determine clustering protocols’ competency, we compared the features and parameters of the clustering and examined the objectives, benefits, and key features of various clustering optimization methods.

Although intelligent intrusion and detection strategies are used to detect any false alarms within the network critical segments of network infrastructures, reducing false positives is still a major challenge. Up to this moment, these strategies focus on either detection or response features, but often lack of having both features together. Without considering those features together, intrusion detection systems probably will not be able to highly detect on low false alarm rates. To offset the abovementioned constraints, this paper proposes a strategy to focus on detection involving statistical analysis of both attack and normal traffics based on the training data of KDD Cup 99. This strategy also includes a hybrid statistical approach which uses Data Mining and Decision Tree Classification. As a result, the statistical analysis can be manipulated to reduce misclassification of false positives and distinguish between attacks and false positives for the data of KDD Cup 99. Therefore, this strategy can be used to evaluate and enhance the capability of the IDS to detect and at the same time to respond to the threats and benign traffic in critical segments of network, application and database infrastructures.

Corporate reputation is companies’ most valuable asset as it can position them to gain competitive advantages that lead to sustainable performance. Therefore, understanding the factors that influence corporate reputation is vital for a company’s survival. The study objectives were to investigate the effects of corporate governance and the quality of environmental and social reporting on corporate reputation. Additionally, this study examined the role of environmental and social reporting quality on the relationship between these two variables. This study used secondary data collected from multiple sources such as the Thomson Data Stream database and annual reports of publicly listed Malaysian companies between 2017 and 2018. The results showed that corporate governance effectiveness and environmental and social reporting quality positively influence corporate reputation. Additionally, the quality of environmental and social reporting mediates the relationship between corporate governance and corporate reputation. This study bridges research gaps by providing evidence for the impact of effective corporate governance, specifically board diversity, on corporate reputation in Malaysia. The findings can help companies to establish criteria and qualifications for the appointment of new board members. The members must have the right combination of skills, knowledge, experience and independent elements that enable them to make decisions to meet companies’ objectives.

Nowadays, the rapid deployment of Wireless Sensor Networks (WSNs) and the integration of Internet of Things (IoT) technology has enabled their application to grow in various industrial fields in our country. Various factors influence the success of WSN development, particularly improvements in Medium Access Control (MAC) protocols, for which WSNs-IoT are deemed vital. Several aspects should be considered, such as energy consumption reduction, performance, scalability for a large deployment of nodes, and clustering intelligence. However, many protocols address this aspect in a constrained view of handling the medium access. This work presents a state-of-the-art review of recently proposed WSN MAC protocols. Different methods and approaches are proposed to enhance the main performance factors. Various performance issue factors are considered to be the main attribute that the MAC protocol should support. A comparison table is given to provide further details about using these approaches and algorithms to improve performance issues, such as network throughput, end-to-end delay, and packet drop, translated into energy consumption.

The process of integrating building information modeling (BIM) and Internet of Things (IoT)-based data sources is a recent development. As a generalization, BIM and IoT data provide complementary perspectives on the project that complement each other’s constraints. Applying the concept of BIM-IoT in the construction industries which has been termed to have a high-risk factor could offer an improvement in the overall performance of the construction industries and thereby reduce the associated risks. This study aims to examine the potential of integrating BIM-IoTs in the construction industries by examining related published literature. Literature analysis revealed that the BIM and IoT have been extensively applied individually to several aspects of construction projects such as construction safety risk assessment, construction conflict management, building construction sustainability, and onsite construction process monitoring. However, there is scanty research awareness on the possibilities of BIM-IoT integration in the construction industries.

Green finance and corporate green innovation have become pivotal in addressing ecological and environmental challenges amidst worsening global environmental conditions. This study aims to provide a systematic explanation of the factors through which green finance promotes corporate green innovation. By qualitatively synthesizing 42 studies published between 2019 and 2024, the research identifies four key factors influencing this relationship. These include two direct aspects—financial support and policy guidance—and two indirect aspects—financing constraints and term restructuring. The analysis reveals a research gap in market-based financing, where most studies focus on credit financing, while discussions on diverse instruments like green bonds and green equities remain limited. Additionally, the impact of policy uncertainty, particularly on long-term investment decisions is underexplored. Existing research primarily emphasizes easing financing constraints, with limited attention to how green finance can also support green innovation by extending funding horizons through term restructuring. Future research should focus on these areas to deepen understanding and enhance practical applications.