Recently, the integration of state-of-the-art technologies, such as modern sensors, networks, and cloud computing, has revolutionized the conventional healthcare system. However, security concerns have increasingly been emerging due to the integration of technologies. Therefore, the security and privacy issues associated with e-health data must be properly explored. In this paper, to investigate the security and privacy of e-health systems, we identified major components of the modern e-health systems (i.e., e-health data, medical devices, medical networks and edge/fog/cloud). Then, we reviewed recent security and privacy studies that focus on each component of the e-health systems. Based on the review, we obtained research taxonomy, security concerns, requirements, solutions, research trends, and open challenges for the components with strengths and weaknesses of the analyzed studies. In particular, edge and fog computing studies for e-health security and privacy were reviewed since the studies had mostly not been analyzed in other survey papers.

Bees play a crucial role in human food production and ecosystem maintenance. However, they face a global crisis characterized by significant population decline, including colony collapse disorder, which threatens their survival. Therefore, the development of precision beekeeping IoT systems is pivotal for enhancing bee colony strength. This study analyzes and categorizes the research focusing on the colony strength into two main areas based on colony activity and threat detection. Research on colony activity aids beekeepers to effectively manage their hives by analyzing behaviors, such as internal status, swarming, and traffic. In threat detection, research focuses on identifying critical predators, such as hornets and varroa destructor that significantly affect colony survival. This study conducts a comprehensive literature review, thoroughly presenting findings on the evolution and diversification of methodologies aimed at enhancing colony strength. By delving into various innovative approaches and assessing their effectiveness, this review highlights key developments that have significantly contributed to improving the resilience of bee populations against emerging threats. Furthermore, this study identifies the limitations of current research and proposes future research directions focused on enhancing the accuracy of bee behavior analysis and threat detection to improve colony strength and productivity.

The Internet of Things (IoT) connects a wide range of objects and the types of environments in which IoT can be deployed dynamically change. Therefore, these environments can be modified dynamically at runtime considering the emergence of other requirements. Self-adaptive software alters its behavior to satisfy the requirements in a dynamic environment. In this context, the concept of self-adaptive software is suitable for some dynamic IoT environments (e.g., smart greenhouses, smart homes, and reality applications). In this study, we propose a self-adaptive framework for decision-making in an IoT environment at runtime. The framework comprises a finite-state machine model design and a game theoretic decision-making method for extracting efficient strategies. The framework was implemented as a prototype and experiments were conducted to evaluate its runtime performance. The results demonstrate that the proposed framework can be applied to IoT environments at runtime. In addition, a smart greenhouse-based use case is included to illustrate the usability of the proposed framework.