Multi-access Edge Computing (MEC) mitigates the high latency and energy consumption issues of computation-intensive applications by offloading some subtasks to edge servers. With the rapid development of the Internet of Things (IoT), the request patterns of edge devices have become increasingly dynamic and unpredictable, posing new challenges to the widely used reinforcement learning (RL) based computation offloading strategies. Additionally, the dependencies between subtasks in edge environments further complicate the design of efficient offloading strategies. To address above issues, this paper proposes a distributed cloud-edge-end collaborative dependent task offloading model based on meta deep reinforcement learning (DDMDRL), which can dynamically update the optimal offloading strategy in real-time dynamic IoT scenarios, and balance the trade-off between latency and energy consumption through a weighted sum optimization objective. In addition, a sparse self-attention mechanism is introduced to efficiently capture the dependencies between subtasks within complex task flows. The experimental results demonstrate that DDMDRL achieves faster convergence during training and reduces total system costs from 15% to 60% compared to baseline methods, underscoring its efficiency and adaptability in dynamic IoT environments.

We consider access control for IoT systems that involves shared accesses to the IoT devices as well as their data. Since IoT devices are dispersed all over the edge of the Internet, traditional centralized access control has problems. Blockchain based decentralized access control is thus the new solution trend. However, existing blockchain based access control methods do not focus on performance issues and may incur a high communication overhead. In this paper, we develop a Pruned Blockchain based Access Control (PBAC) protocol to cutdown the unnecessary message rounds and achieve high efficiency in access validations and policy management. To realize the PBAC protocols, it is necessary to carefully engineer the system architecture, which is also discussed in the paper. Experiments demonstrate the efficacy of the PBAC protocol, specifically, it reduces the access validation time by approximately $43 \%$.

Inter-vehicle communication has the potential to significantly improve driving safety, but also raises security concerns. The fundamental mechanism to govern information sharing behaviors is access control. Since vehicular networks have a highly dynamic and open nature, access control becomes very challenging. Existing works are not applicable to the vehicular world. In this paper, we develop a new access control model, openRBAC, and the corresponding mechanisms for access control in vehicular systems. Our approach lets the accessee define a relative role hierarchy, specifying all potential accessor roles in terms of their relative perception to the accessees. Access control policies are defined for the relative roles in the hierarchy. Since the accessee has a clear understanding of the relative roles defined by itself, the policy definitions can be precise and less flawed.

Role based access control (RBAC) has been used extensively in practice since it naturally capturing the structure of the users in an organization. It is especially useful in multi-tenant cloud platforms. However, with the growing amount of data and growing number of devices, assigning permissions for these resources (such as data and devices) to roles become challenging. We develop a resource hierarchy based permission model and integrate it with RBAC to create the RRBAC (resource and role based access control) model to simplify the permission assignment in RBAC. However, realizing RRBAC requires careful design to ensure efficient permission assignment, validation and revocation. Instead of using policy based solutions, such as XACML, we design a resource tree based approach to achieve high performance for various permission related operations. Preliminary experiments show that RRBAC approach can achieve more efficient permission assignment and validation.

In this paper, we present a new SaaS (software as a service) design for employee job performance appraisals, SaaS-JPA. We use IoT and computer systems to collect data related to the daily works of employees. A semantic model is developed to guide the data collection process, facilitate data interpretation and interoperation, and enable big data analysis to make job performance appraisal decisions. We also propose two new performance assessment models: The similarity-based relative performance model and the revenue-based performance model. These performance models are enabled by the service technologies and big data analytics. Finally, we discuss the design of SaaS-JPA.