With the increasing sensing and communication in cyber physical social system (CPSS), the data volume is growing much rapidly in recent years. Secure deduplication has attracted considerable interests of storage provider for data management efficiency and data privacy preserving. One of the most challenging issues in secure deduplication is how to manage data and the convergent key when users frequently update it. To solve this problem, D. Koo et al. use bilinear paring as the key method. However, bilinear paring requires high computation cost for implementations. In this paper, we propose a session-key-based convergent key management scheme, named SKC, to secure the dynamic update in the data deduplication. Specifically, each data owner in SKC can verify the correctness of the session key and dynamically change it with the data update. Furthermore, to enable group combination and remove the aid of gateway (GW), a convergent key sharing scheme, named CKS, is presented. Security analysis demonstrates that both SKC and CKS can protect the confidentiality of the data and the convergent key in the case of dynamic updates. The simulation results show that our SKC and CKS can significantly reduce computation complexity and communication during the data uploading phase.

Task offloading and real-time scheduling are hot topics in fog computing. This paper aims to address the challenges of complex modeling and solving multi-objective task scheduling in fog computing environments caused by widely distributed resources and strong load uncertainties. Firstly, a task unloading model based on dynamic priority adjustment is proposed. Secondly, a multi-objective optimization model is constructed for task scheduling based on the task unloading model, which optimizes time delay and energy consumption. The experimental results show that M-E-AWA (MOEA/D with adaptive weight adjustment based on external archives) can effectively handle multi-objective optimization problems with complex Pareto fronts and reduce the response time and energy consumption costs of task scheduling.

Emergency exercises are an efficient approach for preventing serious damage and harm, including loss of life and property and a wide range of adverse social effects, during various public emergencies. Among various factors affecting the value of emergency exercises, including their design, development, conduct, evaluation, and improvement planning, this paper emphasizes the focal role of evacuees and their behavior. We address two concerns: What are the intrinsic reasons behind human behavior? How do we model and exhibit human behavior? We review studies investigating the mechanisms of psychological behavior and crowd evacuation animation. A comprehensive analysis of logical patterns of behavior and crowd evacuation is presented first. The interactive effects of information (objective and subjective), psychology (panic, small groups, and conflicting roles), and six kinds of behavior contribute to a more effective understanding of an emergency scene and assist in making scientific decisions. Based on these studies, a wide range of perspectives on crowd formation and evacuation animation models is summarized. Collision avoidance is underlined as a special topic. Finally, this paper highlights some of the technical challenges and key questions to be addressed by future developments in this rapidly developing field.

Virtual Machine Placement (VMP) plays an important role in ensuring efficient resource provisioning of physical machines (PMs) and energy efficiency in Infrastructure as a Service (IaaS) data centers. Efficient server consolidation assisted by virtual machine (VM) migration can promote the utilization level of the servers and switch the idle PMs to sleep mode to save energy. The trade-off between energy and performance is difficult, because consolidation may cause performance degradation, even service level agreement (SLA) violations. A novel residual available capacity (RAC) resource model is proposed to resolve the VM selection and allocation problem from the cloud service provider (CSP) perspective. Furthermore, a novel heuristic VM selection policy for server consolidation, named Minimized Square Root available Resource (MISR) is proposed. Meanwhile, an efficient VM allocation policy, named Balanced Selection (BS) based on RAC is proposed. The effectiveness validation of the BS-MISR combination is conducted on CloudSim with real workloads from the CoMon project. Evaluation results of experiments show that the proposed combinationBS-MISR can significantly reduce the energy consumption, with an average of 36.35% compared to the Local Regression and Minimum Migration Time (LR-MMT) combination policy. Moreover, the BS-MISR ensures a reasonable level of SLAs compared to the benchmarks.