Xiaoting Sun

In this paper, we first identify some unique design requirements in the aspects of security and privacy preservation for communications between different communication devices in vehicular ad hoc networks. We then propose a secure and privacy-preserving protocol based on group signature and identity (ID)-based signature techniques. We demonstrate that the proposed protocol cannot only guarantee the requirements of security and privacy but can also provide the desired traceability of each vehicle in the case where the ID of the message sender has to be revealed by the authority for any dispute event. Extensive simulation is conducted to verify the efficiency, effectiveness, and applicability of the proposed protocol in various application scenarios under different road systems.

In this paper, we propose a Timed Efficient and Secure Vehicular Communication (TSVC) scheme with privacy preservation, which aims at minimizing the packet overhead in terms of signature overhead and signature verification latency without compromising the security and privacy requirements. Compared with currently existing public key based packet authentication schemes for security and privacy, the communication and computation overhead of TSVC can be significantly reduced due to the short message authentication code (MAC) tag attached in each packet for the packet authentication, by which only a fast hash operation is required to verify each packet. Simulation results demonstrate that TSVC maintains acceptable packet latency with much less packet overhead, while significantly reducing the packet loss ratio compared with that of the existing public key infrastructure (PKI) based schemes, especially when the road traffic is heavy.

In today's Internet of Things research community, Cloud-fog framework is a potential technology for Internet of Things to support energy consumption of an IoT system and delay-sensitive applications that require almost real-time responses. However, how to schedule the computational tasks which is to offload to fog nodes or cloud nodes is not fully addressed until now. In this paper, in order to solve the complex task scheduling problem with some priority constraints of IoT applications taking into account the energy consumption and reducing energy consumption on the condition of satisfying the mix deadline, we formulate an associated task scheduling problem into a constrained optimization problem in cloud-fog environment. A laxity and ant colony system algorithm(LBP-ACS) is put forward to tackle this problem. In this algorithm, a strategy of task scheduling is not only considering the priority of a task, but also its finished deadline. In order to handle the sensitivity of task delay, the laxity-based priority algorithm is adopted to construct a task scheduling sequence with reasonable priority. Meanwhile, to minimize the total energy consumption, the constrained optimization algorithm based on ant colony system algorithm is used to obtain the approximate optimal scheduling scheme in the global. Compared with other algorithms, the experimental results show that the proposed algorithm can effectively reduce the energy consumption of processing all tasks, while ensuring reasonable scheduling length and reducing the failure rate of associated tasks scheduling with mixed deadlines.

An important challenge facing cloud computing is how to correctly and effectively handle and serve millions of users' requests. Efficient task scheduling in cloud computing can intuitively affect the resource configuration and operating cost of the entire system. However, task and resource scheduling in a cloud computing environment is an NP-hard problem. In this paper, we propose a three-layer scheduling model based on whale-Gaussian cloud. In the second layer of the model, a whale optimization strategy based on the Gaussian cloud model (GCWOAS2) is used for multiobjective task scheduling in a cloud computing which is to minimize the completion time of the task via effectively utilizing the virtual machine resources and to keep the load balancing of each virtual machine, reducing the operating cost of the system. In the GCWOAS2 strategy, an opposition-based learning mechanism is first used to initialize the scheduling strategy to generate the optimal scheduling scheme. Then, an adaptive mobility factor is proposed to dynamically expand the search range. The whale optimization algorithm based on the Gaussian cloud model is proposed to enhance the randomness of search. Finally, a multiobjective task scheduling algorithm based on Gaussian whale-cloud optimization (GCWOA) is presented, so that the entire scheduling strategy can not only expand the search range but also jump out of the local maximum and obtain the global optimal scheduling strategy. Experimental results show that compared with other existing metaheuristic algorithms, our strategy can not only shorten the task completion time but also balance the load of virtual machine resources, and at the same time, it also has a better performance in resource utilization.