The widespread use of Unmanned Aerial Vehicles (UAV) has made the security and computing resource application efficiency of UAV a hot topic in the security field of the Internet of Things. In this paper, an optimized lightweight identity security authentication protocol, Optimized Identity Authentication Protocol (ODIAP) is proposed for Internet of Drones (IoD) networks. The protocol is targeted to the security risks faced by IoD networks, and proposes the security authentication mechanism consisting of 3 phases and 7 authentication processes, which enables the protocol has both forward and backward security, and can resist mainstream network attacks. Meanwhile, this paper fully considers the computational load and proposes the identity information generation and verification method based on the Chinese residual theorem, which reduces the computational load of resource-constrained nodes and shifts the complex computational process to server nodes with abundant computational resources. Moreover, after security protocol analysis and tool verification based on the automated security verification tool Proverif, the protocol in this paper has complete security. At the same time, the performance analysis and comparison with other mainstream protocols shows that this protocol effectively optimizes the use of computing resources without compromising security. INDEX TERMSUAV, Internet of Drones, Lightweight Authentication, Proverif, Security I. INTRODUCTION A. BACKGROUND

This paper focuses on the two-echelon location routing problem with time constraints in city logistics system. The aim is to define the structure of a system which can optimize the location and the number of two different kinds of logistics facilities as well as the related routes on each echelon. A mathematic model considering the problem characteristics has been set up. Based on probability selection principle, this paper first puts forward a metaheuristic algorithm with comprehensive consideration of time and space accessibility to solve it. Then, random instances of different sizes are generated to verify the effectiveness of our method.

In this paper, we propose an assignment-based local search method for solving vehicle routing problems. This method is a multi-route improvement algorithm that can operate on several routes at a time. To evaluate the performance of the proposed method, extensive computational experiments on the proposed method applied to a set of benchmark problems are carried out. The results show that the proposed method, when coupled with metaheuristics such as simulated annealing, is comparable with other efficient heuristic methods proposed in the literature.

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In the mixed-criticality (MC) system, it is important to achieve better execution quality for the tasks with low criticality (LO tasks) while guaranteeing the completion of tasks with high criticality (HI tasks). To improve the execution of LO tasks, this paper studies the partition and scheduling for dualcriticality tasks on the homogeneous multiprocessor platform. First, we propose a probability MC (PMC) task model that adds a parameter of overload probability for HI tasks to the classic MC model. Second, we optimize the Earliest Deadline First with Virtual Deadline (EDF-VD) algorithm by switching the criticality of the HI tasks in sequence. Third, we analyze the possible cases in an execution period and calculate the expectation of different criticality stages based on the probability theory. Finally, we propose a partitioning algorithm based on probability (PPDC) for tasks with the PMC model. The experiments confirm the effectiveness of our calculations and show that our algorithm has better performance than the existing partition schemes.

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