Trajectory planning is an important subject in the field of unmanned aerial vehicles (UAVs). However, existing methods do not solve some problems well, such as slow convergence speed and low searching efficiency of related algorithms and collisions between UAVs and the obstacles. Therefore, a method is proposed to solve a trajectory planning problem for multi-UAV in a static environment, which includes three main phases: the initial trajectory generation, the trajectory correction, and the smooth trajectory planning. First, the improved algorithm called MACO which introduces the metropolis criterion into the node screening mechanism of the ant colony optimization (ACO) algorithm is presented to generate the initial trajectory that can effectively avoid falling into the local optimal solution and stagnation. Then, considering size constraint of UAVs, this paper gives three trajectory correction schemes to solve the collision avoidance problem and further to optimize the initial trajectory. Last, the discontinuity originated from the sharp turn which occurred in the trajectory planning is solved by the inscribed circle (IC) smooth method, and the produced trajectory has shown better performance in reducing fuel consumption and improving the trajectory safety. Experimental results demonstrate that the proposed method has the high feasibility and effectiveness from aspects of the optimal solution, the collision avoidance, and the smooth trajectory in the trajectory planning problem for UAVs.

This letter mainly considers the cooperative localization problem of 3D mobile cluster network. With the development of Doppler frequency shift measurement technique, high-precision relative velocity measurement has become possible. In this letter, we first incorporate the relative velocity information into the 3D cooperative localization framework. Then the quasi-Newton algorithm BFGS is used to solve the framework, and the vBFGS algorithm suitable for solving the relative position of the mobile cluster network is constructed. Simulation experiments show that using relative velocity measurement to assist positioning can significantly improve positioning accuracy. Compared with the state-of-the-art algorithm, vBFGS has higher positioning accuracy and faster convergence rate for low-connectivity networks, and has better numerical stability and robustness for high-speed moving models.

The widespread application of ultradense and multivariate Internet of Things (IoT) benefits from network function virtualization (NFV) that provides flexible frameworks and effective management. NFV leverages the virtualization technologies to integrate the existing network functions of devices into standard servers, storages, and switches. Then, the network functions are achieved in software form to displace the private, dedicated, and closed network devices. However, NFV also brings instability and challenges to the network management where the network dynamics, lack of visibility, and high frequency and abundant types of faults will increase the difficulty. Therefore, diagnosing the faults embedded in the generic NFV framework is crucial for the effective adoption of NFV to the IoT environment and thus ensuring the user services. This article summarizes the differences and connections of fault diagnosis between the NFV framework and traditional networks, and introduces the challenges faced by NFV. Moreover, we provide a comprehensive survey of the state-of-the-art fault detection methods for the NFV framework. After an in-depth discussion of the fault propagation characteristics, we further present a detailed taxonomy of the fault localization approaches. Finally, we highlight the future research directions to provide ample space for improvement in applying NFV to the IoT environment.