With the rapid development of artificial intelligence technology, the multi-UAV cooperative search has wide applications in the field of Internet of Things, such as resource exploration, emergency rescue, intelligent transportation, etc. However, the communication network in an unknown environment may be inaccessible, and the real-time information sharing among UAVs cannot be guaranteed, resulting in the failure of cooperative search. Aiming at this issue, this article is devoted to the design of the multi-UAV flight strategy to improve the cooperative search capability in an uncertain communication environment. Specifically, a new cooperative architecture oriented to a local communication network is devised to control the observation locations of multiple UAVs in the search process, and some local communication networks are established based on the distance among UAVs to meet the requirements of the search task. On this foundation, we develop a multi-UAV cooperative search model (MCSM) with communication cost and formation benefit as an optimization function to ensure the effectiveness of multi-UAV search. Moreover, in the process of model solving, an improved sparrow search algorithm (ISSA) is presented with some different search strategies to enhance the optimization capability. To verify the superiority of the proposed method, we designed several groups of simulation experiments to analyze the performance of MCSM. Experimental results illustrate that our method can not only maintain high cooperative search accuracy but also has high stability and convergence speed.

Research on multiple unmanned aerial vehicles (UAVs) cooperative surveillance systems serving Internet of Things (IoT) applications, such as smart cities, precision logistics, etc., has become a hot topic. However, the target movement is unpredictable in an uncertain environment, and multiple UAVs are affected by obstacles or inaccessible regions, resulting in the decreased surveillance performance and even the loss of the target. This article is dedicated to determine the current surveillance environment through the 2-D laser scanner. At the cost of the energy consumption and the transmission unreliability, a multi-UAV cooperative path optimization (MCPO) model is designed to adjust the surveillance location of each UAV, which improves the target surveillance performance. Specifically, for different types of obstacles or inaccessible regions, we present a novel obstacle-avoidance selection strategy with two mechanisms in mind: 1) when some of UAVs encounter obstacles, but others can accurately monitor the target, a strict constraint mechanism is established to promptly adjust the surveillance location of each UAV, which ensures the accuracy of formation surveillance and 2) when all UAVs have to avoid obstacles, a fuzzy constraint mechanism is presented and combined with Lucas–Kanade (LK) method to expand the search range of the multi-UAV and enhance the flexible adjustment capability of the formation. To verify the superiority of the proposed optimization method, we develop a 3-D simulation experiment environment based on the UE4 platform and design several groups of experiments to analyze the effectiveness of MCPO. The experimental results demonstrate that MCPO can not only maintain the flight stability of multiple UAVs but also has satisfactory formation flexibility and surveillance accuracy.

UAV surveillance and tracking have attracted great enthusiasm in intelligent transportation, and various approaches have been reported up to now. However, these approaches often ignored the uncertainties in the urban environment, such as occlusion, view change, and background clutter. Ignoring these uncertain factors often leads to a reduction in surveillance performance and tracking quality. This study devotes to improving the cooperative surveillance capability of multi-UAV formation by designing different cooperative strategies in the urban environment. To be specific, a novel cooperative architecture is designed to control the observation locations of multiple UAVs throughout the formation process. For different types of interference, we introduce a novel target recognition rate of each UAV as the decision factor and design corresponding cooperative strategies to guarantee the accuracy of cooperative surveillance. Based on this architecture, we develop a vision-based method of cooperative surveillance and tracking by multiple UAVs (SMART) whose objective function is the motion cost and flight reliability of UAVs to ensure that each UAV can be in the optimal surveillance location for the target. The proposed SMART skillfully integrates the strict, elastic, and flight constraint strategies. During the execution of the multi-UAV formation, the inherent safety constraints of multiple UAVs and the designed strategies are used to solve the quadratic optimization model to adjust the locations of these UAVs. To demonstrate the superiority of our method, we conduct a 3D simulation urban environment and devise several experiments to analyze the performance of SMART on it. The experimental results demonstrate that SMART can not only maintain the high cooperative flight capability, but also provide high flexibility and fault tolerance.

In view of the fact that the types of tasks display an upward trend as the growth of service demands, and the different types of tasks arrive at the system without regularity. Moreover, the resources deployed in cloud are insufficiency to be flexibly provisioned in the face of obvious workload fluctuations. In this paper, we present a method of elastic resource provisioning using date clustering in cloud service platform. The framework of method consists of three core components. In workload classification, we propose a clustering ensemble method, which utilizes a novel distance decision-making method to obtain the final results. Our method can effectively partition the arriving tasks into several clusters based on similarity among tasks. For each cluster, we forecast the amount of tasks arriving at next moment by prediction model based on time-series to provide reference for the follow-up resource provisioning. Afterwards, an energy-saving resource provisioning method is designed to dynamically provide resources for tasks in each cluster. We implement the experiments and the results show that our method achieves 92.3%, 91.2%, and 3679.2 kWh respectively in terms of guarantee ratio, resource utilization and total energy consumption, which demonstrates the effectiveness of proposed method for dynamic resource provisioning.

UAV cooperative formation provides rescue and material delivery for the Industrial Internet of Things (IIoT). To solve issues such as low material distribution efficiency and poor mobility during disaster rescue, we propose a two-layer optimization mechanism based multiple UAV dynamic task planning method (DAWN), which can cope with the problem of the global communication link unreachable caused by disasters. Specifically, we consider the global task allocation as a dynamic vehicle routing problem (VRP) and use deep reinforcement learning (DRL) to solve it so as to minimize the global flight path and energy consumption. Secondly, based on the current communication structure, we establish a local path planning approach based on the trust networkthat maximizes the regional coverage rate while minimizing the flight paths. On the basis of these two layers, a UAV formation dynamic task planning approach is realized. Experimental results prove that the proposed DAWN can obtain the optimal flight paths and achieve higher energy efficiency while providing reasonable region coverage to discover more potential tasks.