Autonomous UAV networks have been deployed in many applications in both military and civil fields. With the ability to handle large data as well as maneuverability, UAVs are capable of completing a wide range of applications such as oil and gas facilities for security [1], surveillance [2], emergency response, and seaport [3]. They are dynamic and effective for sensing and monitoring surveillance purposes [4], and especially, they can be the core technology in Internet of Things vision in which the distributed UAVs can collect sensing data and exchange the data to each other [5, 6]. An UAV network consists of sensing devices, control algorithms, and communications. UAVs in the network cooperatively work together to complete specific missions. Each UAV can obtain visual sensing data by its equipped camera. The sensing data is then exchanged throughout the network for mission purposes. There are two main structures of information sharing: centralized and distributed [7]. In centralized networks, a central processor performs all tasks include collecting, computing, and delivering commands to other nodes in a network. The centralized scheme has a single point failure of the central processor, and other nodes must maintain a connection with the central node. In distributed networks, information is exchanged between nodes, and the computation and decision-making strategies are performed in each UAV itself. Usually, UAV networks operate in a distributed fashion to improve robustness and reduce communication burden as an UAV only needs to connect with its neighbors. Besides information sharing, another consideration is control algorithms for multiple UAV formations. In UAV-based surveillance systems, UAVs have to encounter numerous obstacles because they normally operate at low altitudes in urban environments due to policy restrictions [8]. Control algorithms should be able to drive an UAV formation to targeted areas without collisions with obstacles as well as other UAVs. In [9], a control algorithm for a team of micro-UAVs based on a leader-follower approach was proposed. The above-proposed methods have shown a good performance in terms of formation shape keeping and smooth maneuvering. However, the obstacle avoidance has not been considered. The Artificial Potential Field (APF) method has been investigated to deal with obstacle avoidance problems [10, 11]. In the papers, the impulsive and attractive forces are generated by the potential field for an agent to avoid collision and remain the desired distance in a formation. However, the APF method possesses limitations due to local minimal problems. At these points, the total force due to attractive and repulsive forces is zero, which prevents the UAV to reach targets. In addition, APF methods have shown poor performance in handling obstacles that have convex and concave shapes [12]. Another powerful approach for controlling swarm robots is flocking control which was first proposed by Olfati-Saber [13]. In flocking control, agents in a group only need to keep a certain distance from their neighbors, which is different from formation control algorithms where agents maintain a rigid position respecting their neighbors. Flocking control algorithms allow the formation to effectively change formation shapes when encountering obstacles. This feature makes flocking algorithms become suitable approaches for UAVbased surveillance systems. UAV networks have been providing the most successful application for surveillance systems. An UAV-based platform for drought mapping of agricultural crops is presented in [14]. In [15], multiple UAVs are used to monitor and detect traffic congestions. A framework for wildfire monitoring based on multiple UAV system is developed in [16]. Surveillance tasks often require rapid ability to monitor multiple interested points. As UAVs operate in aerial environments, they have a broader vision and encounter few obstacles than other kinds of robots. These features make UAVs become appropriate approaches in surveillance systems. The intelligent surveillance system (ISS) is a surveillance system with strong data analysis capabilities. An ISS can not only detect or track objects but also analyze data to anticipate behaviors of objects or upcoming events. These kinds of work have been done with minimal intervention from human. Numerous applications of ISS can be found in literature like traffic monitoring [17, 18] or home security [19]. The ISS is a modern technology that makes use of knowledge from various technical fields such as sensing devices, communications, signal processing, and artificial intelligence (AI) [20]. However, due to a large number of cameras deployed in practical surveillance systems, the collected sensing data from the cameras are also large. This leads to numerous issues in terms of system accuracy, time, data complexity, etc. The development of AI technologies has been rapidly increased in recent years. In [21], motion information is combined with a convolution neural network (CNN) to classify and to track a crowd of people. Sultani et al. [22] develop specific classification models to recognize events and correctly identify various activities of human. In [23], the authors propose a knowledge representation framework for describing patterns in video sequences. The proposed framework has shown more advantages in the ability to rapidly detect objects on screen compared to deep learning techniques. AI techniques have been also used in managing network traffic. Ant Colony Optimization (ACO) is applied to improve the performance of software-defined networks (SDN). The quality of experience of SDN increased 24.1% by applying ACO on the weight graph of the SDN controller. Most AI algorithms usually require powerful hardware to process a huge amount of data. This feature limits applications of advanced AI-based signal processing algorithms in practice. The hardware constraints are more strict in UAV network surveillance systems. An UAV can only bring a finite amount of batteries. Equipping more onboard processing devices will increase the weight of UAVs that reduces their operating time. Commercial UAVs can operate within 20- 40 minutes per charged cycle [4]. Most of the energy consumed comes from propulsion [24], which can be solved by optimizing total flight time in case of data collection and analysis tasks in wireless sensor networks application on a single charge [25]. In surveillance applications, the UAVs often perform tasks at a certain altitude and position until the energy runs out, optimizing the flight time may not be appropriate. The monitoring or sensing data could be images or videos that may cost a big amount of memory storage in each UAV. This also consumes a lot of energy consumption in case of UAVs transmitting data to server sides or between UAVs via wireless data transmission. As mentioned in [26], the power consumption of wireless data transmission is proportional with the package size; thus, the smaller the size of transmitted data, the smaller the energy consumption. As shown in Figure 1, in surveillance application, each UAV monitors a certain area. Data from the UAVs can be exchanged between neighboring UAVs. The data collection in the form of a video format of UAVs may cost a big amount of memory storage in each UAV and also the transmission bandwidth. In addition, while performing surveillance, the UAVs often fly in a fixed position; hence, most of the scenes do not change over time, and only moving objects are noticeable. The transmission of redundant data such as background frame and overlapped area is a waste of resources [27]; however, further analysis tasks are only concerned with moving objects. In this work, a framework for high-energy-efficient UAV surveillance networks is proposed. A group of UAVs is deployed to cover an area that needs to be observed. A flocking algorithm is used to drive a group of UAVs moving to sensing areas. The algorithm guarantees that the UAV team Wireless Communications and Mobile Computing.

Figure 1: Illustration of a distributed UAV network monitoring a sensing area. The UAVs can cover a large area for surveillance purposes.

can safely travel to required locations and forms an appropriate shape to cover the sensing areas. Then, an AI-based method is proposed with the aim of reducing redundant data for the UAVs while performing surveillance tasks of collecting data. The data processing algorithm can be divided into three main steps: (i) background modeling which removes all moving objects in scenes and background stitching that combines the background modeled from each UAV, (ii) noticed object extraction of each frame captured by UAVs, and (iii) data reconstruction of combined background modeling in step (i) and noticed objects from step (ii). The methodology can be referred as a kind of compress sensing technique which is aimed at saving power consumption by removing such redundant data of sensors [28, 29]. The rest of this paper is organized as follows: Section 2 provides briefly the system models that describe either the UAV network deploying in the sensing field or the AIbased methods to process the surveillance data collected from the UAVs. In Section 3, the whole problems are addressed. The flocking control algorithm and the AIbased data processing method are provided in detail. Section 4 presents both simulation and experimental results following all the steps modeled in Section 3. Finally, conclusions and future research directions are provided in Section 5.

2. System Model

In this section, the system models are presented. First is a model of an UAV network with the ability to travel, to avoid obstacles, and to collect video streaming data. Each distributed UAV can also be able to exchange the data with neighbors to construct the completed information of sensing regions. The AI-based data processing framework for enhancing an energy-efficient approach of a UAV network is analyzed. 2.1. Multiple UAV Systems. Considering a team of UAVs, the team is deployed in a ground center. After receiving a mis-sion request task, the UAV team will move to a target location. The target location is defined as a virtual leader to be able to lead the UAVs in a flocking control algorithm. The collaborative algorithm in [30] is chosen to drive the UAV team. The UAV formation can safely reconfigure formation shapes to avoid collisions with obstacles while migrating. When UAV team arrives at the target place, the team gradually forms quasilattice formation to fully cover sensing areas. Assuming each UAV can obtain its global position by sensors such as GPS. A downward camera is mounted on an UAV, which provides each UAV a constant sensing range of RS. UAVs are equipped with short-range wireless communication devices that allow them to wirelessly communicate with the others if the Euclidean distance between them is smaller than a constant Rc, noted as the communication range. Different from [30], the sensing range RS is not required to be smaller than the communication range Rc for ensuring nonoverlapped regions. In this work, overlapped regions are acceptable to guarantee coverage performance. While processing, the overlapped data is handled by an AI-based data processing algorithm, which is proposed as follows. 2.2. AI-Based Data Processing Method. The structure of the UAV system is given in Figure 1. An UAV monitors a distinct area, and the areas handled by different UAVs might be overlapped with each other. UAVs form a distributed network and share their local sensing information with others to reconstruct global sensing data. In the first step, background modeling is performed on the UAVs. Captured videos are processed to create backgrounds that consist of only nonmoving objects. Then, the backgrounds are sent to the neighbors at the begging and only updated as there are any changes in backgrounds. The individual backgrounds are then stitched together to form a complete background of the sensing area. In the case of overlapped images, an overlapping detection algorithm is presented. In detecting keypoints and local invariant descriptors, then matching descriptors of overlapping images, a random sample consensus (RANSAC) algorithm is utilized to obtain homography. The obtained homography matrix is then used to warp and stitch overlapped pictures. Secondly, UAVs perform object extraction functions where moving objects are detected by comparing differences in the continuous sequence of frames. If there are motions which is detected, details of moving objects are determined by a convolution neural network (CNN). These useful data are also shared among UAV networks. Finally, the reconstructed images are built based on extracted data sent by other UAVs. Reconstructed processes can be performed on an UAV. As sensing data are reduced by the proposed method, a burden on transmission bandwidth and computational resources is greatly diminished.

3. Problem Formulation

This section presents an overview of approaches to the problems in multiple UAV-based surveillance systems. First, the flocking algorithm to drive the UAV formations to navigate sensing areas is presented. Next, the AI-based data processing method is given. Three steps as shown in Figure 2 are presented in details. After UAVs perform their background modeling processes, the significant data will be sent to neighbors for reconstruction. However, in case of overlapped areas monitored by UAVs, it is not necessary to stitch those areas. In order to do that, the algorithm is called overlapped area detection in which the key points and its corresponding between backgrounds perform the background stitching. Algorithm 1 represents the overlapped area detection steps as follows.

Figure 2: Background modelling and corresponding raw frames

4. Simulation and Experimental Results

In this section, the proposed control algorithm known as the flocking algorithm is simulated with 10 UAVs. Then, the experimental results show all the steps mentioned in Section 3. The experiment results are performed by using Python 3.6

Figure 3: Results of background captured by different UAVs are stitched.

with TensorFlow and Keras library on Intel Xeon E3, 16GB RAM, Windows 10 platform. The dataset is selected from Stanford Drone Dataset [32]. 4.1. Simulation Results with Flocking Algorithm. In this section, a group of 10 UAVs is deployed to perform surveillance tasks in a vast area. We define a unit to calculate Euclidean distances among UAV and obstacles in the sensing field. Each UAV has a constant communication range Rc = 10 units. A virtual leader represents a target location for the group. The group is led by a control algorithm as mentioned in Equation (4). In Figure 3, as the UAVs in the group encounter obstacles, they separate and move around obstacles. Connections among members may be interrupted without affecting control performance. When the group reaches the target, UAV members gradually reconnect to form a quasialpha lattice shape. The shape covers an area around a target point. Figure 4 illustrates the quasialpha lattices shape formed by the UAV group. All 10 UAVs will stay around the virtual leader and start their mission as surveillance tasks. Based on a fixed communication range Rc, they keep their connections to their neighbors as a grid network as shown in the figure. 4.2. Stanford Drone Dataset. Stanford Drone Dataset is a massive dataset of aerial images collected by drone over the Stanford Campus. The dataset is ideal for object detection and tracking problems. It contains about 60 aerial videos. For each video, we have bounding box coordinates for the 6 classes as “Pedestrian,” “Biker,” “Skateboarder,” “Cart,” “Car,” and “Bus”. The dataset is very rich in pedestrians and bikers with these 2 classes covering about 85%- 95% of the annotations. 4.3. Background Modeling. By using multiple frames received from UAV surveillance, the backgrounds are modelled by using a median filter technique. The number of frames needed to perform the background can be selected depending on the scene and location of the monitoring task. In this paper, because the UAVs perform the task of supervising the school staff, the traffic volume of vehicles and people is low. Therefore, 20 frames were randomly selected to perform the background as shown in Figure 5. After the background from different scenes is captured and sent by UAVs to other UAVs, in case of overlap, the overlapping areas will be handled by the algorithm presented in the previous section as shown.

5. Conclusions and Future Developments

This paper proposes new methods either to control multiple UAVs or to process video surveillance data based on AI techniques with CNN. The flocking control algorithms are applied into distributed UAVs to lead the UAVs travelling on the working fields and avoiding collision and obstacles. The AI-based data processing method that reduces significant redundant data streaming among UAVs is proposed. The method also reduces the training time and classification time compared to existing methods, such as YOLO detection. The overall proposed methods help reducing the Figure 9: Nonobjects and objects extracted from Stanford Drone Dataset. Wireless Communications and Mobile Computing 9 storage capacity, transmission bandwidth, and performance in surveillance application of UAVs. Indeed, the proportion of objects in each frame is extremely small, and the transmission of redundancy in each frame is not necessary. The application of the method helps to reduce approximately 90% of the excess data capacity but still ensures the quality of the image. This significantly reduces the energy consumption for UAVs in their tasks. Future research can be done to enhance the proposed solution. In order to improve the system performance, the crucial process is an object classification task that will classify the wrong area detected from the previous step, thereby improving the efficiency of the method. Moreover, when applied to more complex applications such as traffic surveillance and agriculture, more types of object should be.

                                                                                                                         TS. Trần Thuận Hoàng