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Energy-Efficient Unmanned Aerial Vehicle (UAV) Surveillance Utilizing Artificial Intelligence (AI)

1. Introduction

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 UAV-based 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.


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