Energy-Efficient UAV-Aided Target Tracking Systems Based on Edge Computing

Edge computing can be widely used in unmanned aerial vehicle (UAV) inspection, field operation control, power consumption information collection and other businesses in the power Internet of Things scene. Edge computing offloads functions such as data processing and applications to network edge nodes near the terminals to provide low-latency services and ensure service quality. However, with the explosive growth of business terminals, the capacity of single edge node is limited and it is difficult to meet all business requirements at the same time. Therefore, this article proposes a task allocation mechanism based on cooperative edge computing. Firstly, a task allocation model based on cooperation of two edge nodes is established to minimize the average task completion delay while meeting business requirements. Secondly, the Two-edge-node Cooperative-task Allocation based on Improved Particle Swarm Optimization (TCA-IPSO) algorithm is proposed, which applies the crossover and mutation strategy in genetic algorithm to improve the particle swarm optimization algorithm, and solves the problem that the task allocation scheme in cooperation is prone to fall into a local optimum. Finally the simulation results show that the proposed TCA-IPSO algorithm reduces the average task completion delay by 53.8% and 36.0% compared to the benchmark and QoS-based Task Distribution (QBTD) algorithm.

Unmanned aerial vehicles (UAVs) have been widely used to collect data from ground nodes (GNs) in various applications because of their flexibility and mobility. Some emergency situations pose strict requirements for UAV’s mission completion time. The current research focuses on the design and optimization of UAV’s flight trajectory to shorten the mission completion time. But the data collection process also greatly affects the mission completion time, especially when the uplink throughput is large. This article emphasizes the data collection process and proposes a V-shaped collection trajectory for the first time to shorten UAV’s collection time and then jointly optimizes the flight and collection trajectory to minimize the mission completion time. We formulate the mission completion time under energy constraints to an optimization problem and decompose it into three subproblems: 1) mission allocation of UAVs; 2) collection time minimization; and 3) joint optimization of collection and flight trajectory. 1) We establish the mission allocation model by setting the scaling factors for the flight and collection mission, and then solve it by utilizing the min–max multiple traveling salesman problem (MTSP) algorithm; 2) for the data collection process, we propose a V-shaped trajectory to minimize the data collection time and prove its validity; and 3) since the flight and collection trajectory of the adjacent GNs are deeply coupled, we model the joint optimization of the flight and the collection time as a mixed-integer nonlinear programming problem, and solve it by the joint optimization algorithm for flight trajectory and collection trajectory (JOFC). The simulation results show that our proposal outperforms the compared schemes.

The on‐board target tracking of unmanned aerial vehicle (UAV) relies on on‐board computer to process the collected ground image information, and then use the target tracking algorithm to achieve the target tracking function, which provides the user with target position, trajectory and other information. UAVs are limited by load and power consumption. Due to the high resolution and frame rate of UAV's image collection system, traditional target tracking platforms cannot meet the requirements. As a result, the real‐time nature of the tracking platform urgently needs to be resolved. This study designs and implements the airborne ground target tracking accelerator for UAV. The kernelised correlation filters (KCFs) algorithm with low computational complexity and precision is selected to achieve the airborne ground target tracking function and its performance bottleneck is analysed. In order to improve the real‐time performance of the tracking task processing, the ZYNQ platform based on ARM and field programmable gate arrays is chosen to achieve the hardware acceleration of the KCF algorithm. The KCF algorithm is implemented in hardware using the high‐level synthesis technology, and a high‐speed data transmission link is established. Experiments show that the design can achieve a tracking rate of more than 30 FPS under 960 × 540 resolution, which meets the real‐time requirements of UAV target tracking task.

Air–Ground Surveillance Sensor Network based on edge computing for target tracking

Industrial internet of things (IIoT) enables edge computing technology to provide communication between the machines that produce a large amount of data and locate at the edge network. A task scheduling is implemented in the edge node. Furthermore, the real-time data can achieve with the lowest latency that allowed by the edge node near the edge network. However, a mobile machine such as an autonomous guided vehicle can interfere in this situation. Because the vehicle also needs service by the edge node. Over that, quality of service (QoS) performance can decrease. Therefore, this paper deploys an unmanned aerial vehicle (UAV) as an edge node to provide service to the edge network through optimizing the trajectory of UAV, where the edge network request task using a Deep Q-Network (DQN) Learning. The result shows that using machine learning, notably the DQN algorithm, can increase the number of the machine that can be provided service. Subsequently, the real-time data can achieve either the interrupt occurs at the edge node.

The empowering unmanned aerial vehicles (UAVs) have been extensively used in providing intelligence such as target tracking. In our field experiments, a pretrained convolutional neural network (CNN) is deployed at UAV to identify a target (a vehicle) from the captured video frames and enable the UAV to keep tracking. However, this kind of visual target tracking demands a lot of computational resources due to the desired high inference accuracy and stringent delay requirement. This motivates us to consider offloading this type of deep learning (DL) tasks to a mobile-edge computing (MEC) server due to the limited computational resource and energy budget of the UAV and further improve the inference accuracy. Specifically, we propose a novel hierarchical DL tasks distribution framework, where the UAV is embedded with lower layers of the pretrained CNN model while the MEC server (MES) with rich computing resources will handle the higher layers of the CNN model. An optimization problem is formulated to minimize the weighted-sum cost, including the tracking delay and energy consumption introduced by communication and computing of UAVs while taking into account the quality of data (e.g., video frames) input to the DL model and the inference errors. Analytical results are obtained and insights are provided to understand the tradeoff between the weighted-sum cost and inference error rate in the proposed framework. Numerical results demonstrate the effectiveness of the proposed offloading framework.

This research explores the design and implementa- tion of an image and video processing platform using recon- figurable FPGA technology. With the increasing demand for real- time, high-performance multimedia processing in modern applications, a flexible and efficient solution is essential. The project utilizes an FPGA-based platform to handle image and video data processing through edge detection, image scaling, and real-time motion detection techniques. By integrating custom- built hardware components, this study aims to provide a low- power, high-throughput solution that meets the requirements of both image and video processing tasks. Materials and methods involved using Xilinx Virtex-II Pro FPGA for hardware imple- mentation, alongside embedded memory processors and multi- functional design elements such as zoom-in/out utilities. The system was evaluated through several experimental procedures, using various image and video datasets, to measure performance in terms of speed, resource utilization, and power consumption. Results indicated that the system achieved notable improvements in terms of efficiency, utilizing 25% of logic resources, 55% of on- chip memory, and 180mW of power. The discussion focuses on the adaptability of FPGA-based systems in meeting the diverse needs of multimedia processing, emphasizing scalability and low power consumption. In conclusion, the reconfigurable FPGA platform provides a versatile and efficient environment for image and video processing. The system’s resource efficiency, scalability, and potential for further enhancements make it a promising candidate for future multimedia applications. Keywords-Reconfigurable FPGA Architecture, Video and Image Processing, Edge Detection, Image Rescaling.

Driven by the dramatic growth in computing capability and the inherent mobility of the unmanned aerial vehicles (UAVs), the recently advocated UAV edge computing paradigm is expected to enhance the coverage and the on-demand deployment capability of existing terrestrial edge computing systems. Nonetheless, due to the limited onboard resource of the UAV, single- UAV edge computing systems may still be incompetent when serving remote users. Although using multiple UAVs to form a traditional relay network is a viable solution to remote edge computing, it fails to exploit the computing capability of the UAVs. This entails a pressing need to develop multi-UAV remote edge computing mechanisms that allow the UAVs to handle part of the computation tasks using their local processors while conducting multi-hop computation task offloading. To achieve the best performance in such cases, the UAVs have to properly split their power budget for communication and computation and also move to suitable service locations. Nonetheless, finding the optimal UAV power allocation and deployment turns out to be an intractable high-dimensional monotonic optimization problem, even for a mild number of UAVs. To overcome this challenge, a more efficient algorithm that has a complexity only linear in the number of UAVs is developed by exploiting the special structure of this problem. In addition to analysis, numerical results are provided to validate the effectiveness of the proposed scheme.

Due to their distinctive features, unmanned aerial vehicles (UAVs) have been recently exploited to support a wide range of applications. The features include low maintenance cost, compact size, and excellent capability of maneuvering. In particular, UAVs have the potential capabilities to support different technologies such as Internet-of-things (IoT) devices, sensors, cameras, and systems, thus, performing civilian, target tracking, industrial, and military applications. Specifically, target tracking has been recently configured as one of the most attractive applications of UAVs. With this, UAVs estimate and detect the behavior or locate a moving or a stationary item. Accordingly, several research efforts have been conducted to investigate the promising capabilities of UAVs in target-tracking missions, including indoor and outdoor tracking missions. This paper surveys UAV-based target tracking and monitoring for indoor and outdoor environments, where the deployment scenarios of such UAV-based systems are characterized and investigated. Furthermore, we discuss a set of practical design challenges of UAV-based target tracking systems, and thus, we provide a set of potential solutions to deal with these challenges. Specifically, we present a set of recent enabling technologies that might be integrated into UAV target tracking systems, including machine learning (ML), cloud computing, and emerging fifth-generation (5G) technologies. We also demonstrate a use-case scenario in which ML is used to facilitate indoor target tracking and monitoring. Finally, future research directions are outlined that can help in improving the efficiency of the UAV-based target-tracking systems.

Multi-access/Mobile Edge Computing (MEC) and fog computing are promising techniques to satisfy low latency requirements for emerging next generation applications. Moving computation entities closer to a user could reduce the overall serving latency. In terms of green communications, given the latency constraint, how to minimize the power consumption at the user equipment (UE) and the edge node (EN) sides is important. Considering several edge nodes, partially offloading a user's task to one or more edge nodes is key. In this paper, a multi-node partial task offloading MEC scenario is discussed, in which UEs locally compute the task and share the remainder with other edge nodes. In addition, a green task distribution algorithm which minimizes the system power consumption is proposed, considering queueing, transmitting and computing delay. The simulation results show that the proposed algorithm minimizes the power consumption while meeting the latency requirements, and the power saving efficiency outperforms a binary offloading strategy. Moreover, the coupling effects between the latency requirements, offloading signal strength within the edge nodes, computation capability of edge nodes and the number of subcarriers used to transmit the offloading task are discussed.

Edge computing is a novel network architecture that is in proximity to the end devices in an Internet of Things (IoT). As the IoT becoming a major factor in our daily life, provisioning a low response time of the services to IoT users through edge computing is an important problem. Caching necessary program data for the task in an edge node effectively reduces the response time of the computation task. However, due to the increase of IoT users and devices, it is noteworthy that limited-resource edge nodes would receive a number of tasks, having a heavy burden of processing the requests. Therefore, the limited resource and caching space at cloudlet need the careful design of the caching algorithm to utilize the space of multiple edge nodes and relieve the burden of computations. In this paper, we propose a cooperative program caching system that makes different edge nodes cooperatively store program data and cache the replicas of the data requested frequently to handle a number of requests from IoT users. In particular, we develop a cooperative caching algorithm that caches the appropriate number of data replicas depending on the number of requests on each cloudlet and the popularity of the data to minimize the response time. The simulation results show that the proposed cooperative caching algorithm can effectively reduce the response time for IoT users compared to other existing algorithms.

Due to the urgent emergence of computation-intensive intelligent applications on end devices, edge computing has been put forward as an extension of cloud computing, to satisfy the low-latency requirements of these applications. To process heterogenous computation tasks on an edge node, the corresponding services should be placed in advance, including installing softwares and caching databases/libraries. Considering the limited storage space and computation resources on the edge node, services should be elaborately selected and deployed on the edge node and its computation resources should be carefully allocated to placed services, according to the arrivals of computation workloads. The joint service placement and computation resource allocation problem is particularly complicated, in terms of considering the stochastic arrivals of tasks, the additional latency incurred by service migration, and the waiting time of unprocessed tasks. Benefiting from deep reinforcement learning, we propose a novel approach based on parameterized deep Q networks to make the joint service placement and computation resource allocation decisions, with the objective of minimizing the total latency of tasks in a long term. Extensive simulations are conducted to evaluate the convergence and performance achieved by our proposed approach.

A Novel Lyapunov based Dynamic Resource Allocation for UAVs-assisted Edge Computing

In recent years, unmanned aerial vehicle (UAV) has been widely used in target tracking tasks such as security, anti-terrorism, tracking and monitoring because of its wide and flexible perspective. At the same time, deep learning creates new conditions for UAV visual recognition and motion tracking because it has high recognition accuracy and fast recognition speed. This paper presents an UAV dynamic tracking algorithm based on deep learning. The algorithm mainly includes three parts: the PTZ control, relative position calculation and the UAV control. The main work includes: 1. The tracking target is identified through the deep learning network based on YOLOv3, and the PTZ angle is adjusted in real time by using the control method of combining the PID control with the time-delay control to reduce the impact of image transmission delay; 2. The relative position between the UAV and the target is calculated by combining the UAV flight altitude, UAV attitude, PTZ angle and the coordinate of the target in the image. At the same time, the relative position prediction is added to estimate the relative distance between the UAV and the target at the next frame time as a reference value of the UAV speed, so as to improve the dynamic tracking ability; 3. The relative position information is fed back to the flight control function, and the linear velocity of UAV is output by the PID controller. Through the simulation test in Airsim, it is proved that the proposed algorithm can effectively improve the stability and robustness of the UAV tracking system.

The unmanned aerial vehicles (UAVs) have been extensively used in providing intelligence such as target tracking. In our field experiments, a pre-trained deep neural network (DNN) is deployed on the UAV to identify a target from the captured video frames and enable UAV to keep tracking. However, tracking in real time by the DNN requires a lot of computational resources. This motivates us to consider offloading this type of machine learning (ML) tasks to a mobile edge computing (MEC) server. Specifically, we propose a novel hierarchical ML tasks distribution framework for the UAV tracking system, where the UAV is embedded with lower layers of the pre-trained convolutional neural network (CNN) model due to its limited computing capability, while the MEC server with rich computing resources will handle the higher layers of the CNN model. An optimization problem is formulated to minimize the CNN inference delay while taking into account the communications delay, computing time, and ML error. Insights are provided to understand the tradeoff between communications and ML computing in offloading decisions. Numerical results demonstrate the effectiveness of the proposed ML tasks distribution framework with the optimized offloading strategy.