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1. Artificial Intelligence Enabled Distributed Edge Computing for Internet of Things Applications

   https://doi.org/10.1109/DCOSS49796.2020.00077  

   Fragkos, Georgios ; Tsiropoulou, Eirini Eleni ; Papavassiliou, Symeon ( May 2020 , 2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS))

   null (Ed.)

   Artificial Intelligence (AI) based techniques are typically used to model decision making in terms of strategies and mechanisms that can result in optimal payoffs for a number of interacting entities, often presenting antagonistic behaviors. In this paper, we propose an AI-enabled multi-access edge computing (MEC) framework, supported by computing-equipped Unmanned Aerial Vehicles (UAVs) to facilitate IoT applications. Initially, the problem of determining the IoT nodes optimal data offloading strategies to the UAV-mounted MEC servers, while accounting for the IoT nodes' communication and computation overhead, is formulated based on a game-theoretic model. The existence of at least one Pure Nash Equilibrium (PNE) point is shown by proving that the game is submodular. Furthermore, different operation points (i.e. offloading strategies) are obtained and studied, based either on the outcome of Best Response Dynamics (BRD) algorithm, or via alternative reinforcement learning approaches (i.e. gradient ascent, log-linear, and Q-learning algorithms), which explore and learn the environment towards determining the users' stable data offloading strategies. The corresponding outcomes and inherent features of these approaches are critically compared against each other, via modeling and simulation. 

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2. Data Offloading in UAV-Assisted Multi-Access Edge Computing Systems: A Resource-Based Pricing and User Risk-Awareness Approach

   https://doi.org/10.3390/s20082434  

   Mitsis, Giorgos ; Tsiropoulou, Eirini Eleni ; Papavassiliou, Symeon ( April 2020 , Sensors)

   null (Ed.)

   Unmanned Aerial Vehicle (UAV)-assisted Multi-access Edge Computing (MEC) systems have emerged recently as a flexible and dynamic computing environment, providing task offloading service to the users. In order for such a paradigm to be viable, the operator of a UAV-mounted MEC server should enjoy some form of profit by offering its computing capabilities to the end users. To deal with this issue in this paper, we apply a usage-based pricing policy for allowing the exploitation of the servers’ computing resources. The proposed pricing mechanism implicitly introduces a more social behavior to the users with respect to competing for the UAV-mounted MEC servers’ computation resources. In order to properly model the users’ risk-aware behavior within the overall data offloading decision-making process the principles of Prospect Theory are adopted, while the exploitation of the available computation resources is considered based on the theory of the Tragedy of the Commons. Initially, the user’s prospect-theoretic utility function is formulated by quantifying the user’s risk seeking and loss aversion behavior, while taking into account the pricing mechanism. Accordingly, the users’ pricing and risk-aware data offloading problem is formulated as a distributed maximization problem of each user’s expected prospect-theoretic utility function and addressed as a non-cooperative game among the users. The existence of a Pure Nash Equilibrium (PNE) for the formulated non-cooperative game is shown based on the theory of submodular games. An iterative and distributed algorithm is introduced which converges to the PNE, following the learning rule of the best response dynamics. The performance evaluation of the proposed approach is achieved via modeling and simulation, and detailed numerical results are presented highlighting its key operation features and benefits. 

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3. Monitoring the earthquake response of full‐scale structures using UAV vision‐based techniques

   https://doi.org/10.1002/stc.2862  

   Wang, X ; Lo, E ; De Vivo, L ; Hutchinson, T ; Kuester, F ( January 2022 , Structural control health monitoring)

   Vision-based sensing, when utilized in conjunction with camera-equipped unmanned aerial vehicles (UAVs), has recently emerged as an effective sensing technique in a variety of civil engineering applications (e.g., construction monitoring, conditional assessment, and post-disaster reconnaissance). However, the use of these non-intrusive sensing techniques for extracting the dynamic response of structures has been restricted due to the perspective and scale distortions or image misalignments caused by the movement of the UAV and its on-board camera during flight operations. To overcome these limitations, a vision-based analysis methodology is proposed in the present study for extracting the dynamic response of structures using unmanned aerial vehicle (UAV) aerial videos. Importantly, geo-referenced targets were strategically placed on the structures and the background (stationary) region to enhance the robustness and accuracy related to image feature detection. Image processing and photogrammetric techniques are adopted in the analysis procedures first to recover the camera motion using the world-to-image correspondences of the background (stationary) targets and subsequently to extract the dynamic structural response by reprojecting the image feature of the (moving) targets attached to the structures to the world coordinates. The displacement tracking results are validated using the responses of two full-scale test structures measured by analog displacement sensors during a sequence of shake table tests. The high level of precision (less than 3 mm root-mean-square errors) of the vision-based structural displacement results demonstrates the effectiveness of the proposed UAV displacement tracking methodology. Additionally, the limitations and potential solutions associated with the proposed methodology for monitoring the dynamic responses of real structures are discussed. 

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4. Autonomous UAV with Learned Trajectory Generation and Control

   https://doi.org/10.1109/SiPS47522.2019.9020508  

   Li, Yilan ; Li, Mingyang ; Sanyal, Amit ; Wang, Yanzhi ; Qiu, Qinru ( October 2019 , IEEE Workshop on Signal Processing Systems (SIPS))

   Unmanned aerial vehicle (UAV) technology is a rapidly growing field with tremendous opportunities for research and applications. To achieve true autonomy for UAVs in the absence of remote control, external navigation aids like global navigation satellite systems and radar systems, a minimum energy trajectory planning that considers obstacle avoidance and stability control will be the key. Although this can be formulated as a constrained optimization problem, due to the complicated non-linear relationships between UAV trajectory and thrust control, it is almost impossible to be solved analytically. While deep reinforcement learning is known for its ability to provide model free optimization for complex system through learning, its state space, actions and reward functions must be designed carefully. This paper presents our vision of different layers of autonomy in a UAV system, and our effort in generating and tracking the trajectory both using deep reinforcement learning (DRL). The experimental results show that compared to conventional approaches, the learned trajectory will need 20% less control thrust and 18% less time to reach the target. Furthermore, using the control policy learning by DRL, the UAV will achieve 58.14% less position error and 21.77% less system power. 

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5. Gesture commands for controlling high-level UAV behavior

   https://doi.org/10.1007/s42452-021-04583-8  

   Akagi, John ; Morris, T. Devon ; Moon, Brady ; Chen, Xingguang ; Peterson, Cameron K. ( May 2021 , SN Applied Sciences)

   Directing groups of unmanned air vehicles (UAVs) is a task that typically requires the full attention of several operators. This can be prohibitive in situations where an operator must pay attention to their surroundings. In this paper we present a gesture device that assists operators in commanding UAVs in focus-constrained environments. The operator influences the UAVs’ behavior by using intuitive hand gesture movements. Gestures are captured using an accelerometer and gyroscope and then classified using a logistic regression model. Ten gestures were chosen to provide behaviors for a group of fixed-wing UAVs. These behaviors specified various searching, following, and tracking patterns that could be used in a dynamic environment. A novel variant of the Monte Carlo Tree Search algorithm was developed to autonomously plan the paths of the cooperating UAVs. These autonomy algorithms were executed when their corresponding gesture was recognized by the gesture device. The gesture device was trained to classify the ten gestures and accurately identified them 95% of the time. Each of the behaviors associated with the gestures was tested in hardware-in-the-loop simulations and the ability to dynamically switch between them was demonstrated. The results show that the system can be used as a natural interface to assist an operator in directing a fleet of UAVs.

   A gesture device was created that enables operators to command a group of UAVs in focus-constrained environments.

   Each gesture triggers high-level commands that direct a UAV group to execute complex behaviors.

   Software simulations and hardware-in-the-loop testing shows the device is effective in directing UAV groups.

    

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