The rapid evolution of Unmanned Aerial Vehicles (UAVs) has revolutionized target search operations in various fields, including military applications, search and rescue missions, and post-disaster management. In this paper, we propose the use of a multi-armed bandit algorithm for a UAV's search mission in an unknown and adversarial setting. The UAV's objective is to locate a mobile target formation, assuming that their mobility resembles an adversarial behavior. To achieve this, we formulate an optimization problem and leverage the Exp3 (exponential-weighted exploration and exploitation) algorithm to solve it. The targets are assumed to be moving under the assumption of an unknown and potentially non-stationary probability distribution. To enhance the learning process, we integrate environmental observations as contextual information, resulting in a variant called C-Exp3, which optimizes the search process. Finally, we evaluate the performance of C-Exp3 in UAV search missions, focusing on adversarial environments. The primary objective for the UAV is to converge towards an optimal policy as time t approaches the horizon T, reflecting the UAV's capacity to learn the formation's strategy.

The rise of chronic disease patients and the pandemic pose immediate threats to healthcare expenditure and mortality rates. This calls for transforming healthcare systems away from one-on-one patient treatment into intelligent health systems, leveraging the recent advances of Internet of Things and smart sensors. Meanwhile, reinforcement learning (RL) has witnessed an intrinsic breakthrough in solving a variety of complex problems for distinct applications and services. Thus, this article presents a comprehensive survey of the recent models and techniques of RL that have been developed/used for supporting Intelligent-healthcare (I-health) systems. It can guide the readers to deeply understand the state-of-the-art regarding the use of RL in the context of I-health. Specifically, we first present an overview of the I-health systems' challenges, architecture, and how RL can benefit these systems. We then review the background and mathematical modeling of different RL, deep RL (DRL), and multiagent RL models. We highlight important guidelines on how to select the appropriate RL model for a given problem, and provide quantitative comparisons, showing the results of deploying key RL models in two scenarios that can be followed in monitoring applications. After that, we conduct an in-depth literature review on RL's applications in I-health systems, covering edge intelligence, smart core network, and dynamic treatment regimes. Finally, we highlight emerging challenges and future research directions to enhance RL's success in I-health systems, which opens the door for exploring some interesting and unsolved problems.

Service provisioning systems assign users to service providers according to allocation criteria that strike an optimal trade-off between users' Quality of Experience (QoE) and the operation cost endured by providers. These systems have been leveraging Smart Contracts (SCs) to add trust and transparency to their criteria. However, deploying fixed allocation criteria in SCs does not necessarily lead to the best performance over time since the blockchain participants join and leave flexibly, and their load varies with time, making the original allocation sub-optimal. Furthermore, updating the criteria manually at every variation in the blockchain jeopardizes the autonomous and independent execution promised by SCs. Thus, we propose a set of light-weight agents for SCs that are capable of optimizing the performance. We also propose using online learning SCs, empowered by Deep Reinforcement Learning (DRL) agent, that leverage the chained data to continuously self-tune its allocation criteria. We show that the proposed learning-assisted method achieves superior performance on the combinatorial multi-stage allocation problem while still being executable in real-time. We also compare the proposed approach with standard heuristics as well as planning methods. Results show a significant performance advantage over heuristics and better adaptability to the dynamic nature of blockchain networks.

Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services, spanning from recommendation systems and speech processing applications to robotics control and military surveillance. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes of real-time data streams. Designing accurate models using such data streams, to revolutionize the decision-taking process, inaugurates pervasive computing as a worthy paradigm for a better quality-of-life (e.g., smart homes and self-driving cars.). The confluence of pervasive computing and artificial intelligence, namely Pervasive AI, expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising alternative to centralized learning, presenting various challenges, including privacy and latency requirements. In this context, an intelligent resource scheduling should be envisaged among IoT devices (e.g., smartphones, smart vehicles) and infrastructure (e.g., edge nodes and base stations) to avoid communication and computation overheads and ensure maximum performance. In this paper, we conduct a comprehensive survey of the recent techniques and strategies developed to overcome these resource challenges in pervasive AI systems. Specifically, we first present an overview of pervasive computing, its architecture, and its intersection with artificial intelligence. We then review the background, applications and performance metrics of AI, particularly Deep Learning (DL) and reinforcement learning, running in a ubiquitous system. Next, we provide a deep literature review of communication-efficient techniques, from both algorithmic and system perspectives, of distributed training and inference across the combination of IoT devices, edge devices and cloud servers. Finally, we discuss our future vision and research challenges.