CCTV-based surveillance using unmanned aerial vehicles (UAVs) is considered a key technology for security in smart city environments.This article creates a case where the UAVs with CCTV-cameras fly over the city area for flexible and reliable surveillance services. UAVs should be deployed to cover a large area while minimizing overlapping and shadow areas for a reliable surveillance system. However, the operation of UAVs is subject to high uncertainty, necessitating autonomous recovery systems. This article develops a multiagent deep reinforcement learning-based management scheme for reliable industry surveillance in smart city applications. The core idea this article employs is autonomously replenishing the UAV's deficient network requirements with communications. Via intensive simulations, our proposed algorithm outperforms the state-of-the-art algorithms in terms of surveillance coverage, user support capability, and computational costs.

This paper proposes a cloud-assisted joint charging scheduling and energy management framework for unmanned aerial vehicle (UAV) networks. For charging the UAVs those are extremely power hungry, charging towers are considered for plug-and-play charging during run-time operations. The charging towers should be cost-effective, thus it is equipped with photovoltaic power generation and energy storage systems functionalities. Furthermore, the towers should be cooperative for more cost-effectiveness by intelligent energy sharing. Based on the needs and setting, this paper proposes 1) charging scheduling between UAVs and towers and 2) cooperative energy managements among towers. For charging scheduling, the UAVs and towers should be scheduled for maximizing charging energy amounts and the scheduled pairs should determine charging energy allocation amounts. Here, two decisions are correlated, i.e. , it is a non-convex problem. We re-formulate the non-convex to convex for guaranteeing optimal solutions. Lastly, the cooperative energy sharing among towers is designed and implemented with multi-agent deep reinforcement learning and then intelligent energy sharing can be realized. We can observe that the two methods are related and it should be managed, coordinated, and harmonized by a centralized orchestration manager under the consideration of fairness, energy-efficiency, and cost-effectiveness. Our data-intensive performance evaluation verifies that our proposed framework achieves desired performance.

As one of the latest fields of interest in both academia and industry, quantum computing has garnered significant attention. Among various topics in quantum computing, variational quantum circuits (VQCs) have been noticed for their ability to carry out quantum deep reinforcement learning (QRL). This article verifies the potential of QRL, which will be further realized by implementing quantum multiagent reinforcement learning (QMARL) from QRL, especially for Internet-connected autonomous multirobot control and coordination in smart factory applications. However, the extension is not straightforward due to the nonstationarity of classical MARL. To cope with this, the centralized training and decentralized execution (CTDE) QMARL framework is proposed under the Internet connection. A smart factory environment with the Internet of Things (IoT)-based multiple agents is used to show the efficacy of the proposed algorithm. The simulation corroborates that the proposed QMARL-based autonomous multirobot control and coordination performs better than the other frameworks.

The 5G new radio standard defines various functions for forwarding treatments under differentiated quality of services requirements. In particular, a gNB base station helps combat overbuffering due to massive packet flows from associated mobile devices. Several active queue management (AQM) schemes have been proposed previously, with controlled delay (CoDel) proving to be an efficient method that can properly handle excessive buffering, that is called bufferbloat. This article proposes a novel and enhanced AQM scheme based on CoDel for gNB that guarantees low queuing delays, minimizes packet drop rate, and furthermore stabilizes the built-in queue. According to data-intensive performance evaluation results, the proposed scheme verifies that the queuing delay for served packets are decreased and supports ultrareliable low latency communication applications. Especially, the proposed algorithm achieves the packet drop rates and queuing delays tradeoff as $[\mathcal {O}(1/V), {O}(V)]$ where $V$ is a tradeoff parameter; providing efficient control decisions to improve performance in terms of time-average packet drop rate minimization subject to queue stability on demand in 5G mobile system.

Green tide, which is a serious water pollution problem, is caused by the complex relationships of various factors, such as flow rate, several water quality indicators, and weather. Because the existing methods are not suitable for identifying these relationships and making accurate predictions, a new system and algorithm is required to predict the green tide phenomenon and also minimize the related damage before the green tide occurs. For this purpose, we consider a new network model using smart sensor-based federated learning which is able to use distributed observation data with geologically separated local models. Moreover, we design an optimal scheduler which is beneficial to use real-time big data arrivals to make the overall network system efficient. The proposed scheduling algorithm is effective in terms of (1) data usage and (2) the performance of green tide occurrence prediction models. The advantages of the proposed algorithm is verified via data-intensive experiments with real water quality big-data.