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.

Machine learning (ML) is a promising enabler for the fifth-generation (5G) communication systems and beyond. By imbuing intelligence into the network edge, edge nodes can proactively carry out decision-making and, thereby, react to local environmental changes and disturbances while experiencing zero communication latency. To achieve this goal, it is essential to cater for high ML inference accuracy at scale under the time-varying channel and network dynamics, by continuously exchanging fresh data and ML model updates in a distributed way. Taming this new kind of data traffic boils down to improving the communication efficiency of distributed learning by optimizing communication payload types, transmission techniques, and scheduling, as well as ML architectures, algorithms, and data processing methods. To this end, this article aims to provide a holistic overview of relevant communication and ML principles and, thereby, present communication-efficient and distributed learning frameworks with selected use cases.

This paper proposes a novel multi-agent deep reinforcement learning based algorithm which can realize federated learning computation with Internet-of-Underwater-Things (IoUT) devices in ocean environment. According to the fact that underwater networks is relatively not easy to setup reliable links by huge fading compared to wireless free-space air medium, gathering all training data for conducting centralized deep learning training is not easy. Therefore, federated learning (FL) based distributed deep learning can be a suitable solution for this application. In this IoUT network (IoUT-Net) scenario, the FL system needs to construct a global learning model by aggregating the local model parameters those are obtained from individual IoUT devices. In order to reliably deliver the parameters from IoUT devices to a centralized FL machine, base station like devices are needed. Therefore, a joint cell association and resource allocation (JCARA) method is required and it is designed inspired by multi-agent deep deterministic policy gradient (MADDPG) to deal with distributed situations and unexpected time-varying states. The performance evaluation results show that our proposed MADDPG based algorithm achieves 80% and 41% performance improvements than standard actor-critic and DDPG, respectively, in terms of downlink throughput.

On-demand video streaming is becoming a killer application for wireless networks. Recent information-theoretic results have shown that a combination of caching on the users' devices and device-to-device (D2D) communications yields throughput scalability for very dense networks, which represent critical bottlenecks for conventional cellular and wireless local area network (WLAN) technologies. In this paper, we consider the implementation of such caching D2D systems where each device pre-caches a subset of video files from a library, and users requesting a file that is not already in their library obtain it from neighboring devices through D2D communication. We develop centralized and distributed algorithms for the delivery phase, encompassing a link scheduling and a streaming component. The centralized scheduling is based on the max-weighted independent set (MWIS) principle and uses message-passing to determine max-weight independent sets. The distributed scheduling is based on a variant of the FlashLinQ link scheduling algorithm, enhanced by introducing video-streaming specific weights. In both cases, the streaming component is based on a quality-aware stochastic optimization approach, reminiscent of current Dynamic Adaptive Streaming over HTTP (DASH) technology, for which users sequentially request video “chunks” by choosing adaptively their quality level. The streaming and the scheduling components are coupled by the length of the users' request queues. Through extensive system simulation, the proposed approaches are shown to provide sizeable gains with respect to baseline schemes formed by the concatenation of off-the-shelf FlashLinQ with proportional fair link scheduling and DASH at the application layer.

Recently, the Blockchain-based cryptocurrency market witnessed enormous growth. Bitcoin, the leading cryptocurrency, reached all-time highs many times over the year leading to speculations to explain the trend in its growth. In this article, we study Bitcoin and Ethereum and explore features in their network that explain their price hikes. We gather data and analyze user and network activity that highly impact the price of these cryptocurrencies. We monitor the change in the activities over time and relate them to economic theories. We identify key network features that help us to determine the demand and supply dynamics in a cryptocurrency. Finally, we use machine learning methods to construct models that predict Bitcoin price. Based on our experimental results using two large datasets for validation, we confirm that our approach provides an accuracy of up to 99% for Bitcoin and Ethereum price prediction in both instances.

State-of-the-art drone technologies have severe flight time limitations due to weight constraints, which inevitably lead to a relatively small amount of available energy. Therefore, frequent battery replacement or recharging is necessary in applications such as delivery, exploration, or support to the wireless infrastructure. Mobile charging stations (i.e., mobile stations with charging equipment) for outdoor ad-hoc battery charging is one of the feasible solutions to address this issue. However, the ability of these platforms to charge the drones is limited in terms of the number and charging time. This paper designs an auctionbased mechanism to control the charging schedule in multidrone setting. In this paper, charging time slots are auctioned, and their assignment is determined by a bidding process. The main challenge in developing this framework is the lack of prior knowledge on the distribution of the number of drones participating in the auction. Based on optimal second-priceauction, the proposed formulation, then, relies on deep learning algorithms to learn such distribution online. Numerical results from extensive simulations show that the proposed deep learningbased approach provides effective battery charging control in multi-drone scenarios.