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The wireless communication landscape in beyond 5G and 6G systems, particularly in dense smart city environments, presents significant interference challenges. UAV-mounted Reconfigurable Intelligent Surfaces (RIS) offer a promising solution to counter interference from unknown jammers. However, the system’s dynamic nature, especially real-time fluctuations in device and jammer distribution and UAV resources, complicates UAV and RIS management. Current approaches, which rely on a single UAV-mounted RIS or a fixed number of UAVs covering static device clusters, fail to adapt to these dynamic conditions. Smaller swarms may lead to inadequate coverage, while larger swarms can cause inefficiency and higher energy consumption. Additionally, these approaches often target a single objective, such as maximizing sum rates or minimizing energy use, without considering UAV battery constraints. Our work introduces an adaptive UAV swarm formation and dynamic device clustering technique designed for efficient anti-jamming in dynamic multi-user clusters threatened by unknown jammers during critical public events. This approach creates a flexible UAV-borne RIS swarm that dynamically adjusts the number of UAVs and the clustering to real-time changes of mobile devices and jammers, ensuring uninterrupted operations through UAV recharging and swapping while conserving total energy by deploying the minimum sufficient number of UAVs. Using Reinforcement Learning (RL), our solution optimizes the number of UAVs, device-to-UAV associations, UAV trajectories, RIS phase shifts, and base station power to effectively balance the sum rate and energy consumption. Simulations demonstrate the superior performance of our approach in coverage, jamming mitigation, energy conservation, connectivity, and scalability compared to existing methods and baselines.

Over the past decade, Unmanned Aerial Vehicles (UAVs) have attracted significant attention due to their potential applications in emergency-response applications, including wireless power transfer (WPT) and data collection from Internet of Things (IoT) devices in disaster-affected areas. UAVs are more attractive than traditional techniques due to their maneuverability, flexibility, and low deployment costs. However, using UAVs for such critical tasks comes with challenges, including limited resources, energy constraints, and the need to complete missions within strict time frames. IoT devices in disaster areas have limited resources (e.g., computation, energy), so they depend on the UAVs’ resources to accomplish vital missions. To address these resource problems in a disaster scenario, we propose a meta-reinforcement learning (RL)-based energy harvesting (EH) framework. Our system model considers a swarm of UAVs that navigate an area, providing wireless power and collecting data from IoT devices on the ground. The primary objective is to enhance the quality of service for strategic locations while allowing UAVs to dynamically join and leave the swarm (e.g., for recharging). In this context, we formulate the problem as a non-linear programming (NLP) optimization problem aimed at maximizing the total EH IoT devices and determining the optimal trajectory paths for UAVs while adhering to the constraints related to the maximum time duration, the UAVs’ maximum energy consumption, and the minimum data rate to achieve a reliable transmission. Due to the complexity of the problem, the combinatorial nature of the formulated problem, and the difficulty of obtaining the optimal solution using conventional optimization problems, we propose a lightweight meta-RL solution capable of solving the problem by learning the system dynamics. We conducted extensive simulations and compared our approach with two state-of-the-art models using traditional RL algorithms represented by a deep Q-network algorithm, a Particle Swarm Optimization (PSO) algorithm, and one greedy solution. Our simulation results show that the proposed Meta-RL algorithm can outperform the IoT EH of the DQN, PSO algorithm, and the greedy solution by 25%, 32%, and 45%, respectively. The results of our simulations also demonstrate that our proposed approach outperforms the competitive solutions in terms of efficiently covering strategic locations with a high satisfaction rate and high accuracy.

The evolving landscape of beyond 5G and 6G wireless communication systems in smart urban environments faces numerous interference-related challenges posed by legitimate and illicit devices. In this context, Intelligent Reflecting Surfaces (IRS) have emerged as a promising solution to mitigate interference caused by obstacles and unknown jamming devices. Existing techniques mainly focus on mitigating the impact of a single jammer in IRS-assisted communications systems, which affects both stationary and mobile devices. Additionally, these approaches target a single objective, such as minimizing the energy, enhancing the transmission rate, or maximizing the Signal-to-Interference-plus-Noise Ratio (SINR), which restrains the performance of the system. This paper offers a comprehensive anti-jamming solution for securing wireless communications in a smart city urban environment comprising diverse public events such as sporting events, parades, festivals, and exhibitions. The focus is on maintaining essential services like security, law enforcement, logistics, emergency response, crowd management, and public health. We introduce a Reinforcement Learning-based technique for UAV-mounted IRS, optimizing trajectory and phase shift beamforming to counteract the disruptive impact of jammers, ensuring reliable communication in dynamic, security-sensitive settings Our approach also seeks to achieve multi-objective optimization by striking a balance between transmission rate and energy consumption in this highly challenging environment. The formulated optimization is computationally complex due to its combinatorial nature. Hence, we leverage the light-weight Deep Reinforcement Learning (DRL) technique called Deep Deterministic Policy Gradient (DDPG) to optimize trajectory and IRS phase shifts and achieve multiple objectives jointly. Experimental results demonstrate the effectiveness of our proposed DDPG-based approach in outperforming other RL algorithms. It achieves a near-optimal solution compared to the benchmark technique within the close gap and improves both achievable transmission rates and energy efficiency compared to related works by 50-70%.

Abstract In machine learning, an informative dataset is crucial for accurate predictions. However, high dimensional data often contains irrelevant features, outliers, and noise, which can negatively impact model performance and consume computational resources. To tackle this challenge, the Bird’s Eye View (BEV) feature selection technique is introduced. This approach is inspired by the natural world, where a bird searches for important features in a sparse dataset, similar to how a bird search for sustenance in a sprawling jungle. BEV incorporates elements of Evolutionary Algorithms with a Genetic Algorithm to maintain a population of top-performing agents, Dynamic Markov Chain to steer the movement of agents in the search space, and Reinforcement Learning to reward and penalize agents based on their progress. The proposed strategy in this paper leads to improved classification performance and a reduced number of features compared to conventional methods, as demonstrated by outperforming state-of-the-art feature selection techniques across multiple benchmark datasets.

Abstract View-invariant action recognition has been widely researched in various applications, such as visual surveillance and human–robot interaction. However, view-invariant human action recognition is challenging due to the action occlusions and information loss caused by view changes. Modeling spatiotemporal dynamics of body joints and minimizing representation discrepancy between different views could be a valuable solution for view-invariant human action recognition. Therefore, we propose a Dual-Attention Network (DANet) aims to learn robust video representation for view-invariant action recognition. The DANet is composed of relation-aware spatiotemporal self-attention and spatiotemporal cross-attention modules. The relation-aware spatiotemporal self-attention module learns representative and discriminative action features. This module captures local and global long-range dependencies, as well as pairwise relations among human body parts and joints in the spatial and temporal domains. The cross-attention module learns view-invariant attention maps and generates discriminative features for semantic representations of actions in different views. We exhaustively evaluate our proposed approach on the NTU-60, NTU-120, and UESTC large-scale challenging datasets with multi-type evaluation metrics including Cross-Subject, Cross-View, Cross-Set, and Arbitrary-view. The experimental results demonstrate that our proposed approach significantly outperforms state-of-the-art approaches in view-invariant action recognition.

This study aimed to identify typical interactions between adolescents’ Internet addiction and family environment factors. 165 parents were surveyed about family environment and problematic Internet use for themselves and their adolescents. Three distinct clusters were identified. Cluster 1 (assertive interaction) involved non-addicted authoritative parents with adolescents at risk of addiction. These parents had occasional arguments with their adolescents. Cluster 2 (aggressive interaction) included at-risk authoritarian parents with Internet addicted adolescents. These parents often had arguments with their adolescents. Cluster 3 (lenient interaction) comprised non-addicted permissive parents with highly addicted adolescents. These parents constantly argue with their adolescents. As we have not identified a cluster where adolescents’ addiction was below minimal risk, we advocate the need to train parents along with adolescents on healthy technology use. The clusters identified can be used by professionals as a basis to produce diverse interventions that fit each of the identified family types.

5G communication brings substantial improvements in the quality of service provided to various applications by achieving higher throughput and lower latency. However, interactive multimedia applications (e.g., ultra high definition video conferencing, 3D and multiview video streaming, crowd-sourced video streaming, cloud gaming, virtual and augmented reality) are becoming more ambitious with high volume and low latency video streams putting strict demands on the already congested networks. Mobile Edge Computing (MEC) is an emerging paradigm that extends cloud computing capabilities to the edge of the network i.e., at the base station level. To meet the latency requirements and avoid the end-to-end communication with remote cloud data centers, MEC allows to store and process video content (e.g., caching, transcoding, pre-processing) at the base stations. Both video on demand and live video streaming can utilize MEC to improve existing services and develop novel use cases, such as video analytics, and targeted advertisements. MEC is expected to reshape the future of video streaming by providing ultra-reliable and low latency streaming (e.g., in augmented reality, virtual reality, and autonomous vehicles), pervasive computing (e.g., in real-time video analytics), and blockchain-enabled architecture for secure live streaming. This paper presents a comprehensive survey of recent developments in MEC-enabled video streaming bringing unprecedented improvement to enable novel use cases. A detailed review of the state-of-the-art is presented covering novel caching schemes, optimal computation offloading, cooperative caching and offloading and the use of artificial intelligence (i.e., machine learning, deep learning, and reinforcement learning) in MEC-assisted video streaming services.

Objective This study aims to explore the user archetypes of health apps based on average usage and psychometrics. Methods The study utilized a dataset collected through a dedicated smartphone application and contained usage data, i.e. the timestamps of each app session from October 2020 to April 2021. The dataset had 129 participants for mental health apps usage and 224 participants for physical health apps usage. Average daily launches, extraversion, neuroticism, and satisfaction with life were the determinants of the mental health apps clusters, whereas average daily launches, conscientiousness, neuroticism, and satisfaction with life were for physical health apps. Results Two clusters of mental health apps users were identified using k-prototypes clustering: help-seeking and maintenance users and three clusters of physical health apps users were identified: happy conscious occasional , happy neurotic occasional , and unhappy neurotic frequent users. Conclusion The findings from this study helped to understand the users of health apps based on the frequency of usage, personality, and satisfaction with life. Further, with these findings, apps can be tailored to optimize user experience and satisfaction which may help to increase user retention. Policymakers may also benefit from these findings since understanding the populations’ needs may help to better invest in effective health technology.

Existing techniques for incremental learning are computationally expensive and produce duplicate features leading to higher false positive and true negative rates. We propose a novel privacy-preserving intrusion detection pipeline for distributed incremental learning. Our pre-processing technique eliminates redundancies and selects unique features by following innovative extraction techniques. We use autoencoders with non-negativity constraints, which help us extract less redundant features. More importantly, the distributed intrusion detection model reduces the burden on the edge classifier and distributes the load among IoT and edge devices. Theoretical analysis and numerical experiments have shown lower space and time costs than state of the art techniques, with comparable classification accuracy. Extensive experiments with standard data sets and real-time streaming IoT traffic give encouraging results.

In the past decades, the internet has emerged as the fastest way to access information. However, this revolutionary information age comes with its own set of challenges. The privacy of Internet users is at increasing risk with the advances in surveillance techniques. Users' online behavior, activities, and even personal information are being tracked by ISPs and major tech companies. In response to the increasing need for preserving and protecting the privacy of online users, anonymity networks were developed. Tor anonymity network is a low-latency anonymity network that has gained quite a good reputation over the past years and is being adopted by thousands of users. With the great attention Tor's network is getting, the original design of Tor was proven to have performance limiting issues. With the motivation for addressing the performance limitation in Tor, we present QuicTor, a datagram-based design to solve Tor's transport-layer limiting issue. We evaluated the performance of QuicTor in comparison to vanilla Tor as well as other performance-enhancing proposals. QuicTor achieved significant performance improvements for interactive applications as well as streaming applications. Running Tor over a datagram-based protocol entails a careful security analysis. In this article, we assess the behavior of QuicTor under side-channel attacks aiming to de-anonymize Tor's clients. We show that the performance improvements brought by QuicTor do not reduce the anonymity of clients under the investigated types of attacks.

Due to their high computational and memory demand, deep learning applications are mainly restricted to high-performance units, e.g., cloud and edge servers. Particularly, in Internet of Things (IoT) systems, the data acquired by pervasive devices is sent to the computing servers for classification. However, this approach might not be always possible because of the limited bandwidth and the privacy issues. Furthermore, it presents uncertainty in terms of latency because of the unstable remote connectivity. To support resource and delay requirements of such paradigm, joint and real-time deep co-inference framework with IoT synergy was introduced. However, scheduling the distributed, dynamic and real-time Deep Neural Network (DNN) inference requests among resource-constrained devices has not been well explored in the literature. Additionally, the distribution of DNN has drawn the attention to the privacy protection of sensitive data. In this context, various threats have been presented, including white-box attacks, where malicious devices can accurately recover received inputs if the DNN model is fully exposed to participants. In this paper, we introduce a methodology aiming at distributing the DNN tasks onto the resource-constrained devices of the IoT system, while avoiding to reveal the model to participants. We formulate such an approach as an optimization problem, where we establish a trade-off between the latency of co-inference, the privacy of the data, and the limited resources of devices. Next, due to the NP-hardness of the problem, we shape our approach as a reinforcement learning design adequate for real-time applications and highly dynamic systems, namely RL-PDNN. Our system proved its ability to outperform existing static approaches and achieve close results compared to the optimal solution.

Using smartphones, tablets, and other portable/handheld devices, we have become more reliant on the video streaming services for entertainment and remote work. Mobile data traffic has grown eighteen folds over the past five years accounting for the majority of IP traffic. YouTube Live, Facebook Live, Twitch, DouYu and other streaming as well as video conferencing services have increased in popularity so at any given moment they serve thousands of live video streams to millions of users. The Enhanced Multimedia Broadcast Multimedia Service (eMBMS) is the standard multicast protocol for 5G networks. Cellular multicast has gained considerable attention to efficiently utilize the limited spectrum to transmit multimedia content to cellular sites with co-located viewers, lowering the cost, and maximizing the Quality of Experience (QoE). However, popular live video content providers use unicast mode for live video delivery and have limited support in the eMBMS service-oriented network architecture. In this paper, we propose an overlay network architecture to augment eMBMS to address the limitations of the standard eMBMS architecture and enable service-less multicast for crowdsourced live video providers. We propose a Virtual Network Function (VNF) service that identifies potential multicast scenarios based on user requests for a live video within a confined area. The VNF Application Server collects information, validates a potential multicast scenario, and initiates an ad-hoc multicast service on the fly. We use a real-world dataset of Facebook Live videos to evaluate the proposed architecture. The simulation results depict considerable advantages in terms of cost, efficiency, and Quality of Experience (QoE). Our results show that the proposed architecture provides significant benefits in bandwidth saving at the backhaul, transit, and RAN links.

In order to maintain a satisfactory performance in the midst of rapid growth of mobile traffic, the mobile network infrastructure needs to be scaled. Thus there has been significant interest in scalability of mobile core networks and a variety of scaling solutions have been proposed that rely on horizontal scaling or vertical scaling. These solutions handle the scaling of the mobile core networks’ elements on virtual machines (which normally take at while to create) with the help of customized modules at the cost of increased overheads. Utilizing Amazon Web Services (AWS) embedded features, we present two predictive horizontal auto-scalers for containerized and non-containerized versions of EPC that scales the two versions of the EPC according to their respective CPU utilization. Additionally, we propose an efficient task assignment scheme for AWS that aims to maximize throughput and achieve fairness among competing instances. In particular, we propose two solutions: Relaxed Optimized Solution (ROS) and a Heuristic Approach (HA). Leveraging AWS environment, we implemented and evaluated the two proposed auto-scaling models based on the attachment success rate, latency, CPU usage and RAM usage. Our findings show the superiority of container-based model over VM-based model in terms of resource utilization. The obtained results for the two proposed task assignment solutions demonstrates a significant improvement both in fairness and throughput compared to other existing solutions.

Machine learning techniques are becoming mainstream in intrusion detection systems as they allow real-time response and have the ability to learn and adapt. By using a comprehensive dataset with multiple attack types, a well-trained model can be created to improve the anomaly detection performance. However, high dimensional data present a significant challenge for machine learning techniques. Processing similar features that provide redundant information increases the computational time, which is a critical problem especially for users with constrained resources (battery, energy). In this paper, we propose two models for intrusion detection and classification scheme Trust-based Intrusion Detection and Classification System (TIDCS) and Trust-based Intrusion Detection and Classification System- Accelerated (TIDCS-A) for secure network. TIDCS reduces the number of features in the input data based on a new algorithm for feature selection. Initially, the features are grouped randomly to increase the probability of making them participating in the generation of different groups, and sorted based on their accuracy scores. Only the high ranked features are then selected to obtain a classification for any received packet from the nodes in the network, which is saved as part of the node's past performance. TIDCS proposes a periodic system cleansing where trust relationships between participant nodes are evaluated and renewed periodically. TIDCS-A proposes a dynamic algorithm to compute the exact time for nodes cleansing states and restricts the exposure window of the nodes. The final classification decision for both models is estimated by incorporating the node's past behavior with the machine learning algorithm. Any detected attack reduces the trustworthiness of the nodes involved, leading to a dynamic system cleansing. An evaluation of TIDCS and TIDCS-A using the NSL-KDD and UNSW datasets shows that both models can detect malicious behaviors providing higher accuracy, detection rates, and lower false alarm than state-of-art techniques. For instance, for UNSW dataset, the accuracy detection is 91% for TICDS, 83.47%by using online AODE, 88% for CADF, 90% for EDM, 90% for TANN and 69.6% for NB. Consequently, TICDS has better performance than the state of art techniques in terms of accuracy detection, while providing good detection and false alarm rates.

Decentralized cryptocurrencies have gained a lot of attention over the last decade. Bitcoin was introduced as the first cryptocurrency to allow direct online payments without relying on centralized financial entities. The use of Bitcoin has vastly grown as a financial asset rather than just a tool for online payments. A lot of cryptocurrencies have been created since 2011 with Bitcoin dominating the cryptocurrencies' market. With plenty of cryptocurrencies being used as financial assets and with millions of trades being executed through different exchange services, cryptocurrencies are susceptible to trading problems and challenges similar to those traditionally encountered in the financial domain. Price and trend prediction, volatility prediction, portfolio construction and fraud detection are some examples related to trading. In addition, there are other challenges that are specific to the domain of cryptocurrencies such as mining, cybersecurity, anonymity and privacy. In this paper, we survey the application of artificial intelligence techniques to address these challenges for cryptocurrencies with their vast amount of daily transactions, trades and news that are beyond human capabilities to analyze and learn from. This paper discusses the recent research work done in this emerging area and compares them in terms of used techniques and datasets. It also highlights possible research gaps and some potential areas for improvement.

Cloud computing provides flexibility, reliability, and scalability to its consumers. Applications that run on Cloud are now more resource hungry than ever, and the need is constantly growing. The number of Cloud applications with diverse nature is growing, expecting greater performance in low cost. Fulfilling the quality of service (QoS) demands of such diverse applications is a challenging task and requires efficient network architectures, robust scheduling schemes, powerful and adaptable routing algorithms, reliable resource management, effective data exchange policies, and QoS improvement policies. Research community endeavors to provide solutions to various challenges faced by Cloud computing. These solutions must be tested thoroughly before their implementation in a real Cloud. Simulation seems a viable choice for testing a solution in different scenarios. There are a number of simulators available, focusing on certain aspects of Cloud while neglecting others. Their generalized implementation of the Cloud ignores various critical factors, such as networking characteristics, consequently affecting the tests and results of a solution. To tackle these issues, simplify simulation process, and provide the detailed implementation of the Cloud, we present Nutshell: a novel Cloud simulator that makes it easy to model, simulate, and experiment new Cloud solutions. Salient features offered by Nutshell include 1) provision of a platform for modeling and simulating Cloud infrastructure, 2) built-in essential components and helpers to create new architectures, 3) pre-built data center architectures, 4) plug-in-based architecture, 5) communication protocols, 6) pre-built virtual machine scheduler, 7) addressing schemes, 8) user applications, 9) virtual machines, 10) Job splitting, and 11) simulation data collection and exporting.

This paper aims to objectively compare the use of mental health apps between the pre-COVID-19 and during COVID-19 periods and to study differences amongst the users of these apps based on age and gender. The study utilizes a dataset collected through a smartphone app that objectively records the users’ sessions. The dataset was analyzed to identify users of mental health apps (38 users of mental health apps pre-COVID-19 and 81 users during COVID-19) and to calculate the following usage metrics; the daily average use time, the average session time, the average number of launches, and the number of usage days. The mental health apps were classified into two categories: guidance-based and tracking-based apps. The results include the increased number of users of mental health apps during the COVID-19 period as compared to pre-COVID-19. Adults (aged 24 and above), compared to emerging adults (aged 15–24 years), were found to have a higher usage of overall mental health apps and guidance-based mental health apps. Furthermore, during the COVID-19 pandemic, males were found to be more likely to launch overall mental health apps and guidance-based mental health apps compared to females. The findings from this paper suggest that despite the increased usage of mental health apps amongst males and adults, user engagement with mental health apps remained minimal. This suggests the need for these apps to work towards improved user engagement and retention.

Background: As public health strategists and policymakers explore different approaches to lessen the devastating effects of novel coronavirus disease (COVID-19), blockchain technology has emerged as a resource that can be utilized in numerous ways. Many blockchain technologies have been proposed or implemented during the COVID-19 pandemic; however, to the best of our knowledge, no comprehensive reviews have been conducted to uncover and summarise the main feature of these technologies.Objective: This study aims to explore proposed or implemented blockchain technologies used to mitigate the COVID-19 challenges as reported in the literature.Methods: We conducted a scoping review in line with guidelines of PRISMA Extension for Scoping Reviews (PRISMA-ScR). To identify relevant studies, we searched 11 bibliographic databases (e.g., EMBASE and MEDLINE) and conducted backward and forward reference list checking of the included studies and relevant reviews. The study selection and data extraction were conducted by 2 reviewers independently. Data extracted from the included studies was narratively summarised and described.Results: 19 of 225 retrieved studies met eligibility criteria in this review. The included studies reported 10 used cases of blockchain to mitigate COVID-19 challenges; the most prominent use cases were contact tracing and immunity passports. While the blockchain technology was developed in 10 studies, its use was proposed in the remaining 9 studies. The public blockchain technology was the most commonly utilized type in the included studies. All together, 8 different consensus mechanisms were used in the included studies. Out of 10 studies that identified the used platform, 9 studies used Ethereum to run the blockchain. Solidity was the most prominent programming language used in developing blockchain technology in the included studies. The transaction cost was reported in only 4 of the included studies and varied between USD 10−10 and USD 5. The expected latency and expected scalability were not identified in the included studies.Conclusion: Blockchain technologies are expected to play an integral role in the fight against the COVID-19 pandemic. Many possible applications of blockchain were found in this review; however, most of them are not mature enough to reveal their expected impact in the fight against COVID-19.We encourage governments, health authorities, and policymakers to consider all blockchain applications suggested in the current review to combat COVID-19 challenges. There is a pressing need to empirically examine how effective blockchain technologies are in mitigating COVID-19 challenges. Further studies are required to assess the performance of blockchain technologies’ fight against COVID-19 in terms of transaction cost, scalability, and/or latency when using different consensus algorithms, platforms, and access types.

The COVID-19 pandemic has affected the world socially and economically changing behaviors towards medical facilities, public gatherings, workplaces, and education. Educational institutes have been shutdown sporadically across the globe forcing teachers and students to adopt distance learning techniques. Due to the closure of educational institutes, work and learn from home methods have burdened the network resources and considerably decreased a viewer’s Quality of Experience (QoE). The situation calls for innovative techniques to handle the surging load of video traffic on cellular networks. In the scenario of distance learning, there is ample opportunity to realize multi-cast delivery instead of a conventional unicast. However, the existing 5G architecture does not support service-less multi-cast. In this article, we advance the case of Virtual Network Function (VNF) based service-less architecture for video multicast. Multicasting a video session for distance learning significantly lowers the burden on core and Radio Access Networks (RAN) as demonstrated by evaluation over a real-world dataset. We debate the role of Edge Intelligence (EI) for enabling multicast and edge caching for distance learning to complement the performance of the proposed VNF architecture. EI offers the determination of users that are part of a multicast session based on location, session, and cell information. Moreover, user preferences and network’s contextual information can differentiate between live and cached access patterns optimizing edge caching decisions. While exploring the opportunities of EI-enabled distance learning, we demonstrate a significant reduction in network operator resource utilization and an increase in user QoE for VNF based multicast transmission.

Advances and commoditization of media generation devices enable capturing and sharing of any special event by multiple attendees. We propose a novel system to collect individual video streams (views) captured for the same event by multiple attendees, and combine them into multi-view videos, where viewers can watch the event from various angles, taking crowdsourced media streaming to a new immersive level. The proposed system is called Cloud-based Multi-View Crowd sourced Streaming (CMVCS), and it delivers multiple views of an event to viewers at the best possible video representation based on each viewer's available bandwidth. The CMVCS is a complex system having many research challenges. In this paper, we focus on resource allocation of the CMVCS system. The objective of the study is to maximize the overall viewer satisfaction by allocating available resources to transcode views in an optimal set of representations, subject to computational and bandwidth constraints. We choose the video representation set to maximize QoE using Mixed Integer Programming. Moreover, we propose a Fairness-Based Representation Selection (FBRS) heuristic algorithm to solve the resource allocation problem efficiently. We compare our results with optimal and Top-N strategies. The simulation results demonstrate that FBRS generates near optimal results and outperforms the state-of-the-art Top-N policy, which is used by a large-scale system (Twitch).

Modern technology has pushed us into the information age, making it easier to generate and record vast quantities of new data. Datasets can help in analyzing the situation to give a better understanding, and more importantly, decision making. Consequently, datasets, and uses to which they can be put, have become increasingly valuable commodities. This article describes the DroneRF dataset: a radio frequency (RF) based dataset of drones functioning in different modes, including off, on and connected, hovering, flying, and video recording. The dataset contains recordings of RF activities, composed of 227 recorded segments collected from 3 different drones, as well as recordings of background RF activities with no drones. The data has been collected by RF receivers that intercepts the drone's communications with the flight control module. The receivers are connected to two laptops, via PCIe cables, that runs a program responsible for fetching, processing and storing the sensed RF data in a database. An example of how this dataset can be interpreted and used can be found in the related research article “RF-based drone detection and identification using deep learning approaches: an initiative towards a large open source drone database” (Al-Sa'd et al., 2019). Keywords: UAV detection, Drone identification, Classification, Anti-drone systems