Content Requests Research Articles

Energy-Efficient Proactive Content Caching For On-Demand Video Streaming Under Uncertainty

Proactive content caching using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">edge service providers</i> (ESPs) present in wireless heterogeneous networks is an emerging research topic to deal with stringent client requirements. The limited computing resources of an ESP (e.g., small-cell base stations) can be utilized for transcoding the high resolution contents and thereby supporting the bit rate requirements of the clients. Unlike existing works, this paper deals with the case where the storage and computing capacities of the ESPs are random and time varying (e.g., due to internal workloads). This aspect is important because a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">content provider</i> (CP) cannot be aware of the exact resource availability at an ESP when determining proactive content placement. Different from existing works, we also ensure that the bit rate requirements of the clients are maintained throughout their video streaming sessions. Further, uncertainties in the demand (i.e., number of content requests) and link capacities are also considered. Given these unique challenges, we propose a two-stage stochastic problem which jointly determines the content placement decisions and demand allocation decisions for the CP under various uncertainties. The performance of the proposed model is rigorously analyzed in terms of energy consumption and percentage demand allocation, and compared to some benchmark models through extensive simulations.

QAVA: QoE-Aware Adaptive Video Bitrate Aggregation for HTTP Live Streaming Based on Smart Edge Computing

Currently video streaming in heterogeneous network environments is affected by limited network bandwidth availability and consequent low and variable user Quality of Experience (QoE) levels. In particular, for the case of live video streaming, a very high number of end-clients request content at the same time, generating huge concurrent traffic, and putting pressure on the existing network infrastructure. An approach which helps address this issue is deployment of emerging edge computing technologies to smooth the live streaming traffic and improve QoE by adapting client bitrates and caching content at the edge server. In this context, this paper proposes a novel <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</b> oE-aware <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> daptive <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</b> ideo bitrate <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> ggregation scheme for HTTP live streaming based on smart edge computing (QAVA). As an intelligent proxy server, a “smart edge” which deploys QAVA aggregates all the traffic requested by clients for the same live streaming service and adapts their bitrates based on network conditions, client states and video characteristics. The adaptation is performed based on a Deep Reinforcement Learning (DRL)-based algorithm, which is also proposed. The QAVA DRL algorithm is trained and modeled based on a real client experience dataset. The experimental evaluation results presented in this paper show how QAVA outperforms other state-of-the-art adaptive bitrate algorithms in terms of average QoE and QoE fairness.

A user cache management and cooperative transmission mechanism based on edge community computing in opportunistic social networks

With the advent of the 5G era, people's demand for information and bandwidth is increasing. The signal source will mainly be high-frequency radio waves with high bandwidth. However, the coverage of high-frequency radio waves is very limited, which leads to the emergence of micro base stations. Opportunistic social networks are just suitable for this new transmission method, it is easy for users to simultaneously request content with high popularity in a short time, which will cause a heavy load on the micro base station and a high transmission delay. This paper establishes the community mechanism and proposes a user cache management and cooperative transmission mechanism (CMCT) based on edge community computing by using the predictability of users' requests for popular content. The free user equipment space is used to manage cache distribution through the edge community computing to perform cooperative transmission, thereby reducing the content delivery delay and the load of the micro base station. Simulation experiments show that the CMCT algorithm can reduce end-to-end delay and overhead, and improve data transmission efficiency.

Proactive Caching in D2D Assisted Multitier Cellular Network.

Cache-enabled networks suffer hugely from the challenge of content caching and content delivery. In this regard, cache-enabled device-to-device (D2D) assisted multitier cellular networks are expected to relieve the network data pressure and effectively solve the problem of content placement and content delivery. Consequently, the user can have a better opportunity to get their favored contents from nearby cache-enabled transmitters (CETs) through reliable and good-quality links; however, as expected, designing an effective caching policy is a challenging task due to the limited cache memory of CETs and uncertainty in user preferences. In this article, we introduce a joint content placement and content delivery technique for D2D assisted multitier cellular networks (D2DMCN). A support vector machine (SVM) is employed to predict the content popularity to determine which content is to be cached and where it is to be cached, thereby increasing the overall cache hit ratio (CHR). The content request is satisfied either by the neighboring node through the D2D link or by the cache-enabled base stations (BSs) of the multitier cellular networks (MCNs). Similarly, to solve the problem of optimal content delivery, the Hungarian algorithm is employed aiming to improve the quality of satisfaction. The simulation results indicate that the proposed content placement strategy effectively optimizes the overall cache hit ratio of the system. Similarly, an effective content delivery approach reduces the request content delivery delay and power consumption.

A Joint Reinforcement-Learning Enabled Caching and Cross-Layer Network Code in F-RAN With D2D Communications

In this paper, we leverage reinforcement learning (RL) and cross-layer network coding (CLNC) for efficiently pre-fetching requested contents to the local caches and delivering these contents to requesting users in a downlink fog-radio access network (F-RAN) with device-to-device (D2D) communications. In the considered system, fog access points (F-APs) and cache-enabled D2D (CE-D2D) users are equipped with local caches that alleviate traffic burden at the fronthaul and facilitate rapid delivery of the users’ contents. To this end, the CLNC scheme optimizes the coding decisions, transmission rates, and power levels of both F-APs and CE-D2D users, and RL scheme optimizes caching strategy. A joint content placement and delivery problem is formulated as an optimization problem with a goal to maximize system sum-rate. The problem is an NP-hard problem. To efficiently solve it, we first develop an innovative decentralized CLNC coalition formation (CLNC-CF) switch algorithm to obtain a stable solution for the content delivery problem, where F-APs and CE-D2D users utilize CLNC resource allocation. By considering statistics of channel and users’ content request into account, we then develop a multi-agent RL algorithm for optimizing the content placement at both F-APs and CE-D2D users. Simulation results show that the proposed joint CLNC-CF-RL framework can effectively improve the sum-rate by up to 30%, 60%, and 150%, respectively, compared to: 1) an optimal uncoded algorithm, 2) a standard rate-aware-NC algorithm, and 3) a benchmark classical NC with network-layer optimization.

A Cybertwin-Based 6G Cooperative IoE Communication Network: Secrecy Outage Analysis

The sixth-generation (6G) communication networks being highly data-intensive and enabled by the Internet of Everything (IoE) are envisaged to find applications in various domains including smart healthcare, smart industry, and gaming. In this article, a novel hybrid 6G-based cybertwin cooperative architecture is studied and an analytical framework for the secrecy outage analysis is presented. The base station (BS) considered utilizes nonorthogonal-multiple-access for content request transmission to the cybertwin host and the server with a direct communication link present between the BS and the server. A wireless link experiencing Nakagami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> fading is considered which is further assisted in the communication network by a wired link which is a power line communication link and experiences Rayleigh fading, for the communication between the cybertwin host and the server. Secrecy error probability expressions are derived for the cybertwin host and the server for the scenario when both the wireless and the wired links are available for communication and only the wireless link is available for communication. Furthermore, the presented analytical results are corroborated with simulation results which demonstrate the optimality of operating the wireless links at lower signal-to-noise ratio (SNR) values. It is observed that for the lower values of Nakagami fading parameter, better secrecy performance is observed with the usage of wired link along with the wireless link, and also the secrecy performance of the cybertwin host and the server is dependent on the system parameters like the fading parameter and the average SNR.

VABLOCK: A blockchain-based secure communication in V2V network using icn network support technology

Vehicular Ad hoc Network (VANET) provides efficient communication among vehicles (V2V). The communication among all the vehicles complies with the on-demand, which contains a secure and trustable mechanism to ensure trustable communication. The modification in the communication information may result in falsified information. Secure data is very important in V2V communication to save the lives of pedestrians and drives by delivering secure and accurate information. To address the problem and achieve secure communication in V2V, we proposed a new blockchain-based message dissemination technique to secure V2V communication. With the passive need for adaptable and sufficient content delivery, information-centric networking (ICN) is adopted to enhance trustworthiness communication in VANET. We used Cluster-based secure communication through ICN-based VANETs. As VANET is open, ICN provides direct content requests and responses without location dependency. ICN-based VANET enhances caching abilities. The blockchain-based security protocol is implemented to secure efficient communication without altering the messages. The protocol achieves privacy, security, and trust for detecting malicious nodes in VANET. Additionally, the Clustering technique is applied to adopt in-range communication. Proposed-Caching framework enhances security and provides on-demand data to vehicles. NS-2 simulator is used to simulate the Proposed VABLOCK approach. Experimental results are performed and compared with relevant techniques, which show enhanced results based on cache hit ratio, one hop count, malicious node detection, and delivery ratio. We demonstrate that proposed caching improves results on selected parameters based on results.

The Dynamics of Information Search Queries about COVID-19 at the Self-Isolation Stage

IntroductionThe dynamics of search queries in large random samples may reflect both the reaction of the population to the statements of power subjects, and the presence of certain mechanisms of emotional self-regulation.ObjectivesStudying the dynamics of information search queries in the situation of experiencing an objective global threat-the spread of COVID-19.MethodsQuantitative methods of the Data Mining class were used for data collection. Qualitative methods of the Data Mining class and contextual analysis were used for data analysis. Search queries related to COVID-19 and the introduction of self-isolation were analyzed: 5 million queries were randomly selected.ResultsThe number of information requests on the topic of the pandemic increased sharply after V. Putin’s first address, and then steadily decreased, but the overall picture of the content dynamics of search queries, depending on official statements of the authorities, was not observed; requests about the current level of morbidity are constantly presented to the greatest extent, and to the minimum-about the possibilities of psychological assistance and the state of the Russian economy. During the whole time of the study the contents of the Internet search shifted towards positive emotional information.Conclusions. To the maximum extent, the relationship between the experience of a pandemic and self-isolation with the nature of search queries manifests itself at the initial stages and tends to decrease in the future. The dynamics of the content of requests is ambiguously related to official statements of power subjects, influenced by the effect of counter-regulation.DisclosureNo significant relationships.

Resource Allocation Strategy of the Educational Resource Base for MEC Multiserver Heuristic Joint Task.

This paper analyzes the application of MEC multiserver heuristic joint task in resource allocation of the educational resource database. After constructing the scenario of educational resource database, a mathematical model is constructed from the dimensions of local execution strategy, unloading execution, and given educational resource allocation, in order to optimize the optimal allocation of educational resources through MEC. The results show that the DOOA scheme has good performance in terms of calculation cost and timeout rate. Compared with other benchmark schemes, the DQN-based unloading scheme has better performance, can effectively balance the load, and is better than the random unloading scheme and the SNR-based unloading scheme in terms of delay and calculation cost. The results show that the total hits of all category 1 users' content requests account for the proportion of the total content requests. The images have a small downward trend at the 15000 and 30000 time slots and then continue to rise. This shows that the proposed scheme can automatically adjust the caching strategy to adapt to the changes of content popularity, which proves that the agent can correctly perceive the changing trend of content popularity when the popularity of network content is unknown and improve the caching strategy accordingly to improve the cache hit rate. Therefore, the allocation of educational resources based on the MEC multiserver heuristic joint task is more reasonable and can achieve the optimal solution.

Joint Resource, Deployment, and Caching Optimization for AR Applications in Dynamic UAV NOMA Networks

The cache-enabling unmanned aerial vehicle (UAV) non-orthogonal multiple access (NOMA) networks for mixture of augmented reality (AR) and normal multimedia applications are investigated, which is assisted by UAV base stations. The user association, power allocation of NOMA, deployment of UAVs and caching placement of UAVs are jointly optimized to minimize the content delivery delay. A branch and bound (BaB) based algorithm is proposed to obtain the per-slot optimization. To cope with the dynamic content requests and mobility of users in practical scenarios, the original optimization problem is transformed to a Stackelberg game. Specifically, the game is decomposed into a leader level user association sub-problem and a number of power allocation, UAV deployment and caching placement follower level sub-problems. The long-term minimization was further solved by a deep reinforcement learning (DRL) based algorithm. Simulation result shows that the content delivery delay of the proposed BaB based algorithm is much lower than benchmark algorithms, as the optimal solution in each time slot is achieved. Meanwhile, the proposed DRL based algorithm achieves a relatively low long-term content delivery delay in the dynamic environment with lower computation complexity than BaB based algorithm.

The Viewpoint’s Assessment of Physicians About Radiology Reports of Medical Imaging Unit in Kermanshah

Background: Radiology reports are the data obtained from radiography films which as the final and one of the main criteria of service in medical imaging centers, are the only linkage between the radiologist, physicians, and patients. This method is also a usual approach in the transfer of diagnostic data to the physicians. Methods: In this descriptive-cross sectional study, the research community included specialists working in public and private centers of Kermanshah province. A questionnaire including two sections of demographic data (age, sex, specialty, work experience, workplace and level of education) and the questions related to the role of the radiologist and necessary of report providing, quality of reports, radiographic film interpretation ability and attention to request content and in- time delivery of the reports, was investigated. The data were finally analyzed by STATA 11 software. Results: In this study, the opinions of 240 specialists from 14 specialties were examined. Among them, 64.2% were men, 92.2% of them were specialists, 45.5% were working in public centers and 42.5% of them had less than 10 years of work experience. The results showed that 63.3% of the physicians trusted radiologic reports for more than 50%. Among the scopes, the highest score was for the role of the radiologist in preparation of reports, and the lowest mean was obtained for the ability to interpret the radiological film. A comparison of the four scopes with assumed means showed no significant differences (P = 0.001). Conclusions: Regarding the percentage of total attitude score, the studied specialists had a relatively desirable attitude toward the radiological reports. The complexity of medical imaging techniques and their role in in-time disease diagnosis and treatment requires improvement of the relationship between the radiologists and non-radiologists physicians.

Request pattern change-based cache pollution attack detection and defense in edge computing

Through caching popular contents at the network edge, wireless edge caching can greatly reduce both the content request latency at mobile devices and the traffic burden at the core network. However, popularity-based caching strategies are vulnerable to Cache Pollution Attacks (CPAs) due to the weak security protection at both edge nodes and mobile devices. In CPAs, through initiating a large number of requests for unpopular contents, malicious users can pollute the edge caching space and degrade the caching efficiency. This paper firstly integrates the dynamic nature of content request and mobile devices into the edge caching framework, and introduces an eavesdropping-based CPA strategy. Then, an edge caching mechanism, which contains a Request Pattern Change-based Cache Pollution Detection (RPC2PD) algorithm and an Attack-aware Cache Defense (ACD) algorithm, is proposed to defend against CPAs. Simulation results show that the proposed mechanism could effectively suppress the effects of CPAs on the caching performance and improve the cache hit ratio.

Towards efficient device-to-device multicast content delivery with social awareness

Frequent content retrieval leads to significant energy consumption in cellular networks. Thereby, device-to-device (D2D) communication is used for proximal content delivery. Be aware that social friends may have common content interests, D2D content delivery is further improved by considering social network. However, how to implement efficient D2D multicast with the aid of social relationship needs further consideration. In this paper, with a proposed architecture on combined networks, the cooperation range of content sharing is investigated for D2D multicast to minimize the average energy consumption of serving a content request. Based on combined networks, a cooperation group formation scheme is proposed. Simulation results verify our analyzes and the advantage of proposed scheme.

What do women at high risk of breast cancer request of a patient education day? Focus interviews with women before and after deciding about prophylactic interventions.

ObjectiveAt a Danish Hospital, we wished to establish a co‐designed patient education day about prophylactic interventions for women at high risk of developing breast cancer. However, knowledge is lacking on the women's acceptability and requests for content. The objective of this study is to gain knowledge about the acceptability and requests of the content of a patient education day among women at high risk of breast cancer considering prophylactic mastectomy.MethodsA user panel consisting of patients and health care professionals developed an interview guide for two focus interviews with two groups of women at high risk of breast cancer; one group had received a prophylactic mastectomy and one group considered it. Thematic analysis was used to explore the participants' acceptability and requests for content.ResultsMeaningful content was knowledge about prophylactic interventions, how to share knowledge with partners and children, and talking to equals in a safe forum. Not all participants wished to discuss own surgery in a group setting.ConclusionAn education day is an acceptable and supportive format for gaining knowledge about surgery, but since some topics may be vulnerable to discuss in a group setting to some women, we suggest the education day as a valuable supplement to the individual consultations.

Energy-Efficient Hierarchical Collaborative Scheme for Content Delivery in Mobile Edge Computing

With the rapid growth of Internet traffic and smart mobile terminals, ultradense networks are adopted as the key technology of the fifth generation to enhance resource utilization and content distribution while causing serious energy efficiency problem. Mobile edge computing has recently drawn great attention for its advantages in reducing transmission delay and network energy consumption by implementing caching and computing abilities at the edge of mobile networks. To improve network energy efficiency and content transmission, in this paper, we propose a novel energy-efficient hierarchical collaborative scheme by considering the in-network caching, request aggregation, and joint allocation of caching, computing, and communication resources in a layered heterogeneous network including mobile users, small base stations, macro base stations, and the cloud. We formulate the energy consumption problem as a queuing theory-based centralized model, where the same content requests can be aggregated in the queue of each base station. Then, the optimal solution is analyzed based on the distribution characteristic of content popularity at the base stations. Simulation results show that the performance of our proposed model is much better than the existing cloud-edge cooperation solutions without considering the deployment of caching resource and request aggregation policies.

Current Trends in the Manifestation of Socio-Psychological Distress in Adolescents

The subject of the study is the content of the main socio-psychological difficulties and problems that encourage modern adolescents to seek psychological help and are manifestations of socio-psychological distress. The study was conducted on the basis of an analysis of the content of requests for advisory practice for the period 2012-2022. The city center of psychological, pedagogical, medical and social assistance of Yaroslavl. All requests for psychological help were classified by the authors into several groups: communicative problems (reflecting the specifics of relations with the adolescent's society); problems arising in the development of general educational and universal actions; psycho-emotional difficulties and crises (including experiencing eating disorders, suicidal disorders, autoaggression, traumatic experience) and destructive behavior (antisociality, aggression and dependent behavior). A mathematical analysis of the dynamics of appeals of each type was carried out. As a result of the conducted research, conclusions were formulated about the content of the main difficulties and problems that are relevant for modern adolescents and are the subject of an appeal to a psychologist. These include difficulties in relationships with others, the development of a tendency to autoaggression, eating disorders and an increase in a number of problems associated with psychoemotional difficulties, violence. In conclusion, the types of innovative activities of the center for improving the communicative, psycho-emotional and behavioral aspects of the socio-psychological well-being of adolescents are described. The novelty of the described research consists in the development and testing of a system of programs to reduce the socio-psychological distress of adolescents. A special place in them is occupied by programs of work with parents.

Private retrieval of location-related content using [formula omitted]-anonymity and application to ICN

Several platforms to efficiently retrieve content from Internet of Things (IoT) devices installed in various locations have been proposed for information-centric networking (ICN). However, location privacy is at stake in such platforms because consumers retrieve content by specifying the plaintext names of the locations of their interest (LOIs). Previous studies on IP have leveraged k-anonymity of location offered by a trusted proxy called an anonymizer to hide LOIs. Specifically, an anonymizer sends content requests to k locations in a location anonymity set, which comprises an LOI and the other dummy locations. This technique can be applied to ICN; however, two problems need to be solved: the adversary models are unrealistic and the requirements for a location anonymity set have been defined in ad-hoc manners. In this study, we assume a semi-honest anonymizer and define the requirements rigorously using the notions of entropy and t-closeness. Next, we design an architecture for location privacy protection and an algorithm for location anonymity set generation. Finally, we evaluate the overhead incurred by our architecture and the quality of generated location anonymity sets through experiments under a realistic scenario. Our results indicate that our architecture and algorithm offer strong location privacy with marginal overhead.

NDN Content Store and Caching Policies: Performance Evaluation

Among various factors contributing to performance of named data networking (NDN), the organization of caching is a key factor and has benefited from intense studies by the networking research community. The performed studies aimed at (1) finding the best strategy to adopt for content caching; (2) specifying the best location, and number of content stores (CS) in the network; and (3) defining the best cache replacement policy. Accessing and comparing the performance of the proposed solutions is as essential as the development of the proposals themselves. The present work aims at evaluating and comparing the behavior of four caching policies (i.e., random, least recently used (LRU), least frequently used (LFU), and first in first out (FIFO)) applied to NDN. Several network scenarios are used for simulation (2 topologies, varying the percentage of nodes of the content stores (5–100), 1 and 10 producers, 32 and 41 consumers). Five metrics are considered for the performance evaluation: cache hit ratio (CHR), network traffic, retrieval delay, interest re-transmissions, and the number of upstream hops. The content request follows the Zipf–Mandelbrot distribution (with skewness factor α=1.1 and α=0.75). LFU presents better performance in all considered metrics, except on the NDN testbed, with 41 consumers, 1 producer and a content request rate of 100 packets/s. For the level of content store from 50% to 100%, LRU presents a notably higher performance. Although the network behavior is similar for both skewness factors, when α=0.75, the CHR is significantly reduced, as expected.

Analysis of the "Melloni" decision of the Court of Justice of the European Union

The subject of this article is the Melloni decision, which was rendered by the Grand Chamber of the Court of Justice of the European Union on February 26, 2013. The purpose of the work is to analyze this decision, which the modern Dutch researcher L. Besselink considers as determining the modern parameters of the conflict between EU law and the constitutional law of the member states. The article uses such methodological techniques as analysis and synthesis. The structure of the article reflects the circumstances of the case; the content of the preliminary request sent by the national Court; the sequential consideration of issues by the EU Court; as well as the subsequent decision of the Constitutional Court of Spain, which was made on the basis of the position of the Court. The article concludes that the "supremacy, unity and effectiveness" of EU law requires that the national court refuse to apply higher national standards in favor of acts of the Union, including those that do not have direct effect. By its nature, the decision of the EU Court of Justice is rather deaf in relation to the constitutional features of the member states. It is noted that the court should have adhered to a more compromise position taken in the case of Omega, Sayn-Wittgenstein and Filipiak. In general, this solution has great theoretical value for specialists studying EU law.

A source‐driven probabilistic forwarding and caching strategy in NDN and SDN‐based NDN

SummaryNDN forwarding strategies are mainly developed receiver‐driven, where Interest forwarding and Data (content) delivery are controlled from the consumers' viewpoint. The receiver‐driven strategies are greedy by nature and emphasize optimizing the Interest forwarding, which is far from the performance bottleneck as an Interest is of tiny size in compared with a content's size. Thus, optimizing the content delivery can improve the forwarding performance. Moreover, source‐driven optimization of content delivery can alleviate the greedy nature of its counterpart. In this work, a source‐driven forwarding strategy is proposed where the content sources respond to Interest requests in a distributed manner. The content requests hit all the sources, but the sources respond probabilistically such that the content retrieval overhead and latency are minimized. The strategy consents the nearer sources to set a high probability of reacting with the matching content. On the contrary, the distant sources answer with an Offer to save transmission overhead. Besides, a caching strategy is proposed, an integral part of the source‐driven scheme, that caches diverse contents considering the gap between successive copies and content availability while admitting new contents. Next, we extend the source‐driven probabilistic forwarding strategy to operate in SDN‐based NDN architecture. The SDN‐based solution controls the content delivery phase using the global knowledge of NDN cache states. Simulation study conducted using ndnSIM reveals that the proposed forwarding and caching strategy improves user satisfaction and network performance in terms of different performance metrics such as transmission overhead, latency, and cache hit.