Reliability Analysis of Social Network Data Transmission in Wireless Sensor Network Topology

In this paper, the reliability of data transmission in social networks is thoroughly studied and analyzed using wireless sensor network topology technology. This paper, based on the introduction of sensor network reliability analysis-related technology, combined with the characteristics, and needs of the sensor network itself, focuses on the study of the reliability analysis of the sensor network under the state of perturbation scheme. Based on the idea of making full use of data changes to respond to the sensor state, this paper takes the actual monitoring data of the wireless sensor network as the research object, selects the temporal correlation and spatial correlation of the measured environmental data as the reliability index by extracting the features of the wireless sensor network data, and proposes the Evidential reasoning rule- (ER-) based wireless sensor network data reliability assessment model based on Evidential reasoning rule (ER) is proposed. The data are mined, analyzed, and quantified from the perspective of content popularity, and the interest indicators of nodes on data under content popularity are analyzed to derive stable interest quantification values. Combined with the network properties, i.e., node autoassembly community, we analyze the data dissemination characteristics of social networks in wireless sensor network topology environment and derive the upper and lower bounds of data transmission capacity under node interest-driven and its variation on network performance. Social relationships among nodes affected by social attributes are considered; in turn, the data forwarding behavior of nodes is modeled using data transmission probability and data reception probability; finally, the data forwarding process is analyzed and a closed expression for the average end-to-end transmission capacity is derived in turn.

Mode Selection of Content Delivery At Edge Nodes Based On Learning

Pre-caching at edge nodes may improve resource efficiency for future content-centric cellular networks by bringing contents closer to users. However, which mode (muticast or unicast) should be selected for the efficient delivery of those cached contents is still not well addressed, especially at the situation that some key parameters such as content popularity and transmission environments are unknown. To solve this problem, the criterion of delivery mode selection is studied based on the learning policy of multi-armed bandit(MAB). According to the criterion, a mode selection algorithm is proposed. In this algorithm, edge nodes can choose the better delivery modes in the current slot only dependent on existing observations in the previous slot. Performance evaluation results validate our analyses and proposals on the mode selection.

Predicting Popularity of Video Streaming Services with Representation Learning: A Survey and a Real-World Case Study

The Internet’s popularization has increased the amount of content produced and consumed on the web. To take advantage of this new market, major content producers such as Netflix and Amazon Prime have emerged, focusing on video streaming services. However, despite the large number and diversity of videos made available by these content providers, few of them attract the attention of most users. For example, in the data explored in this article, only 6% of the most popular videos account for 85% of total views. Finding out in advance which videos will be popular is not trivial, especially given many influencing variables. Nevertheless, a tool with this ability would be of great value to help dimension network infrastructure and properly recommend new content to users. In this way, this manuscript examines the machine learning-based approaches that have been proposed to solve the prediction of web content popularity. To this end, we first survey the literature and elaborate a taxonomy that classifies models according to predictive features and describes state-of-the-art features and techniques used to solve this task. While analyzing previous works, we saw an opportunity to use textual features for video prediction. Thus, additionally, we propose a case study that combines features acquired through attribute engineering and word embedding to predict the popularity of a video. The first approach is based on predictive attributes defined by resource engineering. The second takes advantage of word embeddings from video descriptions and titles. We experimented with the proposed techniques in a set of videos from GloboPlay, the largest provider of video streaming services in Latin America. A combination of engineering features and embeddings using the Random Forest algorithm achieved the best result, with an accuracy of 87%.

QoS Improvement Using In-Network Caching Based on Clustering and Popularity Heuristics in CCN

Content-Centric Networking (CCN) has emerged as a potential Internet architecture that supports name-based content retrieval mechanism in contrast to the current host location-oriented IP architecture. The in-network caching capability of CCN ensures higher content availability, lesser network delay, and leads to server load reduction. It was observed that caching the contents on each intermediate node does not use the network resources efficiently. Hence, efficient content caching decisions are crucial to improve the Quality-of-Service (QoS) for the end-user devices and improved network performance. Towards this, a novel content caching scheme is proposed in this paper. The proposed scheme first clusters the network nodes based on the hop count and bandwidth parameters to reduce content redundancy and caching operations. Then, the scheme takes content placement decisions using the cluster information, content popularity, and the hop count parameters, where the caching probability improves as the content traversed toward the requester. Hence, using the proposed heuristics, the popular contents are placed near the edges of the network to achieve a high cache hit ratio. Once the cache becomes full, the scheme implements Least-Frequently-Used (LFU) replacement scheme to substitute the least accessed content in the network routers. Extensive simulations are conducted and the performance of the proposed scheme is investigated under different network parameters that demonstrate the superiority of the proposed strategy w.r.t the peer competing strategies.

An Edge Caching Strategy Based on User Speed and Content Popularity for Mobile Video Streaming

Mobile users’ demands to delay-sensitive video streaming media put forward new requirements for mobile networks, such as architecture optimization. Edge caching as a new paradigm is proposed to enhance the quality of service (QoS) for mobile users at the network edge. Due to the limited coverage of edge cache nodes, the frequent handoffs between base stations would aggravate network traffic overhead, resulting in a bad experience of high latency and service interruption when mobile users browse videos. This paper first proposes a three-layer mobile edge network architecture and applied edge caching to video streams to build an efficient caching system. Given the user’s mobility and low latency of mobile video streaming, we propose an edge caching strategy based on user speed and content popularity. Horizontally, the user’s speed affects the spanning area and the buffer size of the cache on edge; vertically, content popularity determines the priority of cached videos. Experimental results demonstrate that our caching strategy outperforms other schemes in terms of the average delay and the cache hit ratio in mobile video streaming scenes compared with the other three classic caching methods.

Popularity-Aware In-Network Caching for Edge Named Data Network

The traditional centralized network architecture can lead to a bandwidth bottleneck in the core network. In contrast, in the information-centric network, decentralized in-network caching can alleviate the traffic flow pressure from the network center to the edge. In this paper, a popularity-aware in-network caching policy, namely, Pop, is proposed to achieve an optimal caching of network contents in the resource-constrained edge networks. Specifically, Pop senses content popularity and distributes content caching without adding additional hardware and traffic overhead. We conduct extensive performance evaluation experiments by using ndnSIM. The experiments showed that the Pop policy achieves 54.39% cloud service hit reduction ratio and 22.76% user request average hop reduction ratio and outperforms other policies including Leave Copy Everywhere, Leave Copy Down, Probabilistic Caching, and Random choice caching. In addition, we proposed an ideal caching policy (Ideal) as a baseline whose popularity is known in advance; the gap of Pop and Ideal in cloud service hit reduction ratio is 4.36%, and the gap in user request average hop reduction ratio is only 1.47%. More simulation results further show the accuracy of Pop in perceiving popularity of contents, and Pop has good robustness in different request scenarios.

Content popularity prediction for cache-enabled wireless B5G networks

In this paper, we study the cache prediction problem for mobile edge networks where there exist one base station (BS) and multiple relays. For the proposed mobile edge computing (MEC) network, we propose a cache prediction framework to solve the problem of contents prediction and caching based on neural networks and relay selection, by exploiting users’ history request data and channels between the relays and users. The proposed framework is then trained to learn users’ preferences by using the users’ history requested data, and several caching policies are proposed based on the channel conditions. The cache hit rate and latency are used to measure the performance of the proposed framework. Simulation results demonstrate the effectiveness of the proposed framework, which can maximize the cache hit rate and meanwhile minimize the latency for the considered MEC networks.

A Novel Cooperative Cache Policy for Wireless Networks

Mobile edge caching is an emerging approach to manage high mobile data traffic in fifth-generation wireless networks that reduces content access latency and offloading data traffic of backhaul links. This paper proposes a novel cooperative caching policy based on long short-term memory (LSTM) neural networks considering the characteristics between the features of the heterogeneous layers and the user moving speed. Specifically, LSTM is applied to predict content popularity. Size-weighted content popularity is utilised to balance the impact of the predicted content popularity and content size. We also consider the moving speeds of mobile users and introduce a two-level caching architecture consisting of several small base stations (SBSs) and macro base stations (MBSs). To avoid content requests of fast-moving users affecting the content popularity distribution of the SBS since fast-moving users frequently handover among SBSs, fast-moving users are served by MBSs no matter which SBS they are in. SBSs serve low-speed users, and SBSs in the same cluster can communicate with one another. The simulation results show that compared to common cache methods, for example, the least frequently used and least recently used methods, our proposed policy is at least 8.9% lower and 6.8% higher in terms of the average content access latency and offloading ratio, respectively.