Content Download Research Articles

Cooperative content downloading in vehicular ad hoc networks

Abstract Content downloading in vehicular ad hoc networks supports the large file sharing, the entertainment experience and the mobile office. However, because of the high velocity, the frequent topology change, the limited bandwidth and a small number of roadside units, it is difficult to directly transmit the contents to their requesters through only one hop. This paper proposes a cooperative content downloading scheme in vehicular ad hoc networks, named CCD. It introduces the direction, range and timing constraints to select appropriate cooperative nodes, which improve the contact probability between the cooperative nodes and the request senders. Additionally, CCD computes the number of copies for each block according to its importance and urgency, and then schedules the block transmission accordingly. Experiments show that compared with existing schemes, CCD has a short delay and a small transmission overhead.

Path Aware Transport Layer Solution for Mobile Networks

The revolutionary Fifth Generation (5G) network technology has already been deployed in many countries to provide end-to-end quality-of-service for a wide variety of services, applications, and users with extremely diverse requirements. To accomplish the same, 5G architecture has enabled Multi-Connectivity (MC) in mobile devices, which aggregates various radio resources and enables simultaneous connectivity. Moreover, the current mobile devices are equipped with multiple network interfaces (Wi-Fi/Cellular) as well as dual-stack networks (IPv4/IPv6) support. But, their performance degrades significantly due to variation in network path quality. The current design of the Internet protocol suite has not adapted well to utilize these multiple interfaces for providing better network reliability. Hence, we propose a client only software solution called Path Aware Transport (PAT), which explores communication paths between client and server and adapts to the dynamics of the network to migrate from one Transmission Control Protocol (TCP) connection to another without hampering the user experience. PAT is prototyped and evaluated in 5G using the ns3 simulator. Moreover, it is implemented in the Samsung flagship mobile device with Android Pie. PAT significantly improves the content downloading time up to 20% and the page loading time up to 29% consistently.

Enhancing Dynamic-Viewport Mobile Applications with Screen Scrolling

The pervasive penetration of mobile smart devices has significantly enriched Internet applications and undoubtedly reshaped the way that users access Internet services. Different from traditional desktop applications, mobile Internet applications require users to input via touch screens and view outputs on the displays with considerably limited size. The significant conflict between the limited-size of touch screens and the richness of online media contents widely exists in dynamic-viewport mobile applications, a class of mobile Internet applications that download contents beyond the user's viewing region (referred to as viewport). As dynamic-viewport mobile applications usually use HTTP for content downloading, to improve their quality of experience (QoE) and cost efficiency, in this paper, we present a Mobile-Friendly HTTP middleware (MF-HTTP), which can interpret user touch screen inputs and optimize the HTTP downloading of media objects for such applications. We first demystify screen scrolling in mobile operating systems and precisely break down the viewport moving process. We identify the key influential factors for media object downloading and develop an optimal download scheme. Towards building a practical middleware, we further discuss and address the implementation issues in detail. We implement a MF-HTTP prototype based on Android platforms and evaluate the performance of MF-HTTP by conducting concrete case studies on two representative dynamic-viewport mobile applications, namely, web browsing and 360-degree video streaming.

Open data for the crowd: an account of citizen science at ETH Library

This paper gives an account of ETH Library’s combined strategy of implementing an open data policy and using crowdsourcing to improve metadata. Both activities go hand in hand and promote each other. ETH Library’s Image Archive was the first unit to provide content for free download in high resolution. This paved the way for a very successful crowdsourcing campaign during which citizen scientists located places, dated photographs, and identified people and artefacts. This positive experience led to further crowdsourcing activities in other ETH Library units. Important conditions for success apart from the open data policy were conscious community management by social media channels and promoting competition within a gamification approach.

Secure Distribution of Protected Content in Information-Centric Networking

The benefits of the ubiquitous caching in ICN are profound, such features make ICN promising for content distribution, but it also introduces a challenge to content protection against the unauthorized access. The protection of a content against unauthorized access requires consumer authentication and involves the conventional end-to-end encryption. However, in information-centric networking (ICN), such end-to-end encryption makes the content caching ineffective since encrypted contents stored in a cache are useless for any consumers except those who know the encryption key. For effective caching of encrypted contents in ICN, we propose a secure distribution of protected content (SDPC) scheme, which ensures that only authenticated consumers can access the content. SDPC is lightweight and allows consumers to verify the originality of the published content by using a symmetric key encryption. Moreover, SDPC naming scheme provides protection against privacy leakage. The security of SDPC was proved with the BAN logic and Scyther tool verification, and simulation results show that SDPC can reduce the content download delay.

Content Placement and User Association for Delay Minimization in Small Cell Networks

In small cell networks, content placement can reduce the content download delay at backhaul links while imposing challenges on user association problems. Specifically, a user may be associated with a small base station (SBS) that has the desired contents but is far away, increasing the delay over radio access links. To this end, we investigate the joint content placement and user association problem to reduce the average download delay over backhaul and radio access links for content delivery. Considering both dynamic user population and natural tandem systems, we model content delivery in each SBS as a tandem queue and thus derive the average sojourn time (the average time contents stay in the tandem queue), i.e., the average download delay. On this basis, we formulate a delay minimization problem, encompassing user characteristics (traffic arrival rates, requested content lengths and content preferences) and SBS constraints (backhaul capacity and storage size). Then, this problem is decomposed into a user association subproblem and a content placement subproblem by exploiting the biconvexity. Finally, a distributed content placement and user association scheme is proposed by exploring the optimal match with the first derivative length method. Extensive simulation results reveal that the delay under the proposed content placement policy may be reduced as user characteristics become heterogeneous, which is opposite to the case under user association policies.

Mobility-Aware Content Preference Learning in Decentralized Caching Networks

Due to the drastic increase of mobile traffic, wireless caching is proposed to serve repeated requests for content download. To determine the caching scheme for decentralized caching networks, the content preference learning problem based on mobility prediction is studied. We first formulate preference prediction as a decentralized regularized multi-task learning (DRMTL) problem without considering the mobility of mobile terminals (MTs). The problem is solved by a hybrid Jacobian and Gauss-Seidel proximal multi-block alternating direction method (ADMM) based algorithm, which is proven to conditionally converge to the optimal solution with a rate ${O}$ (1/ ${k}$ ). Then we use the tool of Markov renewal process to predict the moving path and sojourn time for MTs, and integrate the mobility pattern with the DRMTL model by reweighting the training samples and introducing a transfer penalty in the objective. We solve the problem and prove that the developed algorithm has the same convergence property but with different conditions. Through simulation we show the convergence analysis on proposed algorithms. Our real trace driven experiments illustrate that the mobility-aware DRMTL model can provide a more accurate prediction on geography preference than DRMTL model. Besides, the hit ratio achieved by most popular proactive caching (MPC) policy with preference predicted by mobility-aware DRMTL outperforms the MPC with preference from DRMTL and random caching (RC) schemes.

DeepAuC: Joint deep learning and auction for congestion-aware caching in Named Data Networking

Over the last few decades, the Internet has experienced tremendous growth in data traffic. This continuous growth due to the increase in the number of connected devices and platforms has dramatically boosted content consumption. However, retrieving content from the servers of Content Providers (CPs) can increase network traffic and incur high network delay and congestion. To address these challenges, we propose a joint deep learning and auction-based approach for congestion-aware caching in Named Data Networking (NDN), which aims to prevent congestion and minimize the content downloading delays. First, using recorded network traffic data on the Internet Service Provider (ISP) network, we propose a deep learning model to predict future traffic over transit links. Second, to prevent congestion and avoid high latency on transit links, which may experience congestion in the future; we propose a caching model that helps the ISP to cache content that has a high predicted future demand. Paid-content requires payment to be downloaded and cached. Therefore, we propose an auction mechanism to obtain paid-content at an optimal price. The simulation results show that our proposal prevents congestion and increases the profits of both ISPs and CPs.

Perceptual representation of spam and phishing emails

SummaryUnderstanding how computer users allocate attention to features of potentially dangerous emails could help mitigate costly errors. Which features are salient? How stable is attention allocation across variation in email features? We attempted to measure the mental salience of several email features common in spam and/or phishing emails. We created two email sets: one in which messages contained company logos and urgent actionable links and one without these features. Participants rated pairwise similarity of emails within each set. Multidimensional scaling (MDS) analysis was conducted to quantify psychological similarity between emails. A separate group rated the same emails for presence of five other features: important downloadable content, collecting personal information, account deletion or suspension, advertisement, and large images with clickable content. Regressing feature ratings onto the MDS coordinates revealed that similarity judgments were influenced mostly by advertisement/large images and collecting personal information, regardless of presence or absence of company logos and urgent actionable links.

Collaborative Content Placement Among Wireless Edge Caching Stations With Time-to-Live Cache

Content caching at the Internet edge using a network of wireless edge caching stations (ECSs) is recently considered as a key solution to alleviating the backhaul traffic burden and improving the quality of experience in 5G networks. This paper studies wireless edge caching systems with the following features: first, content files can be partitioned into many coded packets, which then can be cached in multiple ECSs for collaborative content delivery; second, the service provider (SP) deploys time-to-live cache at ECSs and each cached content file has an occupancy time that needs to be guaranteed; third, the content-to-be-cached arrives at the caching system following a stochastic process as users request new content over time. Unlike existing works that determine which content to cache, this paper focuses on how to distribute the coded packets of content-to-be-cached among the network of ECSs in order to reduce the content downloading time. A novel content placement strategy, called stochastic collaborative content placement is proposed based on Lyapunov techniques. The proposed algorithm makes content placement decisions using only currently available information without foreseeing future content arrivals, takes advantage of the spatial content popularity variation with coded caching, and achieves the provable close-to-optimal long-term caching performance. Simulations are carried out on a real-world YouTube video request trace and the results demonstrate a tremendous caching performance improvement against a variety of benchmark schemes.

Private Information Retrieval From a Cellular Network With Caching at the Edge

We consider the problem of downloading content from a cellular network that is cached at the wireless edge while achieving privacy. In particular, we consider private information retrieval (PIR) of content from a library of files, i.e., the user wishes to download a file and does not want the network to learn any information about which file she is interested in. To reduce the backhaul usage, content is cached at the wireless edge in a number of small-cell base stations (SBSs) using maximum distance separable codes. We propose a PIR scheme based on generalized Reed–Solomon codes for this scenario that achieves privacy against a number of spy SBSs that collaborate. The proposed PIR scheme is an extension of a scheme by Kumar et al. to the case of multiple code rates, suitable for the scenario where files have different popularities. We derive the backhaul rate and optimize the content placement to minimize it. We prove that uniform content placement is optimal, i.e., all files that are cached should be stored using the same code rate. This is in contrast to the case where no PIR is required. Furthermore, we show numerically that popular content placement is optimal for some scenarios.

Contract-Based Small-Cell Caching for Data Disseminations in Ultra-Dense Cellular Networks

Evidence indicates that demands from mobile users (MU) on popular cloud content, e.g., video clips, account for a dramatic increase in data traffic over cellular networks. The repetitive downloading of hot content from cloud servers will inevitably bring a vast quantity of redundant data transmissions to networks. A strategy of distributively pre-storing popular cloud content in the memories of small-cell base stations (SBS), namely, small-cell caching, is an efficient technology for reducing the communication latency whilst mitigating the redundant data streaming substantially. In this paper, we establish a commercialized small-cell caching system consisting of a network service provider (NSP), several video providers (VP), and randomly distributed MUs. We conceive this system in the context of 5G cellular networks, where the SBSs are ultra-densely deployed with the intensity much higher than that of the MUs. In such a system, the NSP, in charge of the SBSs, wishes to lease these SBSs to the VPs for the purpose of making profits, whilst the VPs, after pushing popular videos into the rented SBSs, can provide faster local video transmissions to the MUs, thereby gaining more profits. Specifically, we first model the MUs and SBSs as two independent Poisson point processes, and develop, via stochastic geometry theory, the probability of the specific event that an MU obtains the video of its choice directly from the memory of an SBS. Then, with the help of the probability derived, we formulate the profits of both the NSP and the VPs. Next, we solve the profit maximization problem based on the framework of contract theory, where the NSP acts as a monopolist setting up the optimal contract according to the statistical information of the VPs. Incentive mechanisms are also designed to motivate each VP to choose a proper resource-price item offered by the NSP. Numerical results validate the effectiveness of our proposed contract framework for the commercial caching system.

Hybrid collaborative caching in mobile edge networks: An analytical approach

With caching at the base stations (BSs) in a cooperative manner, mobile edge caching (MEC) can alleviate the heavy backhaul burden and reduce the duplicated transmissions of content downloads, which has recently been considered a promising solution to cope with the exponentially increasing data traffic. However, how to maximize the storage utilization while reduce service latency and improve energy savings is still a critical issue in the large-scale mobile edge networks (MENs), since the growth of MENs in size as well as uneven users’ distribution make it difficult to determine which MEC should cache which content. To address this problem, we propose a hybrid collaborative caching (Hy-CoCa) design that jointly leverages local independent, intra-group collaborative and intra-network collaborative caching manners. MECs are clustered into disjoint groups, and then each MEC’s storage is partitioned into local, intra-group and intra-network portions. Local storage is reserved for storing the most popular contents to each MEC locally, so that users can directly fetch them from their associated MEC; Intra-group storage of different MECs inside the same group are regarded as an entity, which is used for collaboratively storing the middle popular contents, so as to reduce the probability for requesting contents from distant MECs; Intra-network storage of all MECs are leveraged for collaboratively storing less popular contents to different MECs in the entire MENs, as a means to improve the overall content diversity. Specifically, we first develop the Hy-CoCa’s framework to support users’ requests locally and conduct the construction of logical groups, considering users’ distribution and MECs’ proximity. Moreover, under the storage and popularity constraints, we formulate the storage allocation optimization problem to minimize average service latency and derive the optimal storage allocation. Furthermore, given an optimal storage allocation, we also formulate the request-aware content placement problem into an integer linear programming problem to maximize the overall energy savings. We prove the submodularity property of the objective function and propose a greedy algorithm with linear computational complexity, which can achieve (1−1/e)-optimality. Simulation results with real-world YouTube trace data demonstrate that our caching strategy can achieve 6% to 28% latency reduction and 9% to 75% energy savings improvement compared with other existing caching strategies.

Collaborative Content Downloading in VANETs with Fuzzy Comprehensive Evaluation

Vehicle collaborative content downloading has become a hotspot in current vehicular ad-hoc network (VANET) research. However, in reality, the highly dynamic nature of VANET makes users lose resources easily, and the transmission of invalid segment data also wastes valuable bandwidth and storage of the users’ vehicles. In addition, the individual need of each customer vehicle should also be taken into consideration when selecting an agent vehicle for downloading. In this paper, a novel scheme is proposed for vehicle selection in the download of cooperative content from the Internet, by considering the basic evaluation information of the vehicle. To maximize the overall throughput of the system, a collaborative content downloading algorithm is proposed, which is based on fuzzy evaluation and a customer’s own expectations, in order to solve the problems of agent vehicle selection. With the premise of ensuring successful downloading and the selection preferences of customer vehicles, linear programming is used to optimize the distribution of agent vehicles and maximize customer’s satisfaction. Simulation results show that the proposed scheme works well in terms of average quality of service, average bandwidth efficiency, failure frequency, and average consumption.

Device-to-Device Communications Enabled Multicast Scheduling for mmWave Small Cells Using Multi-Level Codebooks

Heterogeneous cellular networks with small cells in the millimeter wave (mmWave) band densely deployed have become one of promising candidates for future wireless networks due to huge bandwidth available in the mmWave band. To overcome high path loss in the mmWave band, the beamforming technique of directional antennas is adopted. Consequently, concurrent transmissions of links can be enabled to improve network capacity. On the other hand, multicast services can support many applications like popular content downloading and broadcast communication services. In user intensive region where multicast services usually happen, device-to-device (D2D) communications in physical proximity provide better channel conditions, and can improve multicast efficiency. In this paper, we develop an efficient multicast scheduling scheme for mmWave small cells, referred to as CONMD2D, where both concurrent transmissions and D2D communications are exploited based on a multi-level antenna codebook to optimize network performance. In CONMD2D, the multicast group is partitioned into subgroups, and D2D transmission paths are established between subgroups. Then, a concurrent scheduling algorithm schedules the multicast links concurrently into the transmission stage. Performance evaluation under various system parameters demonstrates CONMD2D achieves significant improvement in network throughput and energy efficiency compared with other state-of-the-art schemes.

Antagonistic framing of sustainability by energy suppliers

PurposeThe purpose of this paper is to determine inasmuch energy suppliers dedicate communicative resources toward sustainable development and corporate social responsibility (CSR), also paying attention to how they frame it, and if they manage to achieve consistency in their communication or fall victim to contradictions.Design/methodology/approachBy use of a qualitative content analysis, online communication tools (information on corporate websites) as well as content for download were examined in detail. The present study sample comprised of 12 case studies from selected countries (Austria, Russia, Germany, the USA, France and Korea).FindingsOverall, findings indicate that CSR has already been implemented in most energy and energy-related industries; however, it is put forward with varying degrees of attention and intensity, depending on which topics energy companies choose to address communicatively (results were classified according to a frame positioning scheme by Weder, 2012, 2018). Results underscore the fact that, at times, companies are struggling to link their CSR projects back to their core businesses. Yet, a clear trend to politicization can be described as a strong correlation of communication strategies of energy suppliers and political programs of the respective country becomes obvious.Research limitations/implicationsLimited research as to how CSR topics are framed in different branches has been conducted to date; likewise, the energy sector, whose motives has been often subject to public questioning, has received little attention in CSR communication research to date. Hence, ambiguities were presumed to exist.Originality/valueThe present study examines the relevance and framing of CSR in a highly competitive, centralized industry that is challenged by a global process of transition to renewable energy. The results show that the analyzed energy suppliers offer only a limited variety of issue-specific frames; instead CSR as well as sustainability are (ab)used as master frames or “buzz words” in a fairly shallow economic or socio-political argumentation.

Cooperative Hierarchical Caching and Request Scheduling in a Cloud Radio Access Network

In this article, we propose a novel cooperative hierarchical caching framework in a Cloud Radio Access Network (C-RAN), in which a new cloud-cache at Cloud Processing Unit (CPU) is envisioned to bridge the storage-capacity/delay-performance gap between the traditional edge-based and core-based caching paradigms. A delay-cost model is introduced and the cache placement problem is formulated that aims at minimizing the average delay-cost of content delivery in the network. Given the NP-completeness of the cache placement problem, we propose a low-complexity heuristic cache-management strategy comprising of a proactive cache-distribution algorithm and a reactive cache-replacement algorithm. Furthermore, a Cache-Aware Request Scheduling (CARS) algorithm is devised in order to optimize online the tradeoff between content download rate and content access delay. Via extensive numerical simulations—carried out using both real-world YouTube video requests and synthetic content requests—it is demonstrated that the proposed cache-management strategy outperforms traditional caching strategies in terms of cache hit ratio, average content access delay, and backhaul traffic load. Additionally, it is shown that the proposed CARS algorithm achieves superior tradeoff performance over traditional approaches that optimize either users’ rate or access delay alone.

Hierarchical Edge Caching in Device-to-Device Aided Mobile Networks: Modeling, Optimization, and Design

The explosive growth of content requests from mobile users is stretching the capability of current mobile networking technologies to satisfy users' demands with acceptable quality of service. An effective approach to address this challenge, which has not yet been thoroughly studied, is to offload network traffic by caching popular content at the edges (e.g., mobile devices and base stations) of mobile networks, thus reducing the massive duplication of content downloads. In this paper, we address the system modeling, large-scale optimization, and framework design of hierarchical edge caching in device-to-device aided mobile networks. In particular, taking into account the analysis of social behavior and preference of mobile users, heterogeneous cache sizes, and the derived system topology, we investigate the maximum capacity of the network infrastructure in terms of offloading network traffic, reducing system costs, and supporting content requests from mobile users locally. Our proposed framework has a low complexity and can be applied in practical engineering implementation. Trace-based simulation results demonstrate the effectiveness of the proposed framework.

Toward Wi-Fi AP-Assisted Content Prefetching for an On-Demand TV Series: A Learning-Based Approach

The emergence of smart Wi-Fi access points (AP), which are equipped with huge storage space, opens a new research area on how to utilize these resources at the edge network to improve users' quality of experience (e.g., a short startup delay and smooth playback). One important research interest in this area is content prefetching which predicts and accurately fetches contents ahead of users' requests to shift the traffic away during peak periods. However, in practice, the different video watching patterns among users and the varying network connection status lead to the time-varying server load, which eventually makes the content prefetching problem challenging. To understand this challenge, this paper first performs a large-scale measurement study on users' AP connection and TV series watching patterns using real traces. Then, based on the obtained insights, we formulate the content prefetching problem as a Markov decision process. The objective is to strike a balance between the increased prefetching and storage cost incurred by incorrect prediction and the reduced content download delay because of successful prediction. A learning-based approach is proposed to solve this problem and another three algorithms are adopted as baselines. In particular, first we investigate the performance lower bound by using a random algorithm and the upper bound by using an ideal offline approach. Then, we present a heuristic algorithm as another baseline. Finally, we design a reinforcement learning algorithm that is more practical to work in the online manner. Through extensive trace-based experiments, we demonstrate the performance gain of our design. Remarkably, our learning-based algorithm achieves a better precision and hit ratio (e.g., 80%) with about 70% (resp. 50%) cost saving compared to the random (resp. heuristic) algorithm

On the Incentive Mechanisms for Commercial Edge Caching in 5G Wireless Networks

Mobile data traffic has dramatically increased by a factor of around 100 over the last five years, while it is still expected to grow exponentially in the near future. Evidence indicates that the repetitive downloading of popular contents accounts for the increase in wireless traffic. This has consequently led to the development of edge caching technology for efficiently mitigating redundant transmissions from backbone networks by pre-caching frequently requested data at radio access networks. Among a variety of research topics in this field, the commercialization of edge caching has attracted augmented attention. In this article, we focus on the design of incentive mechanisms for commercial edge caching in 5G cellular networks, where the edge device providers (EDPs) and content providers (CPs) are the two counter-parties competing to maximize their own welfare. We first propose the architecture of a commercial caching system and analyze the revenue of the entities involved. Due to selfishness, each entity tries to achieve its highest benefit by squeezing profits from others, causing contentions of interest. Game theoretic approaches are then introduced to balance the conflicts. Four incentive frameworks are developed based on different categories of games in the context of the caching system. Afterward, we conduct case studies for three application scenarios to demonstrate the superiority of our proposed game models. Numerical results are also provided to validate the effectiveness of our proposed game frameworks.