Towards Disruption Tolerant ICN

Information-Centric Networking (ICN) is a promi- nent topic in current networking research. ICN design signifi- cantly considers the increased demand of scalable and efficient content distribution for Future Internet. However, intermittently connected mobile environments or disruptive networks present a significant challenge to ICN deployment. In this context, delay tolerant networking (DTN) architecture is an initiative that effec- tively deals with network disruptions. Among all ICN proposals, Content Centric Networking (CCN) is gaining more and more interest for its architectural design, but still has the limitation in highly disruptive environment. In this paper, we design a protocol stack referred as CCNDTN which integrates DTN architecture in the native CCN to deal with network disruption. We also present the implementation details of the proposed CCNDTN. We extend CCN routing strategies by integrating Bundle protocol of DTN architecture. The integration of CCN and DTN enriches the connectivity options of CCN architecture in fragmented networks. Furthermore, CCNDTN can be beneficial through the simultaneous use of all available connectivities and opportunistic networking of DTN for the dissemination of larger data items. This paper also highlights the potential use cases of CCNDTN architecture and crucial questions about integrating CCN and DTNComment: ISCC 201

Data-Driven Capacity Planning for Vehicular Fog Computing

The strict latency constraints of emerging vehicular applications make it unfeasible to forward sensing data from vehicles to the cloud for processing. To shorten network latency, vehicular fog computing (VFC) moves computation to the edge of the Internet, with the extension to support the mobility of distributed computing entities (a.k.a fog nodes). In other words, VFC proposes to complement stationary fog nodes co-located with cellular base stations with mobile ones carried by moving vehicles (e.g., buses). Previous works on VFC mainly focus on optimizing the assignments of computing tasks among available fog nodes. However, capacity planning, which decides where and how much computing resources to deploy, remains an open and challenging issue. The complexity of this problem results from the spatio-temporal dynamics of vehicular traffic, varying computing resource demand generated by vehicular applications, and the mobility of fog nodes. To solve the above challenges, we propose a data-driven capacity planning framework that optimizes the deployment of stationary and mobile fog nodes to minimize the installation and operational costs under the quality-of-service constraints, taking into account the spatio-temporal variation in both demand and supply. Using real-world traffic data and application profiles, we analyze the cost efficiency potential of VFC in the long term. We also evaluate the impacts of traffic patterns on the capacity plans and the potential cost savings. We find that high traffic density and significant hourly variation would lead to dense deployment of mobile fog nodes and create more savings in operational costs in the long term

Video Caching, Analytics and Delivery at the Wireless Edge: A Survey and Future Directions

Future wireless networks will provide high bandwidth, low-latency, and ultra-reliable Internet connectivity to meet the requirements of different applications, ranging from mobile broadband to the Internet of Things. To this aim, mobile edge caching, computing, and communication (edge-C3) have emerged to bring network resources (i.e., bandwidth, storage, and computing) closer to end users. Edge-C3 allows improving the network resource utilization as well as the quality of experience (QoE) of end users. Recently, several video-oriented mobile applications (e.g., live content sharing, gaming, and augmented reality) have leveraged edge-C3 in diverse scenarios involving video streaming in both the downlink and the uplink. Hence, a large number of recent works have studied the implications of video analysis and streaming through edge-C3. This article presents an in-depth survey on video edge-C3 challenges and state-of-the-art solutions in next-generation wireless and mobile networks. Specifically, it includes: a tutorial on video streaming in mobile networks (e.g., video encoding and adaptive bitrate streaming); an overview of mobile network architectures, enabling technologies, and applications for video edge-C3; video edge computing and analytics in uplink scenarios (e.g., architectures, analytics, and applications); and video edge caching, computing and communication methods in downlink scenarios (e.g., collaborative, popularity-based, and context-aware). A new taxonomy for video edge-C3 is proposed and the major contributions of recent studies are first highlighted and then systematically compared. Finally, several open problems and key challenges for future research are outlined

Multi-Tier CloudVR : Leveraging Edge Computing in Remote Rendered Virtual Reality

The availability of high bandwidth with low-latency communication in 5G mobile networks enables remote rendered real-time virtual reality (VR) applications. Remote rendering of VR graphics in a cloud removes the need for local personal computer for graphics rendering and augments weak graphics processing unit capacity of stand-alone VR headsets. However, to prevent the added network latency of remote rendering from ruining user experience, rendering a locally navigable viewport that is larger than the field of view of the HMD is necessary. The size of the viewport required depends on latency: Longer latency requires rendering a larger viewport and streaming more content. In this article, we aim to utilize multi-access edge computing to assist the backend cloud in such remote rendered interactive VR. Given the dependency between latency and amount and quality of the content streamed, our objective is to jointly optimize the tradeoff between average video quality and delivery latency. Formulating the problem as mixed integer nonlinear programming, we leverage the interpolation between client's field of view frame size and overall latency to convert the problem to integer nonlinear programming model and then design efficient online algorithms to solve it. The results of our simulations supplemented by real-world user data reveal that enabling a desired balance between video quality and latency, our algorithm particularly achieves the improvements of on average about 22% and 12% in term of video delivery latency and 8% in term of video quality compared to respectively order-of-arrival, threshold-based, and random-location strategies.Peer reviewe

ViNav

OA-julkaisu. Lisätään artikkeli, kun julkaistu IEEE:n tietokannassa.Smartphone-based indoor navigation services are desperately needed in indoor environments. However, the adoption of them has been relatively slow, due to the lack of ne-grained and up-to-date indoor maps, or the potentially high deployment and maintenance cost of infrastructure-based indoor localization solutions. This work proposes ViNav, a scalable and cost-effcient system that implements indoor mapping, localization and navigation based on visual and inertial sensor data collected from smartphones. ViNav applies structure-from-motion (SfM) techniques to reconstruct 3D models of indoor environments from crowdsourced images, locates points of interest (POI) in 3D models, and compiles navigation meshes for path finding. ViNav implements image-based localization that identifies users' positions and facing directions, and leverages this feature to calibrate dead-reckoning-based user trajectories and sensor fingerprints collected along the trajectories. The calibrated information is utilized for building more informative and accurate indoor maps, and lowering the response delay of localization requests. According to our experimental results in a university building and a supermarket, the system works properly and our indoor localization achieves competitive performance compared with traditional approaches: in a supermarket, ViNav locates users within 2 seconds, with a distance error less than 1 meter and a facing direction error less than 6 degrees.Peer reviewe

Boosting the Performance of Content Centric Networking Using Delay Tolerant Networking Mechanisms

Content-centric networking (CCN) introduces a paradigm shift from a host centric to an information centric communication model for future Internet architectures. It supports the retrieval of a particular content regardless of the physical location of the content. Content caching and content delivery networks are the most popular approaches to deal with the inherent issues of content delivery on the Internet that are caused by its design. Moreover, intermittently connected mobile environments or disruptive networks present a significant challenge to CCN deployment. In this paper, we consider the possibility of using mobile users in improving the efficiency of content delivery. Mobile users are producing a significant fraction of the total Internet traffic, and modern mobile devices have enough storage to cache the downloaded content that may interest other mobile users for a short period too. We present an analytical model of the CCN framework that integrates a delay tolerant networking architecture into the native CCN, and we present large-scale simulation results. Caching on mobile devices can improve the content retrieval time by more than 50%, while the fraction of the requests that are delivered from other mobile devices can be more than 75% in many cases.Peer reviewe

CIDOR: Content distribution and retrieval in disaster networks for public protection

| openaire: EC/H2020/643990/EU//POINTInformation-Centric Networking (ICN) introduces a paradigm shift from a host centric communication model for Future Internet architectures. It supports the retrieval of a particular content regardless of the physical location of the content. Emergency network in a disaster scenario or disruptive network presents a significant challenge to the ICN deployment. In this paper, we present a Content dIstribution and retrieval framework in disaster netwOrks for public pRotection (CIDOR) which exploits the design principle of the native CCN architecture in the native Delay Tolerant Networking (DTN) architecture. We prove the feasibility and investigate the performance of our proposed solution using extensive simulation with different classes of the DTN routing strategies in different mobility scenarios. The simulation result shows that CIDOR can reduce the content retrieval time up to 50% while the response ratio is close to 100%.Peer reviewe

On-demand Vehicular Fog Computing for Beyond 5G Networks

Publisher Copyright: AuthorEmerging compute-intensive and latency-sensitive vehicular applications are expected to be deployed at the edge instead of the cloud to shorten the network latency. Mobile fog nodes carried by moving vehicles, namely vehicular fog nodes (VFNs), have been proposed to complement the stationary fog nodes co-located with base stations to handle the spatiotemporal variations of demand in a cost-efficient way. Existing works on capacity planning for such vehicular fog computing (VFC) scenarios are built on the assumption of certain spatiotemporal patterns of vehicular traffic. They consider long-term capacity planning (e.g., updated every season) but leave the adaptation to temporary changes or unexpected variations out of scope. These solutions typically result in high computational costs and thus are not suitable for short-term capacity planning, which requires low-latency responses. To reduce time complexity, we propose an integer linear programming (ILP)-based framework called on-demand capacity planning (ODCP) to implement two-phase planning through optimizing the routing strategies of VFNs, with the aim of maximizing the profit and quality of service (QoS). More specifically, ODCP first predicts the traffic flow and resource demand using seasonal autoregressive integrated moving average (SARIMA) and estimates the revenue using an economic model defined by service level agreement (SLA). With the estimated workload and revenue, the first phase (i.e., global planning) decides the ratio of tasks that can be served at the city scale and assigns VFNs to each region. The second phase (i.e., regional planning) assigns the VFNs to users within the same region and schedules the routes of VFNs based on the mobility of users. Experimental results show that the proposed solution achieves a higher performance in terms of profit and QoS than the existing single-phase capacity planning solutions. We also find that a large number of VFNs, a small region size, high penalty costs, and low travel and rental costs lead to high service rates, whereas a large region size and low travel, rental, and penalty costs lead to high profits.Peer reviewe