In content-centric networking (CCN), the in-network caching feature provides several attractive advantages such as low dissemination latency and network transport load reduction. Thus, CCN requires less transport energy but additional energy to provide a caching capability at every content router. In this paper, we investigate the minimum energy consumption that CCN can achieve with optimal cache locations by considering different caching hardware technologies, number of downloads per hour, and content popularity. We first set up an energy consumption model for CCN and then formulate linear and nonlinear programming problems to minimize total energy consumption of CCN. Also, a genetic algorithm (GA) approach is proposed to find energy-efficient cache locations. Using reported energy efficiency of computational hardware and network equipment, we show CCN yield greater energy savings for very popular content and small-sized catalog, compared to conventional CDN. Our results also indicate that two aspects of the memory technology, energy-proportional caching and sufficient memory capacity, are critical to the overall energy efficiency gain of CCN.

In-network processing of media streams will be necessary in order to meet the personalization, interactive, and real time requirements of future video centric media services. Using multi-view video (MVV) streaming as an example, we investigate the energy tradeoff between video processing and transport for the delivery of personalized media services. We focus on evaluating the energy benefit of distributed vs. centralized processing architectures. We provide solutions for the relative energy efficiency regions as a function of the user viewing preferences and the processing-transport efficiency ratio. Our results show that a small number of requests and a homogeneous interest among viewing regions favors the centralized processing of personalized video streams and multicast transport to end users, while a larger number of requests and a heterogeneous interest favors the processing of personalized views at a distributed subset of nodes in the network.

We examine the basic energy tradeoffs between video transport and video processing for services such as multi-view video (MVV) streaming, where multiple media streams are combined and processed to create an immersive and personalized user experience. We analyze and compare the energy efficiency of different architectural options for the location of video processing functions and illustrate how the architecture of choice is influenced by the network topology, the users' view preferences, and the relative transport-processing energy efficiency. We provide an integer linear programming formulation for the energy efficient functional resource allocation problem, which we show it can be solved as a linear program, and an easily implementable algorithm that generates optimal solutions in polynomial time.

Smart Grid applications are imposing challenging requirements of security and reliability on the N-way communication infrastructure being designed to support multiple grid applications. These challenges stem from the increasing incorporation of distributed renewable energy sources on to the grid, the rising deployment of electric vehicles, and active consumer participation into power grid operations, all of which communicate with the utility control center with varying degrees of priority and security. To address these challenging requirements, we propose SeDAX, a SEcure Data-centric Application eXtensible platform for Smart Grid applications. SeDAX implements scalable, resilient and secure data delivery and data sharing in a wide area network. The platform can scalably handle high volumes of data generated by both applications and sensors. The SeDAX architecture has as its basis a Delaunay Triangulation (DT) network. The properties of the DT graph are leveraged to scalably support secure data-centric (or information-centric) group communication. The primary goals of this platform are to support communication resilience and data availability. The key functional blocks of the SeDAX platform are: (1) a geographic hash forwarding algorithm that operates over the DT graph (DT-GHF), and (2) a DT-based data replication scheme. The forwarding and replication schemes are scalable and cost effective in terms of communication overhead and memory. We describe the design details of the SeDAX platform and present empirical results on the performance of SeDAX as compared with other geometric-based alternatives such as Geographic Hash Table (GHT) forwarding and Content Addressable Networking (CAN). The operation of SeDAX is illustrated in the context of implementing demand response, a known Smart Grid application.