Approximation algorithms for facility location problems (extended abstract)
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Local search heuristic for k-median and facility location problems
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Introduction to Algorithms
Facility location: distributed approximation
Proceedings of the twenty-fourth annual ACM symposium on Principles of distributed computing
Understanding the wireless and mobile network space: a routing-centered classification
Proceedings of the second ACM workshop on Challenged networks
A socio-aware overlay for publish/subscribe communication in delay tolerant networks
Proceedings of the 10th ACM Symposium on Modeling, analysis, and simulation of wireless and mobile systems
Distributed community detection in delay tolerant networks
Proceedings of 2nd ACM/IEEE international workshop on Mobility in the evolving internet architecture
A Survey of Current Directions in Service Placement in Mobile Ad-hoc Networks
PERCOM '08 Proceedings of the 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications
ContentPlace: social-aware data dissemination in opportunistic networks
Proceedings of the 11th international symposium on Modeling, analysis and simulation of wireless and mobile systems
Scalable service migration in general topologies
WOWMOM '08 Proceedings of the 2008 International Symposium on a World of Wireless, Mobile and Multimedia Networks
Approximation Algorithms for Data Placement Problems
SIAM Journal on Computing
Joint interest- and locality-aware content dissemination in social networks
WONS'09 Proceedings of the Sixth international conference on Wireless On-Demand Network Systems and Services
Distributed facility location algorithms for flexible configuration of wireless sensor networks
DCOSS'07 Proceedings of the 3rd IEEE international conference on Distributed computing in sensor systems
Approximation algorithms for the k-median problem
Efficient Approximation and Online Algorithms
Opportunistic networking: data forwarding in disconnected mobile ad hoc networks
IEEE Communications Magazine
Social network analysis concepts in the design of wireless ad hoc network protocols
IEEE Network: The Magazine of Global Internetworking
Socially-aware gateway-based content sharing and backup
Proceedings of the 2nd ACM SIGCOMM workshop on Home networks
How much off-center are centrality metrics for routing in opportunistic networks
CHANTS '11 Proceedings of the 6th ACM workshop on Challenged networks
Centrality-driven scalable service migration
Proceedings of the 23rd International Teletraffic Congress
Distributed content backup and sharing using social information
IFIP'12 Proceedings of the 11th international IFIP TC 6 conference on Networking - Volume Part I
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
| Hi-index | 0.00 |
As content provisioning becomes the driving application of today's (opportunistic) networking environments and the User Generated Content explodes, the problem of devising scalable approaches to placing it optimally within a networking structure becomes more important and challenging. Since the well-known k-median optimization problem that is typically formulated to address it requires global topology and demand information, different approaches are sought for. The latter is the focus of this paper that aims at exploiting social structures, present in emerging networking environments, in order to devise a scalable approach to the optimal or near-optimal content placement. A new metric that captures the node's social significance or potential for helping establish paths between nodes is introduced and serves as the basis for creating a small scale network sub-graph over which the small-scale content placement problem is solved sequentially until the optimal or near-optimal location is identified. The trade-off between the sub-graph's size and the degree of convergence to the optimal solution is studied through simulations on E-R and B-A random graphs and the effectiveness of the proposed approach is demonstrated.