Proceedings of the 10th international conference on Architectural support for programming languages and operating systems
Probabilistic routing in intermittently connected networks
ACM SIGMOBILE Mobile Computing and Communications Review
Routing in a delay tolerant network
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
Social network analysis for routing in disconnected delay-tolerant MANETs
Proceedings of the 8th ACM international symposium on Mobile ad hoc networking and computing
Bubble rap: social-based forwarding in delay tolerant networks
Proceedings of the 9th ACM international symposium on Mobile ad hoc networking and computing
IEEE Transactions on Mobile Computing
PowerTOSSIM z: realistic energy modelling for wireless sensor network environments
Proceedings of the 3nd ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks
SimBetAge: utilizing temporal changes in social networks for pocket switched networks
Proceedings of the 1st ACM workshop on User-provided networking: challenges and opportunities
Clustering and cluster-based routing protocol for delay-tolerant mobile networks
IEEE Transactions on Wireless Communications
A survey and challenges in routing and data dissemination in vehicular ad hoc networks
Wireless Communications & Mobile Computing
Socially-aware routing for publish-subscribe in delay-tolerant mobile ad hoc networks
IEEE Journal on Selected Areas in Communications
| Hi-index | 0.00 |
When implementing Mobile Ad Hoc Networks, a key characteristic of the network is the mobility pattern of the nodes. Based on the application, nodes can follow semi-predictable patterns, such as the routes followed by Vehicular Ad Hoc Networks, or the more strict schedules followed by aerial reconnaissance. Optimal routing schemes tend to take advantage of information regarding these patterns. In social environments, such as wildlife tracking or sending messages between humans, the devices and/or users will follow regular contact habits, tending to encounter social groups in which they participate. By identifying these groups, the patterns are used to optimize routing through a social environment. Dynamic Social Grouping (DSG), used to route messages strictly from a node to a basestation, is ideal for gathering sensor data and updating a shared data cache. In contrast, Dynamic Social Grouping-Node to Node (DSG-N2) is used to route messages between nodes, generally conventional communications. Both of these algorithms can be implemented ad null, meaning the devices initially have no information about their environment, and they work to reduce bandwith and delivery time while maintaining a high delivery ratio. In addition to presenting these two routing schemas, this article compares and contrasts two methods for estimating nodes' delivery probabilities. The Contact Based Probability is based on encounters with other nodes, and the Performance Based Probability is based on the behavior of previous messages. The probability estimates were then validated with the Oracle analysis, which is based on knowledge of future events. This analysis indicated that DSG-N2 probability estimates are comparable to the ideal.