AGSMR: Adaptive Geo-Source Multicast Routing for Wireless Sensor Networks
WASA '09 Proceedings of the 4th International Conference on Wireless Algorithms, Systems, and Applications
6DMPP: small worlds in mobile peer-to-peer networks
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
EURASIP Journal on Wireless Communications and Networking - Special issue on security and resilience for smart devices and applications
ICISS'11 Proceedings of the 7th international conference on Information Systems Security
Scalable location guide overlay multicast in mobile ad hoc networks using tree partition scheme
Wireless Communications & Mobile Computing
EDR: efficient data routing in wireless sensor networks
International Journal of Ad Hoc and Ubiquitous Computing
Energy-efficient and reliable data delivery in wireless sensor networks
Wireless Networks
GEographic multicast (GEM) for dense wireless networks: protocol design and performance analysis
IEEE/ACM Transactions on Networking (TON)
IPv6 Multicast Forwarding in RPL-Based Wireless Sensor Networks
Wireless Personal Communications: An International Journal
| Hi-index | 0.00 |
Several multicast protocols for mobile ad hoc networks have been proposed that build multicast trees using location information available from GPS or localization algorithms and use geographic forwarding to forward packets down the multicast trees. These stateless multicast protocols carry encoded membership, location and tree information in each packet and are more efficient and robust than stateful protocols (ADMR, ODMRP) as they avoid the difficulty of maintaining distributed state in the presence of frequent topology changes. However, current stateless multicast protocols are not scalable to large groups because of the perpacket encoding overhead, and the centralized group membership and location management. We present the Hierarchical Rendezvous Point Multicast (HRPM) protocol which significantly improves the scalability of stateless multicast with respect to the group size. HRPM consists of two key design ideas: (1) hierarchical decomposition of a large group into a hierarchy of recursively organized manageable-sized subgroups, and (2) use of distributed geographic hashing to construct and maintain such a hierarchy at virtually no cost. Our detailed simulations demonstrates that HRPM achieves significantly enhanced scalability and performance due to hierarchical organization and distributed hashing.