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Multicast ad hoc networks

Multicast ad hoc networks. Multicast in ad hoc nets Review of Multicasting in wired networks Tree based wireless multicast Mesh based wireless multicast – ODMRP Performance comparison Scalable multicast M-LANMAR Backbone Reliable, congestion controlled multicast

alexander-mooney
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Multicast ad hoc networks

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  1. Multicast ad hoc networks • Multicast in ad hoc nets • Review of Multicasting in wired networks • Tree based wireless multicast • Mesh based wireless multicast – ODMRP • Performance comparison • Scalable multicast • M-LANMAR • Backbone • Reliable, congestion controlled multicast • RALM, RALM with M-LANMAR

  2. Logical Subnet Landmark LANMAR • Each team is mapped into a logical subnet • IP-like Node address = <subnet, host> • Address compatible with IPv6 • Team leader (Landmark) elected in each group • Key insight: nodes move in teams/swarms

  3. Scalable Ad hoc multicasting • Multicast (ie, transmit same message to all member of a group) critical in battlefield • “Multiple unicast” does not scale • Current ad hoc multicast solutions:inappropriate • They do not exploit affinity team model • multicast tree approach is “fragile” to mobility; • no congestion control; no reliable end to end delivery • Proposed approach: • TEAM Multicast

  4. Team Multicasting Swarm Leader swarm UAVs: - equipped with video, chemical sensors - read data from ground sensors - “fuse” sensor data inputs - multicast fused data to other teams Command post

  5. Multicast example Attack! Attack! Attack! Attack! Command Post Attack! All Task Force Nodes

  6. Two-tier team multicast: Multicast-Enabled Landmark (M-LANMAR) • Extension of LANMAR enabling multicast • Inter-team communication: unicasttunneling from the source to the representative of each subscribed team • Intra-team communication: scoped flooding within a team

  7. Scope = 2 Flooding Tunneling to landmarks Scope = 2 Flooding M-LANMAR LM2 LM3 Subscribed Teams Source node LM4

  8. Advantages of M-LANMAR • Reduced control traffic overhead • Scalable to thousands of nodes • Enhanced Congestion control and Reliability (because of TCP control on unicast tunnels)

  9. M-LANMAR multicast

  10. Scalability • Objective: test M-LANMAR scalability • Compared with • ODMRP • Flooding • Simulation Environment • QualNet • 1000 nodes forming 36 teams on 6000 x 6000 m2 field • CBR traffic (512 bytes/packet, 1packet/sec)

  11. Simulation Results • As the number of multicast groups increases • M-LANMAR achieves high delivery ratio (by unicast tunneling and flooding) • ODMRP suffers from large control overhead and collisions

  12. Multiple Unicast v.s. Mesh Structure source • Builds a mesh between landmarks • Load Balancing • Better Reliability landmark

  13. dst src Enhanced Scalability with a Mobile Backbone • Multicast across Large systems of swarms does not scale well • excessive protocol overhead • Long paths break • Swarms separate • Enter Mobile Backbone!

  14. Simulation study - swarm motion • Implemented in QualNet 3.6 • Total 300 nodes over a 5000 x 5000 field: • 20 candidate backbone nodes • 14 swarms, 20 ground nodes in each swarm • Traffic generated from single source to 30 receivers across different groups • Bandwidth Allocation - • backbone/ground: 1Mbps • flat network: 2Mbps Mobile backbone significantly improves delivery in swarm/group motion

  15. Simulation study - random roaming • Random distribution of 500 nodes on a 5000x5000 field • Nodes move randomly • Ground network is dense and connected • Single multicast group: one source, 30 receivers • Bandwidth Allocation - • backbone/ground:1Mbs • flat network: 2Mbps Backbone network improvements for random motion even higher than with swarms

  16. Conclusions and Future Work • M-LANMAR is a scalable multicast protocol designed for large ad-hoc networks with affinity team model • Backbone Enhanced multicast: • Backbone + ODMRP achieves better scalability than flat ODMRP (delivery ratio increases up to 50%) • Related work • Coexistence of LANMAR and Backbone • Impact of mobility (random, team, etc) • Mobility and traffic correlation • Delay tolerant appls; “data ferrying”

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