Design and Analysis of an MST-Based Topology Control Algorithm

1. Design and Analysis of an MST-Based TopologyControl Algorithm Ning Li and Jennifer Hou Department of Computer Science University of Illinois at Urbana-Champaign nli@cs.uiuc.edu, jhou@cs.uiuc.edu

2. Outline • Motivation • Topology Control • LMST: Local Minimum Spanning Tree • Simulation Study • Future Work

3. Motivations (2) With Topology Control (1) No Topology Control

4. R&D Roadmap and Opportunities • Maximize information throughput but not data throughput Data Aggregation/Computation Application Layer • Maintain connectivity using the minimum transmission power. • Maintain connectivity by moving some “router” nodes to fill in the“hole”. • Enable nodes to self-organize themselves into clusters. • Load balance with power consideration Transport Layer Admission Control QoS Mapping (e.g. bounded delay) Error Control • Realize Service Differentiation • Provide bounded transmission delay Network Layer Routing Integrated real time scheduling and power control Topology Control MAC Layer Scheduling Contention Resolution Physical Layer GPS Positioning & Synchronizing Power Adjustment Channels Selection (frequency/code) Directional Beam-Forming

5. Routing Topology Control MAC / Power-controlled MAC TopologyControl • Observations • Almost all ad-hoc routing algorithms rely on the cache to inexplicitly build an underlying topology. • Many broadcast/multicast algorithms for ad-hoc wireless networks maintain some kind of underlying topology, upon which the multicast tree/mesh can be built. • Topology control can achieve: • Global connectivity • Low energy consumption • Low interference • High throughput

6. Design Guidelines • Network connectivity should be preserved. • Bi-directional links are preferred. • Algorithms should be distributed. • To be immune to the impact of mobility, the algorithm should depend on local information.

7. LMST:Local Minimum Spanning Tree • Static wireless multihop networks. • Transmission power can be adjusted. • Each node knows its own position. • Each node will build its own minimum spanning tree in its neighborhood and only retain those one-hop neighbors on the tree as its neighbors in the final topology.

8. LMST • Visible neighborhood: the set of nodes that node u can reach by using the maximum transmission power. • Information collection: Each node broadcast periodically a Hello message using its maximal transmission power. • Topology construction • Each node applies Prim’s algorithm independently to obtain its local minimum spanning tree. • Each node takes all the one-hop, on-tree nodes as its neighbors. • The network topology under LMST is all the nodes in V and their individually perceived neighbor relations. • Determination of transmission power: a node transmits using the power that can reach its farthest neighbor.

9. LMST Properties • The resulting topology preserves the connectivity. • After removal of asymmetric links, all links are bi-directional and the connectivity is still preserved. • The degree of any node is bounded by 6.

10. LMST: Example w3 w2 w4 w1 w5 u w6 w7

11. Uni-directional Links w3 w2 w4 w1 d u v dmax dmax

12. Connectivity • G0 is connected with some uni-directional links. • We can either add extra links into G0 so that all uni-directional links become bi-directional or delete all uni-directional links in G0. • Both approaches give us connected graph with bi-directional links.

13. Simulations

14. Simulations (Cont.)

15. Dealing with Mobility

16. Future Work • Extend LMST to mobile networks. • Build the multicast/broadcast protocol upon LMST. • Implement LMST on a Motes testbed at UIUC.