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Energy-Efficient Multicast Protocols in Wireless Ad Hoc Networks

Energy-Efficient Multicast Protocols in Wireless Ad Hoc Networks. Sandeep K. S. Gupta Computer Science and Engineering Department Arizona State University Tempe, AZ, USA {Sandeep.Gupta}@asu.edu. Outline. Multicasting Techniques for Conserving Energy Wireless Network

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Energy-Efficient Multicast Protocols in Wireless Ad Hoc Networks

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  1. Energy-Efficient Multicast Protocols in Wireless Ad Hoc Networks Sandeep K. S. Gupta Computer Science and Engineering Department Arizona State University Tempe, AZ, USA {Sandeep.Gupta}@asu.edu Sandeep Gupta, Arizona State Univ.

  2. Outline • Multicasting • Techniques for Conserving Energy Wireless Network • Multicasting in Wireless Network • Node Metric and Cost Models • Protocols for Constructing Energy-Efficient Multicast Trees • A Framework for Energy-Efficient Multicasting • Conclusions Sandeep Gupta, Arizona State Univ.

  3. Multicasting • Allow one entity to send messages to multiple entities residing in a subset of the nodes in the network • Why multi-destination delivery in a single message? • Transparency; Efficiency; Concurrency • Applications • distributed database, distributed games, teleconferencing Sandeep Gupta, Arizona State Univ.

  4. Techniques for Conserving Energy in Wireless Network • Turn-off non-used transceivers • Scheduling transmission among nodes • Reduce communication overhead, such as defer transmission when channel conditions are poor • Transmission Power Control Sandeep Gupta, Arizona State Univ.

  5. Why Multicasting is different in Wireless Networks? • Wireless medium is broadcast medium (Wireless multicast Advantage) • One time local transmission can possibly reach all the neighbors Sandeep Gupta, Arizona State Univ.

  6. Why Multicasting is different in Wireless Network? • Power control allows a node to determine who are its neighbors. • More power used  • more interference • Reduces # simultaneous transmissions (thrput) • Consumes energy at a faster rate  node can die faster leading to disconnections. Sandeep Gupta, Arizona State Univ.

  7. Why Not Single-Hop Multicast? • Single source multicast: reach a subset of nodes from a given source s • s increases its transmission range to such an extent that it can reach all the group members • Increased interference and power wastage • source may have limited transmission range Sandeep Gupta, Arizona State Univ.

  8. Multi-hop Approach • Multi-hop Solution  Problem of constructing multicast tree • What is a link? • Depends on power level • Using maximum transmission power results in too many links • link weight? 1. & 2.  Link-based view not appropriate! • Node-based view: construct tree with “minimum/maximum summation of node cost” Sandeep Gupta, Arizona State Univ.

  9. Energy Metric • Two Criteria of Energy Optimization • Total Energy Consumption (TEC) • System Lifetime (SL) • Node Cost • Node’ Energy Cost • Lifetime of a Node • Type of Multicast Trees • Source-based(this talk is restricted to source-based trees) • Group-shared Sandeep Gupta, Arizona State Univ.

  10. 1 10 EU/pck 6 EU/pck 2 3 8 EU/pck 1 1 2 2 3 3 Energy Metric Initial battery energy at nodes 1, 2, and 3 are 200 EU Minimum Energy Multicast Tree Maximum Lifetime Multicast Tree Sandeep Gupta, Arizona State Univ.

  11. Node’s Energy Cost • Energy consumed (per bit) at node i in source-based multicast tree T: where and are energy cost (per bit) of transmission processing and reception processing, is maximum energy cost (per bit) of the link between node i and i’s children. Sandeep Gupta, Arizona State Univ.

  12. Node’s Energy Cost • Energy cost (per bit) of node i for reliable multicast in source-based multicast tree T: where is the error rate for node i to forward the multicast packet to all of node i’s children reliably, and is the error rate of node i’s parent to forward the packet to all of its own children. Sandeep Gupta, Arizona State Univ.

  13. 1 10 EU/pck 6 EU/pck 2 3 8 EU/pck Node’s Energy Cost • Node’s energy cost in group-shared Tree • Tree Links attach to the node • Direction of Message coming from • Incorporate message generation rates of all the multicast sources in the tree. Assume message generation rates of nodes 1 and 3 are 7pck/second and 13 packets/second. Average energy cost of node 2: Sandeep Gupta, Arizona State Univ.

  14. Node’s Lifetime • Node i’s lifetime: maximum number of multicast packets that may be forwarded by the node i:where Ri(t) is remaining battery energy of node i at time t. Sandeep Gupta, Arizona State Univ.

  15. Cost of Multicast Tree • The Total Energy Cost (TEC) of a multicast tree T : • The minimum TEC multicast tree T* is:where TG is the set of all possible multicast trees for the multicast group G in a given graph o. • NP-Complete Problem • Minimizing TEC of multicast tree  Minimizing sum energy cost of all the tree nodes. Sandeep Gupta, Arizona State Univ.

  16. Lifetime of Multicast tree T is: The maximum lifetime multicast tree T* is:where TG is the set of all possible multicast trees for the multicast group G in a given graph o. NP-Complete Problem Maximizing multicast tree lifetime  Maximizing the lifetime of tree’s bottleneck node Cost of Multicast Tree Sandeep Gupta, Arizona State Univ.

  17. Protocols for Constructing Energy-Efficient Multicast Trees • Centralized Protocols • Needs global knowledge (High Overhead): Not scalable! • Adaptivity: Expensive to adapt to dynamic changes, such as remaining battery at nodes: Offline Approach. • Distributed Protocols • Local knowledge (Low Overhead): Scalable • Adapt to dynamic changes: Online approach Sandeep Gupta, Arizona State Univ.

  18. BIP/MIP Algorithm • Constructing minimum TEC source-based broadcast tree T. • Centralized ApproachU is the set of all nodes in the networkEi,j is the minimum energy cost of i to cover node j as a child. • MIP Algorithm: Pruning all of the non-group nodes which are leaf nodes in BIP tree. Sandeep Gupta, Arizona State Univ.

  19. 3 3 1 2 1 2 BIP Algorithm • Limitations of BIP • Performance depends on the order of adding nodes in the tree. • View is limited by adding one node at a time. Minimum TEC Tree BIP Tree 3 EU/pck 2 EU/pck 2 EU/pck 2 EU/pck TEC = 4 EU/pck TEC = 3 EU/pck Sandeep Gupta, Arizona State Univ.

  20. Distributed BIP • Distributed Version of BIP • Every node constructs BIP tree locally • Dist-BIP-A: Connect all the locally generated BIP trees (one-hop neighbor information) • Dist-BIP-G: Connect the locally generated BIP tree by the gateway nodes (two-hop neighbor information) Sandeep Gupta, Arizona State Univ.

  21. BIP/MIP Algorithm • Combine energy cost and lifetime of a node as node cost in BIP/MIP: • Limitations: • Minimizing • Ci is not the lifetime of node i, even when =1 • Node Cost is a function of time, so the tree should be periodically refined • , BIP/MIP chooses higher remaining battery nodes minimum TEC(T) or maximum LT(T) Sandeep Gupta, Arizona State Univ.

  22. EWMA Algorithm • EWMA Algorithm: refine MST to minimum TEC source-based broadcast tree (Centralized Approach) • New Transmission Energy Selection: Node i selects a downstream node j. The incremental energy of node i to cover j’s children isEnergy Gain is • Selects the node j with highest positive Gain. Increase node i’s transmission energy to cover all of node j’s children and eliminate the redundant transmissions which are already covered by node i. Sandeep Gupta, Arizona State Univ.

  23. EWMA Algorithm • Limitations • Greedy nature not suitable for multicast tree. EWMA Multicast Tree Minimum TEC Multicast Tree 3 4 4 3 8 EU/pck 4 EU/pck 7 EU/pck 1 1 2 2 2 EU/pck 2 EU/pck TEC = 8 EU/pck TEC = 6 EU/pck Sandeep Gupta, Arizona State Univ.

  24. Distributed EWMA • EWMA-Dist • Two-hop neighbor information • Using breadth first search, Parent tries to reduce TEC by excluding children’s transmission  Shorter and Boarder tree Sandeep Gupta, Arizona State Univ.

  25. REMiT Approach • Refinement-based?- (Take an initial solution and make it better) • Needed anyways because of dynamic changes in the network • Interference • S-REMiT: Minimize TEC of source-based tree • L-REMiT: Maximize Lifetime of source-based tree • G-REMiT: Minimize TEC of group-shared tree • How to distribute the computation? Sandeep Gupta, Arizona State Univ.

  26. Refinement Operation: Change • Reduce TEC of the source-based tree by moving node x’s farthest child (say node i) to another node (say node j) Sandeep Gupta, Arizona State Univ.

  27. Refinement Criterion Sandeep Gupta, Arizona State Univ.

  28. Oscillation & Disconnection Avoidance • Lemma 1: Nodes j and x are the only nodes in the source-based multicast tree whose node cost may be affected by . • Lemma 2: If j is not a descendant of node i in tree T, then the tree remains connected after . Sandeep Gupta, Arizona State Univ.

  29. S-REMiT Algorithm • Minimizing TEC of source-based multicast tree • Two phases • First Phase: Build an initial tree • Second Phase: • Every node starts local refinement • Once node i hears its neighbor just made refinement, it locks all of its neighbors. • Node i selects the new parent for itself with the highest positive energy Gain, say node j. • Node i changes its parent from x to j. (Node x may be pruned if it is leaf node and not in the group.) • Node i unlocks its neighbors Sandeep Gupta, Arizona State Univ.

  30. L-REMiT Algorithm • Maximizing LT of source-based multicast tree • Two phases • First Phase: Build an initial tree • Second Phase: • Find bottleneck node x in the tree, node i is the costliest node of node x. • Node i selects the new parent for itself with the highest positive Lifetime LTGain, say node j. If no such node j exists, terminate L-REMiT. • Node i changes its parent from x to j (Node x may be pruned if it is leaf node and is not in the group). • Recompute the bottleneck node, go to step 1. Sandeep Gupta, Arizona State Univ.

  31. Performance Results Sandeep Gupta, Arizona State Univ.

  32. Performance Results Sandeep Gupta, Arizona State Univ.

  33. Performance Results Sandeep Gupta, Arizona State Univ.

  34. A Framework for Energy-Efficient Multicast Policies & QoS Requirement Protocol of Constructing/Maintenance Energy-Efficient Multicasting Tree Tree Cost Computation Node Cost Computation Energy Cost Model Link layer parameters feedback (mobility, link error rate, etc) Sandeep Gupta, Arizona State Univ.

  35. A Framework for Energy-Efficient Multicast • Energy Cost Model: Nature of wireless transceivers • long range: • short range radios: • Node Cost Computation: QoS constraints (delay), optimization goals (TEC, LT), type of multicast trees (source-based, group-shared) • Cross layer design: combine network layer and link layer Sandeep Gupta, Arizona State Univ.

  36. Conclusions • Wireless Multicasting is different from Wired Multicasting – Wireless Multicast Advantage • Energy-efficient multicast protocols • Power control • Different optimization goals – Lifetime, Energy • Type of trees – source-based, group-shared • Adaptive protocols • Framework for energy-efficient multicasting • Evaluation on actual wireless (sensor) ad hoc – e.g. Berkeley Mica Motes Sandeep Gupta, Arizona State Univ.

  37. Reference [1] B. Wang and S. K. S. Gupta. S-REMiT: “S-REMiT: A Distributed Algorithm for Source-based Energy Efficient Multicasting in Wireless Ad Hoc Networks ”, In Proceedings of IEEE GlobleCOM, San Francisco, CA, Dec. 2003, pp. 3519-3524 [2] B. Wang and S. K. S. Gupta,"On Maximizing Lifetime of Multicast Trees in Wireless Ad hoc Networks", In Proceedings of Thirty-Second International Conference on Parallel Processing (ICPP), Kaohsiung, Taiwan, China, October 2003, pp. 333-340. [3] B. Wang and S. K. S. Gupta, "G-REMiT: An Algorithm for Building Energy Efficient of Multicast Trees in Wireless Ad Hoc Networks", In Proceedings of IEEE International Symposium on Network Computing and Applications (NCA),Cambridge, MA, April 2003, pp. 265-272. [4] J. E. Wieselthier, G. D. Nguyen, and A. Ephremides, Resource management in energy-limited, bandwidth-limited, transceiver-limited wireless networks for session-based multicasting. Computer Networks, 39(2):113–131, 2002. [5] J. E. Wieselthier, G. D. Nguyen, and A. Ephremides, Distributed algorithms for energy-efficient broadcasting in ad hoc networks, Proceedings of MilCom, Anaheim, CA, Oct. 2002. [6] M. Cagalj, J.P. Hubaux, and C. Enz. Minimum-energy broadcast in All-wireless networks: NP-completeness and distribution issues. In Proceedings of ACM MobiCom 2002, pages 172 – 182,Atlanta, Georgia, September 2002. [7] F. Li and I. Nikolaidis. On minimum-energy broadcasting in all-wireless networks. In Proceedings of the 26th Annual IEEE Conference on Local Computer Networks (LCN 2001), pages 193–202, Tampa, Florida, November 2001. Sandeep Gupta, Arizona State Univ.

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