Localized Operations for Distributed minimum energy multicast algorithm in mobile ad hoc networks

1. Paper by Song Guo and Oliver Yang; supporting images and definitions from Wikipedia Presentation prepared by Al Funk, VT CS 6204, 10/30/07 Localized Operations for Distributed minimum energy multicast algorithm in mobile ad hoc networks

2. Table of Contents • Background and related work • Models: system, network, mobility • DMEM algorithm • Operations • Performance • Conclusions

3. Background and related work • Multicast: communication technique which enables a source to send a single packet to reach multiple receivers. • Objective: Create a distributed algorithm to solve the Minimum Energy Multicast (MEM) problem • Definition of MEM: Find a route for multicast transmission with the minimum total energy consumption for a given communication session. • Challenges: MANET changing network topology, lack of central authority; problem is NP-hard

4. Background and related work • Prior research focused on: • Creating centralized, not distributed, algorithms • Efficient heuristic algorithm design • Weaknesses of prior research • Examination of static, not dynamic, network topologies • Little examination of performance impact of node mobility

5. Models: System Model • Discrete Power Level Management Model • Transmission range based on power level, but power level increases at an exponential rate as distance increases • Identify discrete power levels appropriate to reach nodes at various distances from the transmitter • Vary transmitter power in granular increments to balance power use with the bandwidth usage necessary to constantly adjust transmission strength

6. Models: System Model • Pvu = Power level required to transmit from node v to node u • lvu=Layer (concentric ring from prior slide) of u relative to v • K = Number of discrete power levels of the transmitter (and therefore number of layers) • rK = Distance of ring K • α = Parameter (2 to 4) representing rate of signal attenuation

7. Models: Network Model • Represent network as a directed graph, G(N,A,p) • N = set of nodes, A = set of arcs, p = function representing power required for each arc • Rooted tree: directed acyclic graph with a source node that has no incoming arcs and where other nodes have a single incoming arc • Leaf vs. internal/relay nodes

8. Models: Network Model • For any node v in the rooted tree,there exists a single acyclic source route πv • Our goal is to set lv, the transmission layer of node v, to the minimum necessary for v to reach all of its child nodes • Once this is known, we can calculate pv, the necessary power level for the node

9. Models: Mobility • Mobility is a differentiator for the contribution, as alternative models require the significant overhead associated with central coordination. • Authors use “Random Waypoint Model” • Calculate random speeds bounded by Vmin and Vmax; assume random start and end points; introduce pause between journeys. • Objective: calculate the steady-state average speed:

10. Algorithm: Data Structure • We need to store the forwarding state at each tree node v. • Membership status – sender, receiver, forwarder (can be receiver and forwarder) • Source route π – directed path from the source to node v (used to avoid loops) • Tree neighborhood table TNv – stores neighbors, along with whether is a father, child or other, along with layer lvu

11. Algorithm: Tree Construction • Minimum Spanning Tree: Given a connected, undirected graph with weighted edges, an MST is a subgraph which connects all vertices together resulting in the minimum total weight.

12. Algorithm: Tree Construction • MULTICAST-JOIN-REQUEST (MJREQ): Broadcast message initiated by the source used when no route information is known • MULTICAST-JOIN-REPLY (MJREP):Response message sent to previous hop node • MJREQ: Transmitted at maximum transmission power • MJREP: Returned at necessary power • Necessary power determined by strength of the original MJREQ message

13. Algorithm: Tree Flood • MULTICAST-ALIVE (MA): Message sent periodically during session to refresh the tree (otherwise tree routes are cleared) • Message sent at maximum power • Used to adjust power dynamically • Only sent if received from father (but then always sent) • Supports tree repair and energy saving operations • Nodes update neighborhood information to identify nearby nodes

14. Localized Operations • Normal Energy Saving (NES): Upon receipt of MA from children, node adjusts its transmission power to the minimum necessary. • Reactive approach which could lower total power utilization • Keeps the tree connected but not with maximum efficiency

15. Localized Operations: SHO • Soft Hand-Off (SHO): Initiated by a node that detects it is leaving its father’s transmission range (K). • Goal is to identify a new father s.t.and power utilization is minimized • Node severs link with previous father (via MULTICAST-LEAVE (ML) message), selects the new father • Tree is maintained.

16. Localized Operations: MTR • Multicast Tree Repair (MTR): In the case where loss of a node results in a tree partition, we need a way to repair the multicast tree. • Occurs when a forwarder or receiver fails to receive successive MAs from its father • Nodes furthest from the source attempt to reconnect first • MULTICAST-JOIN-SOLICITATION (MJS): Hop-limited message

17. Localized Operations: MTR • Disconnected node closest to source notifies the subtree that it is initiating repair procedures using an MA message • The closest node to the source initiates an MJREP message and attempts to reconnect the subtree back to the multicast tree • If an appropriate node responds, the tree is reconnected; if not, other nodes in the subtree attempt to reconnect, and the node(s) that failed must rejoin through a network flood.

18. Localized Operations: AES • Advanced Energy Savings (AES): A proactive method of reallocating child nodes s.t. overall power utilization of the system is reduced. • The major contribution of the paper • We must be able to retain the MST structure for multicast • Operation performed as part of MA • Approach: Each child node attempts to extend its transmission range to become the parent of a current child of its father – but only if such a change reduces the total power utilization of the system • More sophisticated than NES • They are not mutually exclusive

19. Localized Operations: AES • Using the MA message header means that no separate message is necessary for the operation • Use of MA messages fits the algorithm -- father to child propagation enables communication of power levels and supports child decision-making. • At each transmission from its father, a node modifies header with its own information and propagates to its neighbors • Because MA messages are at full power, neighbors of multicast tree nodes will receive. • As a result, non-multicast tree nodes can join, but must consider potential added cost of the link from a father node

20. Localized Operations: AES • AES-REQUEST: When a node identifies a power savings, it sends an AES-REQUEST to the source • Source reviews AES-REQUEST messages and sends AES-REPLY to the node with the greatest power savings

21. Localized Operations: AES • Finalizing the update • Selected node sends local broadcast TREE-UPDATE and assigns itself as father to the node to move • Moving node leaves father, sending MULTICAST-LEAVE. • If selected node is a non-tree node, it must find a father • It will be a forwarding node only, otherwise it would have been part of the original tree • Multiple nodes may become children of the selected node if power savings justify

22. Localized Operations: AES • Examples of AES tree revision

23. Performance Evaluation • Simulations • Ad hoc network with size 1,000 meters sq. • Each node can transmit 250 meters • K=10 • α = 2 • Modeled max node movement speeds of:1, 5, 10, 15, 20 and 25 m/s • Multicast groups 5, 25, 50, 75, 100 • Static networks considered • 50 scenarios for each multicast group

24. Performance Evaluation • Measures • Relative tree power: Ratio of actual total tree power for heuristic algorithm vs. ideal of MST algorithm • Average tree power: Power used over time for the tree • Communication overheads: Overhead for AES, SHO and MTR as a total number of these operations over each simulation

25. Performance Evaluation • Static network evaluation • Compared DMEM against prior work • Not key to the paper, but demonstrates that DMEM is a useful heuristic compared with prior research

26. Performance Evaluation • Mobile network evaluation: Consider with and without optional protocol components

27. Performance Evaluation • Examine AES performance considering node speed and multicast group size.

28. Performance Evaluation • Examine SHO operations given node speed and multicast group size.

29. Performance Evaluation • MTR operations considering node speed and multicast group size.

30. Conclusions • In a static network, DMEM is superior to alternative algorithms for medium and large multicast groups. • Measures heuristics, but major contribution is on dynamic network • DMEM is efficient in reducing energy utilization • AES provides significant value relative to base case • SHO is mostly redundant when using AES • DMEM proven correct for maintaining tree structure using localized operations

31. Critique • Graphs are not presented in such a way to visually support the analysis • e.g., authors require visual comparison of separate charts to compare AES and SHO, rather than presenting a single chart • Is it scalable? Authors indicate that AES becomes saturated; this seems to occur rapidly in “large” networks even at slow speed. • Authors indicate that it is scalable with regard to mobility – but AES saturation seems to put this in question, as do some of their comments right before the conclusion • If scalability is an issue, possible approaches to address it would have been welcome • Do the arbitration performed by the source node along with the broadcast approach amount to centralization that reduces scalability and creates a bottleneck?