1	Energy Level Accuracy in Mobile Ad-Hoc Networks Using OLSR Rana Alhalimi and Thomas Kunz
2	Outline Motivation OLSR and Our Modifications Initial Results Prediction Strategies Guessing Prediction Smart Prediction Conclusions and Future Work
3	Motivation QoS routing in MANETs Supports interesting applications Requires “accurate” state information Little work to quantify accuracy of state information QoS routing protocols exist State information aggregation discussed for LARGE wired networks, including loss of information (ATM, hierarchical networks, etc.) Some work on quantifying amount and accuracy of topology information Existing evidence seems to suggest that More accurate state information results in “better” routing decisions More accurate state information is difficult to collect
4	Motivation: Energy-Efficient OLSR
5	OLSR and Our Modification OLSR: uses MPRs to Efficiently propagate topology information Constrain routing to MPRs while guaranteeing shortest path -> only partial topology is known Two key protocol messages Hello: propagate 1-hop and 2-hop neighbor information, used to route to close neighbors and to select MPRs TC (topology control) messages: propagate partial topology information to all nodes, allows them to build a (partial) view of topology and determine shortest paths Modification: Piggyback nodal energy level onto Hello and TC messages, including a timestamp Nodes build a database for all known/reachable nodes and their known energy levels, based on most recent report Periodically report actual and perceived nodal energy levels
6	Initial Results All tests done with NS2 version 2.27 with OOLSR version 0.99.15 Initial question: how accurate is the energy level information and do the OLSR protocol parameters (Hello interval, TC interval, MPR coverage, and TC redundancy) significantly impact the accuracy For example: decreasing TC interval should result in more frequent propagation of state information, but comes at a cost of increased control message traffic
7	Initial Results (cont.)
8	Initial Results (cont.)
9	Prediction Strategies: Lots of “old” information
10	Prediction Strategy: Guessing
11	Prediction: Two prediction strategies
12	Conclusions and Future Work Provided some empirical evidence on QoS state information inaccuracy under OLSR Initial results not too surprising, except that little control via OLSR protocol parameters Improvements in state information accuracy are possible Guessing was not a good idea J Prediction can be made to work for a metric like energy Future Work Explore results under mobility Demonstrate more clearly the impact of more accurate state information on routing performance Apply similar ideas to other state information, for example Queue Length (used for load-balanced routing)