Energy Level Accuracy in Mobile Ad-Hoc Networks Using OLSR

	Rana Alhalimi and Thomas Kunz

Outline

	Motivation
	OLSR and Our Modifications
	Initial Results
	Prediction Strategies
		Guessing
		Prediction
		Smart Prediction
	Conclusions and Future Work

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

Motivation: Energy-Efficient OLSR

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

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

Initial Results (cont.)

Initial Results (cont.)

Prediction Strategies: Lots of “old” information

Prediction Strategy: Guessing

Prediction: Two prediction strategies

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)