QoS in ad hoc nets: distributed fair scheduling SCOPE: Self-coordinating Localized FQ H. Luo et al “A Self-Coordinating Approach to Distributed FairQueueing in Ad Hoc Wireless Networks”, Infocom 01 Support mission critical applications –ad-hoc network, rooftop-neighbor network –sensor network Better coordination to resolve resource competition from inside the network

Challenges I Location-dependent channel contention Spatial channel reuse Distributed scheduling information Notion of fairness

Challenges II F1 F2 F3 X Spatial Collision Spatial Channel Reuse No Spatial Channel Reuse F4

Flow Contending Graph F1 F2 F4 F3 F1 F4 F2 F3 Flow Contending Graph Node Graph RTS-CTS-(DS)-DATA-ACK

Challenges III Distributed scheduling information –NO single entity has complete flow information Fairness notion –No consistent contending flow set –Indirect contention –Conflict with max t’put F1 F4 F2 F3 Flow Contending Graph

Fairness Flow with minimum service should be guaranteed to receive service –Identify the flow with global minimum service w/o global search? – MLM-FQ Simultaneous transmissions should be scheduled whenever possible, subject to max-min and BW constraint –EMLM-FQ

MLM-FQ Maximize Local Min-FQ Identify all flows that receive local minimum services Global minimum must be among local minima Maximize-global- min => maximize- local-min

MLM-FQ A node tags its own flows (STFQ) Piggyback service tags in control messages: –Current service tag in RTS-CTS –Updated service tag in DS-ACK A node maintains one-hop neighboring flows’ tags – a local table A node transmits only if one of its flows has the local minimum service tag

MLM-FQ F1 F3 F2 F4 F2 F4 2 4 F11 F2 F4 2 4 F11 F44 F33 F2 F3 2 3 F11 F44 F1 be scheduled

Drawbacks Low channel utilization – spatial reuse may be prohibited –Worst-case C/N Deadlock due to collisions on service tag propagation F11 F22 F32 F11 F32 F41 F11 F22 F41 F22 F32 F41 F111 F411 F11 F41 F11 F41

Enhanced MLM-FQ Set a backoff value for each flow before it contends for channel Backoff value set to be the total number of flows that have smaller service tags in local table

How E-MLM works

EMLM-FQ Min Fair Service Worst-case: C/Nc –Nc is the total number of flows in the maximum clique of the flow contending graph –Independent from total number of flows

Comparing Scheduling Schemes

Performance Evaluation FTP/TCP CBR/UDP

Throughputs Aggregate: 205.8kbps Aggregate: 141.4kbps

SCOPE Summary Ensure fair service to individual flows through localized coordination Table-driven distributed algorithms Addresses: –Location-dependent resource sharing – self-coordination from inside the network –Scalability - both state maintenance and communication overhead Mobility & wireless link dynamics due to outside interferences and attacks