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Adaptive Batch Resolution Algorithm for CSMA Wireless Networks

SIGNET. Special Interest Group on NEtworking & Telecommunications. Adaptive Batch Resolution Algorithm for CSMA Wireless Networks. Andrea Zanella zanella@dei.unipd.it. Problem statement. What’s a “batch”? Set of mutually interfering nodes simultaneously solicited to send a packet

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Adaptive Batch Resolution Algorithm for CSMA Wireless Networks

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  1. SIGNET Special Interest Group on NEtworking & Telecommunications AdaptiveBatchResolutionAlgorithmfor CSMA Wireless Networks Andrea Zanella zanella@dei.unipd.it

  2. Problem statement • What’s a “batch”? • Set of mutually interfering nodes simultaneously solicited to send a packet • RF tags illuminated by a reader • Wireless nodes that reply to neighbour-discovery request • Mobile terminals that compete to reserve a channel slot • What’s the “Batch resolution problem”? • Simultaneous transmissions by multiple nodes result into collision  all packets are lost! • Nodes need to arbitrate the channel access in order to transmit their packet avoiding collisions • A node that successfully transmits is said to be resolved • What’s a “Batch Resolution Algorithm” (BRA)? • The BRA arbitrates the channel access in order to minimizing the batch resolution interval, ie, the mean time required to resolve all the nodes in the batch Broadcast inquiry message Solicited nodes form the “batch” Unicast reply messages Inquirer A. Zanella - Globecom 2010

  3. BRA vs MAC • The batch resolution problem looks like the MAC problem but… • MAC protocolsgenerally look at the channelcontentionas a steady-state phenomenon • BRAsaddressscenarioswherecontentionhas a bursty nature • BRAs can beappliedas MAC protocol, calledobvious MAC • nodeswithpendingpacketsform a batch • batchisresolvedusing BRA • onepckdelivered per node • No othernodesisadmittedinto the batchtill the end of the BRA • Processstartsoveragain, with a newbatchformedbynodeswithstillpendingpackets A. Zanella - Globecom 2010

  4. Performance measures • Batchresolutioninterval (BRI) • Tau(n) = E[timerequiredtoresolve a batch of sizen] • Batch Throughput • Asymptotic throughput • Corresponds to the maximal sustainable arrival rate when BRA is used as obvious MAC A. Zanella - Globecom 2010

  5. Literature: immediate feedback • Feedback (idle, successful, collision) is returned after each slot • Collisions are recursive resolved by random binary splitting • Nodes are randomly split in two subsets: Left and Right • Left subset is activated first (nodes transmit) • Collision? apply recursively the algorithm from step (1) • Idle or successful slot?  activate Right subset & goto step (3) I/S/C Feedback packet (bp) Successful tx (ts) Idle slot (ti) Collision (tc) I S S S ti tc bp ts A. Zanella - Globecom 2010 activated backlogged resolved C C

  6. Splitting-tree BRAs • Time is slotted • Slots may have unequal duration in CSMA networks • In each slot, some nodes are “activated”, that is to say, enabled to transmit • Feedback is returned after each slot • Idle slot: no nodes transmit • Successful slot: a single node transmit • Collided slot: two or more nodes transmit • BRA works recursively, driven by feedback, as follows • Idle: activate nodes in the next slot • Successful: activated node is resolved and leaves the batch • Collision: activated nodes are randomly split in left (L) and right (R) subgroups • BRA is applied to L first • Once L is resolved, BRA is applied to R A. Zanella - Globecom 2010

  7. Example: BT Initial batch: {1,2,3,4,5} L= {1,2} R= {3,4,5} RL= {} RR= {3,4,5} LL= {1,2} LR= { } LLL= {1} LLR= {2} RRL= {3} RRR= {4,5} RRRL= {4} RRRR= {5} 2 is resolved 1 is resolved 3 is resolved A. Zanella - Globecom 2010 4 is resolved 5 is resolved

  8. Ex: MBT Initial batch: {1,2,3,4,5} L= {1,2} R= {3,4,5} RL= {} RR= {3,4,5} LL= {1,2} LR= { } LLL= {1} LLR= {2} RRL= {3} RRR= {4,5} RRRL= {4} RRRR= {5} 2 is resolved 1 is resolved 3 is resolved A. Zanella - Globecom 2010 4 is resolved 5 is resolved

  9. Ex: CMBT Initial batch: {1,2,3,4,5} Clipped L= {1,2} LL= {1,2} LR= { } LLL= {1} LLR= {2} 2 is resolved 1 is resolved A. Zanella - Globecom 2010

  10. Ex: CMBT (2) Clipped batch: {3,4,5} L= {} R= {3,4,5} RR= {4,5} RRR= {4,5} RL= {3} RRR= {5} RRL= {4} 3 is resolved 4 is resolved 5 is resolved A. Zanella - Globecom 2010

  11. Shortcomings of existing solutions In theory In practice • Slots are assumed to have constant time duration • Feedback overhead is negligible • Maximizing the per-frame throughput will minimize the batch resolution time • In CSMA systems, slots duration depends on the channel status • Each transmission brings along a certain overhead • Maximizing per-frame throughput does not necessarily minimize the overall batch resolution interval A. Zanella - Globecom 2010

  12. The cost of neglecting feedback cost… A. Zanella - Globecom 2010

  13. Contribution of this work A. Zanella - Globecom 2010

  14. ABRA: principles • ABRA works in successive resolutionrounds • At each round, unresolvednodestransmittheirpackets in a random slot in the frame • Slotted CSMA ALOHA • At the end of the contention frame, the inquirerbroadcasts a probe messagethatcontains: • aggregate feedback field • frame lengthwtobeused in the next round • ACKednodesleave ABRA, the otherkeepcompeting in the next frame A. Zanella - Globecom 2010

  15. ABRA core: frame size optimization! • We assume that the inquirer perfectly knows the number “n” of still unresolved nodes at the end of each round • The frame size w(n) of the next round is selected in order to minimize the BRI for the residual batch: • Batch • ResolutionInterval Dynamic programming optimization ts=tc=1 b=ti/ts Frame duration Residual batch resolution interval • w*n:optimal frame lengthfor a batch of sizen A. Zanella - Globecom 2010

  16. Optimal frame length A. Zanella - Globecom 2010

  17. ABRA’s throughput A. Zanella - Globecom 2010 l(n)

  18. Case study • Parameters set accordingtoWiFi (WF) & ZigBee (ZB) specifications • BatchsizenwithPoissondistribution of parameterN • Simplebatchsizeestimator [Schoute83]: A. Zanella - Globecom 2010

  19. Throughput comparison • Throughputgain of ~9% for WF and ~6% for ZB wrt best competitor • High and ratherconstantthroughputforallbatchsizes A. Zanella - Globecom 2010 ABRADE ABRADE

  20. Energy efficiency comparison • Meannumber of tx per slot (proportionaltoenergyconsumption) comparableto the best performingalgorithms A. Zanella - Globecom 2010 ABRADE ABRADE

  21. SIGNET Special Interest Group on NEtworking & Telecommunications AdaptiveBatchResolutionAlgorithmfor CSMA Wireless Networks Andrea Zanella zanella@dei.unipd.it

  22. Appendix: asymptotic throughput A. Zanella - Globecom 2010

  23. Appendix: asymptotic throughput Taking the derivative wrtmu_infty A. Zanella - Globecom 2010

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