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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 Adaptive Batch Resolution Algorithm for 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 protocols generally look at the channel contention as a steady-state phenomenon • BRAs address scenarios where contention has a bursty nature • BRAs can be applied as MAC protocol, called obvious MAC • nodes with pending packets form a batch • batch is resolved using BRA • one pck delivered per node • No other nodes is admitted into the batch till the end of the BRA • Process starts over again, with a new batch formed by nodes with still pending packets A. Zanella - Globecom 2010

  4. Performance measures • Batch resolution interval (BRI) • Tau(n) = E[time required to resolve a batch of size n] • 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) Idle slot (ti) Successful tx (ts) 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} 1 is resolved 2 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} 1 is resolved 2 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} 1 is resolved 2 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 resolution rounds • At each round, unresolved nodes transmit their packets in a random slot in the frame • Slotted CSMA ALOHA • At the end of the contention frame, the inquirer broadcasts a probe message that contains: • aggregate feedback field • frame length w to be used in the next round • ACKed nodes leave ABRA, the other keep competing 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 • Resolution Interval Dynamic programming optimization ts=tc=1 b=ti/ts Frame duration Residual batch resolution interval • w*n: optimal frame length for a batch of size n 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 according to WiFi (WF) & ZigBee (ZB) specifications • Batch size n with Poisson distribution of parameter N • Simple batch size estimator [Schoute83]: A. Zanella - Globecom 2010

  19. Throughput comparison • Throughput gain of ~9% for WF and ~6% for ZB wrt best competitor • High and rather constant throughput for all batch sizes A. Zanella - Globecom 2010 ABRADE ABRADE

  20. Energy efficiency comparison • Mean number of tx per slot (proportional to energy consumption) comparable to the best performing algorithms A. Zanella - Globecom 2010 ABRADE ABRADE

  21. SIGNET Special Interest Group on NEtworking & Telecommunications Adaptive Batch Resolution Algorithm for CSMA Wireless Networks Andrea Zanella zanella@dei.unipd.it

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

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

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