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Mobile Communications Protocols and Performance Analysis

This document discusses various mobile communication protocols and their performance analysis, including RMAV and Aboelaze protocols. It also explores the challenges in predicting the number of sources and achieving fairness. Additionally, the SMAC protocol for sensor networks is introduced.

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Mobile Communications Protocols and Performance Analysis

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  1. CSE 4215/5431:Mobile Communications Winter 2011 Suprakash Datta datta@cs.yorku.ca Office: CSEB 3043 Phone: 416-736-2100 ext 77875 Course page: http://www.cs.yorku.ca/course/4215 Some slides are adapted from the book website CSE 4215, Winter 2011

  2. Some proposed protocols • All demand assignment protocols • All two phase protocols • Reservation phase (minislots) • Data transmission phase (slots) • Differ in how reservation phase works CSE 4215, Winter 2011

  3. Protocol 1 (RMAV) • D. Jeong, C. Hoi, and W. Jeon, “Design and performance evaluation of a new medium access control protocol for local wireless data communications,” IEEE/ACM Transactions on Networking, vol. 3, no. 6, pp. 742–752, 1995. • 1 reservation minislot every round • Adaptive change of backoff probability • If collision, p doubled • If idle p halved • Else p unchanged • Advantages and Disadvantages? CSE 4215, Winter 2011

  4. Protocol 2 • M. Abolelaze and R. Radhakrishnan, “The performance of a new wireless MAC protocol,” in 3G Wireless 2002. • Adapt number of minislots each reservation round • if the number of minislots with collisions is larger than the number of empty minislots, then the number of minislots are doubled in the next round. • Else halve the number of minislots • Advantages and Disadvantages? CSE 4215, Winter 2011

  5. Performance - 1 • At low arrival rates CSE 4215, Winter 2011

  6. Performance - 2 • At all traffic rates CSE 4215, Winter 2011

  7. Questions? • Are these algorithms similar? Are they making the right decisions? • Can we do better? How? • What about fairness? • RMAC: a randomized adaptive medium access control algorithm for sensor networks, Suprakash Datta, SANPA, 2004. CSE 4215, Winter 2011

  8. Rationale behind protocols • A simple model for the problem • Use balls and bins analysis from elementary discrete math (Combinatorial analysis) • Can we analyze the problem and the algorithms? • First, we need (to specify) a clean mathematical model CSE 4215, Winter 2011

  9. Our model • Network model: clustered, heterogeneous • Node model: clock synchronization • Traffic model: • Random traffic • Bursty traffic • 2 state On-OFF model • Performance metric: delay CSE 4215, Winter 2011

  10. Analysis • No node knows the number of sources contending for resources • Can we estimate this number? How? • We know • Number of minislots • Number of collisions • Number of idle minislots • Number of successful transmissions CSE 4215, Winter 2011

  11. Maximum likelihood estimation • Given number of sources, we can find expected values of number of collisions, idle, successful minislots • Can we solve the inverse problem? • Our result: MLE(#sources) = ns + 2nc What does this mean? CSE 4215, Winter 2011

  12. Maximum likelihood estimation Intuitively: • if MLE(#sources) > # minislots • Increase #minislots • else decrease #minislots • RMAV: nc (=1) > ne (=0) • Aboelaze et al: nc > ne • MLE = ns + 2nc > N = nc + ne + ns • Yieldsnc > ne !!! CSE 4215, Winter 2011

  13. Questions? • Are these algorithms similar? Are they making the right decisions? • Can we do better? How? • What about fairness? • Need to decide on number of minislots • Need to implement fairness CSE 4215, Winter 2011

  14. Number of minislots • Q1: if we predict n sources how many minislots should we have? • Q2: How do we predict the number of sources? • Using Queueing theory, we can solve Q1. Having #minislots = #predicted packets minimizes expected delay. • Also max throughput = 1/N(e+S+1) CSE 4215, Winter 2011

  15. Predicting number of sources • Very difficult to do in practice • Varies a lot with application • We used a very simple linear model p(t + 1) = n(t) + a(n(t) − n(t − 1)). p(t) = number of minislots at time t a = smoothing factor n(t) = predicted #sources at time t CSE 4215, Winter 2011

  16. Dealing with fairness • Use piggybacked requests • The algorithm rejects piggybacked requests with probability f • f=1: original demand assignment, most fair, bad QoS to long sessions • f=0: bad fairness, great QoS to long sessions. • Designer can choose the desired f CSE 4215, Winter 2011

  17. What next? • Centralized algorithm • Do not need to worry about hidden terminals • Can this be made distributed? CSE 4215, Winter 2011

  18. SMAC • Designed for sensor networks • slides adapted from www.cs.wmich.edu/wsn/cs691_sp03/ • Major components in S-MAC • Periodic listen and sleep • Collision avoidance • Overhearing avoidance • Message passing CSE 4215, Winter 2011

  19. sleep sleep listen listen sleep sleep listen listen Node 1 Node 2 Energy Latency Periodic Listen and Sleep • Reduce long idle time • Reduce duty cycle to ~ 10% (120ms on/1.2s off) • Schedules can differ • Prefer neighbors have same schedule • — easy broadcast & low control overhead CSE 4215, Winter 2011

  20. Periodic Listen & Sleep • Nodes are in idle for a long time if no sensing event happens • Put nodes into periodic sleep mode • i.e. in each second, sleep for half second and listen for other half second CSE 4215, Winter 2011

  21. Coordinated Sleeping • Nodes coordinate on sleep schedules • Nodes periodically broadcast schedules • New node tries to follow an existing schedule Schedule 1 Schedule 2 1 2 • Nodes on border of two schedules follow both • Periodic neighbor discovery • Keep awake in a full sync interval over long time CSE 4215, Winter 2011

  22. Choose & Maintain Schedule • Each node maintains a schedule table that stores schedules of all its neighbors • Nodes exchange schedules by broadcasting them to its neighbors • Try to synchronize neighboring nodes together CSE 4215, Winter 2011

  23. Collision Avoidance • Adopt IEEE 802.11 collision avoidance • Virtual carrier sense • During field • Network allocation vector (NAV) • Physical carrier sense • RTS/CTS exchange (for hidden terminal problem) • Broadcast packets (SYNC) are sent without RTS/CTS • Unicast packets (DATA) sent with RTS/CTS CSE 4215, Winter 2011

  24. Overhearing Avoidance • Problem: Receive packets destined to others • Solution: Sleep when neighbors talk • Basic idea from PAMAS (Singh, Raghavendra 1998) • But we only use in-channel signaling • Who should sleep? • All immediate neighbors of sender and receiver • How long to sleep? • The duration field in each packet informs other nodes the sleep interval CSE 4215, Winter 2011

  25. Example • Who should sleep when node A is transmitting to B? • All immediate neighbors of both sender & receiver should go to sleep CSE 4215, Winter 2011

  26. Message Passing • How to efficiently transmit a long message? • Single packet vs. fragmentations • Single packet: high cost of retransmission if only a few bits have been corrupted • Fragmentations: large control overhead (RTS & CTS for each fragment), longer delay • Problem: Sensor network in-network processing requires entire message CSE 4215, Winter 2011

  27. Energy Msg-level latency Fairness Message Passing • Solution: Don’t interleave different messages • Long message is fragmented & sent in burst • RTS/CTS reserve medium for entire message • Fragment-level error recovery — ACK — extend Tx time and re-transmit immediately • Other nodes sleep for whole message time CSE 4215, Winter 2011

  28. Average energy consumption in the source nodes 1800 1600 1400 802.11-like protocol without sleep 1200 1000 Energy consumption (mJ) Overhearing avoidance 800 600 400 S-MAC w/o adaptive listen 200 0 2 4 6 8 10 Message inter-arrival period (second) Experiment Result • Average source nodes energy consumption • S-MAC consumes much less energy than 802.11-like protocol w/o sleeping • At heavy load, overhearing avoidance is the major factor in energy savings • At light load, periodic sleeping plays the key role CSE 4215, Winter 2011

  29. Experiment Result (contd.) • Percentage of time source nodes in sleep CSE 4215, Winter 2011

  30. Experiment Result (contd.) • Energy consumption in the intermediate node CSE 4215, Winter 2011

  31. Effect of duty cycle • Important tradeoff: CSE 4215, Winter 2011

  32. S-MAC Conclusions • Advantages: • Periodically sleep reduces energy consumption in idle listening • Sleep during transmissions of other nodes • Message passing reduces contention latency and control packet overhead • Disadvantages: • Reduction in both per-node fairness and increase in latency CSE 4215, Winter 2011

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