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SENSE: Scalable and Efficient Networking of Sensor Elements

SENSE: Scalable and Efficient Networking of Sensor Elements. J.J. Garcia-Luna-Aceves CCRG Computer Engineering Department University of California, Santa Cruz. Discussion Topics. Implications of fundamental limitations to the scaling of ad hoc networks Cross-layer optimization

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SENSE: Scalable and Efficient Networking of Sensor Elements

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  1. SENSE: Scalable and Efficient Networking of Sensor Elements J.J. Garcia-Luna-Aceves CCRG Computer Engineering Department University of California, Santa Cruz

  2. Discussion Topics • Implications of fundamental limitations to the scaling of ad hoc networks • Cross-layer optimization • Impact of the physical layer on communication protocol stack. • Importance of modular protocol stacks and good understanding of their distributed algorithms.

  3. Scaling

  4. Known Results on Network Capacity • Definition: A source-destination throughput of λ(n) bits/sec is feasible if every source node can send information at a rate of λ(n) bits/sec to its destination. • Gupta and Kumar (for static networks) • Grossglauser and Tse (Multiuser diversity: One-copy two phase packet relay to nearest neighbor strategy for mobile networks)

  5. Preliminary Results • Multiuser diversity with multi-copy two- phase packet relay to close neighbors strategy for mobile networks where • For fixed n • Interference analysis:

  6. Single-copy forward Multi-copy forward r0 r0 n total users r0 r0 n total users First relay reaching destination delivers the packet (More than one relay looking for destination) Only one relay looking for destination Preliminary Results:Node Trajectories Are IID

  7. D Conventional close straight line path S Path of least interference subject to constraints Outlook:Need More than Min-Hop Routing How can we reduce interference subject to multiple constraints (power consumption, e-t-e delays, bandwidth requirements)? Exploit diversity (user, space, time, code, freq) and cross-layer optimization!

  8. S Scalable & Efficient Network Control T R Need for Cross-Layer Optimization scheduling establishes links and decides which nodes are awake; needs multicast group affiliations and routes to destinations of flows routing needs links for collision-free transmission of control packets; packet forwarding needs links for collision-free transmission of data packets Multicasting needs a convenient topology topology control determines nodes & links that can be used for certain functions; needs links for collision-free transmission of control packets, and dissemination of neighborhood data Signaling to support functions should not be redundant

  9. Importance of analytical models

  10. Simulations: Specific to each scenario and setup Results for each parameter value of interest Statistical fitting not a trivial task Many physical layer features not readily available Physical layer has to be implemented How far can we go? Analytical Models: Aim to cover different scenarios: general behavior! Quick answers for the impact of different parameter values on system performance Upper/lower bounds Insights: help in the design Physical layer issues at least as accurate as in simulations Why Do We Need Analytical Models?

  11. Limits of Simulation Effort • Consider executing a simulation in a Sun blade 100 running Solaris 5.8 • 50 seeds of a 100-node, 5-min data traffic scenario required 16.41 hours for a given set of PHY-level parameters. • Analyzing the impact of different combinations of PHY-level parameters will take a very long time, and testbeds are hard to control.

  12. CTS RTS Multihop Networks Interference is network-wide!

  13. Previous Work • Single-hop (mostly) or “weak-interactions” approach (to avoid interference from distant nodes) • Scheduling rates are independentPoisson point processes • Packet lengths exponentially distributed and independently generated at each transmission attempt = backoff retransmissions ignored! • Instantaneous acknowledgments • Error-free Links • Assumptions on spatial distributions (e.g., Poisson)

  14. Modeling the Effect of the PHY:Highlights[Mobicom 04] • Framework for any MAC protocol in ad hoc networks • Focus on PHY / MAC layer interactions • No assumptions on spatial probability distributions or specific arrangement of nodes • Individual (per-node) performance metrics for any given network topology (node location) and radio channel model • Linear model that provides remarkable correlation with simulation results. • Key Benefit: Analytical results are obtained much faster than in simulations (same example as before takes 0.44 sec in Matlab). M. Carvalho and J.J. Garcia-Luna-Aceves, " A Scalable Model for Channel Access Protocols in Multihop Ad Hoc Networks," Proc. ACM Mobicom 2004, Philadelphia, Pennsylvania, Sept. 26--Oct. 1, 2004.

  15. Modeling Rationale • Focus on the essentials of MAC and PHY layers: • PHY:Ensure that frames are correctly received • MAC:Scheduling discipline to share the channel • MAC/PHY interactions depend on connectivity among the nodes: • Network topology is key! • Model each layer’s functionality in probabilistic terms: • PHY: successful frame reception probability • MAC: transmission probability • Model topology with an interference matrix

  16. Application: Modeling IEEE 802.11 [Mobicom 04] • Based on the works by • M. Carvalho and J. J. Garcia-Luna-Aceves, “Delay Analysis of IEEE 802.11 in Single-Hop Networks,” Proc. ICNP, Atlanta, 2003. • G. Bianchi, “Performance Analysis of the IEEE 802.11 Distributed Coordination Function,” IEEE JSAC, 2000.

  17. Application: Modeling IEEE 802.11[Mobicom 04] • Per-node performance metric: throughput Simulator used: Qualnet 3.5

  18. Percentage of Prediction Error [Mobicom 04] Histogram over 10 random topologies (100 nodes) Sample topologies

  19. Modular protocols and distributed algorithms

  20. APPLICATION collaborative sensor processing applications… TRANSPORT end-to-end transport protocols… NETWORK routing-structure maintenance opportunistic packet forwarding node interconnection LINK synchronization transmission scheduling neighborhood discovery PHYSICAL prototype radios simulated PHY Modular Protocol Stack

  21. Routing Issues • Routing protocols are monolithic • One flavor of signaling for all destinations • One flavor of routes (single path) for all traffic to destinations. • Routing layer in MANETs assumes that routing takes place over a given topology, just like Internet routing protocols like OSPF and RIP do. • The existence of radio connectivity does not imply the availability of a logical link in a MANET.

  22. Not All Nodes and Traffic Are Created Equal! Most communication is multipoint and for particular purposes command center Image from sensor

  23. S Scalable & Efficient Network Control T R Need for Cross-Layer Optimization scheduling establishes links and decides which nodes are awake; needs multicast group affiliations and routes to destinations of flows routing needs links for collision-free transmission of control packets; packet forwarding needs links for collision-free transmission of data packets Multicasting needs a convenient topology topology control determines nodes & links that can be used for certain functions; needs links for collision-free transmission of control packets, and dissemination of neighborhood data Signaling to support functions should not be redundant

  24. Routing Issues • Timers and sequence numbers can be a problem when the networks become very large and partitions can happen (disruption tolerance): • How long should a node remember its “state” for a destination? • What are the implications of forgetting? • Similarly, path information becomes obsolete very quickly in large dynamic/disrupted networks. • How should path information be used to ensure correct routing? • Same mechanisms repeated in different protocols.

  25. Outlook:Develop Flow Adaptive Routing Mechanisms (FARM) • Develop routing techniques that are “role”-centric (no clusters) and adapt dynamically to the flows in the network. • How a routing table entry for a destination is obtained and maintained is a function of the type of flow towards the destination. • Proactive and on-demand mechanisms used according to flow types. • Different flows are given resources (paths) according to their types and priorities. • Routing works in coordination with scheduling and topology management.

  26. multicast group i g C1 R f h e c d C2 b R special services, sink of data a Outlook:Integrated Routing and Multicasting Each common node keeps paths to the cores of groups and well-known nodes. Paths to common nodes are found on demand. Much of the traffic in sensor nets is to groups and common nodes!

  27. Thanks!

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