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SENSE: Scalable and Efficient Networking of Sensor Elements

This paper explores scalable methods for networking sensor elements in ad-hoc networks, focusing on cross-layer optimization and the fundamental limitations of scaling such networks. We examine the implications of physical layer characteristics on upper-layer communication protocols and stress the importance of modular protocol stacks. By analyzing the interactions between layers and leveraging diversity, this work aims to enhance packet relay strategies to minimize interference and optimize network performance. The approach includes both analytical models and simulations to predict system behavior under various parameters. ###

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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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