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

2. 2 2 Discussion Topics  Implications of fundamental limitations to the scaling of ad hoc networks u 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. 4 4  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) Known Results on Network Capacity

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

6. 6 6 n total users r0r0 Only one relay looking for destination Single-copy forward r0r0 n total users r0r0 First relay reaching destination delivers the packet (More than one relay looking for destination) Multi-copy forward r0r0 Preliminary Results: Node Trajectories Are IID

7. 7 7 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! S D Conventional close straight line path Outlook: Need More than Min-Hop Routing Path of least interference subject to constraints

8. 8 8 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 S T R Scalable & Efficient Network Control Signaling to support functions should not be redundant

9. Importance of analytical models

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

11. 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. 12 Multihop Networks RTS CTS Interference is network-wide!

13. 13 Previous Work  Single-hop (mostly) or “weak-interactions” approach (to avoid interference from distant nodes)  Scheduling rates are independent Poisson 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. 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. 15 Modeling Rationale  Focus on the essentials of MAC and PHY layers: u PHY: Ensure that frames are correctly received u MAC: Scheduling discipline to share the channel  MAC/PHY interactions depend on connectivity among the nodes: u Network topology is key!  Model each layer’s functionality in probabilistic terms: u PHY: successful frame reception probability u MAC: transmission probability  Model topology with an interference matrix

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

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

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

19. Modular protocols and distributed algorithms

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

21. 21 Routing Issues  Routing protocols are monolithic u One flavor of signaling for all destinations u 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. 22 Image from sensor command center Not All Nodes and Traffic Are Created Equal! Most communication is multipoint and for particular purposes

23. 23 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 S T R Scalable & Efficient Network Control Signaling to support functions should not be redundant

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

25. 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. 26 Outlook: Integrated Routing and Multicasting f e g R h i dc b a R C1 C2 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! special services, sink of data multicast group

27. Thanks!