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By David Culler etc.

Towards a Network Architecture for Wireless Sensor Networks - where do we need to “think different” about these networks -. By David Culler etc. service. data mgmt. network. system. architecture. The Quest…. Monitoring & Managing Spaces and Things. applications. Store. Comm.

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By David Culler etc.

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  1. Towards a Network Architecture for Wireless Sensor Networks - where do we need to “think different” about these networks - By David Culler etc.

  2. service data mgmt network system architecture The Quest… Monitoring & Managing Spaces and Things applications Store Comm. uRobots actuate MEMS sensing Proc Power technology Miniature, low-power connections to the physical world

  3. A.M. (ante-mote) emphasis Applications Collaborative sig. Proc. tuple-based naming cluster formation Redundancy utilization Dissem. Alg. Topology formation Xmit control MAC alg Technology

  4. P.M. emphasis Applications Mech. design deployments Robust gateways data vis. transit pub/sub Mhop collect bcast logging net prog prog. env Quer. Proc. nD store agg. Nbr mgmt O.S. Mhop anal. watchdog Power mgmt sensor i/f loss character MAC impl. link estimation Technology

  5. Now time to work on the architecture Applications Sensor Network System Architecture Technology

  6. FTP Telnet HTTP SMNP NTP SNMP UDP TCP BGP IGMP OSPF ICMP IP Network Access Physical Starting points ??? 0. Many ind. Pt-pt flows • Multiplex utilization of diverse nets • Internet continue despite loss of nets • Multiple types of comm svc • Accommodate variety of nets • Distributed mgmt • Cost effective • Easy host attach • Accountable • E2E • Reliable byte stream over best effort packet • hard layering • 1% net loss rule • Evolution of arch from multiple implementations

  7. Capability Catalog • Traffic Patterns => collectives, topology sensitive • N-1: Data collection / Aggregation • 1-N: Broadcast / Disseminate • 1-k: Local neighborhood • 1-1, K-K: Few dynamic Pt – Pt • Transport • Low frequency, small packet, best effort • Bursts of high fidelity bulk xfer, reliable • Time Correlation • Discovery • Connectivity, neighborhoods, routing, groups • Route / Role formation and management • Management • Debugging • Security • DEEP power management • Limited storage • Intermittent, infrequent, changing connectivity telnet, ftp http, smtp, … tcp, udp ntp igmp ospf, bgp snmp icmp

  8. Example: Classical Thinking about Routing • Discover connectivity graph • Determine routing subgraph • relative to traffic pattern • Route data hop-by-hop • Queuing, multiplexing, scheduling, retransmission, coding, …

  9. non-isotropic Informed from below • Connectivity Determination • Underlies routing, comm. Scheduling, in-network processing • Under appreciated in mobile ad hoc work • Reams of geo-based studies • Numerous recent studies characterizing ‘links’ • Connectivity not a simple binary relation • many occasional receptions • asymmetries, affinities, … • stable long links • temporal changes • Fundamental to mhop sensor net routing • Not just “bad radios” • Quality of neighborhood info fundamentally limited by storage, bandwidth, duty cycle, power • Engages multiple layers

  10. What is connectivity? • CS: Ability to correctly receive a large fraction of transmitted packets • EE: Signal-to-noise ratio exceeds some threshold

  11. Signal Noise The Amoeboed “cell” Distance

  12. Which node do you route through?

  13. What does this mean? • Always routing through nodes “at the hairy edge” • Wherever you set the threshold, the most useful node will be close to it • The underlying connectivity graph changes when you use it • More connectivity when less communication • Discovery must be performed under load • Estimation & Blacklist not enough • Need to deal meaningfully with the variation and uncertainty • Impact basic algorithmic structure

  14. In-concert above • Example: Radio Power Management • Traditionally restricted to MAC or Phy layer • Early rejection of non-dest packets, protocol response • TDMA, cluster head, … • Transparent savings of <2x, not 100x • Monitoring applications have natural schedules • Sample / sleep epochs • Sleep / Log / burst • Alarm triggers • Sleep closely related to storage management, reliability technique, fidelity & deadline requirement • Informed below: passive vigilance techniques • Other Examples: channel back-off, neighborhood management, …

  15. Challenge in the middle • Example: Fine-grain Time Synch • Below • RBS and post-MA timestamp yield good fidelity • Above • Appln requirements well-defined, but diverse • Data correlation, co-operative scheduling, protocol timeouts • Challenge is in the middle • NTP ‘vco’ game map to usecs • cannot convert all local readings • Mark and interpolate • Conversion per hop tends to accumulate error • Coarse conversion to reference (global or specific)

  16. Reliability at many levels • We will use links that have reliability too low to use without regeneration • Nbr and Routing must deal explicitly with loss rate • Pt-Pt reliability often not meaningful • Aggregate behavior as nodes come and go • Need to define at appln level and carry down • Epidemic dissemination • Eventual consistency with convergence rate and response time • Transactional routing • Custody transfer • Security too…

  17. Internet On-site data center Real Sensor Nets have Multiple Layers • New challenges at lowest tier • Higher layers will be primarily IP • Intermittent connectivity • Power-aware • Reframe the lowest layer • Integrate at many points • Deep info-mgmt around the sensor net Patch Networks Verification Network GW Transit Network Client Data Browsing and Processing Site Link data storage & processing

  18. Wither Interoperability? • Large sensor networks will be deployed for particular application(s) • Valuable even without interoperability • Multiple distinct services / applns share network • Interoperable factoring of the stack • Competitive impl. of MACs, nbr, routing, discovery • Increasingly multiple WS-nets will cooperate • Backbone nodes vs sensor nodes • Lightbulb nodes + AC control nodes + temp. nodes + tag + … • Routing problem  routing service(s) • Discovery, profile, role determination, …

  19. Design Elements • Design for continual change • Not the traditional “chaotic diverse nets” of IP • Change in connectivity • Change in components, protocols, platforms • Change in population (loss, addition, partition) • Change in application usage • Change in node diversity (new layers) • Want State-free / soft-state / easy reactivity and repair of E2E, with in-network processing • Application (aggregation, region detection, CSP) • Network layers too • Leery of “maintain network structure” approaches • Hard TDMA schedules, cluster head / gateway / node, … • Probabilistic schemes more attractive

  20. Design Elements (cont) • Establish traffic models / requirements • Not statistical multiplexing of independent flows • Collect / Aggregate / Neighborhood / Broadcast / Disseminate • Net Structure and Physical Constraints DO MATTER • Nodal scaling constraints • Power • everything costs: listening, protocol, and headers • often at very low duty cycle – not saturation • Storage • algorithmic limits of network picture • Complexity • every protocol msg is another opportunity for loss • every additional piece of info can be wrong • Environmental change

  21. Towards Architecture Application CoS Net Prog Agg MCast Diffuse Pt-Pt Dissem Time Handler dispatch Collect Hood Mgmt Disc NBR Protocol dispatch SP Net Acc Net Acc timer Sensor Cross Cutting NBR mgmt Power Mgmt Scheduling Health Link Link Phy Phy RFM, CC, 802.15.4, ???

  22. Protocol Suite Protocol Multiplexing Sharing and independence Encapsulation & dispatch Minimal set (mgmt) In-network proc. Intercept Storage limitations RAM, flash Segmentation / Frag. Security Naming & addressing Mac, logical, routable, physical, topological Protocol / port Time Candidate links CC, RFM, 802.15.4, BT?, … System / platform substrate Encapsulation w/I stack dispatch, Pwr Scheduling and power i/f ‘Above-link’ packet Std?, diverse, IP? Fixed format? Nbr mgmt inter & intra node info Estimation Min. trans. Rate? Transport suite Small best-effort vs Reliable bulk Time for Careful Design & Engineering

  23. Understanding fundamentals • Localized algorithms: Distributed computation where each node performs local operations and communicates within some neighborhood to accomplish a desired global behavior • D. Estrin, “21st Century Challenges…” • It takes energy to maintain ‘structure’ from local interactions. • How much? • To maintain a routing tree? • To aggregate? • To disseminate info? • Compression / reliability, ….

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