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Sensor Networks Evolution & Design for the 21st Century

Explore sensor network technologies, design issues, and applications for ubiquitous computing. Learn about sensors, networks, and power management in the wireless domain. This course covers topics like sensor deployment, transducers, and communication protocols.

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Sensor Networks Evolution & Design for the 21st Century

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  1. Sensor Network Overview Taekyoung Kwon tk@mmlab.snu.ac.kr

  2. For starters • The problems of engineering education • Problem solving • English • Communication skills

  3. For starters • What you can achieve by taking this course • Problem solving • Problem definition • Topics in the wireless/sensor network • Idea • Verify/evaluate • sensor network • Ubiquitous computing • standardization

  4. Evolution (size and number)

  5. Confluence of technologies

  6. Ubiquitous computing • 21st century computers • Embedded in our world (ubiquitous, pervasive) • They weave themselves into the fabric of everyday life until they are indistinguishable from it [Mark Weiser, 1991] • The anti-thesis of “virtual reality” • Like motor technology, embedding computers everywhere and having them “disappear in the background” is easy

  7. Wired vs. wireless • Bandwidth • Reliability • CSMA/CD vs CSMA/CA

  8. Wireless networks • Wireless network  ad hoc network • Ad hoc network  sensor network? • Wireless WAN: Cellular • Wireless MAN: IEEE 802.16 • Wireless LAN: IEEE 802.11 series • Wireless PAN: IEEE 802.15 family

  9. What is sensor? • Sensor: a transducer that converts a physical, chemical, or biological parameter into an electrical signal • Actuator: a transducer that accepts an electrical signal and converts it into a physical, chemical, or biological action • Transducer: a device converting energy from one domain into another. The device may either be a sensor or an actuator

  10. Sensor network • Tens of thousand nodes • Densely deployed Sink Internet, Satellite, etc Sink Task Manager

  11. Sensor node hardware • Small • Low power • Low bit rate • High density • Low cost (dispensable) • Autonomous • Adaptive Mobilizer Location Finding System Transceiver Sensor ADC Processor Memory Power Unit

  12. Sensor network • Power constraint • Battery powered  mains powered • Energy harvest • Light(solar), vibration, temperature • Tradeoff between energy and QoS • Prolong network lifetime by sacrificing application requirements • Delay, throughput, reliability, data fidelity,… • Still QoS is attractive • Deterministic or probabilistic bound

  13. Application Layer Transport Layer Task Management Plane Mobility Management Plane Network Layer Power Management Plane Data Link Layer Physical Layer Sensor network • Traffic type: streaming, periodic, event • Low cost, Low bit rate, low duty cycle • IEEE 802.15.4: 250Kbps

  14. Ad hoc vs. sensor • Number of sensor nodes can be several orders of magnitude higher • Sensor nodes are densely deployed and are prone to failures • The topology of a sensor network changes very frequently due to node mobility and node failure • May leverage broadcasting than point-to-point communications • May operate in aggregate fashion • In-network processing • Sensor nodes are limited in power, computational capacities, and memory • May not have global ID like IP address • Need tight integration with sensing tasks

  15. Design issues • Fault tolerance • Battlefield application • Scalability • Node density: (NR^2)/A (transmission) • Production costs • Hardware constraints • Topology • Deployment phase • Post-deployment phase • Environment • Transmission media: ISM, IR • Power consumption: sensing, processing, communication

  16. PHY layer • Sync • Self-organization • Beacon scheduling (periodic) • Directional/smart antenna • Ultra-wideband (UWB) • Transmit-only device • pros: cost, energy • Cons: uncontrollable, communications/networking overhead

  17. MAC layer • TDMA vs. CSMA • TDMA: inter-cluster, scalability • CSMA: idle listening, overhearing • Sleep cycle • Coordination • Spatial correlation • Clustering (MAC vs NWK) • Additional control channel • FDMA or TDMA • Location awareness • Exposed terminal problem

  18. network layer • Attribute-based addressing • Information-centric delivery • Routing • Route discovery • Data aggregation/coordination • Location awareness • Directional antenna (AOA) • UWB (distance measure via signal flight time) • GPS

  19. routing • Route discovery (AODV, DSR,…) • Route selection metric: hop count • Metric can be generalized to cost • Hierarchical tree routing • Gradient routing: data broadcasting

  20. Transport layer • Goodput decreases drastically as the offered traffic exceeds the network capacity • Flow control vs. Congestion control • open loop vs closed loop • Proactive vs. reactive

  21. Transport layer • Reliability concept should be relaxed • Event-to-sink reliability • Not all event-sensing nodes need to report • N reception among M transmission might be OK (M > N) • Hop-by-hop approaches

  22. Middleware/Language/Appl. • query/advertisement • Publish/subscribe • nesC, Mate, SQTL • Declarative rather than procedural • TEDS (IEEE 1451)

  23. Some of the commercial applications • Industrial automation (process control) • Defense (unattended sensors, real-time monitoring) • Utilities (automated meter reading), • Weather prediction • Security (environment, building etc.) • Building automation (HVAC controllers). • Disaster relief operations • Medical and health monitoring and instrumentation

  24. What to consider: application requirements • Energy-saving • QoS • Throughput/Goodput • Reliability • timeliness • Traffic/application scenario • Amdahl’s law • Every possible case • Self-organization

  25. What to consider: enabling technologies • Directional (smart, MIMO) antenna • Multi-hop reachability • AoA • Hidden node problem • Heterogeneous node type • E.g., Transmit-only device • GPS: too costly • UWB (distance measurement) • Location aware • Energy harvesting device • Additional (separate) control channel

  26. Possible approaches • Conservative vs. aggressive • Pessimistic vs. opportunistic vs. optimistic • Proactive (a priori) vs reactive (on demand) • Information amount vs. performance (better control/decision) • History • Neighbors within some hops • Deterministic (e.g. threshold) vs. probabilistic • N * p = 1? • Reservation vs. random access • Heterogeneous functionalities • E.g, cluster head, member

  27. Possible enhancements: • Flexibility vs. efficient • adaptivity • Stability vs. throughput (utilization) • Goodput • Reliable vs. fault-tolerant vs. error-resilient vs. robust • fairness • Legacy-system support, standard-compliant, backward compatibility

  28. Final goal • Tradeoff • Quantitative trend • Qualitative feature • How to verify? • Analysis • Simulation • Implementation

  29. analysis • assumptions • Whole system vs key element • Steady state probability • Upper/lower bound • Worst/average case • Complexity: O() • Temporal vs. spatial

  30. Simulation • Arbitrary level of detail • Still too many ambiguities • Follow the norm, other reference • How to emphasize the strength? • Also show the weakness

  31. Implementation • Most time and energy consuming • Good luck!

  32. Leverage other techniques • Algorithm • Combination theory • AI • e.g., self-learning • Operations Research • optimization • Network Flow, scheduling theory • Probability • Queuing theory

  33. Let’s make team!

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