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Real-Time Communication in Wireless Sensor Networks

Real-Time Communication in Wireless Sensor Networks. Richard Arps, Robert Foerster, Jungwoo Lee, Hui Cao SPEED Routing RAP Event Detection Power Management. Introduction. Wireless sensor networks (WSN) Small sensor devices Equipped with wireless communication interfaces

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Real-Time Communication in Wireless Sensor Networks

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  1. Real-Time Communication in Wireless Sensor Networks Richard Arps, Robert Foerster, Jungwoo Lee, Hui Cao • SPEED • Routing • RAP • Event Detection • Power Management

  2. Introduction • Wireless sensor networks (WSN) • Small sensor devices • Equipped with wireless communication interfaces • In very large numbers • The distances between nodes are in the order of meters • The network density is very high, sometimes as high as tens of nodes / m2

  3. Sink Sink Source Event Common Network Architecture • Sensor nodes are responsible for • Detection of events • Observation of environments • Relaying of third party messages • Information is generally gathered at sinks • Sinks are responsible for higher level processing and decision making

  4. Limited processing capability • Limited storage space • Simple sensing devices • Limited range and rate • Limited power supplies Sensor Node Hardware • Components: • Processor unit • Memory • Sensor unit(s) • Transceiver • Power Unit • Optional Components: • Mobilizers • Localization hardware • Power generators

  5. Example Sensor Nodes MICA Motes JPL Sensor Webs UC Berkeley Dust weC Rene Rockwell WINS

  6. Sensor Types Seismic Magnetic Thermal Visual Infrared Acoustic Radar Pressure … Sensor Tasks Periodic sampling Event-based sampling Movement detection Direction of movement Object detection Object classification Chemical composition Mechanical stress … Sensor Types and Tasks

  7. Sensor Network Applications • General applications are geared towards • Command, Control, Communications, Computing, Intelligence, Surveillance, Reconnaissance, Targeting (C4ISRT) • Example military applications • Monitoring friendly forces, equipment, and ammunition • Battlefield surveillance • Reconnaissance of opposing forces and terrain • Targeting • Battle damage assessment • Nuclear, biological and chemical (NBC) attack detection and reconnaissance

  8. Sensor Network Applications • Example military applications • Intrusion detection (mine fields) • Detection of firing gun (small arms) location • Chemical (biological) attack detection • Targeting and target tracking systems • Enhanced navigation systems • Battle damage assessment system • Enhanced logistics systems

  9. Sensor Network Applications • Environmental applications • Habitat monitoring • Monitoring environmental conditions for farming • Irrigation, Precision agriculture • Earth monitoring and planetary exploration • Biological, Earth, and environmental monitoring in marine, soil, and atmospheric contexts • Meteorological or geophysical research • Pollution study • Biocomplexity mapping of the environment • Flood detection and forest fire detection

  10. Sensor Network Applications • Health applications • Providing interfaces for the disabled • Integrated patient monitoring • Diagnostics • Telemonitoring of human physiological data • Tracking and monitoring doctors and patients inside a hospital • Drug administration in hospitals

  11. Sensor Network Applications • Commercial applications • Smart homes and office spaces • Interactive toys • Monitoring disaster areas • Machine diagnosis • Interactive museums • Inventory control • Environmental control in office buildings • Detecting and monitoring car thefts • Vehicle tracking and detection • Parking lot management

  12. Factors Affecting Sensor Network Design • Fault Tolerance (Reliability) • Scalability • Production Costs • Hardware Constraints • Sensor Network Topology • Operating Environment • Transmission Media • Power Consumption

  13. SPEED • Goals • Stateless • Information regarding only the immediate neighbors • Soft Real Time • Provides uniform speed delivery across the network • Minimum MAC layer support • Traffic load balancing • Localized behavior • Void Avoidance

  14. SPEED • Soft real-time guarantees • “SPEED aims at providing a uniform packet delivery speed across the sensor network, so that the end-to-end delay of a packet is proportional to the distance between the source and the destination. With this service, real-time applications can estimate end-to-end delay before making admission decisions.”

  15. SPEED • Neighbor beacon exchange • Periodically broadcasts a beacon to neighbors to exchange location information • In order to reduce traffic we can piggyback the information • Assume all neighbors fit in the neighborhood table • Possible enhancement • Advertising state changes (rather than on fixed intervals) may reduce the number of beacons transmitted • On-demand beacons • Delay estimation • Back pressure • Fields in beacon • Neighbor ID • Position • Send to delay • TTL

  16. SPEED • Delay estimation • Due to scarce bandwidth, cannot use probe packets • Delay is measured at the sender as the round trip time minus the processing time at the receiver. • Exponential weighted moving average is used to keep a running estimation • Delay estimation beacon is used to communicate estimated delay to neighbors

  17. SPEED • Stateless non-deterministic geographic forwarding (SNGF) • Neighbor set of node I • NSi = {n | d(n,i) < range(i)} • Forwarding candidate set • FSi(destination) = {n e NSi| L-Lnext >0 } • Where L = d(i, destination) and Lnext = d(next,destination)

  18. SPEED • Back pressure rerouting

  19. SPEED • Void avoidance

  20. SPEED • Last mile processing • Since SPEED is targeted at sensor networks where the ID of a node is not important, SPEED only cares about the location. • Called “last mile” since this function will only be invoked when the packet enters the destination area • Area-multicast, area-anycast

  21. SPEED- results E2E delay under different congestion

  22. SPEED results (2) Deadline Miss ratio under different congestion

  23. Routing in Sensor Networks • Different than regular network routing • Power • Mobility • Congestion

  24. Parametric Probabilistic Routing • Partial flooding • When a node receives a packet it calculates if it is closer or further from the destination. • If closer, probability of retransmission goes up • If farther, probability goes down

  25. Parametric Probabilistic Routing • Test of probability of retransmission with origin at (0,0) and destination at (1,0)

  26. Parametric Probabilistic Routing • Pro’s • Allows for dynamic network topology. • Completely stateless. • Reduced transmission load at sensors close to base station. • Simple to impliment. • Con’s • Wasted power. • Flooding doesn’t utilize bandwidth very well. • Possible packet loss.

  27. Packet Priority Routing • Packets in sensor networks have deadlines. • Hard deadlines can give priority to those who don’t need it. • Packets originating farther from the base station need to travel more hops but have the same time to do it. • A new protocol is needed to address the issues of late packets • RAP protocol suite

  28. RAP Protocol Suite • Lightweight set of protocols aimed to reduced the percentage of missed deadlines. • Velocity Monotonic Scheduling (VMS) • Designates packet’s velocity instead of hard deadline • If a packet travels through the network at this velocity it will make its deadline. • Velocity can be static or dynamic. • Static Vel=distance(origin, dest)/deadline • Dynamic Vel=distance(current, dest)/(deadline-elapsed time)

  29. VMS • Simulations • Miss ratio Vs. packet throughput • Overall miss ratio • Miss ratio from far corner

  30. RAP • RAP can reduce deadline miss ratio from 90% to 17.9% for packets originating far from the destination.

  31. Wireless Sensor Networks • Event Detection Services • Radio-Triggered Wake-Up Capability

  32. Event Detection Services Using Data Service Middleware in Distributed Sensor Networks • Data Service Middleware (DSWare): • Exists between the application layer and the network layer • Integrates various real-time data services • Provides data service abstractions • Event Detection: dig meaningful information out of the huge volume of data produced

  33. Framework of DSWare • Data Storage • Data lookup • Robustness • Data Caching • provides multiple copies of the data • monitors current usages of copies • determines whether to increase or reduce the number

  34. Framework of DSWare (Cond.) • Group Management • provides localized cooperation among sensor nodes to accomplish a more global objective • nodes decides whether to join this group by checking the criterion • Event Detection • Data Subscription • places copies of the data at some intermediate nodes to minimize the total amount of communication scheduling • changes the data feeding paths when necessary • Scheduling • energy-aware • real-time scheduling

  35. Event Hierarchy Event: activity that can be monitored or detected in the environment and is of interest to the application Atomic event and compound event Confidence, Confidence Function and Phase Confidence: return value of the confidence function Confidence > 1.0 , confirmed , event actually occurred Confidence function: specifies the relationships among sub-events of a compound event (relative importance, sensing reliability, historic data, statistical model, fitness of a known pattern, proximity of detection) Phase: there is a set of events that are likely to occur Event Detection Services

  36. Real-Time Semantics AVI: absolute validity interval Temporal consistency btw environment and its measurement Preserve a time window to allow all possible reports of sub-event to arrive to the aggregating node Registration and Cancellation Registration: application submits a request in SQL-like statement Subevent_Set defines a set of sub-events and their timing constrains Cancellation: similar to event detection, only needs to specify the event’s id instead of describing an event’s cirteria Event Detection Services (Cond.)

  37. Evaluation of Real-Time Event Detection • Simulation • Detection of Explosion: temp. light and acoustic event • Baseline: sensor detect atomic event, report to the registrant registrant decide whether there is a compound event happening • Communication cost • Save energy since communication cost dominates the energy consumption • Reaction Time • Baseline causes severe traffic congestion • Completeness • Number of missing report around 1 or 2 out of 100 nodes • Impact of Node Density • 400 node experiment • Low density →Low missing rate, • high density →high energyconsumption, reaction time

  38. Conclusions • Sensor Network should be able to provide the abstraction of data services to applications • DSWare • Hide unattractive characteristics of sensor network (Unreliability, Complexity and necessity of group coordination) • Present a more general data service interface to applications • Accommodates the data semantics of real-life compound events and tolerates the uncertainty and unreliability

  39. Radio-Triggered Wake-Up Capability for Sensor Networks • Power Management Scheme • High power running mode • Low-power sleep mode • Problem • Network node has its CPU halted • Unaware of the external events • Periodical wake up

  40. Basic Radio-Triggered Power management • Aims to avoid the useless wake-up periods • Special radio signal wakes up the sleeping node • Saves energy spent in wake-up listen intervals • Requirements • Wake up almost instantly when it receives a wake-up packet • Use approximately the same amount of energy in sleep mode as in power mag. protocol without radio-triggered support • Should not wake up when the event of interest does not happen • Should not miss wake-up calls

  41. Design of the Basic Radio-Triggered circuit • Essential Tasks • Collect energy from radio signals • Distinguish trigger signal from other radio signals • Basic radio triggered circuit • Antenna provide suitable selectivity and efficiency • Reacts to electromagnetic wave and generates an input voltage

  42. Effectiveness of the circuit • Electric signal of 0.6V is sufficient to trigger an interrupt • Berkeley Mica2 mote • Wake up logic is implemented as an interrupt caused by a timer • Wake up logic can work with the radio-triggered interrupt • SPICE simulation • SPICE is a circuit level simulator developed by Berkeley • Output voltage, Vout > 0.6 • Simulation shows Vout is 0.62V

  43. Evaluation of the potential power saving • Tracking application system • Berkeley Mica2 mote • Total 1,000 nodes randomly deployed • 10 events/day, Each event lasts 2 minutes • Each network node uses two 1600mAh AA batteries • Average wake up current: 20 mA, sleep mode: 100uA • Comparison • Energy saving • 98% saved to always-on scheme • 70% saved to rotation-based scheme • Lifespan • 3.3 days (always-on), 49.5 days (rotation –based), 178 days (radio-triggered)

  44. Conclusions • Extracting energy from the radio signals • Hardware provides wake-up signals to the network node without using internal power supply • Adequate antenna : does not respond to normal data communication, not prematurely wake up • highly flexible and efficient • Zero stand-by power consumption and timely wake-up capability

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