Real-Time Communication in Wireless Sensor Networks

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