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
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
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 and Tasks

• 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

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) lt range(i)
• Forwarding candidate set
• FSi(destination)
• n e NSi L-Lnext gt0
• 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

• Pros
• Allows for dynamic network topology.
• Completely stateless.
• Reduced transmission load at sensors close to
  base station.
• Simple to impliment.
• Cons
• Wasted power.
• Flooding doesnt 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
  dont 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 packets 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 Veldistance(origin, dest)/deadline
• Dynamic Veldistance(current, dest)/(deadline-ela
  psed 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 Detection Services

• 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 gt 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

36
Event Detection Services (Cond.)

• 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 events id instead of
  describing an events cirteria

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 energy consumption, 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 gt 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