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 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

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

Event

Source

Sink

Sink

Sensor Node Hardware

Components: Processor unit Memory Sensor unit(s) Transceiver Power Unit

Optional Components: Mobilizers Localization hardware Power generators

Limited processing capability

Limited storage space

Simple sensing devices

Limited range and rate

Limited power supplies

Example Sensor Nodes

JPL Sensor Webs UC Berkeley Dust MICA Motes

Rockwell WINS weC Rene

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 …

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

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

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

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

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

Factors Affecting Sensor Network Design

Fault Tolerance (Reliability) Scalability Production Costs Hardware Constraints Sensor Network Topology Operating Environment Transmission Media Power Consumption

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

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.”

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

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

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)

SPEED

Back pressure rerouting

SPEED

Void avoidance

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

SPEED- results

E2E delay under different congestion

SPEED results (2)

Deadline Miss ratio under different congestion

Routing in Sensor Networks

Different than regular network routing Power Mobility Congestion

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

Parametric Probabilistic Routing

Test of probability of retransmission with origin at (0,0) and destination at (1,0)

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.

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

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)

VMS

Simulations Miss ratio Vs. packet throughput

Overall miss ratio Miss ratio from far corner

RAP

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

Wireless Sensor Networks

Event Detection Services Radio-Triggered Wake-Up Capability

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

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

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

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 > 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 (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 event’s id instead of describing an event’s cirteria

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

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

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

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

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

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

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)

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