introduction to wireless sensor networks biljana stojkoska biljana.stojkoska@finki.ukim.mk...

Introduction to Wireless Sensor Networks

Biljana StojkoskaBiljana.Stojkoska@finki.ukim.mk

University “Ss. Cyril and Methodius”,Skopje

Wireless Sensor NetworksA wireless sensor network (WSN) is a wireless network

consisting of spatially distributed autonomous devices

using sensors to cooperatively monitor physical or

environmental conditions, such as temperature, sound,

vibration, pressure, motion or pollutants, at different

locations.

Network Model for WSN

A base station links the sensor network to another

network (like a gateway) to disseminate the data

sensed for further processing. Base stations have

enhanced capabilities over simple sensor nodes since

they must do complex data processing.

continue …

How the story startedThe development of wireless sensor networks was originally motivated by military applications such as battlefield surveillance. However, wireless sensor networks are now used in many civilian application areas, including environment and habitat monitoring, healthcare applications, home automation, and traffic control.

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Military

Remote deployment of sensors for tactical monitoring of enemy troop movements.

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Sensors• The overall architecture of a sensor

node consists of:– The sensor node processing

subsystem running on sensor node main CPU

– The sensor subsystem and – The communication subsystem

• The processor and radio board includes:– TI MSP430 microcontroller with

10kB RAM– 16-bit RISC with 48K Program

Flash– IEEE 802.15.4 compliant radio at

250 Mbps– 1MB external data flash– Runs TinyOS 1.1.10 or higher– Two AA batteries or USB– 1.8 mA (active); 5.1uA (sleep)

Crossbow MoteTPR2400CA-TelosB

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What is a mote?

• mote noun [C] LITERARYsomething, especially a bit of dust, that is so small it is almost impossible to see---Cambridge Advanced Learner’s Dictionaryhttp://dictionary.cambridge.org/define.asp?key=52014&dict=CALD

Evolution of Sensor Hardware Platform (Berkeley), [Alec Woo 2004]

Imote2 06 withenalab camera

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Mica2 Wireless Sensors

New MicaZ follows IEEE 802.15.4 Zigbee standard with direct sequence sprad spectrum radio and 256kbps data rate

MTS310 Sensor Boards• Acceleration, • Magnetic, • Light, • Temperature, • Acoustic,• Sounder

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Motes and TinyOS• Motes (Mica2, Mica2dot, MicaZ)

– Worked well with existing curriculum • ATMega128L microcontroller• 128KB program flash; 512KB measurement Flash; 4KB EEPROM

– Standard platform with built-in radio chicon1000 (433MHz, 916MHz, 2.4GHz) 38.4kb; 256kbps for MicaZ IEEE 802.15.4. (1000ft, 500ft; 90/300ft) range

– AA battery– Existing TinyOS code base– Convenient form factor for adding sensors

• TinyOS– Event-based style helped students understand:

• Time constraints• Code structure (need to write short non-blocking routines)

– Existing modular code base saved time• Made a more complex project possible • Provided a degree of abstraction

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Networked vs. individual sensors• Extended range of sensing:

– Cover a wider area of operation• Redundancy:

– Multiple nodes close to each other increase fault tolerance

• Improved accuracy:– Sensor nodes collaborate and combine their data to

increase the accuracy of sensed data• Extended functionality:

– Sensor nodes can not only perform sensing functionality, but also provide forwarding service.

Sensor Network

Interface electronics, radio

and microcontroller

Soil moisture probe Mote

Antenna

Gateway

Server

Internet

Communications barrier

Sensor field

Sensor Network

Gateway

Server

Internet

Sensor fieldWatershed

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Network Architectures

Layer 1

Layer 2

Layer 3

Layered Architecture

BaseStation

Clustered Architecture

BaseStation

Larger Nodes denote Cluster Heads

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Wireless Sensor Network

Stargate

• 802.11a/b

• Ethernet

• Mica2

• PCMCIA

• Compactflash

• USB

• JTAG

• RS232

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Why Wireless Sensors Now?

• Moore’s Law is making sufficient CPU performance available with low power requirements in a small size.

• Research in Materials Science has resulted in novel sensing materials for many Chemical, Biological, and Physical sensing tasks.

• Transceivers for wireless devices are becoming smaller, less expensive, and less power hungry.

• Power source improvements in batteries, as well as passive power sources such as solar or vibration energy, are expanding application options.

Computer Revolution

0.5 oz, 2.25 x 1.25 x 0.25 inch0.5 oz, 2.25 x 1.25 x 0.25 inch25 lb, 19.5 x 5.5 x 16 inch25 lb, 19.5 x 5.5 x 16 inch

~14 mW~14 mW~ 64 W~ 64 W

~ $35~ $35~ $6K (today)~ $6K (today)

512 KB Flash512 KB Flash160 KB Floppies160 KB Floppies

128 KB RAM128 KB RAM16-256 KB RAM16-256 KB RAM

4 MHz4 MHz4.77 MHz4.77 MHz

MICAZ Mote (2005)MICAZ Mote (2005)Original IBM PC (1981)Original IBM PC (1981)

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Advances in Wireless Sensor Nodes

Consider Multiple Generations of Berkeley Motes

Model Rene Mica Mica-2 Mica-Z

Date 1999 2002 2003 2004

CPU 4 MHz 4 MHz 4 MHz 4 MHz

Flash Memory

8 KB 128 KB 128 KB 128 KB

RAM 512 B 4 KB 4 KB 4 KB

Radio 10 Kbps 40 Kbps 76 Kbps 250 Kbps

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Characteristics and challenges • Deeply distributed architecture: localized coordination to reach

entire system goals, no infrastructure with no central control support

• Autonomous operation: self-organization, self-configuration, adaptation, exception-free– TCP/IP is open, widely implemented, supports multiple physical

network, relatively efficient and light weight, but requires manual intervention to configure and to use.

• Energy conservation: physical, MAC, link, route, application• Scalability: scale with node density, number and kinds of networks• Data centric network: address free route, named data,

reinforcement-based adaptation, in-network data aggregation

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Event Detection

Localization &Time Synchronization Calibration

Programming Model

In Network Processing

Needed: Reusable, Modular, Flexible, Well-characterized Services/Tools• Routing and Reliable transport • Time synchronization, Localization, Calibration, Energy Harvesting• In Network Storage, Processing (compression, triggering), Tasking• Programming abstractions, tools• Development, simulation, testing, debugging

Routing and Transport

Common system services

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Key Properties• Networks meaningfully

distributed over physical space• Large numbers of nodes• Long duration• Irregular, varying connectivity• Variations in density• Loss & interference• Constrained resources & Energy• Connected to deeper

infrastructure

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Reguirements

• Wireless sensors need to operate in conditions that are not encountered by typical computing devices:– Rain, sleet, snow, hail, etc.– Wide temperature variations

• May require separating sensor from electronics

– High humidity– Saline or other corrosive substances– High wind speeds

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Sensor deployment

• Sensor deployment is a critical issue because it affects the cost and detection capability of a wireless sensor network

• A good sensor deployment should consider both coverage and connectivity

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Coverage Connectivity

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Coverage Problems

• Coverage: is a measure of the Quality of service of a sensor network

• How well can the network observe (or cover) a given event?– For example, intruder detection; animal or fire

detection

• Coverage depends upon:– Range and sensitivity of sensing nodes– Location and density of sensing nodes in given region

Routing

Multihop routing is common due to limited transmission range

Phenomenonbeing sensed

Sink

• Limited node mobility• Power aware• Irregular topology• MAC aware• Limited buffer space

Some routing issues in WSNs

Data aggregationtakes place here

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Why not port Ad Hoc Protocols?• Ad Hoc networks require significant amount of

routing data storage and computation– Sensor nodes are limited in memory and CPU

• Topology changes due to node mobility are infrequent as in most applications sensor nodes are stationary– Topology changes when nodes die in the network due

to energy dissipation• Scalability with several hundred to a few

thousand nodes not well established• GOAL: Simple, scalable, energy-efficient protocols

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WSN vs. MANET• Wireless sensor networks may be considered a subset

of Mobile Ad-hoc NETworks (MANET).• WSN nodes have less power, computation and

communication compared to MANET nodes.• MANETs have high degree of mobility, while sensor

networks are mostly stationary.– Freq. node failures in WSN -> topology changes

• Routing protocols tend to be complex in MANET, but need to be simple in sensor networks.

• Low-power operation is even more critical in WSN.• MANET is address centric, WSN is data centric.

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Wireless Sensor Network(WSN) vs. Mobile Ad Hoc Network (MANET)

WSN MANET

Similarity Wireless Multi-hop networking

Security Symmetric Key Cryptography Public Key Cryptography

Routing Support specialized traffic pattern. Cannot afford to have too many node states and packet overhead

Support any node pairsSome source routing and distance vector protocol incur heavy control traffic

Resource Tighter resources (power, processor speed, bandwidth)

Not as tight.

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A Rosy Future for Wireless Sensors?

• Is the effort on wireless sensor protocols a waste of time??

• Can we just wait 10-15 years until we have sensors that are very powerful??

NO!! Will still face:– Very limited storage– Modest power supplies

Data processing in WSN

• Energy Management Issues

Taxonomy of data driven energy saving

Data processing in WSN

Data Aggregation

• In-network fusion of data from different sensors to eliminate redundant transmissions to the base station.

• Aggregation takes place if the data arriving the common node have same attributes of the phenomenon being sensed

Phenomenon being sensed

• This significantly reduces the amount of data traffic as well as the distances over which the data needs to be transmitted

• In the process of in-network aggregation, the value generated at each sensor in each round must influence the value that reaches the base-station in that round !

• Problem: generating aggregation tree

Data processing in WSN

Dual Prediction Scheme• each node runs a filter (or a model) that

estimates next sensor reading• sink node runs exactly the same models for

each sensor in the network and makes the same predictions

• sensor makes measurements of the sensed quantity

• Used filters (models)– Kalman filter– Least Mean Square adaptive filter– AR, MA, ARMA, ARIMA

Dual Prediction Scheme• Simulation results for two variants of LMS algorithm for

temperature readings from Intel network laboratory

Dual Prediction Scheme• Simulation results for two variants of LMS algorithm for

humidity readings from Intel network laboratory

Dual Prediction Scheme• Simulation results for two variants of LMS algorithm for

temperature readings from Intel network laboratory considering cluster-based approach

LocalizationLocation necessary in order for sensed data to be meaningful e.g. forest fire detection

Location information is taken for granted in many network designs, e.g. geographic routing

Equipping each node with GPS is not always feasible due to power constraints and other limitations inherent to sensor networks

Localize using inter-node distances

Nodes can often measure their distances to nearby nodes, e.g. ultra-wideband ranging

The network localization problem is to determine the positions of all the nodes

Anchors are nodes whose positions are known

The distances between some nodes are known

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The network is localizable if there exists exactly one position in the plane corresponding to each non-anchor node so that all known inter-node distances are satisfied

A network in the plane

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A node is localizable if its position is uniquely determined by the known inter-node distances and anchor positions

Anchor positions from GPS or manual configuration.

Localization

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WSN Applications• Wide area monitoring tools supporting Scientific Research

– Wild life Habitat monitoring projects Great Duck Island (UCB), James Reserve (UCLA), ZebraNet (Princeton.

– Building/Infrastructure structure study (Earthquake impact)• Military Applications

– Shooter Localization– Perimeter Defense (Oil pipeline protection)– Insurgent Activity Monitoring (MicroRadar)

• Commercial Applications– Light/temperature control– Precision agriculture (optimize watering schedule)– Asset management (tracking freight movement/storage)

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Traffic Management & Monitoring

Future cars could use wireless sensors to:– Handle Accidents– Handle Thefts

Sensors embedded in the roads to:

–Monitor traffic flows–Provide real-time route updates

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Smart Home / Smart Office

• Sensors controlling appliances and electrical devices in the house.

• Better lighting and heating in office buildings.

• The Pentagon building has used sensors extensively.

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Industrial & Commercial

• Numerous industrial and commercial applications:– Agricultural Crop Conditions– Inventory Tracking– In-Process Parts Tracking– Automated Problem Reporting– RFID – Theft Deterrent and Customer Tracing– Plant Equipment Maintenance Monitoring

Usage of Sensor NetworksHealthcare:

Sensors can be used in biomedical applications to

improve the quality of the provided care. Sensors are

implanted in the human body to monitor medical

problems like cancer and help patients maintain their

health.

Mercury: A Wearable Sensor Network Platform for High-Fidelity Motion Analysis

SHIMMER wearable mote

• developed by the Digital Health Group at Intel• TI MSP430 processor, • CC2420 IEEE 802.15.4 radio, • triaxial accelerometer, • rechargeable Li-polymer battery• MicroSD slot supporting up to 2 GBytes of

Flash memory

Mercury network• Harvard University and Spaulding Hospital• long-term motion analysis studies in a home

setting• Each sensor samples multiple channels of

accelerometer, gyroscope, and/or physiological data and stores raw signals to local flash

• Sensors also perform feature extraction on the raw signals, which may involve expensive on-board computation

• Nodes also save energy by dropping down to a low-power state when the sensor is not moving.

Mercury network• Parkinson's disease

– disease affects about 3% of the population over the age of 65 years– number of hours of ON (i.e., when medications effectively attenuate tremor)

and OFF time– 9 nodes on the body (2 on each arm and leg, one on the back)– recording triaxial accelerometer and gyroscope data device for measuring or

maintaining orientation at 100 Hz– SVM for data classification (transmitting features estimated from the raw

data, instead of transmitting the raw data itself)• Epilepsy

– starting up project– 4 sensors on arms and only 2 on legs– Accel, gyro, and EMG data is collected– Electromyography (EMG) is a technique for evaluating and recording the

electrical activity produced by skeletal muscles– EMG is be localized (say, one arm) and sampled at 500 Hz

Wireless Body Area Network

E. Jovanov, et al., “A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation,” Journal of NeuroEngineering and Rehabilitation, 2005, 2:6

Wireless Body Area NetworkThe personal server can be implemented on an Internet-enabled PDA or a 3G mobile phone, or a regular laptop of desktop computer. It can communicate with remote upper-level services in a hierarchical type architecture. Its tasks include:

• Initialization, configuration, and synchronization of WBAN nodes

• Control and monitor operation of WBAN nodes

• Collection of sensor readings from physiological sensors

• Processing and integration of data from the sensors

• Secure communication with remote healthcare provider

Framework for Medical Image AnalysisThe remote medical image repositories communicate through different types of network connections with the central computing site that coordinates the distributed analysis.

V, Megalooikonomou, et al., “Medical Data Fusion for Telemedicine,” IEEE Engineering in Medicine and Biology Magazine, pp. 36-42, September/October 2007

Usage of Sensor Networks Building Monitoring:

Sensors can also be used in large buildings or factories

monitoring climate changes. Thermostats and

temperature sensor nodes are deployed all over the

building’s area. In addition, sensors could be used to

monitor vibration that could damage the structure of a

building.

Illinois Structural Health Monitoring Project (ISHMP)

• Manual inspection of bridges (measure acceleration),– costs millions of dollars – relatively unreliable – can only be carried out sporadically

• But ….Why WSN, why not traditional sensors?• Tsing Ma Bridge and Kap Shui Mun Bridge in Hong

Kong, which uses 326 channels of sensors in total and produces about 65 MBytes of data every hour, is an attempt toward in-depth monitoring.

• Limited deployment• Cost

– the total system cost of the monitoring system (including installation) on the Bill Emerson Memorial Bridge in Cape Girardeau, Missouri, USA is approximately $1.3M for 86 accelerometers, which makes the average installed cost per sensor a little over $15,000.

Start from scratchdistributed implementation of vibration-based

damage detection algorithms

• Imote2 hardware (homogenous network)• TinyOS middleware services• hierarchical network topology (base station, manager

node, cluster head nodes, and leaf nodes), but their roles can be reassigned in the case when some nodes stop working due to battery exhaustion, OS failure, etc

• newly developed damage detection algorithm (most of the SHM methods mentioned above employ modal analysis to obtain modal parameters such as natural frequencies, damping ratios, and mode shapes)

• the most powerful and promising smart wireless sensor platform

• built with 32 bit XScale processor• RAM of 32 MB • flash memory of 32 MB, • Integrated radio with a built-in 2.4 GHz antenna

cost as little as $200/node and prices are falling rapidly while functionality is improving

Usage of Sensor Networks Environmental Observation:

Sensor networks can be used to monitor environmental

changes. An example could be water pollution detection in a

lake that is located near a factory that uses chemical substances.

Sensor nodes could be randomly deployed in unknown and

hostile areas and relay the exact origin of a pollutant. Other

examples include forest fire detection, air pollution and rainfall

observation in agriculture.

Camalie VineyardsCase Study in Crossbow Mote

Deployment.

Background

• Camalie Vineyards is a 4.4 acre hillside vineyard on the western slopes of Napa Valley: Mt Veeder

• Highly varied soil, slope, sun exposure, and water flow, At least 12 distinct areas.

• World class Cabernet Sauvignon grapes; $5K/ton– French clones 337, 338, 191 on rootstocks selected to

compensate for varying vigor across vineyard areas. • Water is scarce, most wells and reservoirs are dry

by the end of the growing season. 60 gal/vine/yr. 300K gal.

Water in the Vineyard

Initial Installation 2005 growing season

• Monitor water getting to the vines and the irrigation system getting it there.

• 1 mote with 3 sensors in each of 4 irrigation blocks

• 2 pressure sensors at irrigation manifold, pre filter and post filter. Monitor tank level and filter status. .

2003-2004 used weather station with 3 soil moisture sensors at one location

Vineyard Installation• At each Mote location:

• 2 soil moisture sensors • 12” and 24” depth• 1 soil temp sensor to calibrate soil moisture sensors

Irrigation Manifold

Vineyard Mote Prototype

• 433MHz Mica2dot• Solar power supply• Up to 6 resistive sensor inputs

Power Supply

• 2 month max battery life now with 10 minute sampling interval

• Decided to use solar power, always there when doing irrigation. Solar cell $10 in small quantities though and need a $.50 regulator.

Network Maps

Irrigation Block Map

13 nodes late 05, Now 18 nodes

Soil Moisture Data

• Red = 12” depth soil moisture• Green= 24” depth soil moisture• Note delay deeper• More frequent, shorter watering keeps water shallow

Irrigation Pressure Sensors

Temp Data

Software for WSN

• Multi-tier architecture• Each layer must be carefully designed• Programming sensor nodes

– TinyOS– NesC– TinyDB

• Xmesh• XServe• Tossim (Power Tossim)

event result_t Timer.fired(){call Leds.redToggle();call MyI2C.sendStart();return SUCCESS;}

event result_t MyI2C.sendEndDone(){call Leds.yellowToggle();return SUCCESS;

}

Software for WSN

• Mote-VIEW is a full-featured data analyzing application developed by Crossbow– visualize the topology, – produce graphs from selected motes, – check status of the nodes– export sensor readings to a database

Software for WSN

• jWebDust• Java based application • consists of software components that use

interfaces to communicate with each other • new services can be developed and integrated

with the modification of existing services• University of Patras, Patras, Greece• A framework for service provisioning in virtual

sensor networks (april 2012)

Web Interface for Habitat Monitoring using Wireless Sensor Network

FUNCTIONALITIES OF THE WEB INTERFACE

• Sensor readings• Topology control• Statistics• Dynamic graphs

FUNCTIONALITIES OF THE WEB INTERFACE

• Smart phone controlling the WSN• Interacting with the WSN

What will Wireless Sensor Networks Look Like in the Near Future?

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Large-Scale Deployments

• Sensor networks will grow in size because of:– Lower cost– Better protocols– Advantages of

dense networks

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Heterogeneous Sensors

• Homogeneous network of sensors has been the typical assumption, but not the future!!– Combining sensors with different functions– Hierarchy of sensors – a few expensive

powerful sensors with more cheap sensors• Useful for special communication nodes

– A few sensor nodes with expensive sensors, such as GPS-equipped sensors

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Mobile Sensors

Sensors with MicromachinesLow-Power Motors that Support Mobility

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General Purpose Sensors Single-purpose network is the typical assumption, but not the future!!

– Sensors for evolving applications– Sensors that can adapt to changing objectives– More memory and CPU will allow more

complex applications – Flexibility increases marketability

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Overlapping Coverage Areas• Sensors will be deployed for specific

applications, but– These deployments will overlap physically– Sensors will have different properties– Users will want to combine these different

sensors for new applications:• Temperature sensors for HVAC control• Location tracking of employees• Combine these for fire rescue operations

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Mixture of Wired and Wireless• Wireless sensors will become a seamless

part of larger networks!– Combining wired sensors with wireless sensors

• Wired sensors can have more power• Wired sensors can run TCP/IP

– Accessing wireless sensors through the Internet• Need a gateway to translate requests• Uploading/downloading information remotely• Modifying wireless sensor tasks remotely

– Increased direct user interaction

The Traditional WSN Myth The wireless sensor network

paradigm was a myth from the late 1990s

Usual “assumptions”:

– 1000s of homogeneous “sensing only” nodes

– Mesh routing all nodes

This market is marginal Sink

• Luckily, the ideas and algorithms that were developed can be applied to ubiquitous wireless applications

• Huge research and market potential

Sensors EverywhereSome current issues:There are already many deployed sensors

– Mobile phones

– Surveillance cameras

– GPS receivers

– Motion and light sensors

How to organize them in networksHow to retrieve, store, and index data from sensorsChange the attention from “network” to “data”Combine data processing with data delivery

Convergent WSNsHow do we integrate the Internet and

Intranets with sensor networks?– Where is the intelligence?– Heterogeneous protocol interfaces?– Scalability and security are important issues– Mobility support

Gateways play an important role, as they communicate with TCP/IP and sensor networks– A proxy application often used to translate and

shield one level from another

Convergent WSNsLooking at the sales of uCs, there is a

potential for billions of networked devices – much larger than the Internet itself

Huge impact also on the core Internet

– IPv6 will be key to supporting convergent sensor networks

– Intelligent data processing to reduce the network traffic

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Economic Factors

• New technologies replace existing technologies or fill new niches when there are economic advantages.– Wireless sensors will replace wired sensors

• No wiring – lower costs• More flexible deployments

– Wireless sensors will provide new services• Provide cost advantages or lower overhead• Improve product quality or product features

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Summary and Conclusions

• Wireless sensor networks have a bright future– Many applications have been proposed– Potential to revolutionize human-computer

interactions– Availability of sensors will lead to new and exciting

applications

• A lot of research remains to be done– Wireless sensors will not evolve into traditional

computers– Many obstacles to overcome– Allow realism to guide research efforts