Wireless Network Sensors

8/7/2019 Wireless Network Sensors

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1. INTRODUCTION

Sensors integrated into structures, machinery, and the environment, coupled with the ef¿cient

delivery of sensed information, could provide tremendous bene¿ts to society. Potential bene¿ts

include: fewer catastrophic failures, conservation of natural resources, improved manufacturing

productivity, improved emergency response, and enhanced homeland security. However, barriers

to the widespread use of sensors in structures and machines remain. Bundles of lead wires and

¿ber optic ³tails´ are subject to breakage and connector failures. Long wire bundles represent a

signi¿cant installation and long term maintenance cost, limiting the number of sensors that may

be deployed, and therefore reducing the overall quality of the data reported. Wireless sensing

networks can eliminate these costs, easing installation and eliminating connectors. The ideal

wireless sensor is networked and scaleable, consumes very little power, is smart and software

programmable, capable of fast data acquisition, reliable and accurate over the long term, costs

little to purchase and install, and requires no real maintenance. Selecting the optimum sensorsand wireless communications link requires knowledge of the application and problem de¿nition.

Battery life, sensor update rates, and size are all major design considerations. Examples of low

data rate sensors include temperature, humidity, and peak strain captured passively. Examples of

high data rate sensors include strain, acceleration, and vibration. Recent advances have resulted

in the ability to integrate sensors, radio communications, and digital electronics into a single

integrated circuit (IC) package. This capability is enabling networks of very low cost sensors that

are able to communicate with each other using low power wireless data routing protocols.

Figure: The figure shows the complexity of wireless sensor networks



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A wireless sensor network (WSN) generally consists of a base station (or ³gateway´) that can

communicate with a number of wireless sensors via a radio link. Data is collected at the wireless

sensor node, compressed, and transmitted to the gateway directly or, if required, uses other

wireless sensor nodes to forward data to the gateway. The transmitted data is then presented to

the system by the gateway connection.



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2. INDIVIDUAL WIRELESS SENSOR NODE ARCHITECTURE

A functional block diagram of a versatile wireless sensing node is provided in the figure given

below. A Modular design approach provides a Àexible and versatile platform to address the

needs of a wide Variety of applications. For example, depending on the sensors to be deployed,

the signal conditioning block can be re-programmed or replaced. This allows for a wide variety

of different sensors to be used with the wireless sensing node. Similarly, the radio link may be

swapped out as required for a given application¶s wireless range requirement and the need for

bidirectional communications. The use of Àash memory allows the remote nodes to acquire data

on command from a base station, or by an event sensed by one or more inputs to the node.

Furthermore, the embedded ¿rmware can be upgraded through the wireless network in field.

Figure: Architecture of Wireless Sensor Node

Figure: Example of WSN- MICAZ MOTE



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Sensing Unit:

Sensing units are usually composed of two subunits: sensors and analog to digital converters

(ADCs). Sensor is a device which is used to translate physical phenomena to electrical signals.

Sensors can be classified as either analog or digital devices. There exists a variety of sensors that

measure environmental parameters such as temperature, light intensity, sound, magnetic fields,image, etc. The analog signals produced by the sensors based on the observed phenomenon are

converted to digital signals by the ADC and then fed into the processing unit.

Processing Unit:

The processing unit mainly provides intelligence to the sensor node. The processing unit

consists of a microprocessor, which is responsible for control of the sensors, execution of

communication protocols and signal processing algorithms on the gathered sensor data.

Commonly used microprocessors are Intel's Strong ARM microprocessor, Atmelµs AVR

microcontroller and Texas Instruments' MP430 microprocessor. For example, the processing unit

of a smart dust mote prototype is a 4 MHz Atmel AVR8535 micro-controller with 8 KB

instruction flash memory, 512 bytes R AM and 512 bytes EEPROM. TinyOS operating system is

used on this processor, which has 3500 bytes OS code space and 4500 bytes available code

space. The processing unit of AMPS wireless sensor node prototype has a 59±206 MHz SA-

1110 micro-processor. In general, four main processor states can be identified in a

microprocessor: off, sleep, idle and active. In sleep mode, the CPU and most internal peripherals

are turned on, and can only be activated by an external event (interrupt). In idle mode, the CPU

is still inactive, but other peripherals are active.

Transceiver Unit:

The radio enables wireless communication with neighbouring nodes and the outside world. It

consists of a short range radio which usually has single channel at low data rate and operates at

unlicensed bands of 868-870 MHz (Europe), 902-928 MHz (USA) or near 2.4 GHz (global ISM

band). For example, the TR1000 family from RF Monolithics works in the 800±900 MHz range

can dynamically change its transmission power up to 1.4 mW and transmit up to 115.2 Kbps.

The Chipcon s CC2420 is included in the MICAZ mote that was built to comply with the IEEE

802.15.4 standard [8] for low data rate and low cost wireless personal area networks. There are

several factors that affect the power consumption characteristics of a radio, which includes the

type of modulation scheme used, data rate, transmit power and the operational duty cycle. At

transmitted power levels of -10dBm and below, a majority of the transmit mode power is

dissipated in the circuitry and not radiated from the antenna. However, at high transmit levels



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(over 0dBm) the active current drown by the transmitter is high. The transmit power levels for

sensor node applications are roughly in the range of -10 to +3 dBm. Similar to microcontrollers,

transceivers can operate in Transmit, Receive, Idle and Sleep modes. An important observation

in the case of most radios is that, operating in Idle mode results in significantly high power

consumption, almost equal to the power consumed in the Receive mode. Thus, it is important to

completely shut down the radio rather than set it in the idle mode when it is not transmitting or

receiving due to the high power consumed. Another influencing factor is that, as the radio's

operating mode changes, the transient activity in the radio electronics causes a significant

amount of power dissipation. The sleep mode is a very important energy saving feature in

WSNs.

Battery:

The battery supplies power to the complete sensor node. It plays a vital role in determiningsensor node lifetime. The amount of power drawn from a battery should be carefully monitored.

Sensor nodes are generally small, light and cheap, the size of the battery is limited. AA batteries

normally store 2.2 to 2.5 Ah at 1.5 V. However, these numbers vary depending on the

technology utilized. For example, Zinc±air-based batteries have higher capacity in Joules/cm3

than lithium batteries. Alkaline batteries have the smallest capacity, normally around 1200

J/cm3. Furthermore, sensors must have a lifetime of months to years, since battery replacement

is not an option for networks with thousands of physically embedded nodes. This causes energy

consumption to be the most important factor in determining sensor node lifetime.

Figure: Wireless Sensor Node Functional Block Diagram



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A key feature of any wireless sensing node is to minimize the power consumed by the system.

Generally, the radio subsystem requires the largest amount of power. Therefore, it is

advantageous to send data over the radio network only when required. This sensor event-driven

data collection model requires an algorithm to be loaded into the node to determine when to send

data based on the sensed event. Additionally, it is important to minimize the power consumed by

the sensor itself. Therefore, the hardware should be designed to allow the microprocessor to

judiciously control power to the radio, sensor, and sensor signal conditioner.



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3. WIRELESS SENSOR NETWORKS ARCHITECTURE

There are a number of different topologies for radio communications networks. A brief

discussion of the network topologies that apply to wireless sensor networks are outlined below.

3.1. STAR NETWORK (SINGLE POINT-TO-MULTIPOINT)

Figure: Star Network Topology

A star network is a communications topology where a single base station can send and/or receive

a message to a number of remote nodes. The remote nodes can only send or receive a message

from the single base station, they are not permitted to send messages to each other. The

advantage of this type of network for wireless sensor networks is in its simplicity and the ability

to keep the remote node¶s power consumption to a minimum. It also allows for low latency

communications between the remote node and the base station. The disadvantage of such a

network is that the base station must be within radio transmission range of all the individual

nodes and is not as robust as other networks due to its dependency on a single node to manage

the network.

3.2. MESH NETWORK

A mesh network allows for any node in the network to transmit to any other node in the network

that is within its radio transmission range. This allows for what is known as multihop

communications; that is, if a node wants to send a message to another node that is out of radio



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communications range, it can use an intermediate node to forward the message to the desired

node.

Figure: Mesh Network Topology

This network topology has the advantage of redundancy and scalability. If an individual node

fails, a remote node still can communicate to any other node in its range, which in turn, can

forward the message to the desired location. In addition, the range of the network is not

necessarily limited by the range in between single nodes, it can simply be extended by adding

more nodes to the system. The disadvantage of this type of network is in power consumption for

the nodes that implement the multihop communications are generally higher than for the nodes

that don¶t have this capability, often limiting the battery life. Additionally, as the number of

communication hops to a destination increases, the time to deliver the message also increases,

especially if low power operation of the nodes is a requirement.

3.3. HYBRID STAR ± MESH NETWORK



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Figure: Hybrid Star-Mesh Topology

A hybrid between the star and mesh network provides for a robust and versatile communications

network, while maintaining the ability to keep the wireless sensor nodes power consumption to aminimum. In this network topology, the lowest power sensor nodes are not enabled with the

ability to forward messages. This allows for minimal power consumption to be maintained.

However, other nodes on the network are enabled with multihop capability, allowing them to

forward messages from the low power nodes to other nodes on the network. Generally, the nodes

with the multihop capability are higher power, and if possible, are often plugged into the

electrical mains line. This is the topology implemented by the up and coming mesh networking

standard known as zigbee.



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4. RADIO OPTIONS FOR THE PHYSICAL LAYER IN

WIRELESS SENSOR NETWORKS

The physical radio layer de¿nes the operating frequency, modulation scheme, and hardware

interface of the radio to the system. There are many low power proprietary low power radiointegrated circuits that are appropriate choices for the radio layer in wireless sensor networks,

including those from companies such as atmel, microchip, micrel, melexis, and chipcon. If

possible, it is advantageous to use a radio interface that is standards based. This allows for

interoperability among multiple companies networks. A discussion of existing radio standards

and how they may or may not apply to wireless sensor networks is given below.

4.1. IEEE802.11X

IEEE802.11 is a standard that is meant for local area networking for relatively high bandwidth

data transfer between computers or other devices. The data transfer rate ranges from as low as 1

mbps to 150 mbps. Typical transmission range is upto 820 feet with a standard antenna; the

range can be greatly improved with use of a directional high gain antenna. Both frequency

hopping and direct sequence spread spectrum modulation schemes are available. While the data

rates are certainly high enough for wireless sensor applications, the power requirements

generally preclude its use in wireless sensor applications.

4.2. BLUETOOTH (IEEE802.15.1 AND .2)

Bluetooth is a personal area network (pan) standard that is lower power than 802.11. It was originallyspecied to serve applications such as data transfer from personal computers to peripheral devices such

as cell phones or personal digital assistants. Bluetooth uses a star network topology that supports up to

seven remote nodes communicating with a single base station. While some companies have built

wireless sensors based on bluetooth, they have not been met with wide acceptance due to limitations of

the bluetooth protocol including:

1) Relatively high power for a short transmission range.

2) Nodes take a long time to synchronize to network when returning from sleep mode, which

increases average system power.

3) Low number of nodes per network (<=7 nodes per piconet).

4) Medium access controller (mac) layer is overly complex when compared to that required for

wireless sensor applications.



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4.3. IEEE 802.15.4

The 802.15.4 standard was speci¿cally designed for the requirements of wireless sensing

applications.the standard is very Àexible, as it speci¿es multiple data rates and multipletransmission frequencies. The power requirements are moderately low; however, the hardware is

designed to allow for the radio to be put to sleep, which reduces the power to a minimal amount.

Additionally, when the node wakes up from sleep mode, rapid synchronization to the network

can be achieved. This capability allows for very low average power supply current when the

radio can be periodically turned off. The standard supports the following characteristics:

1) Transmission frequencies, 868 mhz/902±928 mhz/2.48±2.5 ghz.

2) Data rates of 20 kbps (868 mhz band) 40 kbps (902 mhz band) and 250 kbps (2.4 ghz band).

3) Supports star and peer-to-peer (mesh) network connections.

4) Standard speci¿es optional use of aes-128 security for encryption of transmitted data.

5) Link quality indication, which is useful for multi-hop mesh networking algorithms.

6) Uses direct sequence spread spectrum (dsss) for robust data communications.

It is expected that of the three afore mentioned standards, the IEEE 802.15.4 will become most

widely accepted for wireless sensing applications. The 2.4-ghz band will be widely used, as it is

essentially a worldwide license-free band. The high data rates accommodated by the 2.4-ghz

speci¿cation will allow for lower system power due to the lower amount of radio transmissiontime to transfer data as compared to the lower frequency bands.

ZIGBEE

The ZIGBEE� alliance is an association of companies working together to enable reliable,

cost-effective, low-power, wirelessly networked monitoring and control products based on an

open global standard. The ZIGBEE alliance speci¿es the IEEE 802.15.4 as the physical and mac

layer and is seeking to standardize higher level applications such as lighting control and hvac

monitoring. It also serves as the compliance arm to IEEE802.15.4 much as the wi-fi allianceserved the IEEE802.11 speci¿cation. The ZIGBEE network speci¿cation, to be rati¿ed in 2004,

will support both star network and hybrid star mesh networks. As can been seen in figure below,

the ZIGBEE alliance encompasses the IEEE802.15.4 speci¿cation and expands on the network

speci¿cation and the application interface.



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Figure: ZIGBEE Stack

4.4. IEEE1451.5

While the IEEE802.15.4 standard speci¿es a communication architecture that is appropriate for

wireless sensor networks, it stops short of de¿ning speci¿cs about the sensor interface. The

IEEE1451.5 wireless sensor working group aims to build on the efforts of previous IEEE1451

smart sensor working groups to standardize the interface of sensors to a wireless network.

Currently, the IEEE802.15.4 physical layer has been chosen as the wireless networking

communications interface, and at the time of this writing the group is in the process of de¿ningthe sensor interface.



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5. POWER CONSIDERATION IN WIRELESS SENSOR

NETWORKS

The single most important consideration for a wireless sensor network is power consumption.

While the concept of wireless sensor networks looks practical and exciting on paper, if batteriesare going to have to be changed constantly, widespread adoption will not occur. Therefore, when

the sensor node is designed power consumption must be minimized. Figure below shows a chart

outlining the major contributors to power consumption in a typical 5000-ohm wireless strain

gage sensor node versus transmitted data update rate. Note that by far, the largest power

consumption is attributable to the radio link itself.

Figure : Power Consumption of a 5000-Ohm Strain Gauge Wireless Sensor Node

There are a number of strategies that can be used to reduce the average supply current of the

radio, including:

Reduce the amount of data transmitted through data compression and reduction.

Lower the transceiver duty cycle and frequency of data transmissions.

Reduce the frame overhead.



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Implement strict power management mechanisms (power-down and sleep modes).

Implement an event-driven transmission strategy; only transmit data when a sensor event

occurs.

Power reduction strategies for the sensor itself include:

Turn power on to sensor only when sampling.

Turn power on to signal conditioning only when sampling sensor.

Only sample sensor when an event occurs.

Lower sensor sample rate to the minimum required by the application.



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6. ROUTING CHALLENGES AND DESIGN ISSUES IN WSN

Despite plethora of applications of WSN, these networks have several restrictions, e.g. limited

energy supply, limited computing power, and limited bandwidth of the wireless links connecting

sensor nodes. One of the main design goals of WSN is to carry out data communication while

trying to prolong the lifetime of the network and prevent connectivity degradation by employing

aggressive energy management techniques. In order to design an efficient routing protocol,

several challenging factors should be addressed meticulously. The following factors are

discussed below:

Node deployment: Node deployment in WSN is application dependent and affects the

performance of the routing protocol. The deployment can be either deterministic or randomized.

In deterministic deployment, the sensors are manually placed and data is routed through pre-

determined paths; but in random node deployment, the sensor nodes are scattered randomly

creating an infrastructure in an ad hoc manner. Hence, random deployment raises several issues

as coverage, optimal clustering etc. which need to be addressed.

Energy consumption without losing accuracy: Sensor nodes can use up their limited supply of

energy performing computations and transmitting information in a wireless environment. As

such, energy conserving forms of communication and computation are essential. Sensor node

lifetime shows a strong dependence on the battery lifetime. In a multihop WSN, each node plays

a dual role as data sender and data router. The malfunctioning of some sensor nodes due to

power failure can cause significant topological changes and might require rerouting of packets

and reorganization of the network.

Node/Link Heterogeneity: Some applications of sensor networks might require a diversemixture of sensor nodes with different types and capabilities to be deployed. Data from different

sensors, can be generated at different rates, network can follow different data reporting models

and can be subjected to different quality of service constraints. Such a heterogeneous

environment makes routing more complex.

Fault Tolerance: Some sensor nodes may fail or be blocked due to lack of power, physical

damage, or environmental interference. The failure of sensor nodes should not affect the overall

task of the sensor network. If many nodes fail, MAC and routing protocols must accommodate

formation of new links and routes to the data collection base stations. This may require actively

adjusting transmit powers and signaling rates on the existing links to reduce energy consumption,or rerouting packets through regions of the network where more energy is available. Therefore,

multiple levels of redundancy may be needed in a fault-tolerant sensor network.



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Scalability: The number of sensor nodes deployed in the sensing area may be in the order of

hundreds or thousands, or more. Any routing scheme must be able to work with this huge

number of sensor nodes. In addition, sensor network routing protocols should be scalable enough

to respond to events in the environment. Until an event occurs, most of the sensors can remain in

the sleep state, with data from the few remaining sensors providing a coarse quality.

Network Dynamics: Most of the network architectures assume that sensor nodes are stationary.

How-ever, mobility of both BSµs and sensor nodes is sometimes necessary in many applications.

Routing messages from or to moving nodes is more challenging since route stability becomes an

important issue, besides energy, bandwidth etc. Moreover, the sensed phenomenon can be either

dynamic or static depending on the application, e.g., it is dynamic in a target detection/tracking

application, while it is static in forest monitoring for early fire prevention. Monitoring static

events allows the network to work in a reactive mode, simply generating traffic when reporting.

Dynamic events in most applications require periodic reporting and consequently generate

significant traffic to be routed to the BS.

Transmission Media: In a multi-hop sensor network, communicating nodes are linked by a

wireless medium. The traditional problems associated with a wireless channel (e.g., fading, high

error rate) may also affect the operation of the sensor network. As the transmission energy varies

directly with the square of distance therefore a multi-hop network is suitable for conserving

energy. But a multi-hop network raises several issues regarding topology management and media

access control. One approach of MAC design for sensor networks is to use CSMA-CA based

protocols of IEEE 802.15.4 that conserve more energy compared to contention based protocols

like CSMA (e.g. IEEE 802.11). So, Zigbee which is based upon IEEE 802.15.4 LWPAN

technology is introduced to meet the challenges.

Connectivity: The connectivity of WSN depends on the radio coverage. If there continuously

exists a multi-hop connection between any two nodes, the network is connected. The

connectivity is intermittent if WSN is partitioned occasionally, and sporadic if the nodes are only

occasionally in the communication range of other nodes.

Coverage: The coverage of a WSN node means either sensing coverage or communication

coverage. Typically with radio communications, the communication coverage is significantly

larger than sensing coverage. For applications, the sensing coverage defines how to reliably

guarantee that an event can be detected. The coverage of a network is either sparse, if only parts

of the area of interest are covered or dense when the area is almost completely covered. In caseof a redundant coverage, multiple sensor nodes are in the same area.

Data Aggregation: Sensor nodes usually generate significant redundant data. So, to reduce the

number of transmission, similar packets from multiple nodes can be aggregated. Data

aggregation is the combination of data from different sources according to a certain aggregation

function, e.g., duplicate suppression, minima, maxima and average. It is incorporated in routing



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protocols to reduce the amount of data coming from various sources and thus to achieve energy

efficiency. But it adds to the complexity and makes the incorporation of security techniques in

the protocol nearly impossible.

Data Reporting Model: Data sensing and reporting in WSNs is dependent on the application

and the time criticality of the data reporting. In wireless sensor networks data reporting can becontinuous, query-driven or event-driven. The data-delivery model affects the design of network

layer, e.g., continuous data reporting generates a huge amount of data therefore, the routing

protocol should be aware of data-aggregation.

Quality of Service: In some applications, data should be delivered within a certain period of

time from the moment it is sensed; otherwise the data will be useless. Therefore bounded latency

for data delivery is another condition for time-constrained applications. However, in many

applications, conservation of energy, which is directly related to network lifetime, is considered

relatively more important than the quality of data sent. As the energy gets depleted, the network

may be required to reduce the quality of the results in order to reduce the energy dissipation inthe nodes and hence lengthen the total network lifetime. Hence, energy-aware routing protocols

are required to capture this requirement.



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7. APPLICATIONS OF WIRELESS SENSOR NETWORKS

According to a new report from research firm on world the home market for wireless sensor

networks (WSN) will reach us$6 billion a year by 2012. The prediction includes both products

and services centred on in-home energy management and health monitoring. Meanwhile, on

world predicts the market for "home area network" (HAN) energy management solutions to

reach 20 million homes worldwide by 2013.

Wireless sensor networks may consist of many different types of sensors such as seismic,low

sampling rate magnetic, thermal, visual, infrared, acoustic and radar. They are able to monitor a

wide variety of ambient conditions that include temperature, humidity, vehicular movement,

lightning condition, pressure, soil makeup, noise levels, the presence or absence of certain kinds

of objects, mechanical stress levels on attached objects, and the current characteristics such as

speed, direction and size of an object. WSN applications can be classified into two categories as

shown in figure below:

y Monitoring

y Tracking

Figure: Overview of Wireless Sensor Network Applications



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Monitoring applications include indoor/outdoor environmental monitoring, health and wellness

monitoring, power monitoring, inventory location monitoring, factory and process automation,

and seismic and structural monitoring.

Tracking applications include tracking objects, animals, humans, and vehicles and categorize

the applications into military, environment, health, home and other commercial areas. It ispossible to expand this classification with more categories such as space exploration, chemical

processing and disaster relief.

Military applications: The rapid deployment, self-organization and fault tolerance

characteristics of sensor networks make them a very promising sensing technique for military

command, control, communications, computing, intelligence, surveillance, reconnaissance and

targeting (C4ISRT) systems. Military sensor networks could be used to detect and gain as much

information as possible about enemy movements, explosions, and other phenomena of interest,such as battlefield surveillance, nuclear, biological and chemical attack detection and

reconnaissance. As an example, PinPtr is an experimental counter-sniper system developed to

detect and locate shooters. The system utilizes a dense deployment of sensors to detect and

measure the time of arrival of muzzle blasts and shock waves from a shot. Sensors route their

measurements to a base station (e.g., a laptop or PDA) to compute the shooterµs location. Sensors

in the PinPtr system are second-generation Mica2 motes connected to a multi-purpose acoustic

sensor board. Each multi-purpose acoustic sensor board is designed with three acoustic channels

and a Xilinx Spartan II FPGA. Mica2 motes run on a TinyOS operating system platform that

handles task scheduling, radio communication, time, I/O processing, etc. Middleware services

developed on TinyOS that are exploited in this application include time synchronization,message routing with data aggregation, and localization.

Environmental applications: Wireless Sensor Networks have been deployed for

environmental monitoring, which involves tracking the movements of small animals and

monitoring environmental conditions that affect crops and livestock. In these applications, WSNs

collect readings over time across a space large enough to exhibit significant internal variation.

Other applications of WSNs are chemical and biological detection, precision agriculture,

biological, forest fire detection, volcanic monitoring, meteorological or geophysical research,flood detection and pollution study.

Macroscope of redwood is a case study of a WSN that monitors and records the redwood trees

in Sonoma, California. Each sensor node measures air temperature, relative humidity, and

photosynthetically active solar radiation. Sensor nodes are placed at different heights of the tree.



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Plant biologists track changes of spatial gradients in the microclimate around a redwood tree and

validate their biological theories.

Underwater monitoring study in developed a platform for underwater sensor networks to be

used for long term monitoring of coral reefs and fisheries. The sensor network consists of static

and mobile underwater sensor nodes. The nodes communicate via point-to-point links using highspeed optical communications. Nodes broadcast using an acoustic protocol integrated in the

TinyOS protocol stack. They have a variety of sensing devices, including temperature and

pressure sensing devices and cameras. Mobile nodes can locate and move above the static nodes

to collect data and perform network maintenance functions for deployment, re-location, and

recovery.

Similarly, ZebraNet system is a mobile wireless sensor network used to track animal migrations.

Healthcare applications: WSN based technologies such as Ambient Assisted Living and

Body Sensor Networks provide dozens of solutions to healthcare's biggest challenges such as an

aging population and rising healthcare costs. Body sensor networks can be used to monitor

physiological data of patients The Body sensor networks can provide interfaces for disabled,

integrated patient monitoring. It can monitor and detect elderly people's behaviour, e.g., when a

patient has fallen. These small sensor nodes allow patients a greater freedom of movement and

allow doctors to identify pre-defined symptoms earlier on. The small installed sensor can also

enable tracking and monitoring of doctors and patients inside a hospital. Each patient has small

and lightweight sensor nodes attached to them, which may be detecting the heart rate and blood

pressure. Doctors may also carry a sensor node, which allows other doctors to locate them withinthe hospital.

AT&T recently introduced a telehealth monitoring service that uses ZigBee and WiFi. Mote

Track is the patient tracking system developed by Harvard University, which tracks the location

of individual patientµs devices indoors and outdoors, using radio signal information from the

sensor attached to the patients.

Heart@Home is a wireless blood pressure monitor and tracking system. Heart@Home uses a

SHIMMER mote located inside a wrist cuff which is connected to a pressure sensor. A userµs

blood pressure and heart rate is computed using the oscillometric method. The SHIMMER mote

records the reading and sends it to the T-mote connected to the userµs computer. A software

application processes the data and provides a graph of the userµs blood pressure and heart rate

over time.



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Home applications: With the advance of technology, the tiny sensor nodes can be embedded

into furniture and appliances, such as vacuum cleaners, microwave ovens and refrigerators. They

are able to communicate with each other and the room server to learn about the services they

offer, e.g., printing, scanning and faxing. These room servers and sensor nodes can be integratedwith existing embedded devices to become self-organizing, self-regulated and adaptive systems

to form a smart environment. Automated homes with Personal Area Networks such as ZigBee

can provide the ability to monitor and control mechanisms like light switches and lights, HVAC

(heating, ventilating, air conditioning) controls and thermostats; computers, TVs and other

electronic devices, smoke detectors and other safety equipment; alarm panels, motion sensors,

and other security devices; and electricity, water and gas meters.

Traff ic control: Traffic conditions can be easily monitored and controlled at peak times byWSNs. Temporary situations such as roadworks and accidents can be monitored in situ. Further,

the integration of monitoring and management operations, such as signpost control, is facilitated

by a common WSN infrastructure.



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8. CONCLUSION

Wireless sensor networks are enabling applications that previously were not practical. As new

standards based networks are released and low power systems are continually developed, we will

start to see the widespread deployment of wireless sensor networks.



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9. REFERENCES

1. LEWIS, F.L., ³WIRELESS SENSOR NETWORKS,´ SMART ENVIRONMENTS:

TECHNOLOGIES, PROTOCOLS, AND APPLICATIONS, ED. D.J. COOK AND S.K. DAS,

JOHN WILEY, NEW YORK, 2004.

2. ARMS, S.W., NEWHARD, A.T., GALBREATH, J.H., TOWNSEND, C.P., ³REMOTELY

REPROGR AMMABLE WIRELESS SENSOR NETWORKS FOR STRUCTUR AL HEALTH

MONITORING APPLICATIONS,´ ICCES INTERNATIONAL CONFERENCE ON

COMPUTATIONAL AND EXPERIMENTAL ENGINEERING AND SCIENCES, MEDEIR A,

PORTUGAL, JULY 2004.

3. JENNIFER YICK, BISWANATH MUKHERJEE, DIPAK GHOSAL, "WIRELESS SENSOR

NETWORK SURVEY," COMPUTER NETWORKS,ELSEVIER, VOL. 52, PP. 2292-2330, 2008.

4. G. TOLLE, D. CULLER, W. HONG, ET AL., "A MACROSCOPE IN THE REDWOODS,"IN PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON EMBEDDED

NETWORKED SENSOR SYSTEMS, SAN DIEGO, CA, 2005, PP. 51-63

5. JON S. WILSON, SENSOR TECHNOLOGY HANDBOOK, VOLUME 1, NEWNES, 2005,

CHAPTER 22.

Wireless Network Sensors

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