Low Power Wireless Sensor Netwok

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LOW POWER WIRELESS SENSOR

CHAPTER 1

INTRODUCTION

1. INTRODUCTION TO WIRELESS SENSOR

Wireless distributed microsensor systems will enable fault tolerant monitoring and control of a

variety of appli-cations. Due to the large number of microsensor nodes that may be deployed and the

long required system lifetimes, replacing the battery is not an option. Sensor systems must utilize the

minimal possible energy while operating over a wide range of operating scenarios. This paper presents

an overview of the key technologies required for low-energy distributed microsensors.These include

power aware computation communication component technology, low-energy signaling and

networking, system parti-tioning considering computation and communication trade-offs, and a poweraware software infrastructure.

The design of micropower wireless sensor systems has gained increasing importance for a

variety of civil and military applications. With recent advances in MEMS technology and its associated

interfaces, signal processing, and RF circuitry, the focus has shifted away from limited macrosensors

communicating with base stations to creating wireless networks of communicating microsensors that

aggregate complex data to provide rich, multi-dimensional pictures of the environment. While

individual microsensor nodes are not as accurate as their macrosensor counterparts, the networking of a

large number of nodes enables high quality sensing networks with the additional advantages of easy

deployment and faulttolerance. These characteristics that make microsensorsideal for deployment in

otherwise inaccessible environmentswhere maintenance would be inconvenient or impossible.

The potential for collaborative, robust networks of microsensors has attracted a great deal of

research attention. The WINS and PicoRadio and projects, for instance, aim to integrate sensing,

processing and radio communication onto a microsensor node. Current prototypes are custom circuit

boards with mostly commercial, off-the-shelf components. The Smart Dust project seeks a minimum-

size solution to the distributed sensing problem, choosing optical communication on coin-sized

motes. The prospect of thousands of communicating nodes has sparked research into network

protocols for information flow among microsensors, such as directed diffusion [7].The unique operating

environment and performance requirements of distributed microsensor networks require

Dept. of ECE TOCE MAY 2012 Page 1


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fundamentally new approaches to system design. As an example,consider the expected performance

versus longevity of the microsensor node, compared with current battery-powered portable devices.

Fig1.1-wireless sensor network

The node, complete with sensors, DSP, and radio, is capable of a tremendous diversity of

functionality.Throughout its lifetime, a node may be called upon to be a data gatherer, a signal

processor, and a relay station. Its lifetime, however, must be on the order of months to years, since

battery replacement for thousands of nodes is not an option. In contrast, much less capable devices such

as cellular telephones are only expected to run for days on a single battery charge. High diversity also

exists within the environment and user demands upon the sensor network. Ambient noise in the

environment, the rate of event arrival, and the users quality requirements of the data may vary

considerably over time. A long node lifetime under diverse operating conditions demands power-aware

system design. In a power-aware design, the nodes energy consumption displays a graceful scalability

in energy consumption at all levels of the system hierarchy, including the signal processing algorithms,



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operating system, network protocols, and even the integrated circuits themselves. Computation and

Fig1.2-wireless sensor network with gateway sensor node

communication are partitioned and balanced for minimum energy consumption. Software that understands

the energy-quality tradeoff collaborates with hardware that scales its own energy consumption

accordingly.Using the MIT AMPS project as an example, this paper surveys techniques for system-level

power-awareness.



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CHAPTER 2

SENSOR

2.1 SENSOR NODE

A sensor node, also known as a 'mote' (chiefly in North America), is a node in a wireless

sensor network that is capable of performing some processing, gathering sensory information andcommunication with other connected nodes in the network

. History of development of sensor nodes dates back to 1998 in Smart dustproject. One of the

objectives of this project is to create autonomous sensing and communication in a cubic millimeter. Though

this project ended early on, it has given birth to many more research projects. They include major research

centre in Berkeley NEST and CENS. The researchers involved in these projects coined the term 'mote' to

refer to a sensor node. Sensor nodes have not increased in power as one would expect from Moore's Law.

They typically have very small compute and storage capabilities compared to desktop computers. This can

be attributed to the low volume of the current market for them and their use of very low power

microcontrollers.

http://en.wikipedia.org/wiki/Motehttp://en.wikipedia.org/wiki/North_Americahttp://en.wikipedia.org/wiki/Wireless_sensor_networkhttp://en.wikipedia.org/wiki/Wireless_sensor_networkhttp://en.wikipedia.org/wiki/Smartdusthttp://en.wikipedia.org/wiki/Smartdusthttp://en.wikipedia.org/wiki/Microcontrollershttp://en.wikipedia.org/wiki/Microcontrollershttp://en.wikipedia.org/wiki/Motehttp://en.wikipedia.org/wiki/North_Americahttp://en.wikipedia.org/wiki/Wireless_sensor_networkhttp://en.wikipedia.org/wiki/Wireless_sensor_networkhttp://en.wikipedia.org/wiki/Smartdusthttp://en.wikipedia.org/wiki/Microcontrollers


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FIG 2.1 BLOCK DIAGRAM OF SENSOR NODE

2.2. Components of a Sensor Node

Microcontroller

Transceiver

Memory

Ram (random access memory)

Rom (read only memory )

Power source

One or more sensors.

2.2.1. Microcontroller

Microcontrollerperforms tasks, processes data and controls the functionality of other components in the

sensor node. Other alternatives that can be used as a controller are: General purpose desktop microprocessor,

http://en.wikipedia.org/wiki/Microcontrollerhttp://en.wikipedia.org/wiki/Transceiverhttp://en.wikipedia.org/wiki/Memoryhttp://en.wikipedia.org/wiki/Power_sourcehttp://en.wikipedia.org/wiki/Sensorshttp://en.wikipedia.org/wiki/Microcontrollerhttp://en.wikipedia.org/wiki/Desktophttp://en.wikipedia.org/wiki/Microprocessorhttp://en.wikipedia.org/wiki/Microcontrollerhttp://en.wikipedia.org/wiki/Transceiverhttp://en.wikipedia.org/wiki/Memoryhttp://en.wikipedia.org/wiki/Power_sourcehttp://en.wikipedia.org/wiki/Sensorshttp://en.wikipedia.org/wiki/Microcontrollerhttp://en.wikipedia.org/wiki/Desktophttp://en.wikipedia.org/wiki/Microprocessor


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Digital signal processors, Field Programmable Gate Array and Application-specific integrated circuit.

Microcontrollers are most suitable choice for sensor node. Each of the four choices has their own advantages and

disadvantages. Microcontrollers are the best choices forembedded systems. Because of their flexibility to connect

to other devices, programmable, power consumption is less, as these devices can go to sleep state and part of

controller can be active. In general purpose microprocessor the power consumption is more than the

microcontroller; therefore it is not a suitable choice for sensor node.

Digital Signal Processors are appropriate for broadband wireless communication. But in Wireless Sensor

Networks, the wireless communication should be modest i.e., simpler, easier to process modulation and signal

processing tasks of actual sensing of data is less complicated. Therefore the advantages of DSPs are not that much

of importance to wireless sensor node. Field Programmable Gate Arrays can be reprogrammed and reconfigured

according to requirements, but it takes time and energy. Therefore FPGA's is not advisable. Application Specific

Integrated Circuits are specialized processors designed for a given application. ASIC's provided the functionality in

the form of hardware, but microcontrollers provide it through software

2.2.2.Transceiver

Sensor nodes make use ofISM band which gives free radio, huge spectrum allocation and global

availability. The various choices of wireless transmission media are Radio frequency, Optical

communication (Laser) and Infrared. Laser requires less energy, but needs line-of-sight forcommunication

and also sensitive to atmospheric conditions. Infrared like laser, needs no antenna but is limited in its

broadcasting capacity. Radio Frequency (RF) based communication is the most relevant that fits to most of

the WSN applications. WSNs use the communication frequencies between about 433 MHz and 2.4 GHz.

The functionality of both transmitterand receiverare combined into a single device know as transceivers

are used in sensor nodes. Transceivers lack unique identifier. The operational states are Transmit, Receive,

Idle and Sleep.

Current generation radios have a built-in state machines that perform this operation automatically. Radios

used in transceivers operate in four different modes: Transmit, Receive, Idle, and Sleep. Radios operating in

Idle mode results in power consumption, almost equal to power consumed in Receive mode [4]. Thus it is

better to completely shutdown the radios rather than in the Idle mode when it is not Transmitting or

http://en.wikipedia.org/wiki/Digital_signal_processorshttp://en.wikipedia.org/wiki/Field_Programmable_Gate_Arrayhttp://en.wikipedia.org/wiki/Application-specific_integrated_circuithttp://en.wikipedia.org/wiki/Microcontrollershttp://en.wikipedia.org/wiki/Embedded_systemshttp://en.wikipedia.org/wiki/Wireless_communicationhttp://en.wikipedia.org/wiki/Wireless_Sensor_Networkshttp://en.wikipedia.org/wiki/Wireless_Sensor_Networkshttp://en.wikipedia.org/wiki/Modulationhttp://en.wikipedia.org/wiki/Signal_processinghttp://en.wikipedia.org/wiki/Signal_processinghttp://en.wikipedia.org/wiki/Softwarehttp://en.wikipedia.org/wiki/Transceiverhttp://en.wikipedia.org/wiki/ISM_bandhttp://en.wikipedia.org/wiki/Radiohttp://en.wikipedia.org/wiki/Radio_frequencyhttp://en.wikipedia.org/wiki/Optical_communicationhttp://en.wikipedia.org/wiki/Optical_communicationhttp://en.wikipedia.org/wiki/Infraredhttp://en.wikipedia.org/wiki/Line-of-sight_propagationhttp://en.wikipedia.org/wiki/Communicationhttp://en.wikipedia.org/wiki/Antenna_(radio)http://en.wikipedia.org/wiki/Broadcastinghttp://en.wikipedia.org/wiki/MHzhttp://en.wikipedia.org/wiki/GHzhttp://en.wikipedia.org/wiki/Transmitterhttp://en.wikipedia.org/wiki/Receiverhttp://en.wikipedia.org/wiki/Transceivershttp://en.wikipedia.org/wiki/State_machineshttp://d/vivek/project,%20seminar/seminar/vivek/Sensor_node.htm#cite_note-3#cite_note-3http://d/vivek/project,%20seminar/seminar/vivek/Sensor_node.htm#cite_note-3#cite_note-3http://en.wikipedia.org/wiki/Digital_signal_processorshttp://en.wikipedia.org/wiki/Field_Programmable_Gate_Arrayhttp://en.wikipedia.org/wiki/Application-specific_integrated_circuithttp://en.wikipedia.org/wiki/Microcontrollershttp://en.wikipedia.org/wiki/Embedded_systemshttp://en.wikipedia.org/wiki/Wireless_communicationhttp://en.wikipedia.org/wiki/Wireless_Sensor_Networkshttp://en.wikipedia.org/wiki/Wireless_Sensor_Networkshttp://en.wikipedia.org/wiki/Modulationhttp://en.wikipedia.org/wiki/Signal_processinghttp://en.wikipedia.org/wiki/Signal_processinghttp://en.wikipedia.org/wiki/Softwarehttp://en.wikipedia.org/wiki/Transceiverhttp://en.wikipedia.org/wiki/ISM_bandhttp://en.wikipedia.org/wiki/Radiohttp://en.wikipedia.org/wiki/Radio_frequencyhttp://en.wikipedia.org/wiki/Optical_communicationhttp://en.wikipedia.org/wiki/Optical_communicationhttp://en.wikipedia.org/wiki/Infraredhttp://en.wikipedia.org/wiki/Line-of-sight_propagationhttp://en.wikipedia.org/wiki/Communicationhttp://en.wikipedia.org/wiki/Antenna_(radio)http://en.wikipedia.org/wiki/Broadcastinghttp://en.wikipedia.org/wiki/MHzhttp://en.wikipedia.org/wiki/GHzhttp://en.wikipedia.org/wiki/Transmitterhttp://en.wikipedia.org/wiki/Receiverhttp://en.wikipedia.org/wiki/Transceivershttp://en.wikipedia.org/wiki/State_machineshttp://d/vivek/project,%20seminar/seminar/vivek/Sensor_node.htm#cite_note-3#cite_note-3


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Receiving. And also significant amount of power is consumed when switching from Sleep mode to

Transmit mode to transmit a packet. An example of transceiver is cc2420 radio which have configuration is

following----

CC2420 Radio

IEEE 802.15.4 Compliant

Fast data rate, robust signal

250kbps : 2Mchip/s : DSSS

2.4GHz : Offset QPSK : 5MHz

16 channels in 802.15.4

-94dBm sensitivity

Low Voltage Operation

1.8V minimum supply

Software Assistance for Low Power Microcontrollers

128byte TX/RX buffers for full packet support

Automatic address decoding and automatic acknowledgements

Hardware encryption/authentication

Link quality indicator (assist software link estimation)

samples error rate of first 8 chips of packet (8 chips/bit)

2.2.3-External Memory

From an energy perspective, the most relevant kinds of memory are on-chip memory of a microcontroller and

FLASH memory - off-chip RAM is rarely if ever used. Flash memories are used due to its cost and storage

capacity. Memory requirements are very much application dependent. Two categories of memory based on the



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purpose of storage a) User memory used for storing application related or personal data. b) Program memory used

for programming the device. This memory also contains identification data of the device if any.

Ram (random access memory)-

Rom (read only memory )-

2.2.4. Power sources

Power consumption in the sensor node is for the Sensing, Communication and Data Processing. More energy

is required for data communication in sensor node. Energy expenditure is less for sensing and data processing. The

energy cost of transmitting 1 Kb a distance of 100 m is approximately the same as that for the executing 3 million

instructions by 100 million instructions per second/W processor. Power is stored either in Batteries or Capacitors.

Batteries are the main source of power supply for sensor nodes. Namely two types of batteries used are chargeable

and non-rechargeable. They are also classified according to electrochemical material used for electrode such as

NiCad(nickel-cadmium), NiZn(nickel-zinc), Nimh (nickel metal hydride), and Lithium-Ion. Current sensors are

developed which are able to renew their energy from solar, thermo generator, or vibration energy. Two major

power saving policies used are Dynamic Power Management (DPM) and Dynamic Voltage Scaling (DVS)[5]. DPM

takes care of shutting down parts of sensor node which are not currently used or active. DVS scheme varies the

power levels depending on the non-deterministic workload. By varying the voltage along with the frequency, it is

possible to obtain quadratic reduction in power consumption.

2.2.5. Sensor

Sensors arehardware devices that produce measurable response to a change in a physical condition

like temperature and pressure. Sensors sense or measure physical data of the area to be monitored. The

continual analog signal sensed by the sensors is digitized by an Analog-to-digital converter and sent to

controllers for further processing. Characteristics and requirements of Sensor node should be small size,

consume extremely low energy, operate in high volumetric densities, be autonomous and operate

unattended, and be adaptive to the environment. As wireless sensor nodes are micro-electronic sensor

device, can only be equipped with a limited power source of less than 0.5 Ah and 1.2 V. Sensors are

classified into three categories.

http://en.wikipedia.org/wiki/Thermogeneratorhttp://en.wikipedia.org/wiki/DPMhttp://en.wikipedia.org/wiki/DVShttp://d/vivek/project,%20seminar/seminar/vivek/Sensor_node.htm#cite_note-4#cite_note-4http://d/vivek/project,%20seminar/seminar/vivek/Sensor_node.htm#cite_note-4#cite_note-4http://en.wikipedia.org/wiki/Sensorshttp://en.wikipedia.org/w/index.php?title=Hardware_devices&action=edit&redlink=1http://en.wikipedia.org/w/index.php?title=Hardware_devices&action=edit&redlink=1http://en.wikipedia.org/wiki/Analog_signalhttp://en.wikipedia.org/wiki/Analog-to-digital_converterhttp://en.wikipedia.org/wiki/Thermogeneratorhttp://en.wikipedia.org/wiki/DPMhttp://en.wikipedia.org/wiki/DVShttp://d/vivek/project,%20seminar/seminar/vivek/Sensor_node.htm#cite_note-4#cite_note-4http://en.wikipedia.org/wiki/Sensorshttp://en.wikipedia.org/w/index.php?title=Hardware_devices&action=edit&redlink=1http://en.wikipedia.org/wiki/Analog_signalhttp://en.wikipedia.org/wiki/Analog-to-digital_converter


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Passive, Omni Directional Sensors: Passive sensors sense the data without actually manipulating the

environment by active probing. They are self powered i.e energy is needed only to amplify their

analog signal. There is no notion of direction involved in these measurements.

Passive, narrow-beam sensors: These sensors are passive but they have well-defined notion of

direction of measurement. Typical example is camera.

Active Sensors: These group of sensors actively probe the environment, for example, a sonar or

radar sensor or some type of seismic sensor, which generate shock waves by small explosions.

The overall theoretical work on WSNs considers Passive, Omni directional sensors. Each sensor node has

a certain area of coverage for which it can reliably and accurately report the particular quantity that it is

observing. Several sources of power consumption in sensors are a) Signal sampling and conversion of

physical signals to electrical ones, b) signal conditioning, and c) analog-to-digital conversion. Spatial

density of sensor nodes in the field may be as high as 20 nodes .

List of sensor node

Table 2.1 -List of Sensor Nodes available

Sensor Node

Name

Microcontr-

-ollerTransceiver

Program +

Data

Memory

External

Memory

Programm

ingRemarks

COOKIES ADUC841 ETRX2

TELEGESIS

4 Kbytes +

62 Kbytes

4 Mbit C Plattform

with

hardware

reconfigurab



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ility

( Spartan

3FPGA

based)

BEAN MSP430F169

CC1000

(300-1000

MHz) with

78.6 kbit/s

4 MbitYATOS

Support

BTnode

Atmel

ATmega

128L (8 MHz

@ 8 MIPS)

Chipcon

CC1000

(433-915

MHz) and

Bluetooth

(2.4 GHz)

64+180 K

RAM

128K

FLASH

ROM,

4K

EEPRO

M

C and nesC

Programmin

g

BTnut and

TinyOS

support

COTS

ATMEL

Microcontroll

er 916 MHz

Dot ATMEGA163 1K RAM8-16K

FlashWeC

EPIC Mote

Texas

Instruments

MSP430

microcontroll

er

250 kbit/s 2.4

GHz IEEE

802.15.4

Chipcon

Wireless

Transceiver

10k RAM 48k Flash TinyOS

http://www.btnode.ethz.ch/http://store.archrock.com/ProductDetails.asp?ProductCode=RMB-1010Shttp://www.btnode.ethz.ch/http://store.archrock.com/ProductDetails.asp?ProductCode=RMB-1010S


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CHAPTER 3

POWER OPERATION IN NETWORK

3.1. Low power operation

The protocol stack combines power and routing awareness, integrates data with networking

protocols, communicates power efficiently through the wireless medium. The protocol stack consists of

the application layer, transport layer, network layer, data link layer, physical layer, power management

plane,mobility management plane, and task management plane.Depending on the sensing tasks,

different types of application software can be built and used on the application layer. The transportlayer helps to maintain the flow of data if the sensor networks application requires it. The network layer

takes care of routing the data supplied by the transport layer. Since the environment is noisy and

sensor nodes can be mobile, the MAC protocol must be power aware and able to minimize collision

with neighbors broadcast. The physical layer addresses the needs of different types of modultions,

transmission and receiving techniques. In addition, the power, mobility, and task management planes



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monitor the power, movement, and task distribution among the sensor nodes. These planes help the

sensor nodes coordinate the sensing task and to keep low the overall power consumption.

3.2.Power awareness from Radio Communication Hardware

The characteristics of sensor networks call for interesting considerations in communication models

that differ from multimedia networks. The average energy consumption for a sensor radio (Figure 3.1)

when sending a burst packet is given by the following equation:

Ptx/rx is the power consumption of the transceiver, Ton-tx/rx is the transmit/receive on-time (actual

data transmission/reception time), Tstartup-tx/rx is the start-up time of the transceiver, Pout is the

output transmit power which drives the antenna and d is the duty cycle of the receiver. Although the

primary purpose of the sensor node is to transmit data, a receiver is also necessary to support a

communication protocol in the network (i.e., time syn-chronization, acknowledgment signal,etc.)

Fig 3.1 radio architectre



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It is important to note that the power consumption of the transceiver (Ptx/rx) does not vary with the

data rate to first order.For short-range transmission (e.g., under 10 meters) at gigahertz carrier

frequencies, the radios power is dominated by the frequency synthesizer which generates the carrier

frequency rather than the actual transmit power. Hence, data rate, to first order,does not affect the

power consumption of the transceiver [15].But as packets become shorter, the radios start-up time

becomes significant. To reduce energy, the nodes radio module is duty cycled, or turned on/off during

the active/idle periods.Figure 3.2 illustrates the effect of start-up time on transmitter energy

consumption when sending a 100 bit packet at 1 Mbps.As the start-up time increases, the radio energy

becomes dominated by the start-up transient rather than the active transmittime. Unfortunately,

transceivers today require initial start-up times on the order of milliseconds due to an inherent feedback

Fig 3.2- Effects of start-up time on short packet transmission

loop in the PLL-based frequency synthesizer. The start-up time must be lowered to a few tens of

microseconds to minimize energy consumption for the short packets expected in microsensor

communication.



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Fig 3.3 Local data aggregation can reduce energy dissipation

a factor of eight reduction in energy over conventional routing protocols such as multi-hop routing.

However, the effectiveness of a clustering network protocol is highly dependent on the performance of the

algorithms used for data aggregation and communication. It is important to design and implement energy

efficient sensor algorithms for data aggregation and link-level protocols for the wireless sensors. Beam

forming algorithms are one class of algorithms which can be used to combine data. Beam forming can

enhance the source signal and remove uncorrelated noise or interference. Since many types of beam

forming algorithms exist, it is important to make a careful selection based upon their computation energy

and beam forming quality. Comparing the Max Power beam forming algorithm and the LMS beam forming

algorithm, for instance, measurements on the SA-1100 indicate that the Max Power algorithm requires

more than 5 times the energy of the LMS algorithm.

3.3.2. System Partitioning

Algorithm implementations for a sensor network can take advantage of the networks inherent

capability for parallel processing to further reduce energy. Partitioning a computation among multiple

sensor nodes and performing the computation in parallel permits a greater allowable latency per

computation, allowing energy savings through frequency and voltage scaling.

As an example, consider a target tracking application that requires sensor data to be transformed into the

frequency domain through 1024-point FFTs. The FFT results are phase shifted and summed in a frequency-



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domain beam former to calculate signal energies in 12 uniform directions, and the line-of-bearing (LOB) is

estimated as the direction with the most signal energy. By intersecting multiple LOBs at the base station,

the sources location can be determined. Figure 3.4 demonstrates the tracking application performed with

traditional clustering techniques for a 7 sensor cluster. The sensors (S1-S6) collect data and transmit the

data directly to the cluster-head (S7), where the FFT, beamforming and LOB estimation are performed.

Measurements on the SA-1100 at an operating voltage of 1.5V and frequency of 206 MHz show that the

tracking application dissipates 27.27 mJ of energy. Distributing the FFT computation among the sensors

reduces energy dissipation. In the distributed processing scenario of Figure 3.4, the sensors collect data and

perform the FFTs before transmitting the FFT results to the cluster-head.

Fig 3.4 a) Approach 1: All computation is done at the cluster-head.

b) Approach 2: Distribute the FFT computation among all sensors.

At the clusterhead, the FFT results are beamformed and the LOB estimate is found. Since the 7 FFTs are

done in parallel, we can reduce the supply voltage and frequency without sacrificing latency. When the

FFTs are performed at 0.9V, and the beamforming and LOB estimation at the cluster-head are performed at

1.3V, then the tracking application dissipates 15.16 mJ, a 44% improvement in energy dissipation.

3.3.3. Energy-Efficient Link Layer



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Energy-quality tradeoffs appear at the link layer as well. One of the primary functions of the link

layer is to ensure that data is transmitted reliably. Thus, the link layer is responsible for some basic form of

error detection and correction. Most wireless systems utilize a fixed error correction scheme to minimize

errors and may add more error protection than necessary to the transmitted data. In a energy-constrained

system, the extra computation becomes an important concern. Thus, by adapting the error correction

scheme used at the link layer, energy consumption can be scaled while maintaining the bit error rate (BER)

requirements of the user Error control can be provided by various algorithms and techniques, such as

convolutional coding, BCH coding, and turbo coding. The encoding and decoding energy consumed by the

various algorithms can differ considerably

.

Table 3.1 Energy per useful bit for BCH codes (@1.5V))

Table I shows the energy per useful bit to encode and decode messages using various BCH codes

on the SA-1100. As the code rate increases, the algorithms energy also increases. Hence, given bit error

rate and latency requirements, the lowest power FEC algorithm that satisfies these needs should

continuously be chosen. Power consumption can be further reduced by controlling the transmit power of

the physical radio. For a given bit error rate, FEC lowers the transmit power required to send a given

message. However, FEC also requires additional processing at the transmitter and receiver, increasing both

the latency and processing energy. This is another computation versus communication trade-off that divides

available energy between the transmit power and coding processing to best minimize total system power.



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3.4 Read, Write and Erase Energy Usage

Read and write energy costs of single pages were measured, and these are presented in Table 2.

The results are presented in units of energy per byte, to account for the difference in page and erase block

sizes of the devices. The energy cost for read, write and erase operations on the Telos NOR is seen to be

18x less than the Atmel NOR and 3x less than the Hitachi MMC. The Toshiba 16MB NAND flash is 21x

more efficient in comparison to the Telos NOR, 65x better than the Hitachi MMC and 407x better than the

Atmel NOR. The Micron 512MB NAND flash was found to be 3.6x less efficient than the Toshiba 16MB

NAND flash, though offering 32 times the storage capacity. Many factors affect the energy consumption of

flash memories, but we are unable to discuss these due to the space constraints of this paper. We consider

the Toshiba 16MB NAND flash for further discussions. The results of the erase operation may also be seen

in Table 2; the minimum erase block size was tested for the serial NOR and the NAND devices - 1 and 32

pages respectively. The MMC interface defines both a single page erase and a block erase command; both

were tested. We find that erasing a single page is 140 times more expensive than a block erase of several

16-page blocks. Under continuous usage, one byte must be erased for

Fig 3.5. Affect of size of data being read or written on the energy consumption.

every byte written. Thus, in this case the total energy used to write a single byte should also consider the

erase operation that precedes it. In either case, accounting for erase energy does not significantly increase



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the total energy cost.The energy consumed by each read and write operation has two components - a

constant overhead associated with the operation and a variable component that is dependant on the size of

data being read or written. Figure 5 shows how the energy consumption varies with varying data sizes on

the NAND flash, and this is representative of the energy Wireless sensors are being used to monitor vital

signs of patients in a hospital environment. Compared to conventional approaches, solutions based on

wireless sensors are intended to improve monitoring accuracy whilst also being more convenient for

patients. The system consists of four components: a patient identifier, medical sensors, a display device,

and a setup pen. The patient identifier is a special sensor node containing patient data (e.g., name) which is

attached to the patient when he or she enters the hospital. Various medical sensors (e.g.,

electrocardiogram) may be subsequently attached to the patient. Patient data and vital signs may be

inspected using a display device. The setup pen is carried by medical personnel to establish and remove

associations between the various devices. The pen emits a unique ID via infrared to limit the scope to a

single patient. Devices which receive this ID form a body area network.



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CHAPTER 4

ADVANTAGES AND DISADVANTAGES

4.1. Advantage

Limited power they can harvest or store

Ability to withstand harsh environmental conditions

Ability to cope with node failures

Mobility of nodes

Dynamic network topology

Communication failures

Heterogeneity of nodes

Low cost

Low power

Small size

4.2. Disadvantage

Short communication distance

Its damn easy for hackers to hack it as we cant control propagation of waves

Comparatively low speed of communication

Gets distracted by various elements like Blue-tooth

Still Costly at large



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In this section we justify our design space model by locating a number of applications at different

points in the design space. For this, we have selected concrete applications that are well-documented and

that have advanced beyond a mere vision. Some of the applications listed are field experiments, some are

commercial products, and some are advanced research projects that use sensor networks as a tool. For

classification, we have used the reported parameters that were actually used in practical settings and we

have deliberately refrained from speculation as to what else could have been done. Note that there are

usually different technical solutions for a single application, which means that the concrete projects

described below are only examples drawn from a whole set of possible solutions. However, these examples

reflect what was technically possible and desirable at the time the projects were set up. Therefore, we have

decided to base our discussion on these concrete examples rather than speculating about the inherent

characteristics of a certain type of application. Table 1 classifies the sample applications according to the

dimensions of the design space described in the previous section. Depending on the application, the

required lifetime of a sensor network may range from some hours to several years. The necessary lifetime

has a high impact on the required degree of energy efficiency and robustness of the nodes. A WSN is being

used to monitor power consumption in large and dispersed office buildings. The goal is to detect locations

or devices that are consuming a lot of power to provide indications for potential reductions in power

consumption. The system consists of three major components: sensor nodes, transceivers, and a central

unit. Sensor nodes are connected to the power grid (at outlets or fuse boxes) to measure power consumption

and for their own power supply. Sensor nodes directly transmit sensor readings to transceivers. The

transceivers form a multi-hop network and forward messages to the central unit. The central unit acts as a

gateway to the Internet and forwards sensor data to a database system.



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CHAPTER 5

APPLICATION AND FUTURE SCOPE

5.1. Application

Industrial control & monitoring

Health care

Security & military surveillance

Environmental sensing

Home automation & consumer electronics

5.2. Industrial control & monitoring

5.2.1 Water/Wastewater Monitoring

There are many opportunities for using wireless sensor networks within the water/wastewater

industries. Facilities not wired for power or data transmission can be monitored using industrial wireless

I/O devices and sensors powered using solar panels or battery packs. As part of the American Recovery and

Reinvestment Act (ARRA), funding is available for some water and wastewater projects in most states.

5.2.2. Landfill Ground Well Level Monitoring and Pump Counter

Wireless sensor networks can be used to measure and monitor the water levels within all ground

wells in the landfill site and monitor leach ate accumulation and removal. A wireless device and

submersible pressure transmitter monitors the leach ate level. The sensor information is wirelessly



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applicable for a large class of applications. This raises the question of whether appropriate abstractions and

middleware concepts can be devised that are applicable for a large portion of the sensor network design

space. This is not an easy task, since some of the design space dimensions (e.g., network connectivity) are

very hard to hide from the system developer. Moreover, exposing certain application characteristics to the

system and vice versa is a key approach for achieving energy and resource efficiency in sensor networks.

Even if the provision of abstraction layers is conceptually possible, it would often introduce significant

resource overheads which is problematic in highlresource-constrained sensor networks. At the workshop

mentioned above, some possible directions were discussed for providing general abstractions despite these

difficulties. One approach is the definition of common service interfaces independent of their actual

implementation. The interfaces would, however, contain methods for exposing application characteristics to

the system and vice versa. Different points in the design space would then require different

implementations of these interfaces. A modular software architecture would then be needed, together with

tools that would semi-automatically select the implementations that best fitted the application and hardware

requirements. One possible approach here is the provision of a minimal fixed core functionality that would

be dynamically extended with appropriate software modules. We acknowledge that all this is somewhat

speculative.



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Fig 5.1 Security & military surveillance

Fig.5.2 Home automation

A WSN is being used to assist people during the assembly of complex composite objects such as

do-it-yourself furniture. This saves users from having to study and understand complex instruction

manuals, and prevents them from making mistakes. The furniture parts and tools are equipped with sensor

nodes. These nodes are equipped with a variety of different sensors: force sensors (for joints), gyroscope

(for screwdrivers), and accelerometers (for hammers). The sensor

nodes form an ad hoc network for detecting certain actions and sequences thereof and give visual feedbackto the user via LEDs integrated into the furniture parts.



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CHAPTER 6

CONCLUSION

6. Conclusion

To realize the ubiquitous computing in human life a sensor network may be the powerful tool,

because they can be deployed at the places where a man can not reach. However it is negative sides also

because the power of sensor node can not be refreshed. To realize the power control and power saving

every layer take care of that. At Physical layer modulation schemes are chosen according to that. At MAC

layer contention free (TDMA/FDMA) schemes are used. At Network layer multihop routing and data

centric routing is used. Normally at transport layer UDP protocol is used. At the time when guaranteed

delivery is required TCP can also be used. TCP is used in addition to link layer retransmission. Software

which is used on application layer also should be power aware software.



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REFERENCES

1. Rajgopal Kannan, Ram Kalidindi, S. S. Iyengar Energy and Rate based MAC Protocol for Wireless

Sensor Networks , International Symposium on Communication Theory and Applications,

Louisiana State University, Dec2003

2. N. Bulusu, D. Estrin, L. Girod, J. Heidemann, Scalable coordination for wireless sensor networks:

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3. Heidemann, F. Silva, C. Intanagonwiwat, Building efficient wireless sensor networks with low-

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4. Adam Dunkels, Juan Alonso, Thiemo Voigt Making TCP/IP Viable for Wireless Sensor Networks

Swedish Institute of Computer Science, June2003.

5. Michele Zorzi and Ramesh R. Rao Energy and latency performance of geographic random

forwarding for ad hoc and sensor networks UdR CNIT, University of Ferrara Saragat, Ferrara, Italy,

June2002

Low Power Wireless Sensor Netwok

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