-low-power-wireless-sensor visi

8/7/2019 -Low-Power-Wireless-Sensor visi

1/26

MOTES - Low power wireless sensor network

CHAPTER 1

1. INTRODUCTION

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 technolo-gies required for low-energy

distributed microsensors.These include power aware computation communication component

technology, low-energy signaling and networking, system parti-tioning conside

computation and communication trade-offs, and a power aware 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 inacce

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

Department of Electronics and Communication, GSSIT


2/26


3/26


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.

CHAPTER 2

2.1 SENSOR NODE



4/26


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

wireless sensor networkthat is capable of performing some processing, gathering sensory

information and communication with other connected nodes in the network

. History of development of sensor nodes dates back to 1998 inSmartdust project [1].

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 [2] and CENS [3]. 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.

FIG 2.1 BLOCK DIAGRAM OF SENSOR NODE

2.2. Components of a Sensor Node

Microcontroller

Transceiver

http://en.wikipedia.org/wiki/Motehttp://en.wikipedia.org/wiki/North_Americahttp://en.wikipedia.org/wiki/Wireless_sensor_networkhttp://en.wikipedia.org/wiki/Smartdusthttp://d/vivek/project,%20seminar/seminar/vivek%20ppt%20,%20project%20report/vivek/vivek/Sensor_node.htm#cite_note-0#cite_note-0http://d/vivek/project,%20seminar/seminar/vivek%20ppt%20,%20project%20report/vivek/vivek/Sensor_node.htm#cite_note-1#cite_note-1http://d/vivek/project,%20seminar/seminar/vivek%20ppt%20,%20project%20report/vivek/vivek/Sensor_node.htm#cite_note-2#cite_note-2http://d/vivek/project,%20seminar/seminar/vivek%20ppt%20,%20project%20report/vivek/vivek/Sensor_node.htm#cite_note-2#cite_note-2http://en.wikipedia.org/wiki/Microcontrollershttp://en.wikipedia.org/wiki/Microcontrollershttp://en.wikipedia.org/wiki/Microcontrollerhttp://en.wikipedia.org/wiki/Transceiverhttp://en.wikipedia.org/wiki/Motehttp://en.wikipedia.org/wiki/North_Americahttp://en.wikipedia.org/wiki/Wireless_sensor_networkhttp://en.wikipedia.org/wiki/Smartdusthttp://d/vivek/project,%20seminar/seminar/vivek%20ppt%20,%20project%20report/vivek/vivek/Sensor_node.htm#cite_note-0#cite_note-0http://d/vivek/project,%20seminar/seminar/vivek%20ppt%20,%20project%20report/vivek/vivek/Sensor_node.htm#cite_note-1#cite_note-1http://d/vivek/project,%20seminar/seminar/vivek%20ppt%20,%20project%20report/vivek/vivek/Sensor_node.htm#cite_note-2#cite_note-2http://en.wikipedia.org/wiki/Microcontrollershttp://en.wikipedia.org/wiki/Microcontrollerhttp://en.wikipedia.org/wiki/Transceiver


5/26


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, 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 for

embedded 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 acco

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

http://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/Digital_signal_processorshttp://en.wikipedia.org/wiki/Field_Programmable_Gate_Arrayhttp://en.wikipedia.org/wiki/Application-specific_integrated_circuithttp://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_communicationhttp://en.wikipedia.org/wiki/Wireless_Sensor_Networkshttp://en.wikipedia.org/wiki/Modulationhttp://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/Infraredhttp://en.wikipedia.org/wiki/Line-of-sight_propagationhttp://en.wikipedia.org/wiki/Communicationhttp://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/Digital_signal_processorshttp://en.wikipedia.org/wiki/Field_Programmable_Gate_Arrayhttp://en.wikipedia.org/wiki/Application-specific_integrated_circuithttp://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_communicationhttp://en.wikipedia.org/wiki/Wireless_Sensor_Networkshttp://en.wikipedia.org/wiki/Modulationhttp://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/Infraredhttp://en.wikipedia.org/wiki/Line-of-sight_propagationhttp://en.wikipedia.org/wiki/Communication


6/26


7/26


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 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

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://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-4


8/26


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 are hardware 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 converterand 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.

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 theparticular 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 .

http://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_converterhttp://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


9/26


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 ADUC841ETRX2

TELEGESIS

4 Kbytes +

62 Kbytes4 Mbit C

Plattform

with

hardwarereconfigurab

ility

( Spartan

3FPGA

based)

BEAN MSP430F169

CC1000

(300-1000

MHz) with

78.6 kbit/s

4 Mbit

YATOS

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

http://www.btnode.ethz.ch/http://www.btnode.ethz.ch/


10/26


DotATMEGA16

31K RAM

8-16K

FlashweC

EPIC Mote

Texas

Instruments

MSP430

microcontroll

er

250 kbit/s

2.4 GHz

IEEE

802.15.4

Chipcon

Wireless

Transceiver

10k RAM48k

FlashTinyOS

CHAPTER 3

3.1. Low power operation

The protocol stack combines power and routing awareness, integrates data

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 managemen

plane.Depending on the sensing tasks, different types of application software can be built and

used on the application layer. The transport layer 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 m

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

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


11/26


planes 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.)



12/26


Fig 3.1 radio architectre

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 initialstart-up times on the order of milliseconds due to an inherent feedback



13/26


Fig 3.2- Effects of startup 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 exp

microsensor communication.

3.3. Energy-efficient networks

Once the power-aware micro sensor nodes are incorporated into the framework of a larger

network, additional power-aware methodologies emerge at the network level. Decisions about

local computation versus radio communication, the partitioning of computation across nodes,

and error correction on the link layer offer a diversity of operational points for the network.

3.3.1. Signal Processing in the Network

A network protocol layer for wireless sensors allows for sensor collaboration. Sensor

collaboration is important for two reasons. First, data collected from multiple sensors can offer

valuable inferences about the environment. For example, large sensor arrays have been used

for target detection, classification and tracking. Second, sensor collaboration can provide



14/26


tradeoffs in communication versus computation energy. Since it is likely that the data acquired

from one sensor are highly correlated with data from its neighbours, data aggregation can

reduce the redundant information transmitted in the network. Figure 7 shows the amount of

energy required to aggregate data from 2, 3 and 4 sensors and to transmit the result to the basestation, compared to all sensors transmitting data to the base station individually. When the

distance to the base station is large, there is a large advantage to using local data aggregation

(e.g. beam forming) Rather than direct communication. Since wireless sensors are energy-

constrained, it is important to exploit such trade-offs to increase system lifetimes and improve

energy efficiency. The energy-efficient network protocol LEACH (Low Energy Adaptive

Clustering Hierarchy) utilizes clustering techniques that greatly reduce the energy dissipated by

a sensor system [12]. In LEACH, sensor nodes are organized into local clusters. Within the

cluster is a rotating clusterhead. The cluster-head receives data from all other sensors in the

cluster, performs data aggregation, and transmits the aggregate data to the end-user. This

greatly reduces the amount of data that is sent to the enduser for increased energy-efficiency.

LEACH can achieve up to

Fig 3.3 Local data aggregation can reduce energy dissipation



15/26


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-levelprotocols 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-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 of206 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.



16/26


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, a44% improvement in energy dissipation.

3.3.3. Energy-Efficient Link Layer

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



17/26


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.

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



18/26


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 alsobe 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 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 the total energy cost.

Fig 3.5. Affect of size of data being read or written on t

consumption.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



19/26


being used to monitor vital signs of patients in a hospital environment. Compared to

conventional approaches, solutions based on wireless sensors are intended to imp

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. Thepatient 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

(e.g.electrocardiogram) may be subsequently attached to the patient. Patient data and vital

signs may be inspected using a display device.

CHAPTER 4

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



20/26


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

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 asto 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.



21/26


CHAPTER 5

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



22/26


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. Thinformation is wirelessly transmitted to a central data logging system to store the level data,

perform calculations, or notify personnel when a service vehicle is needed at a specific well.

It is typical for leach ate removal pumps to be installed with a totalizing counter mounted at the

top of the well to monitor the pump cycles and to calculate the total volume of leach ate

removed from the well. For most current installations, this counter is read manually. Instead of

manually collecting the pump count data, wireless devices can send data from the pumps back

to a central control location to save time and eliminate errors. The control system uses this

count information to determine when the pump is in operation, to calculate leach ate extraction

volume, and to schedule maintenance on the pump.

5.2.3 Flare Stack Monitoring

Landfill managers need to accurately monitor methane gas production, removal, venting, and

burning. Knowledge of both methane flow and temperature at the flare stack can define when

methane is released into the environment instead of combusted. To accurately determine

methane production levels and flow, a pressure transducer can detect both pressure and

vacuum present within the methane production system.

Thermocouples connected to wireless I/O devices create the wireless sensor network that

detects the heat of an active flame, verifying that methane is burning. Logically, if the meter is

indicating a methane flow and the temperature at the flare stack is high, then the methane is

burning correctly. If the meter indicates methane flow and the temperature is low, methane is

releasing into the environment.

5.3. Health care

Gather data to infer activities of daily living

Give clues to a persons state of health



23/26


Monitor patients with dementia and other ills of aging

Detect early signs of disease and prevent its progression

A WSN is being used to track the path of military vehicles (e.g., tanks) [19]. The sensor

network should be unnoticed design space, since each application would potentially require the

use of software with different interfaces and properties. In conventional distributed systems,

middleware has been introduced to hide such complexity from the software developer by

providing programming abstractions that are 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.



24/26


Fig 5.1 Security & military surveillance

Fig.5.2 Home automation



25/26


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 feedback to the user via LEDs integrated into the furniture parts.

CHAPTER6

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.



26/26


REFERENCES

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

Wireless Sensor Networks , International Symposium on Communication Theory

Applications, Louisiana State University, Dec2003

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

networks: self-configuring localization systems, International Symposium on Communication

Theory and Applications (ISCTA2001), Ambleside, UK, July 2001.

[3] Heidemann, F. Silva, C. Intanagonwiwat, Building efficient wireless sensor networks with

low-level naming, Proceedings of the Symposium on Operating Systems Principles, Banff,Canada, 2001.

[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 visi

Documents