Low Power Wireless Sensor Network--document

LOW POWER WIRELESS SENSOR NETWORK A SEMINAR REPORT Submitted in the partial fulfillment of the requirement of degree of Bachelor of Technology in

Electronics & Communication Engineering by VIVEK KUMAR GANGWAR

(0608231121) Under the guidance of Saurabh verma

Shyam Lal Rao

(Lecturer)

(Asst. Professor)

Seminar Guide

Seminar Coordinator

DEPARTMENT OF ELECTRONICS & COMMUNICATION ENGINEERING MORADABAD INSTITUTE OF TECHNOLOGY Ram Ganga Vihar, Phase –II, Moradabad-244001 (U.P) Session: 2009-10

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ACKNOWLEDGEMENT

I thank the almighty for giving me the courage and perseverance in completing the seminar report. I am grateful to the following persons for various help rendered by them. I am greatly indebted to MR. KSHITIJ SHINGHAL head of the department for his valuable advices at every stage of this work. Without the supervision and many hours of devoted guidance, stimulating and constructive criticism, this seminar report would never have come out in this form. I also thankful to my seminar guide MR. SAURABH VERMA and seminar coordinator MR. SHYAM LAL RAO, for providing the excellent motivation and valuable guidance throughout the seminar work. With this co-operation and encouragement I completed the seminar report in time. Last but not least I would like to express my deep sense of gratitude and earnest thanks to my dear parents for their moral support and heartfelt co-operation in doing the seminar. I would also like to thank all the teaching and non teaching staff and my friends, whose direct or indirect help has enabled me to complete this work successfully.

With sincere thanks from VIVEK KUMAR GANGWAR ROLL NO-0608231121

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MORADABAD INSTITUTE OF TECHNOLOGY MORADABAD CERTIFICATE

This is to certify that the seminar entitled, “Low Power Wireless Sensor Network” submitted by Vivek Kumar Gangwar, roll no. 0608231121, in the partial fulfillment of the requirement for the award of the degree of Bachelor of Technology in Electronics & Communication Engineering embodies the work done by him in my guidance .

Seminar Guide

Seminar Coordinator

(Sourabh Verma)

(Shyam Lal Rao)

Lecturer,

Assistant Professor,

Department of E&C Engineering,

Department of E&C Engineering,

Moradabad Institute of Technology,

Moradabad Institute of Technology,

Moradabad -244001,India

Moradabad -244001,India.

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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 partitioning considering 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, multidimensional 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 4

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 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 user’s 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,

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the node’s energy consumption displays a graceful scalability in energy consumption at all levels of the system hierarchy, including the signal processing algorithms, 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 µAMPS project as an example, this paper surveys techniques for system-level power-awareness.

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CHAPTER 2 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 and communication with other connected nodes in the network .

History of development of sensor nodes dates back to 1998 in Smartdust 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

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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 Microcontroller performs 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 Applicationspecific 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 DSP’s 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

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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 of ISM 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 for communication 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. WSN’s use the communication frequencies between about 433 MHz and 2.4 GHz. The functionality of both transmitter and receiver are 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 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

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 -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 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.

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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(nickelzinc), 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 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 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.  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 WSN’s 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 11

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

Microcontr-

Node Name

-oller

Program Transceiver

+ Data Memory

External Programm-Memory

ing

Remarks

Plattform with hardware COOKIES

ADUC841

ETRX2

4 Kbytes +

TELEGESIS 62 Kbytes

4 Mbit

C

reconfigurab ility ( Spartan 3FPGA based)

CC1000 BEAN

MSP430F169

(300-1000

YATOS

4 Mbit

MHz) with

Support

78.6 kbit/s

BTnode

Chipcon

128K

Atmel

CC1000

FLASH

ATmega

(433-915

128L (8 MHz MHz) and @ 8 MIPS)

COTS

64+180 K

ROM,

RAM

4K

Bluetooth

EEPRO

(2.4 GHz)

M

ATMEL

12

C and nesC

BTnut and

Programmin

TinyOS

g

support

Microcontroll er 916 MHz Dot

ATMEGA16

1K RAM

3

8-16K Flash

weC

250 kbit/s

EPIC Mote

Texas

2.4 GHz

Instruments

IEEE

MSP430

802.15.4

10k RAM

microcontroll Chipcon er

Wireless Transceiver

13

48k Flash

TinyOS

CHAPTER 3 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 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 modultions, transmission and receiving techniques. In addition, the power, mobility, and task management 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

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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 synchronization, acknowledgment signal,etc.)

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 radio’s 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 radio’s start-up time becomes significant. To reduce energy, the node’s 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 15

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 expected in 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

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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 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 base station, 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

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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 network’s 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 LOB’s at the base station, the source’s 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 18

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 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

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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 algorithm’s 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

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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 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

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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 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 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

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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 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 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.

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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 person’s state of health  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 26

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 highl resource-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

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

CHAPTER 6 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: 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

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