Project Report on Super nodes- An Embedded Approach

7/31/2019 Project Report on Super nodes- An Embedded Approach

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1.ABSTRACT :Advances in wireless sensor networks make many of the impossible possible.

Roadway safety warning , habitat monitoring , smart classroom etc., are prosperous

applications tied to our daily life. Such networks rely on the collaboration of

thousands of resource-constrained error-prone sensors for monitoring and control.

One of the most contemporary challenges is to design efficient methods for

exploiting the new technology of wireless sensor networks (WSN).

WSN divided to two important types: homogenous wireless sensor network

and heterogeneous wireless sensor network. In homogenous WSNs all nodes in the

network have the same power, resources, quality and etc, but heterogeneous WSNs

consisting of two types of wireless devices: resource-constrained wireless sensor

nodes deployed randomly in a large number and a much smaller number of

resource-rich super nodes placed at known locations. The super nodes are

comparatively resource richer than other nodes in the network..

Direct transmission networks are very simple to design but can be very power

consuming due to the long distances from sensors to the target node. Alternative

designs that shorten or minimize the communication distances can extend network

lifetimes. The use of super nodes for transmitting data to a target node leverages

the advantages of small transmit distances for most nodes, requiring only a few

nodes to transmit far distances to the target node. The super nodes gather the data

and send it directly to the target node. This model can greatly reduce

communication costs of most nodes because they only need to send data to the

nearest Super node, rather than directly to a target node that may be further away.

Here in our project, we have clustered the wireless nodes in the same region.


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We place one super node in to the cluster. The super node is responsible for

all inter cluster communications. All the communications inside the cluster are

always destined to the super node, from where the data is transmitted further.

1.1. INTRODUCTION :WSN is an emerging technology that can be deployed in such situation where

human interaction is not possible like border area tracking enemy moment or fire

detection system. Figure 1 shows an overview of WSN. Sensor are deployed in the

environment which can be fire area, border or open environment. These tiny

devices sense the area of interest and then communicate with Base Station (BS).

On BS the gathered information is analyzed.

Advances in wireless sensor networks make many of the impossible possible.

Roadway safety warning, habitat monitoring , smart classroom , etc., are

prosperous applications tied to our daily life. Such networks rely on the

collaboration of thousands of resource-constrained error-prone sensors for

monitoring and control. One of the most contemporary challenges is to design

efficient methods for exploiting the new technology of wireless sensor networks

(WSN). A WSN consists of a large number of sensor nodes deployed over a certain

area, providing real-time data about certain phenomena . The deployment of a

WSN can be random (for example, dropping sensors in a hostile terrain or a

disaster area) or deterministic (for example, placing sensors along a pipeline to

monitor pressure and/or temperature, and boundary surveillance). WSN divided to

two important types: homogenous wireless sensor network and heterogeneous

wireless sensor network. In homogenous WSNs all nodes in the network have the

same power, resources, quality and etc, but heterogeneous WSNs consisting of two

types of wireless devices: resource-constrained wireless sensor nodes deployed


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randomly in a large number and a much smaller number of resource-rich super

nodes placed at known locations. The super nodes have two transceivers: one

connects to the WSN, and the other connects to the super node network. The upper

node network provides better Quality of Service (QoS) and is used to quickly

forward sensor data packets to the user. A study by Intel shows that using a

heterogeneous architecture results in improved network performance such as a

lower data-gathering delay and a longer network lifetime. Hardware components of

the heterogeneous WSNs are now commercially available .

2.LITERATURE SURVEY :Relocation of Gateway for Enhanced Timeliness in Wireless Sensor Networks

-Kemal Akkaya and Mohamed Younis ,Department of Computer Science and

Electrical Engineering ,University of Maryland, Baltimore County ,Baltimore, MD

21250 ,kemal1

In recent years, due to increasing interest in applications of wireless sensor

networks that demand certain quality of service (QoS) guarantees, new routing

protocols have been proposed for providing energy-efficient real-time relaying of

data. However, none of these protocols considered any possible movement of the

sink node for performance purposes. In this paper, we propose possible relocation

of sink (gateway) for improving the timeliness of real-time packets. Our approach

searches for a location close to the most loaded node. The gateway is then

relocated to the new location so that the load of that node is alleviated and the real-

time traffic can be split. As long as the gateway stays within the transmission range

of all last hop nodes, it can be moved to that location without affecting the current


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route setup. Otherwise routes are adjusted by introducing new forwarders.

Simulation results demonstrate the effectiveness of the proposed approach.

An Energy-Aware QoS Routing Protocol for Wireless Sensor Networks -

Kemal Akkaya and Mohamed Younis, Department of Computer Science and

Electrical Engineering, University of Maryland, Baltimore County,Baltimore, MD

21250, kemal1

Recent advances in wireless sensor networks have led to many new routing

protocols specifically designed for sensor networks. Almost all of these routing

protocols considered energy efficiency as the ultimate objective in order to

maximize the whole network lifetime. However, the introduction of video and

imaging sensors has posed additional challenges. Transmission of video and

imaging data requires both energy and QoS aware routing in order to ensure

efficient usage of the sensors and effective access to the gathered measurements. In

this paper, we propose an energy-aware QoS routing protocol for sensor networks,

which can also run efficiently with best-effort traffic. The protocol finds a least-

cost, delay-constrained path for real-time data in terms of link cost that captures

nodes energy reserve, transmission energy, error rate and other communication

parameters. Moreover, adjusting the service rate for both real-time and non-real-

time data at the sensor nodes maximizes the throughput for non-real-time data.

Simulation results have demonstrated the effectiveness of our approach for

different metrics.

Energy Aware Routing for Wireless Sensor Networks - Department of

Information Technology, PSG College of Technology, Coimbatore, INDIA

Self organizing, wireless sensors networks are an emergent and challenging

technology that is attracting large attention in the sensing and monitoring

community. Impressive progress has been done in recent years even if we need to


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assume that an optimal protocol for every kind of sensor network applications

cannot exist. The energy constraint sensor nodes in sensors networks operate on

limited batteries, so it is a very important issue to use energy efficiently and reduce

power consumption. Many routing protocols have been proposed among these

protocols, the adaptive routing protocols are very attractive because they have low

routing overhead. As a result, the routes tend to have the shortest hop count and

contain weak links, which usually provide low performance and are susceptible to

breaks. In this paper we introduce an adaptive routing protocol called energy aware

routing that is intended to provide a reliable transmission environment with low

energy consumption. This protocol efficiently utilizes the energy availability and

the received signal strength of the nodes to identify the best possible route to the

destination. Simulation results show that the energy aware routing scheme achieves

much higher performance than the classical routing protocols, even in the presence

of high node density and overcomes simultaneous packet forwarding.

Delay-Energy Aware Routing Protocol for Sensor and Actor Networks - Arjan

Durresi, Vamsi Paruchuri, Department of Computer Science, Louisiana StateUniversity, Baton Rouge, Louisiana, USA

We present a novel Delay-Energy Aware Routing Protocol (DEAP) for for

heterogeneous sensor and actor networks. DEAP enable a wide range of tradeoffs

between delay and energy consumption. The two major components of DEAP are:

(a) an adaptive energy management scheme that controls the wake up cycle of

sensors based on the experienced packet delay; and (b) a loose geographic routingprotocol that in each hop distributes the load among a group of neighboring nodes.

The primary result of DEAP is that it enables a flexible range of tradeoffs between

the packet delay and the energy use. Therefore, DEAP supports delay sensitive

applications of heterogeneous sensor and actor networks.


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Energy Aware Intra Cluster Routing for Wireless Sensor Networks-

International Journal of Hybrid Information Technology Vol.3, No.1, January,

2010.

Wireless Sensor Network (WSN) is an emerging technology that is predicted

to change the human life in future. This technology is composed of tiny sensing

objects called sensors that are wirelessly scattered in the environment.

Due to wireless nature and having limited lifetime (battery operated) there

are many challenges for researchers to make this technology more useful. In this

research work an energy efficient routing technique Energy Aware Intra Cluster

Routing (EAICR) is presented that has increased energy efficiency up to 17% and

increased the network lifetime up to 12% when compared with a well known

routing algorithm MultiHop Router [1] .

Energy-Efficient Communication Protocol for Wireless Micro sensor

Networks Proceedings of the 33rd Hawaii International Conference on System

Sciences2000.

Wireless distributed micro sensor systems will enable the reliable

monitoring of a variety of environments for both civil and military applications. In

this paper, we look at communication protocols, which can have significant impact

on the overall energy dissipation of these networks. Based on our findings that the

conventional protocols of direct transmission, minimum-transmission-energy,

multihop routing, and static clustering may not be optimal for sensor networks, we

propose LEACH (Low-Energy Adaptive Clustering Hierarchy), a clustering-based

protocol that utilizes randomized rotation of local cluster base stations (cluster-

heads) to evenly distribute the energy load among the sensors in the network.

LEACH uses localized coordination to enable scalability and robustness for


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dynamic networks, and incorporates data fusion into the routing protocol to reduce

the amount of information that must be transmitted to the base station. Simulations

show that LEACH can achieve as much as a factor of 8 reduction in energy

dissipation compared with conventional routing protocols. In addition, LEACH is

able to distribute energy dissipation evenly throughout the sensors, doubling the

useful system lifetime for the networks we simulated.

Energy Conservation in Wireless Sensor Networks: Department of Information

Engineering #Institute for Informatics and Telematics (IIT), University of Pisa,

Italy National Research Council (CNR), Italy.

In the last years, wireless sensor networks (WSNs) have gained increasing

attention from both the research community and actual users. As sensor nodes are

generally battery-powered devices, the critical aspects to face concern how to

reduce the energy consumption of nodes, so that the network lifetime can be

extended to reasonable times. In this paper we first break down the energy

consumption for the components of a typical sensor node, and discuss the main

directions to energy conservation in WSNs. Then, we present a systematic and

comprehensive taxonomy of the energy conservation schemes, which are

subsequently discussed in depth. Special attention has been devoted to promising

solutions which have not yet obtained a wide attention in the literature, such as

techniques for energy efficient data acquisition. Finally we conclude the paper with

insights for research directions about energy conservation in WSNs

A Novel Real-Time Power Aware Routing Protocol in Wireless Sensor

Networks - IJCSNS International Journal of Computer Science and Network

Security, VOL.10 No.4, April 2010 300 Manuscript received April 5, 2010

Manuscript revised April 20, 2010.


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One of the most important and challenging issues in real-time applications of

resource-constrained wireless sensor networks (WSNs) is providing end-to-end

delay requirement. To address such an issue a few QoS routing protocols have

been proposed. THVR (Two-Hop Velocity based routing protocol) is newly

proposed real-time protocol while it is based on the concept of using two-hop

neighbor information for routing decision. In this paper we propose a novel real-

time Power-Aware Two-Hop (PATH) based routing protocol. PATH improves

real-time performance by means of reducing the packet dropping in routing

decisions. PATH is based on the concept of using two-hop neighbor information

and power-control mechanism. The former is used for routing decisions and the

latter is deployed to improve link quality as well as reducing the delay. PATH

dynamically adjusts transmitting power in order to reduce the probability of packet

dropping. Also PATH addresses practical issue like network holes, scalability and

loss links in WSNs .We simulate PATH and compare it with THVR. Our

simulation results show that PATH can perform better than THVR in term of

energy consumption and delay.

Feedback Based Dynamic Energy Aware Routing Protocol - Haimasree

Bhattacharya , Krishnendu Mukhopadhyaya, Jadavpur University, Indian

Statistical Institute.

With the advancement of wireless sensor network many routing strategies

have been developed which deal with distinguishable features of wireless sensor

networks like energy, bandwidth, high rate of interaction with environment etc.

Tiny wireless sensors could be deployed in wilderness areas, where they would

remain for many years without the need to recharge or replace their power

supplies. Thus power management is a very important issue in this kind of

networks because of the battery driven nodes. The sensor nodes should be routed


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in such a way that the energy consumed along the routing path is as less as

possible. The energy aware routing protocols for WSNs developed so far are static

in nature in terms of node energy. Energy efficient routes are being developed on

the basis of energy initially available in nodes. Some node energy is used up in

transmission of messages. The energy of the node continually gets depleted with

transmission. But this dynamic behavior of node energy is not taken into

consideration in the following rounds. This paper proposes a Feedback based

Dynamic Energy aware Routing Protocol(FDERP)which deals with this dynamic

behavior of node energy. In contrast with LEACH it decreases the average energy

consumed per node increasing the network lifetime. The paper concludes with

open research issues.

3.Project Description3.1 Introduction To Project

The use of super nodes for transmitting data to a target node leverages the

advantages of small transmit distances for most nodes, requiring only a few nodesto transmit far distances to the target node. The super nodes gather the data and

send it directly to the target node. This model can greatly reduce communication

costs of most nodes because they only need to send data to the nearest Super node,

rather than directly to a target node that may be further away.


We place one super node in to the cluster. The super node is responsible for all

inter cluster communications. All the communications inside the cluster are always

destined to the super node, from where the data is transmitted further.


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3.2 Existing System

In most of the sensor nodes, a power source supplies the energy needed by the

device to perform the programmed task. This power source often consists of a

battery with a limited energy budget. In addition, it could be impossible or

inconvenient to recharge the battery, because nodes may be deployed in a hostile

or unpractical environment. On the other hand, the sensor network should have a

lifetime long enough to fulfill the application requirements. In many cases a

lifetime in the order of several months, or even years, may be required.

The existing systems are following either one hop model or multi hop model.

The one hop model is a simple model that uses direct data sending towards the BS.

In The multi hop model, nodes choose their neighbors to forward data toward the

BS, this model is an energy efficient model of routing.

Routing of sensor data has been one of the challenging areas in wireless sensor

network research. It usually involves multi-hop communications and has been

studied as part of the network layer problems. Despite the similarity between

sensor and mobile ad-hoc networks, routing approaches for ad-hoc networks

proved not to be suitable to sensors networks. This is due to different routing

requirements for ad-hoc and sensor networks in several aspects. For instance,

communication in sensor networks is from multiple sources to a single sink, which

is not the case in ad-hoc networks. Moreover, there is a major energy resource

constraint for the sensor nodes.

Nodes in sensor networks have restricted storage, computational and energy

resources; these restrictions place a limit on the types of deployable routing

mechanisms. Additionally, ad hoc routing protocols, for conventional wireless

networks support IP style addressing of sources and destinations. They also use


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intermediate nodes to support end-to-end communication between arbitrary nodes

in the network. It is possible for any-to-any communication to be relevant in a

sensor network; however this approach may be unsuitable as it could generate

unwanted traffic in the network, thus resulting in extra usage of already limited

node resources. Many to- one-communication paradigms are widely used in regard

to sensor networks since sensor nodes send their data to a common sink for

processing. This many-to-one paradigm also results in non-uniform energy

drainage in the network

Existing Topology:

3.3 Proposed System

In the proposed architecture sensor nodes are grouped into clusters controlled

by a single command node. Sensors are only capable of radio-based short-haul

communication and are responsible for probing the environment to detect a

target/event. Every cluster has a gateway node that manages sensors in the cluster.

Clusters can be formed based on many criteria such as communication range,

number and type of sensors and geographical location. In this project, we assume


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that sensor and gateway nodes are stationary and the gateway node is located

within the communication range of all the sensors of its cluster.

Clustering the sensor network is performed by the command node and is

beyond the scope of this paper. The command node will inform each gateway node

of the ID and location of sensors allocated to the cluster. Cluster routing is an

energy efficient routing model as compared with direct routing and multihop

routing. But there are some issues in cluster routing as well. We discussed the

problem of load balancing in cluster based routing and introduced a novel idea of

rotation of CH role inside the cluster named LEACH (Low-Energy Adaptive

Clustering Hierarchy), thus doing load balancing in the network. In this research

work this problem is kept in mind and a solution for limited energy source has

been proposed. The proposed solution is Energy Aware Intra Cluster Routing. In

this algorithm while keeping the scope to intra cluster communication each node is

not identical to other for routing the data. Some nodes are considered in close

region and they perform direct routing and outside the region nodes adopt multihop

routing. In this way the closer nodes are not having extra load on them. InTraditional routing like multihop the closer nodes exhaust energy very quickly

because they are Perform two task in their life time, one is sensing their own data

and second is routing the Data of other nodes.

The sensing nodes sense the environment and then transmit the data towards

the CH and on other hand CH get the data aggregates it and then transmit toward

the BS. By introducing CH with high powered batteries the network lifetime canbe increased.


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Proposed Topology:

3.4 Project Task

The work introduces another type of heterogeneous WSN called actor

networks, consisting of sensor nodes and actor nodes. The role of actor nodes is to

collect sensor data and perform appropriate actions. A sensor node is a tiny device

that includes three basic components: a sensing subsystem for data acquisition

from the physical surrounding environment, a processing subsystem for local data

processing and storage, and a wireless communication subsystem for data

transmission. In addition, a power source supplies the energy needed by the device

to perform the programmed task. This power source often consists of a battery with

a limited energy budget. In addition, it could be impossible or inconvenient to

recharge the battery, because nodes may be deployed in a hostile or unpractical


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environment. On the other hand, the sensor network should have a lifetime long

enough to fulfill the application requirements. In this paper we will refer mainly to

the sensor network model depicted and consisting of one sink node (or base

station) and a (large) number of sensor nodes deployed over a large geographic

area (sensing field). Data are transferred from sensor nodes to the sink through a

multi-hop communication paradigm. Experimental measurements have shown that

generally data transmission is very expensive in terms of energy consumption,

while data processing consumes significantly less. The energy cost of transmitting

a single bit of information is approximately the same as that needed for processing

a thousand operations in a typical sensor node. The energy consumption of the

sensing subsystem depends on the specific sensor type.

3.5 Flow Diagram

Sensor Node:

SENSOR

PIC 16F877A

BATTERY

SOURCE

RF

TRANSCEIVER


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Super Node:

4. IMPLEMENTATION AND METHODOLOGY:

4.1. SYSTEM SPECIFICATION:

4.1.1.PIC16F87XA :High-Performance RISC CPU

Only 35 single-word instructions to learn.

All single-cycle instructions except for program branches, which are two-cycle.

Operating speed: DC 20 MHz clock input DC200 ns instruction cycle

ARM

Processor

LCD

AC/DC

CONVERTER

Regulator

RF Transceiver


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Up to 8K x 14 words of Flash Program Memory, Up to 368 x 8 bytes of Data

Memory (RAM), Up to 256 x 8 bytes of EEPROM Data Memory.

Pinout compatible to other 28-pin or 40/44-pin PIC16CXXX and PIC16FXXX

microcontrollers .

Peripheral Features

Timer0: 8-bit timer/counter with 8-bit prescaler.

Timer1: 16-bit timer/counter with prescaler, can be incremented during Sleep via

external crystal/clock.

Timer2: 8-bit timer/counter with 8-bit period register, prescaler and

postscaler.

Two Capture, Compare, PWM modules

- Capture is 16-bit, max. resolution is 12.5 ns

- Compare is 16-bit, max. resolution is 200 ns

Synchronous Serial Port (SSP) with SPI (Master mode) and I2C

(Master/Slave)

Universal Synchronous Asynchronous Receiver Transmitter (USART/SCI) with

9-bit address detection.

Parallel Slave Port (PSP) 8 bits wide with external RD, WR and CS controls

(40/44-pin only) .

Brown-out detection circuitry for Brown-out Reset (BOR).

Analog Features


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10-bit, up to 8-channel Analog-to-Digital Converter (A/D)

Brown-out Reset (BOR)

Analog Comparator module with:

- Two analog comparators

- Programmable on-chip voltage reference (VREF) module

- Programmable input multiplexing from device inputs and internal voltage

reference

Comparator outputs are externally accessibleSpecial Microcontroller Features:

100,000 erase/write cycle Enhanced Flash program memory typical

1,000,000 erase/write cycle Data EEPROM memory typical

Data EEPROM Retention > 40 years

Self-reprogrammable under software control

In-Circuit Serial Programming (ICSP) via two pins

Single-supply 5V In-Circuit Serial Programming

Watchdog Timer (WDT) with its own on-chip RC oscillator for reliable operation

CMOS Technology:

Low-power, high-speed Flash/EEPROM technology

Fully static design


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Wide operating voltage range (2.0V to 5.5V)

Commercial and Industrial temperature ranges

Low-power consumption

Pin diagram:

Device overview

This document contains device specific information about the following devices:

PIC16F873A

PIC16F874A

PIC16F876A

PIC16F877A


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PIC16F873A/876A devices are available only in 28-pin packages, while

PIC16F874A/877A devices are available in 40-pin and 44-pin packages. All

devices in the PIC16F87XA family share common architecture with the following

differences:

The PIC16F873A and PIC16F874A have one-half of the total on-chip memory of

the PIC16F876A and PIC16F877A .

The 28-pin devices have three I/O ports, while the 40/44-pin devices have five .

The 28-pin devices have fourteen interrupts, while the 40/44-pin devices have

fifteen.

The 28-pin devices have five A/D input channels, while the 40/44-pin devices

have eight .

The Parallel Slave Port is implemented only on the 40/44-pin devices.

4.1.2.LPC2119/LPC2129 :Single-chip 16/32-bit microcontrollers; 128/256 kB ISP/IAPFlash with 10-bit ADC and CAN

General description

The LPC2119/LPC2129 are based on a 16/32 bit ARM7TDMI-S CPU with

real-time emulation and embedded trace support, together with 128/256 kilobytes

(kB) of embedded high speed ash memory. A 128-bit wide memory interface and

a unique accelerator architecture enable 32-bit code execution at maximum clock

rate. For critical code size applications, the alternative 16-bit Thumb Mode

reduces code by more than 30 % with minimal performance penalty. With their

compact 64 pin package, low power consumption, various 32-bit timers,


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4-channel 10-bit ADC, 2 advanced CAN channels, PWM channels and 46 GPIO

lines with up to 9 external interrupt pins these microcontrollers are particularly

suitable for automotive and industrial control applications as well as medical

systems and fault-tolerant maintenance buses. With a wide range of additional

serial communications interfaces, they are also suited for communication gateways

and protocol converters as well as many other general-purpose applications.

Key features

16/32-bit ARM7TDMI-S microcontroller in a tiny LQFP64 package. 16 kB on-chip Static RAM.

128/256 kB on-chip Flash Program Memory. 128-bit wideinterface/accelerator enables high speed 60 MHz operation.

In-System Programming (ISP) and In-Application Programming (IAP) viaon-chip boot-loader software. Flash programming takes 1 ms per 512 byte

line. Single sector or full chip erase takes 400 ms.

EmbeddedICE-RT interface enables breakpoints and watch points. Interruptservice routines can continue to execute while the foreground task is

debugged with the on-chip RealMonitor software.

Four channel 10-bit A/D converter with conversion time as low as 2.44 ms. Multiple serial interfaces including two UARTs (16C550), Fast I2C (400

kbits/s) and two SPIs.

60 MHz maximum CPU clock available from programmable on-chipPhase-Locked Loop with settling time of 100 ms.

Two 32-bit timers (with four capture and four compare channels), PWM unit(six outputs), Real Time Clock and Watchdog.


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Pin Diagram:

4.2. METHODOLOGY:

Direct transmission networks are very simple to design but can be very power

consuming due to the long distances from sensors to the target node. Alternative

designs that shorten or minimize the communication distances can extend network

lifetimes. The use of super nodes for transmitting data to a target node leverages

the advantages of small transmit distances for most nodes, requiring only a fewnodes to transmit far distances to the target node. The super nodes gather the data

and send it directly to the target node. This model can greatly reduce

communication costs of most nodes because they only need to send data to the

nearest Super node, rather than directly to a target node that may be further away.


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We place one super node in to the cluster. The super node is responsible for all

inter cluster communications. All the communications inside the cluster are always

destined to the super node, from where the data is transmitted further.

4.3. MODULES:

Speciation analysis and modules splitting Programming(super node, Sensor node) Simulation (graphical verification) Downloading (ARM,PIC) Testing (emulator) Proto Type Product

4.4 APPLICATIONS:

Wireless distributed microsensor systems will enable the reliable

monitoring of a variety of environments for both civil and militaryapplications. Recent advances in MEMS-based sensor technology, low-

power analog and digital electronics, and low-power RF design have enabled

the development of relatively inexpensive and low-power wireless

microsensors. These sensors are not as reliable or as accurate as their

expensive macrosensor counterparts, but their size and cost enable

applications to network hundreds or thousands of these microsensors in

order to achieve high quality, fault tolerant sensing networks. A wireless

sensor network consists of light-weight, low power, small size of sensor

nodes.


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The areas of applications of sensor networks vary from military, civil,

healthcare, and environmental to commercial.

Examples of application include forest fire detection, inventory control,

energy management, surveillance and reconnaissance, and so on .

Due to the low-cost of these nodes, the deployment can be in order of

magnitude of thousands to million nodes. The nodes can be deployed either

in random fashion or a pre-engineered way. WSN is an emerging technology

that can be deployed in such situation where human interaction is not

possible like border area tracking enemy moment or fire detection system.

Networking unattended wireless sensors are expected to have significant

impact on the efficiency of many military and civil applications such as

combat field surveillance, security and disaster management.

5. REFERENCES:

[1] K. Xing, X. Cheng, and M. Ding, Safety Warning Based on Roadway

Sensor Networks, submit to IEEE Wireless Communications and

Networking Conference 2005.

[2] A. Mainwaring, J. Polastre, R. Szewczyk, D. Culler, and J. Anderson,

Wireless Sensor Networks for Habitat Monitoring, ACM WSNA02,

Atlanta GA, September 2002.

[3] S. S. Yau, S. K. S. Gupta, F. Karim, S. I. Ahamed, Y. Wang, and B.

Wang, Smart Classroom: Enhancing Collaborative Learning Using

Pervasive Computing Technology, Proc. of 6th WFEO World

Congress on Engineering Education and Second ASEE International

Colloquium on Engineering Education (ASEE), June 2003.


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[4] J. Agre and L. Clare, "An Integrated Architecture for Cooperative

Sensing Networks," IEEE Computer, vol. 5, pp, 106-108, 2000.

[5] I.F. Akyildiz, W. Su, Y. Sankara subramaniam and E. Cayirci,

Wireless Sensor Networks: a Survey, Computer Networks, vol. 38,

no. 4, pp. 393-422, 2002.

[6] David Culler, D. Estrin and M. Srivastava, Overview of Sensor

Networks, IEEE Computer, August, pp. 41-49, 2004.

[7] Mihaela Cardei, Shuhui Yang, Algorithms for Fault-Tolerant

Topology in Heterogeneous Wireless Sensor Networks, IEEE

TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS,

VOL. 19, NO. 4, APRIL 2008.

[8] Heterogeneous Networks with Intel XScale, http://www.intel.com/

research/ exploratory/ heterogeneous.htm, 2007.

[9] Crossbow Mica2 Motes and Stargate-Xscale, http://www.xbow.com,

2007.

[10] Nima Jafari Navimipour, Leili Mohammad Khanli, "The LGR

Method for Task Scheduling in Computational Grid,"ICACTE, IEEE,

pp.1062-1066, 2008 International Conference on Advanced Computer

Theory and Engineering, Phuket, Thailand, 2008

[11] Linda Murphy, Hoda S. Abdel-Aty-Zohdy, M. Hashem-Sherif, A

Genetic Algorithm Tracking Model for Product Deployment in

Telecom Services, pp 1729-1732, IEEE, 2005.

[12] MlLENA KAROVA, Solving Timetabling Problems Using Genetic

Algorithms, 27th Int'l Spring Seminar on Electronics Technology, pp

96-98, IEEE, 2004.

Project Report on Super nodes- An Embedded Approach

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