project report on super nodes- an embedded approach
TRANSCRIPT
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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.
Here in our project, we have clustered the wireless nodes in the same region.
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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Here in our project, we have clustered the wireless nodes in the same region.
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.
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