Hi I am Ashik i would like to get details on circuit diagram for pollution less mobile horn system.

ABSTRACT
Sensor networks are currently an active research area mainly due to the potential of their applications. In
this paper we investigate the use of Wireless Sensor Networks (WSN) for air pollution monitoring in
Mauritius. With the fast growing industrial activities on the island, the problem of air pollution is
becoming a major concern for the health of the population. We proposed an innovative system named
Wireless Sensor Network Air Pollution Monitoring System (WAPMS) to monitor air pollution in
Mauritius through the use of wireless sensors deployed in huge numbers around the island. The proposed
system makes use of an Air Quality Index (AQI) which is presently not available in Mauritius. In order to
improve the efficiency of WAPMS, we have designed and implemented a new data aggregation algorithm
named Recursive Converging Quartiles (RCQ). The algorithm is used to merge data to eliminate
duplicates, filter out invalid readings and summarise them into a simpler form which significantly reduce
the amount of data to be transmitted to the sink and thus saving energy. For better power management we
used a hierarchical routing protocol in WAPMS and caused the motes to sleep during idle time.
KEYWORDS
Sensor Networks, Routing Protocol, Data Aggregation, Air Pollution Monitoring, Data Fusion
1. INTRODUCTION
Sensor networks are dense wireless networks of small, low-cost sensors, which collect and
disseminate environmental data. Wireless sensor networks facilitate monitoring and controlling
of physical environments from remote locations with better accuracy [1]. They have
applications in a variety of fields such as environmental monitoring, indoor climate control,
surveillance, structural monitoring, medical diagnostics, disaster management, emergency
response, ambient air monitoring and gathering sensing information in inhospitable locations [2,
3, 4, 5]. Sensor nodes have various energy and computational constraints because of their
inexpensive nature and ad-hoc method of deployment. Considerable research has been focused
at overcoming these deficiencies through more energy efficient routing, localization algorithms
and system design.
In this paper we proposed a wireless sensor network air pollution monitoring system (WAPMS)
for Mauritius. Indeed, with the increasing number of vehicles on our roads and rapid
urbanization air pollution has considerably increased in the last decades in Mauritius. For the
past thirty years the economic development of Mauritius has been based on industrial activities
and the tourism industry. Hence, there has been the growth of industries and infrastructure
works over the island. Industrial combustion processes and stone crushing plants had
contributed to the deterioration of the quality of the air. Further, the economic success of

  
      
   
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Mauritius has led to a major increase in the number of vehicles on the roads, creating additional
air pollution problem with smoke emission and other pollutants.
Air pollution monitoring is considered as a very complex task but nevertheless it is very
important. Traditionally data loggers were used to collect data periodically and this was very
time consuming and quite expensive. The use of WSN can make air pollution monitoring less
complex and more instantaneous readings can be obtained [6, 7]. Currently, the Air Monitoring
Unit in Mauritius lacks resources and makes use of bulky instruments. This reduces the
flexibility of the system and makes it difficult to ensure proper control and monitoring.
WAPMS will try to enhance this situation by being more flexible and timely. Moreover,
accurate data with indexing capabilities will be able to obtain with WAPMS. The main
requirements identified for WAMPS are as follows:
1. Develop an architecture to define nodes and their interaction
2. Collect air pollution readings from a region of interest
3. Collaboration among thousands of nodes to collect readings and transmit them to a gateway,
all the while minimizing the amount of duplicates and invalid values
4. Use of appropriate data aggregation to reduce the power consumption during transmission
of large amount of data between the thousands of nodes
5. Visualization of collected data from the WSN using statistical and user-friendly methods
such as tables and line graphs
6. Provision of an index to categorize the various levels of air pollution, with associated
colours to meaningfully represent the seriousness of air pollution
7. Generation of reports on a daily or monthly basis as well as real-time notifications during
serious states of air pollution for use by appropriate authorities
At present, our scientific understanding of air pollution is not sufficient to be able to accurately
predict air quality at all times throughout the country. This is where monitoring can be used to
fill the gap in understanding. Monitoring provides raw measurements of air pollutant
concentrations, which can then be analysed and interpreted. This information can then be
applied in many ways. Analysis of monitoring data allows us to assess how bad air pollution is
from day to day, which areas are worse than others and whether levels are rising or falling. We
can see how pollutants interact with each other and how they relate to traffic levels or industrial
activity. By analysing the relationship between meteorology and air quality, we can predict
which weather conditions will give rise to pollution episodes.
2. RELATED WORKS
Wireless Sensor Network (WSN) is an active field of research due to its emerging importance in
many applications including environment and habitat monitoring, health care applications,
traffic control and military network systems [8]. With the recent breakthrough of Micro-ElectroMechanical
Systems (MEMS) technology [9] whereby sensors are becoming smaller and more
versatile, WSN promises many new application areas in the near future. Typical applications of
WSNs include monitoring, tracking and controlling. Some of the specific applications are
habitat monitoring, object tracking, nuclear reactor controlling, fire detection, traffic
monitoring, etc.
Initial development into WSN was mainly motivated by military applications. However, WSNs
are now used in many civilian application areas for commercial and industrial use, including
environment and habitat monitoring, healthcare applications, home automation, nuclear reactor
controlling, fire detection and traffic control [8]. This transition from the use of WSN solely in

  
      
   
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military applications has been motivated due to the nature of WSNs which can be deployed in
wilderness areas, where they would remain for many years, to monitor some environmental
variables, without the need to recharge/replace their power supplies. Such characteristics help to
overcome the difficulties and high costs involved in monitoring data using wired sensors. Below
are some areas where WSN have been successfully deployed to monitor the environment.
2.1. Fire and Flood Detection
Large number of environmental applications makes use of WSNs. Sensor networks are
deployed in forest to detect the origin of forest fires. Weather sensors are used in flood detection
system to detect, predict and hence prevent floods. Sensor nodes are deployed in the
environment for monitoring biodiversity.
The Forest-Fires Surveillance System (FFSS) [10] was developed to prevent forest fires in the
South Korean Mountains and to have an early fire-alarm in real time. The system senses
environment state such as temperature, humidity, smoke and determines forest-fires risk-level
by formula. Early detection of heat is possible and this allows for the provision of an early alarm
in real time when the forest-fire occurs, alerting people to extinguish forest-fires before it grows.
Therefore, it saves the economical loss and environment damage. Similarly, a typical
application of WSN for flood detection and prevention is the ALERT system [11] deployed in
the US. Rainfalls, water level and weather sensors are used in this system to detect, predict and
hence prevent floods. These sensors supply information to a centralized database system in a
pre-defined way.
2.2. Biocomplexity Mapping and Precision Agriculture
Wireless sensor networks can be used to control the environment which involves monitoring air,
soil and water. Sensors are deployed throughout the field and these sensors form a network that
communicate with each other to finally reach some processing centre which analyse the data
sent and then accordingly adjust the environment conditions (e.g., if the soil is too dry, the
processing centre send signals which actuators recognise accordingly and thus can start the
sprinkling system. Biocomplexity mapping system helps to control the external environment.
Sensors are used to observe spatial complexity of dominant plant species [12]. An example is
the surveillance of the marine ground floor where an understanding of its erosion processes is
important for the construction of offshore wind farms [13].
Precision agriculture is an emerging WSN application area to monitor and control the amount of
pesticides present in drinking water, monitor the level of soil erosion and the level of air
pollution [14]. Precision agriculture encompasses different aspects such as monitoring soil, crop
and climate in a field. Huge amount of sensor data from large-scale agricultural fields are
frequently generated in such an application.
2.3. Habitat Monitoring
Concerns associated with the impacts of human presence in monitoring plants and animals in
field conditions have to a large extent been overcome by WSNs [15]. Sensors can now be
deployed prior to the onset of the breeding season and while plants are dormant or the ground is
frozen as well as on small islets where it is unsafe or unwise to repeatedly attempt field studies.
Such deployment represents a substantially more economical method for conducting studies
than traditional personnel-rich methods where substantial proportion of logistics and
infrastructure must be devoted to the maintenance of field studies, often at some discomfort and
occasionally at some real risk.

  
      
   
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Perhaps the best known application demonstrator for WSN in this domain is the Great Duck
Island project at Berkley [15]. Sensors monitored the microclimates in and around nesting
burrows used by the Leach's Storm Petrel in a non-intrusive and non-disruptive manner. Motes
were deployed on the island, with each of them having a microcontroller, a low-power radio,
memory, and batteries. Readings such as Infrared levels, humidity, rainfall and temperature
were monitored on a constant basis to better understand the movements of the petrels. Motes
periodically sampled and relayed their sensor readings to computer base stations on the island
which in turn fed into a satellite page link that allows researchers to access real-time environmental
data over the Internet.
Researchers at University of Florida and University of Missouri, Colombia are studying the role
of wildlife in maintaining diversity, tracking invasive species and the spread of emerging
diseases by obtaining unobtrusive visual information. They are using DeerNet [16] which is a
WSN-based system for analysing wildlife behaviour by tracking deer’s actions. The overall goal
is to develop a long-lived and unobtrusive wildlife video monitoring system capable of real-time
video streaming. The captured video will be transmitted over to a remote monitoring center for
real-time viewing and camera control. Advanced scene classification and object recognition
algorithms together with fusion of data from other sensors like GPS and motion can be applied
to remove essential visual information from the captured video. Then, statistical models about
animals' food selection, activity patterns and close interactions can be made consequently.
3. RECURSIVE CONVERGING QUARTILES (RCQ) DATA AGGREGATION
ALGORITHM
Most wireless sensor networks involve the collection of high amounts of data. For this reason,
during last years considerable research effort has been devoted to data fusion and aggregation
algorithms [17, 18]. In general, if we consider the problem to route data packets, representing
measurements collected by sensors, to a single managing entity, i.e., a network sink, it is often
efficient to exploit the correlation among similar data collected by the sensors in order to
decrease overhead [19, 20]. At this point, however, a trade-off arises between the amount of
transmitted data in the aggregated flows and their reliability. Data aggregation is a technique
which tries to alleviate the localized congestion problem. It attempts to collect useful
information from the sensors surrounding the event. It then transmits only the useful
information to the end point thereby reducing congestion and its associated problems. We have
developed a new data aggregation algorithm for WAPMS named Recursive Converging
Quartiles (RCQ). The algorithm includes two basic operations namely duplicate elimination and
data fusion.
3.1. Duplicate Elimination Technique
In WAPMS a packet consists of two parts: the data, which is the reading collected by the source
node, and an id, which identifies the node uniquely in the network such as a network address.
The cluster head collects readings from every node and stores them in a list. After collection, it
goes through each item in the list and check for the occurrence of packets with the same id,
thereby detecting the presence of duplicate packets. It then keeps only one instance of them.
Figure 1and figure 2 illustrate our proposed duplication elimination technique.

  
      
   
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Figure 1. Multihop routing of data during a collection instance
Figure 2. Illustration of duplicate elimination technique
3.2. Proposed Data Fusion Technique
There are several statistical methods to summarise a list of data. We have considered the use of
the three quartiles - lower, median and upper. We have considered the use of quartiles since they
are unaffected by extreme values; this is required in our system whereby extreme and invalid
values can sometimes be transmitted to the cluster head and these should not influence the data
fusion mechanism. Moreover, quartiles reduce the amount of data to only three values while still
reflecting the original data in an accurate way. The novel data fusion algorithm works as
follows:
1. The list is partitioned into several smaller groups
• We consider the length of the list
• We find its multiples in the form (x1, y1), (x2, y2)…
• E.g., length = 200, multiples = (1, 200), (2, 100), (4, 50), (5, 40), (10, 20), (20, 10),
(24, 5)
• We choose the pair which will give the highest number of groups (Maximise x) and the
minimum number of elements per group, while keeping it above a threshold (Minimise
y, y > threshold value)

  
      
   
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E.g., length = 50, multiples = (1, 50), (2, 25), (5, 10), (10, 5), threshold = 5, optimal pair
= (10, 5).
2. We calculate the quartiles for each of the smaller lists
3. Merge the resulting quartiles for the sub lists into one list
4. Repeat the whole process until the eventual number of groups, in which the list can be
broken, becomes one and the final list obtained has only three values.
Figure 3 below shows our proposed data fusion algorithm, Recursive Converging Quartiles, at
work to achieve 3 values out of the original 33.
Figure 3. Using RCQ to aggregate a list of 33 values to only 3 values
4. WAPMS: THE PROPOSED AIR POLLUTION MONITORING SYSTEM
The proposed wireless sensor network air pollution monitoring system (WAPMS) comprises of
an array of sensor nodes and a communications system which allows the data to reach a server.
The sensor nodes gather data autonomously and the data network is used to pass data to one or
more base stations, which forward it to a sensor network server. The system send commands to
the nodes in order to fetch the data, and also allows the nodes to send data out autonomously.
Figure 4 shows the architecture diagram of WAPMS.

  
      
   
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Figure 4. Architecture diagram of WAMPS
Below is a brief description of each component of WAMPS:
• Reading Sensor: generates a random value whose range is set based on the value of a
“seriousness” variable.
• Reading Transmitter: gets the generated value from the reading sensor and transmits it
through the communicator.
• Power Controller: Each node will have a method called “turn on” that will start the node
and we just call it. As for power-saving modes, this will depend on what the simulator will
provide to us.
• Communicator: this is implemented by the simulator. Inter-Process communication is
usually done using sockets; so, we expect the simulator to provide us with sockets as well
as methods such as “send” and “receive”.
• Launcher: informs the data collector to start collection based on the delivery mode set by
the user.
• Data Collector: gets a list of nodes from which it has to collect readings, then sends
messages to inform them and finally receives the required values.
• Aggregator: implements the RCQ algorithm for data aggregation that we will discuss in the
next section.
• Data Extractor: Use SQL queries to extract data from database
• Data Displayer: This extracts data as required by the user and displays them in a table as
well as evaluates the AQI for the selected area.
• Trend Analyser: Gets previous readings and determines relationship between them to be
able to extrapolate future readings.
• Nodes Deployment Viewer: Displays deployment of nodes in the WSN field and their AQI
colours.
• Connection Initiator: The java DriverManager allows for a method to open a database,
providing it the name of the database, user name and password as parameters. So, this
component just has to make a call to this method and store the return reference to the
connection.
• Connection Destructor: Connection object, in java.sql package, usually provides for a
close method that closes the latter safely and frees associated memory as well as save the
state of the latter. Therefore, this component just has to call this method.

  
      
   
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The following table shows the various types of nodes that are present in WAPMS:
Table 1. Types of Nodes
Type of
Node
Energy
Requirements
Location Role
Source
(sensor
node)
Constrained Random
Sensing and
multihop
routing
Cluster
Head
(collector)
NotConstrained
Fixed
Collection
and
aggregation
Sink
/Gateway
NotConstrained
Fixed Collection
These nodes will form a hierarchy that is shown in figure 5 below:
Figure 5. Hierarchy of nodes
The strategy to deploy the WSN for our system is as follows:
o We first partition our region of interest into several smaller areas for better management of
huge amount of data that will be collected from the system and for better coordination of the
various components involved
o We deploy one cluster head in each area; these will form cluster with the nodes in their
respective areas, collect data from them, perform aggregation and send these back to the
sink.
o We, then, randomly deploy the sensor nodes in the different areas. These will sense the data,
send them to the cluster head in their respective area through multihop routing
o We will use multiple sinks that will collect aggregated from the cluster heads and transmit
them to the gateway. Each sink will be allocated a set of cluster heads.
o The gateway will collect results from the sinks and relay them to the database and
eventually to our application.
Figure 6 illustrates our deployment strategy:

  
      
   
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Figure 6. Deployment strategy
The system is simulated over a small region as a prototype and then it will be extended to the
whole island. The town of Port Louis, the capital of the country, is chosen for the prototype
implementation as it is an urban area and therefore, more exposed to air pollution than rural
areas. The site is partitioned the site into 6 smaller areas as shown in figure 7. With this small
number of areas, we will use a single sink and we further simplify the system by allowing the
gateway to play the role of the latter.
Figur.