CHAPTER 7

CONCLUSION AND FUTURE SCOPE

The real success of the sensor network technology depends mainly on its application in

eradicating a harmful situation or in maintaining a good one. Designing an efficient application

is one of the major challenges and sensor network challenges are application dependent.

Air quality monitoring is a prospective application domain which is of particular value to

our country. Large cities with high concentration of industry, intensive transport networks and

high population density are major sources of air pollution. Predicting air quality from multiple

sources by using modeling is very complicated. So, air quality models are best used for isolated

sources or situations.

As per the World Bank report quoted earlier, industrial pollution in India is on the more

alarming state than industrial production. Hence, controlling and monitoring air pollution round

the clock is a social imperative. This study proves that WSN could be a useful mechanism for

this double task.

The air quality data generation through air quality monitoring network available today,

involves large number of monitoring agencies, personal and equipment for sampling, chemical

analysis and data reporting etc. The involvement of several agencies increases the probability of

variations and personal biases reflecting on the data. Therefore, the air quality data statistics

available today is being recognized to be more indicative rather than absolute and perfect.

To carry out perfect air pollution models, namely scientific research, air management and

decision making, air pollution control, environmental impact and air pollution episodes,

continuous air pollution monitoring using sensor network is the only solution. It is mandatory to

expand the existing monitoring network. Many more on-line stations need to be established to

get real time information about the spatial distribution of pollution and areas of acute pollution.

The drawbacks of existing air pollution monitoring methods and limited study on

industrial air pollution monitoring have motivated this study. This study is mainly focused on

continuous industrial stack monitoring and reporting mechanism and energy efficient WSNs

design.

To design WSNs, the application domain space and network domain space are the two

avenues to be considered. Design requires domain knowledge and application needs to be

examined to solve a problem. Without thorough knowledge of the application domain, one

cannot design an effective sensor network. The application domain space and network domain

space characteristics of industrial air pollution monitoring application is decided based on the

knowledge gathered from some large scale industrial visit in Tamilnadu.

The choices of application domain space of industrial air pollution monitoring application

are analyzed with the view of 15 evaluation metrics. The set of evaluation metrics form a

multidimensional space that can be used to describe the capabilities of a SN. From the analysis, it

is noted that many of the evaluation metrics are interrelated. Often it may be necessary to

decrease performance in one metric, such as sample rate, in order to increase another that is

lifetime. It is concluded that the characteristics of the air pollution monitoring application are

large scale – long lived, with fixed sensor nodes, static physical topology, cost driven and no

delay in control.

Conceptually, the network domain space comes next to application domain space. The

network domain space refers to the configuration of connection between peripherals involved in

the network like sensor, computer, transmission media and levels of communication. The

possibilities of network design of building an efficient data collecting system for continuous air

pollution level monitoring using sensor network in an industrial area, with available resources

are discussed. The models outlined are

- Generic architecture of sensor network in Industrial Air Pollution Monitoring (IAPM)

through internet equipped with micro server in industrial premises and meta server in

pollution control board

- Design of proposed District Air Pollution Network (DAPNET)

- Design of simple short distance sensor field setup and sample long distance sensor field

that is topo sketch showing air quality monitoring sensor locations in XYZ industry

- One of the interesting three dimensional node location scenarios to monitor SPM

(Suspended Particulate Matter) level in stack of an industrial area

- Large scale industries are having industrial control systems namely Distributed

Control System (DCS) to form communication network of various critical

infrastructures of electric, water, oil, gas etc., In addition to these it is proposed to

form a modern field bus system with Sensors Marshalling Panel (SMP) to collect

data from various sensors available in different units of an industry.

- Multi source and single sink topology model to collect air pollution data.

The network design methodology can be very useful for management and control of

environmental pollution to ensure a pollution free environment and also to get real picture of air

pollution models.

In WSNs design logical and physical topology plays major role. The logical topology is a

method used to pass information between them. From the existing air pollution monitoring and

reporting methods, the following points are concluded.

Spot or short sampling cannot give adequate data on the nature and the magnitude

of an air pollution problem.

Collected data is treated as indicative rather than absolute.

Factors related to continuous monitoring are number of communication, energy

consumption and bandwidth.

The possible alternate method of reporting is aggregation. There is a lack of

objective criteria for choosing an appropriate aggregating method.

In statistical point of view, if the number of samples increases then the possible

error rate decreases.

Hence, in air pollution monitoring system, instead of reduced packet size and number of

communication, usefulness of data is important. If the samples are collected and maintained

once, it is possible to answer a wide range of queries out of network with accuracy.

An approach of monitoring continuously using various sampling techniques,

implemented using Castalia simulator. Initially, stack monitoring through single source and a

sink is carried out based on two schemes, namely at the rate of particular sampling interval and

sampling greater than threshold value. Next, to test scalability, the sampling pattern of small

network is carried out by constructing a network with four sources and a sink. The four schemes

tested are

- Periodic time sampling – The sensors communicate their data continuously at a pre

specified rate that is at the rate of particular sampling interval (Application Sample

Interval = 1000s for all nodes)

- Multiple sample rate - Different sampling interval for different nodes in a network to

record large emission sources frequently (Application Sample Interval = 1000s for

one node and 2000s for all other nodes).

- Threshold value sampling or event driven – The sensors report information only if an

event of interest occurs that is to report values greater than the defined pollution

threshold (150µgm)

- Time period sampling like duration of first shift, morning hours, peak hours, shut

down time etc. to measure variation in pollution levels (for example sampling from

the time period 15 minutes before - 27900s to 15 minutes after - 29700s for the shift

starts at night 12‟o clock -28800s).

Various sampling methods can be used to determine the concentration of air pollution

and these may be taken as guidance to compare results in different ways. Also, the benefit of

different sampling technique is to collect only valuable data and hence the amount of data

transmitted to the sink, RAM memory used and transmission channel utilization are reduced.

The optimization in sampling may be possible in terms of the parameters, namely node

number (self), sensed value (thedatavalue), and simulation time (simTime).

The major outcome of the simulation schemes is the value of spent energy obtained for

each node. The energy consumed for each node is same irrespective of the distance between

source and sink. This is because the energy consumption is based on the time the radio is ON

(Listening, Transmitting or Receiving). If two nodes have the same ON time then they will have

the same energy consumed. So, it is necessary to consider the possibilities of minimizing

consumed energy.

One of the key challenges in physical topology of WSN design is to provide energy

efficient communication. To do energy efficient communication, the impact of parameters like

Tx power, duty cycle and their correspondence is tested in order to reduce idle listening and

unnecessary high transmitting power that can be considered more in case of industrial stack

monitoring scenario. The simulation steps carried out are

- By tuning transmission power level

- For one-to-one network (Base work).

- For many-to-one network ( Two sources and a sink).

- By tuning duty cycle and listen interval

The findings are, in many-to-one network

(i) Nodes within the communication range receive all sensed data

(ii) With greater Tx power, greater transmission distances can be achieved.

So, selection of opt Tx power level based on the distance between source and sink is one

of the design criteria for energy efficient SN design. Opt Tx power means the lowest power level

able to successfully transmit messages from source to sink. When Tx power level increases, the

consumed energy decreases and this leads to energy save. For more than one source, it is not

necessary to assign same Tx power level for all sources in a network.

The following points are derived regarding duty cycle and their correspondence.

- Fix minimum duty cycle based on interference since maximum duty cycle will lead to

maximum energy

- Select opt listen interval for both source and sink and

- It is not necessary to fix the same listen interval for all sources and sink in many-to-

one network.

It is concluded that proper tuning of parameters, namely Tx power, Duty cycle, Listen

interval makes improvement in energy saving of 2.542% and 1.68% in Node(1) and Node(2)

respectively for just 60s of simulation time and 10s of application sample interval. To generalize

the result, the same study may be extended by tuning both simulation time and application

sample interval.

The design of the air quality monitoring network basically involves determining the

number of stations and their locations, with a view of the objectives, costs and available

resources. To assist an industrialist, an expert system should be developed to fix the exact

number and distribution of monitoring locations of a sensor. The expert system must contain

some guidance to energy efficient continuous air pollution monitoring sensor network.

One more challenge is, effective data warehousing and mining process for air pollution

monitoring data. The data stored within the central data warehouse will often need to be queried

by the end-users in order to find regularities and fluctuations in pollution levels. Furthermore,

there is a need to identify the long-term patterns of pollution, key relationship between certain

pollutants etc.

Society looks for a pollution-free globe for happy living. The global warming threat is

waiting at the door. Government rules, governing pollution control in private sector industries

are not implemented that effectively. This scenario stresses the need for an efficient monitoring

system with the collaboration of users, domain experts, hardware designers and software

developers. This study is an attempt in this direction.

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