**8. Application example - air quality monitoring**

The methods presented in this Chapter can be applied to several WSN deployment scenarios. An important application of WSNs is the monitoring of environmental parameters. With the advancements in the production of small, accurate, low power sensors, it is becoming more and more possible to deploy a WSN for continuous monitoring of air quality. The WSN would report the concentration of several pollutants in the atmosphere, and the reported measurements can be made available to the general public via dedicated websites, mobile applications, etc. In addition, the stored measurements can be made available to expert environmental scientists to analyze and assess pollution information in order to submit recommendations to the relevant authorities in order to take appropriate action.

In this section, we present a high level description of the system architecture for air pollution monitoring and describe the role of the SNs where the presented communication protocol will be applied. The system model for air pollution monitoring is displayed in Fig. 9. Each BS covers a cell of certain area, where several SNs are deployed to monitor environmental parameters. The architecture follows a three-tier approach:


**Figure 9.** Implementation scenario for air pollution monitoring.

20 Will-be-set-by-IN-TECH

algorithms. Consequently, the results of Section 5.3 correspond to a worst-case scenario, and

The multihop and clustering methods are based on selecting certain nodes that transmit the data of other SNs in addition to their own data. This could lead to an increase in energy consumption for some of these nodes compared to the non-cooperative scenario, although the overall energy consumption in the network is minimized. In [29], it was shown that, within a single cluster, fading variations lead to selecting a different cluster head for each fading realization, and this was shown to lead to fairness in energy consumption in the cluster on the long term. Thus, in the case of WSNs deployed for long term measurement and monitoring of certain parameters, different training phases (as explained in Section 7.1), will occur. Consequently, the techniques presented in this Chapter can be considered to be fair. In fact, different SNs will take turn to relay the SR data when the fading varies, which

The methods presented in this Chapter can be applied to several WSN deployment scenarios. An important application of WSNs is the monitoring of environmental parameters. With the advancements in the production of small, accurate, low power sensors, it is becoming more and more possible to deploy a WSN for continuous monitoring of air quality. The WSN would report the concentration of several pollutants in the atmosphere, and the reported measurements can be made available to the general public via dedicated websites, mobile applications, etc. In addition, the stored measurements can be made available to expert environmental scientists to analyze and assess pollution information in order to submit

In this section, we present a high level description of the system architecture for air pollution monitoring and describe the role of the SNs where the presented communication protocol will be applied. The system model for air pollution monitoring is displayed in Fig. 9. Each BS covers a cell of certain area, where several SNs are deployed to monitor environmental

1. The sensor nodes (SNs): these include the sensors, measuring pollutants to be monitored, e.g., CO, NOx, Ozone, and Particulate Matter (PM), in addition to other environmental parameters like relative humidity and temperature. An SN usually can accommodate one or more sensors, with each sensor measuring one of the mentioned parameters. The SNs transmit the measured data using the presented communication methods. Thus, the nodes can form cooperative clusters, and relay the data in a multihop fashion ensuring energy

2. The database server: the data received at the BS is sent to a database server where it is stored using a common format in order to automate its extraction and analysis. The measured data might contain missing, noisy, or erroneous values. Appropriate data integrity checks should be performed before storing the data for subsequent use. Afterwards, the data becomes ready for analysis and display. Analysis techniques include statistics (for computation of daily, monthly, or yearly averages of a certain air pollutant),

recommendations to the relevant authorities in order to take appropriate action.

the complexity in practical scenarios is generally lower.

averages out the energy consumption levels among SNs.

**8. Application example - air quality monitoring**

parameters. The architecture follows a three-tier approach:

efficiency.

**7.2. Fairness considerations**

advanced interpolation, neural networks, principal component analysis, and data mining techniques.

3. The Client tier: it consists of client-side applications running on computers or mobile devices, e.g. smart phones. These applications access the network via the server, which forwards the stored data received from the sensors. Examples of applications include periodically updated web sites with data summaries and statistics, data visualization with display of sensor locations on a map (along with each SN's measurements), and data dissemination applications like SMS alerts relating to pollution levels in certain areas.
