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Urban Air Pollution Monitoring Using Wireless Sensor Networks: A Comprehensive Review.

1. Introduction

Air pollution is primary concern for the human kind, as it is degrading the environmental health and inhaling the pollutant air is dangerous for the health of the humankind. Due to these adverse effects of the air pollution on the human kind, the demand for the development of high-quality air monitoring systems has been in high demand and drawing the attention of researchers [1]. The air pollution monitoring systems detect the concentration levels of air pollutants and the analysis of the collected information is required for the policymakers to take necessary and proper steps to decrease the level of air pollution for the wellbeing of their citizens. For controlling air pollution first, we need to track Air Quality Index (AQI) which provides the levels of air pollution by using different sensors [2]. For the preservation of environment and health of the people measuring the air pollutants is a significant and a vital task.In the urban areas due to increase in the number of industries and automobile vehicles, emission of toxic gases like CO, NO2 and C[O.sub.2] etc. are also increasing causing the severe impact on the health conditions of the general public like coughing, wheezing and asthma. Poor air quality may cause increased short-term health problems such as fatigue and nausea as well as chronic respiratory diseases, heart disease, and lung cancer. To monitor the indoor and outdoor air pollution, the necessary steps involved in the design of the monitoring systems are data collection, analyzing the collected data and transmission of collected data. Wireless Sensor Networks (WSNs) are extensively used in the real-time applications to gather and examine the data smartly without the human involvement. In the present day, WSNs are widely employed in all the commercial, industrialized and military applications [2].Air pollution is monitored by conventional systems with stationary devices traditionally[1]. These monitoring stations are highly reliable, accurate and able to measure pollutants by employing the traditional analytical equipment, such as gas chromatograph-mass spectrometers. The drawbacks of the conventional monitoring devices are their large size, heavyweight and extraordinarily expensive. The locations of the monitoring stations need to be carefully placed to be useful as the concentration levels of air pollutants are sensitive to the human habitations and also depend on construction activities, movement of automobiles. Conventional air pollution systems are not able to provide air pollution data of high spatiotemporal resolution due to non-scalability and limited data availability

In the proposed paper, different air pollution monitoring methods using WSN is presented. It also brings the difference between conventional and WSN based air pollution monitoring systems [24].It describes the implementation, simulation methods to monitor Air pollution

2. Related work

Wei Ying Yi et al. 2015 [1] presented the concept of The Next Generation Air Pollution Monitoring System (TNGAPMS) with emphasis on the need for developing a real-time air pollution monitoring systems with low cost, energy efficient and high spatiotemporal resolution. The Next Generation Air Pollution Monitoring System (TNGAPMS) classified into three types of sensor networks based upon the carriers of the sensing elements are Sensors static Sensor Network (SSN), Community Sensor Network (CSN), Vehicle Sensor Networks (VSN). The working of these sensor networks was discussed along with the advantages, disadvantages, comparisons and limitations of present works

Liu, J.H. et al. 2011[3] proposed urban air quality monitoring system based on wireless sensor networks. Designed system architecture consists of sensor nodes comprising of a wide range of CO sensors powered by batteries; a gateway communication link includes a Global System for Mobile (GSM) and centralized control system provided by the Lab view program. All the CO sensors on the sensor nodes integrate with Zigbee communications. Air pollutant data sensed by the sensor nodes were sent to the central system through the gateway. The designed system was implemented in the main roads of Taipei city which were able to collect finest air pollution information in real time scenario. The presented work is categorized under the static sensor networks.

Kadri, A et al. 2013 [4] developed a wireless sensor network for outdoor air pollution monitoring and tested the designed prototype on the real-time basis. In the designed system the sensors are used to collect the pollution data of [O.sub.3], N[O.sub.2], CO, [H.sub.2]S and sensed information is send to the server through the GPRS. A solar panel was used for solving the power limitation of the stationary sensor nodes. Air pollution data captured by the sensor nodes can be made available to the public through the customized mobile and web Apps etc. after obtaining the authorization for the data dissemination. The presented work is categorized under the static sensor networks.

Mansour, S et al. 2014 [5] presented the outdoor air quality monitoring system based on wireless sensor networks for industrialized and urban areas. Designed system is capable of sensing [O.sub.3], CO and N[O.sub.2].These sensors are mounted on the sensor nodes Libelium's [43] Wasp mote. ZigBee communication link is used to upload the collected data by the gas sensors to the central server. Approved air pollution data is made available to the community through customized Web App, Emails or SMS. Designed system is simple, and it can be easily integrated with other applications. Clustering Protocol of Air Sensor (CPAS) was implemented in the designed system to improve the efficiencies in network lifetime, communication rate and network energy consumption in the simulation environment. In the proposed protocol, detection of failed sensor nodes and minimization of power consumption by the sensor nodes was achieved. The presented work is categorized under the static sensor networks in which the reliability, delay and accuracies of the system are also considered.

Khedo, K.K et al. 2010 [6] proposed a novel wireless sensor based system for air pollution monitoring (WAPMS).It consists of a communication system for collecting the air pollution information to the server from the array of sensor nodes. Air pollution data sensed by the sensor nodes sent to the server through the cluster heads autonomously. A Data aggregation algorithm Recursive Converging Quartiles (RCQ) was used to minimize the power consumption and to maximize the energy efficiency of the sensor nodes; a hierarchical routing protocol [33] was used. The designed system is implemented in the Mauritius city to monitor the outdoor air pollution. The presented work is categorized under the static sensor networks.

Jelicic, V et al. 2013 [7] proposed an indoor air pollution monitoring system based on WSN, which had addressed the issues related to the power consumption levels of the sensors, sensor nodes. In the proposed work, 36 sensor nodes were deployed on the first floor of the four-storied building to study the lifetime of the designed system. Various sensors are used on the sensor nodes for capturing the information related to temperature, humidity, carbon monoxide, VOC, accelerometers, motion sensors and obtained information is provided only to the researchers. The presented work is categorized under the static sensor networks

Hasenfratz, D et al. 2012 [9] proposed the mobile based outdoor air pollution monitoring system which is a low-cost sensing system consist of sensor nodes with small sensors for detecting the [O.sub.3] and a smartphone. Using the cellular network, location-based (GPS module built in smartphones) air pollutants information are uploaded to the server, and it is made available to the community with the customized mobile apps. In the proposed paper techniques are used to improve the quality of the data sensed by the sensors and it showed high spatial resolution. The presented work is categorized under the community sensor networks. Mendez, D et al. 2011 [10] presented the participatory sensing technology for monitoring outdoor air pollution based on community sensor networks (P-Sense). Information sensed by the various sensors(CO, C[O.sub.2], VOCs, [H.sub.2], temperature and humidity) on the sensors nodes are sent to the smart phones through the Bluetooth link. Cellular networks are used to sense the information and then uploaded the gathered information to the server to make it available to the community with the customized mobile apps. Practical issues related to the deployment of the designed system are also discussed.

Lo Re, G et al. 2014 [20] proposed a low cost and portable Urban Air Quality Monitoring Using Vehicular Sensor Networks. The designed system consists of various sensors mounted on the public transportation vehicles like buses, a GPS module and Zigbee communication link. Sensors on the sensor nodes are used to collect the levels of temperature, relative humidity, N[O.sub.2], C[O.sub.2], CO and[O.sub.3] which are transmitted to the central server through the Zigbee communication link. By using the mobility of public transportation vehicles, it is possible to achieve broad area coverage resulting in high spatial resolution level of information even with few sensors. Air pollution information is shared only for the research purposes.

Devarakonda, S et al. 2013 [21] presented a real-time air quality monitoring through mobile sensing in metropolitan areas. In the proposed work, portable sensor nodes are used consisting of Mobile Sensing Box (MSB) and other individual sensing devices to collect the fine-grained air pollution information.CO and PM concentration levels are sensed by the sensors of the Mobile Sensing Box. MSB mounted on the dedicated car travels around the city for the data collection, and this information was transmitted to the cloud server through the cellular network. Air pollution data captured by the sensor nodes are made available to the public through the customized mobile and web Apps etc.

Sherin Abraham et al. 2014[22] presented a low-cost wireless sensor network based indoor air quality monitoring system Designed system consist of a micro gas sensor, Arduino board and XBee modules which are capable of measuring six air quality parameters simultaneously. Sensor calibration is performed using Least Square Estimation method (LSM). Designed system results are compared with the commercially available indoor air quality monitoring systems, and the system showed an appreciable performance. The overall architecture for the WSN based Indoor air quality monitoring system is shown in the figure 1. James J.Q.YU et al. 2015 [23] proposed a vehicular sensor network based air pollution monitoring system. In the designed system public transport vehicles are chosen as carriers of the sensor nodes, and bus routes are selected for deploying these sensors called as Bus Sensor Deployment Problem (BSDP). Chemical Reaction Optimization (CRO) is an optimization tool used to choose the buses to install the sensors. The project was implemented in the Hong Kong Island bus route and the real-time data acquired showed the CRO technique could solve optimization problems efficiently.

Elias Yaacoub et al. 2013[25] presented the wireless sensor network for monitoring and analyzing the air quality in Doha, Qatar. Multi-gas monitoring stations (MGMS) are used machine-to-machine (M2M) communications to communicate with a backend server. A smart platform was deployed on the server. The platform handles data cleaning filtering and data processing tasks. It presented the data presentation for environmental experts using dedicated software tools, e.g., the R software system and its OpenAir package. Analysis and assessment of real measurement data was also investigated. Air Quality Monitoring System Architecture is as shown in the figure

Joonhee Kang et al. 2012 [26] proposed a low cost and portable RF based Sensor system to measure air pollution parameters like VOC, [O.sub.3], N[O.sub.2], and dust. Designed system was also used for measuring the pH and DO of water. It consists of Sensor unit, GPS unit, a Central processing unit and RF unit. Performance and efficiency of the designed system was tested against the commercially available units for the [O.sub.3] and N[O.sub.2] gases, and it showed the accuracy was comparable with the standard commercial unit. Project was implemented for the air pollution monitoring in the city of Incheon in South Korea.

Tanuj Ahuja et al. 2016[27] presented a low-cost portable air pollution monitoring system by using the Internet of things (IoT) to create awareness to the public about the air quality enabling them to make better choices regarding traveling a route or purchasing of houses in the better area.

Ruiyun Yu et al. 2016[28] suggested a random forest method for finding the air quality (RAQ) for urban areas. Urban sensing systems collect the data regarding the meteorology, the historical air quality data, road information, point of Interest (POI) and real-time traffic pollution data. The algorithm was used for the data training and prediction, and it was evaluated with the city data. This algorithm showed a better prediction precision when compared with three algorithms Box model, Gaussian model, Lagrangian model. Gyu-Sik Kim et al., 2016[29] presented the environmental sensors based air quality monitoring station to capture the information related to the temperature, humidity, PM10, and C[O.sub.2].The proposed work was implemented at the Seoul Metro and Seoul Metropolitan Rapid Transit Corporation. In the subways [37], the accuracy and precision of the PM instrument to monitor the PM10 concentrations using the light scattering method was enhanced by the linear regression analysis technique. It was observed that use of ventilation fans experimentally in the subways could improve the air quality level and reduce the P[M.sub.10] level Movva Pavani et al. 2016 [30,31] presented a real-time pollution monitoring using wireless sensor networks (WSN).The sensors sense the concentration levels of carbon monoxide,carbon dioxide and oxygen gases deployed on sensor motes (Libelium Waspmote) which were calibrated correctly and the project was implemented in the industrial belt of Hyderabad city. A multihop data aggregation algorithm was employed, and air pollutant data collected from the designed test beds are made available onto the internet through the middleware and web interface. The developed system is capable of obtaining the fine grain pollution data on the real-time basis.

3. WSN applications in air pollution monitoring

With the technology development of the sensors, integrated circuits and wireless communications have paved the way for the fast growth of wireless sensor networks.WSNs are capable of detecting, measuring and gathering data from the real world (air quality, water quality, weather and traffic conditions, etc.) and transferring the information to end users. A sensor is a class of transducer which converts physical quantities like heat, light, motion, vibration, and sound into electrical signals. The primary unit in a sensor network is sensor node, which contains onboard sensors, processor, memory, transceiver and power supply. The sensor system consists of a vast number of sensor nodes deployed either inside or very close to the sensed phenomenon. Wireless sensors systems can be placed anywhere as the conventional wired network cannot be deployed, e.g., volatile places like high-temperature areas, chemical and toxins prone areas. The capability of self-organization, ability to process concurrently, economical, limited energy resources, limited range of operation, fault-tolerance and rapid deployment characteristics of Wireless Sensor Networks make them resourceful for versatile applications like military, intelligence, control, monitoring, and surveillance of targeted areas.

The primary elements in air quality monitoring system using Wireless Sensor Networks are gas sensors (Fig. 5).

In Figure 6, the classification of gas sensors based on their working principles is shown.

The gas sensors of metal oxide semiconductor operate on the principle that the conductivity of the sensing element on the surface of the gas sensors will vary when they are exposed to the gas to be measured. Metallic oxide materials are used as sensing element. In polymer semiconductor gas sensors, the layer phtalocyanines are used as the sensitive element which has high thermal chemical stability. It works on the principle that the layer conductivity varies with the presence of gases. In the catalytic gas sensor, because of the catalytic reactions, there is a variation in the heat energy with the exposure to the gases.

In the Electrochemical gas sensors, a chemical reaction takes place between the two electrodes which are made up of catalytic metals. These widely used gases sensors are classified further into potentiometric and amperometric gas sensors. The working principle involved in the optical gas sensors is the variation in the radiation characteristics like polarization, intensity when the surface is exposed to the gases. In the Gravimetric sensors, there is variation in the mass due to the absorption of gas molecules leading to change in the mechanical properties. In Gas sensors based on MOS- structure, the characteristics of the transition metal electrodes vary with the exposure of gas. For developing the low-cost air quality monitoring systems, metal oxide gas sensors are preferred as they have high sensitivity and fast response time. The main drawbacks of these sensors are low selectivity and more sensitive to the temperature, humidity which makes identification of gases difficult. The characteristics of metal oxide gas sensors of the various companies are provided in Table 2.

4. Impact of air pollution on the health

Air Quality Index (AQI) is also referred as Pollutant Standard Index (PSI) or Air Pollution Index (API) gives the quality of the air in the range of 1 to 500 at a particular location.

Higher the AQI value, higher the health risks. It is calculated for the primary air pollutants like Carbon monoxide, sulphur dioxide, particle matters and ozone.AQI value up to 50 is considered to be good quality air and inhaling this air will not affect the public health. AQI value above 300 is deemed to be hazardous causing severe concerns on the public health. AQI value of 100 is set as a standard air quality by the Environmental Protection Agency (EPA). AQI value less than 100 is acceptable and above 100 is considered to be harmful to specific sensitive groups. AQI norms will differ for various countries and the AQI standards for the US is given in the table 3.

Air Quality Index, sub-categories proposed by IITM (MoES) for the Indian standards. Each group corresponds to the various levels of health risks.

* Good: Air quality is good and no health hazard when the AQI value is between 0 to 50

* Moderate: Air quality is acceptable and can cause respiratory problems to those who are sensitive to ozone or particle pollutants when the AQI value is in the range of 51100.

* Unhealthy: Only Sensitive group will experience health effects when the AQI value is in the range of 101-150.

* Unhealthy: Every person will experience the risk of health concerns, and the sensitive group will have serious health issues when AQI is between 150 - 200.

* Very Unhealthy: Poses severe health problems to all when AQI value is in the range of 201 - 300.

* Hazardous: AQI values above 300 represent acute health effects; it's a health emergency condition.

Air pollution is a complex mix of various components like particulate matter (PM) , gaseous pollutants like ozone, nitrogen dioxide (N[O.sub.2]), carbon monoxide (CO), sulphur dioxide (S[O.sub.2]) and volatile organic compounds (including benzene). It is posing serious health concerns of respiratory diseases like asthma, chronic obstructive pulmonary disease, lung cancer and even cardiovascular diseases.Gaseous pollutant, Ozone will have a severe health impact on the people suffering from lung disease, children and old people. Particle pollutants will have a serious health effect on the sections of people suffering from heart or lung diseases, older adults and children. It is suggested that the research should take place in this area to find the methods to reduce the air pollution levels to avoid cardiovascular diseases.

5. Existing air pollution monitoring techniques using Sensor networks

Based upon the carriers of the sensor nodes, the present air pollution monitoring systems are classified broadly into three categories [1].

* Static Sensor Network (SSN)

* Community Sensor Network (CSN)

* Vehicle Sensor Network (VSN)

5.1 Static Sensor Network (SSN)

Low-cost sensors are deployed on fixed structures like traffic lights, street lights, walls or at meticulously planned areas as shown in Figure 4. The number of sensors used in Static Sensor Network can be increased by using low-cost sensors, and the density of the sensor nodes in Static Sensor Network is much larger than the conventional air pollution monitoring systems. Air pollution data captured by the sensor nodes can be made available to the public through various devices /platforms like internet pages, mobile and Web Apps, etc. after obtaining the authorization for the data dissemination. Advantages of Static Sensor Networks

1. No constraint on energy consumption

2. No limitation on the size and weight of sensors

3. Multiple sensors can be placed per node

4. Accurate and reliable data

5. Network connectivity guaranteed

6. Well-calibrated and maintained sensors

Disadvantages of Static Sensor Networks

1. Need careful deployment of sensor nodes

2. Requires large number of sensor nodes

3. Resource wastage

4. Inconvenience in calibration and maintenance

5. Requirement of customised network

5.2 Community Sensor Network (CSN)

Low-cost portable sensors are used, and these nodes are carried by the public or the professional users usually environmental volunteers who are concerned about the quality of air as shown in Figure 5. Analysed air pollution data captured by the sensor nodes can be made available to the public through various devices /platforms like internet pages, mobile and web Apps, etc. after obtaining the authorization for the data dissemination.

Advantages of Community Sensor Networks

1. Cost efficiency

2. Public driven property

3. Mobility of sensor nodes

Disadvantages of Community Sensor Networks

1. Low data accuracy and reliability

2. Poorly calibrated and maintained sensors

3. Severe energy consumption constraints

4. Uncontrolled/semi-controlled mobility

5. Severe limitation on size and weight of sensors

5.3 Vehicle Sensor Network (VSN)

Low-cost portable sensors are used for acquiring the air pollution data in Vehicle Sensor Network (VSN) systems, typically carried by the public transportations like buses, trains, taxis as shown in Figure 6. By the mobility of vehicles, a large geographic area can be covered by the single sensor node which can achieve sufficient broad geographic coverage. Air pollution data captured by the sensor nodes can be made available to the public through various devices /platforms like internet pages, mobile and Web Apps, etc. after obtaining the authorization for the data dissemination.

Advantages of Vehicle Sensor Networks

1. Loose constraint on energy consumption

2. No limitation on size and weight of the sensors

3. Can place multiple sensors per node

4. Accurate and reliable data

5. High mobility of sensor nodes

6. Well-calibrated and maintained sensors

7. Feasibility in maintenance

Disadvantages of Vehicle Sensor Networks

1. Uncontrolled/semi-controlled mobility

2. Redundant sampling issues

3. Requirement of locating and communication devices equipment

4. Customised network requirement

Traditional and conventional air pollution monitoring systems use complex measurement techniques with sophisticated and high precision equipment with proper calibration techniques. To ensure the quality and data accuracy obtained from these systems, they are assisted by the build-in calibrator, air filtering equipment, temperature controller (cooler and heater) and relative humidity controllers. As a result, these equipment consume high power and they are in large volumes with heavyweight and cost.Due to significant size, power limitations, high maintenance cost and high initial investment traditional Air Quality Monitoring stations are used in the limited number. Wireless Sensor Network presents an attractive and alternate solution for the large conventional air monitoring stations as we could able to deploy the sensing stations with smaller size and low-cost sensors. The sensing stations are capable of collecting and transmitting the sensed information to the other sensing stations in the network. Comparison of methods and instruments employed in the existing air quality monitoring systems are given in Table 7.

6. Research challenges and future directions

Critical issues that determine the performance and design of a wireless sensor network [34] for the air pollution monitoring are as follows:

Energy Efficiency: Designing the energy efficient [35] software algorithms, protocols [33] and developing the energy-efficient scalable hardware architectures are the critical challenges for the researchers in this domain. Sensors operate on the batteries and energy is consumed in data collection, data processing, and data communication and also in idle condition. Regularly batteries are needed to be replaced or recharged after they have been drained out. It is difficult sometimes to restore or replace the batteries because of demographic conditions.

Self Management: Wireless sensor networks should able to manage the network configuration, maintenance and repair by itself once deployed and function without any human intervention [36, 37].

Hardware and Software Issues: Hardware components in Sensor Networks consist of hundreds of thousands of nodes, Flash memories and FPGA-based microcontrollers which are to be inexpensive and practically economically viable. Software in WSN should be light and less energy consuming which is hardware independent [38, 39].

Operating System: Operating System for wireless sensor networks should support a simple programming environment [40]. Various Operating Systems developed for Sensor nodes include TinyOS, Mantis Operating System and Nano-Qplus.

Quality of Service (QoS): Air pollution monitoring systems using WSNs are used in real time, and it is mandatory for the network to provide an excellent level of service QoS to its users. Unbalanced traffic, constantly changing network topology and imprecise routing affects the QoS and these issues should be addressed for improving the QoS. WSNs QoS should not be affected by adding or removing the nodes in the network [41, 42].

Security: Security of data is a relatively challenging task in WSNs as it should provide the safety of data traveling between the sensor nodes of the network or between the sensors and the base station. The sensor networks should be designed adequately with each sensor node and the base station having the capability to verify the data received from the trusted sender. A false data can change the way a network could be predicted. The integrity of data has to be maintained without any change in the data, and accurate data must reach at the user end [44, 45].

Architecture: Properly designed sensor network architecture should be employed as they will support the durability and scalability of the system without affecting the QoS [46] There is a significant need for research required in the design and development of air monitoring stations using the wireless sensor networks. Some of the future areas of research to be addressed are (i) Designing the energy efficient software protocols and developing the energy-efficient scalable hardware architectures (ii) Design of the self management systems (iii) Security and privacy (iv) Approaches for handling and utilization of the big data from numerous sensor deployed in the network. Hardware engineers should work towards designing the scalable architectures providing desirable QoS whereas software engineers should work towards developing efficient energy protocols. Wireless sensor networks present various practical issues which are needed to be addressed and feasible solutions should be evolved in building the future efficient and portable systems.

7. Conclusion

Air pollution is evolving as a severe environmental concern due to its enormous impact on the wellbeing of the people universal environment and also on the global economy. Conventional air pollution systems are not able to provide air pollution data of high spatiotemporal resolution due to non-scalability and limited data availability. Air pollution is majorly contributed by the industries and automobile vehicles. Hence there is a need to investigate and develop real-time, continuous air pollution monitoring systems of low cost and energy efficient. There is a need of proposing new methods in future to reduce Air pollution by introducing the proper controlling mechanisms.In the present review, various issues and challenges faced by the researchers during the implementation of the existing systems were presented.

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[46] Franco Frattolillo, "A Deterministic Algorithm for the Deployment of Wireless Sensor Networks," International Journal of Communication Networks and Information Security (IJCNIS), Vol. 8, No. 1, pp 1-10, 2016.

Movva Pavani (1), P.Trinatha Rao (2)

(1) Department of ECE, Faculty of Science and Technology, IFHE University, Hyderabad, India

(2) Department of ECE, School of Technology, GITAM University, Hyderabad, India
Table 1. Summary of Existing Air Pollution Monitoring Systems

S.No   Approach            WSN Type         Sensor Type

 1     Murty R.N et al.    WiFi             Not mentioned
       2008 [2]            802.11a/b/g
 2     Liu J.H. et al.     GSM and          CO sensor
       2011[3]             Zigbee

 3     Kadri A et al.      GPRS             [O.sub.3], N[O.sub.2], CO
                                             and [H.sub.2]S
       2013[4]
 4     Mansour S et al.    ZigBee           Electrochemical
       2014[5]                              ([O.sub.3], CO, N[O.sub.2])
 5     Khedo K.K et al.    Not              Not mentioned
       2010[6]             mentioned
 6     Jelicic V et al.    ZigBee           Solid-state (CO,
       2013[7]                              VOCs)
 7     Postolache O.A      Wi-Fi            Solid-state
       et al. 2009[8]      (802.11          (CO, VOCs)
                           b/g)
 8     Hasenfratz D et     Cellular         Solid-state ([O.sub.3])
       al. 2012[9]         network
 9     Mendez D et.al      Cellular         Solid-state
       2011[10]            network          (C[O.sub.2], VOCs),
                                            Catalytic ([H.sub.2]),
                                            Electrochemical (CO)
10     Jiang Y. et.al      Wi-Fi            NDIR (C[O.sub.2]),
       2011[11]                             Solid-state
                                            (CO, [O.sub.3])
11     Honicky R et al.    Not              Not mentioned
       2008[12]            mentioned
12     Maisonneuve N       Cellular         Microphone
       et al. 2009 [13]    network
13     Tsow F et.al        Cellular         QTF (VOCs)
       2009[14]            network
14     Volgyesi P et al.   Wi-Fi            Solid state (CO, N[O.sub.2],
       2008[15]                             [O.sub.3])
15     Ma Y et.al          Wi-Fi or         DUVAS ([O.sub.3],
       2008[16]            ZigBee           NO,N[O.sub.2], S[O.sub.2],
                           or Others        VOCs)
16     Hu S.C et.al        Cellular
       2009, 2011          network          NDIR (C[O.sub.2])
       [17 &18]            (GSM)
                           Wi-Fi or         Optical analyser
17     Wong K.J et al.     Cellular         (PM),
       2009 [19]           network          Solid-state
                           (GPRS)           (CO, N[O.sub.2],
                                            NO, VOCs)
18     Lo Re, G et al.     ZigBee           Solid-state (CO,
       2014[20]                             N[O.sub.2],[O.sub.3],
                                            C[O.sub.2])
19     Devarakonda S       Cellular         Solid-state (CO),
       et.al 2013 [21]     network          Optical analyzer
                                            (PM)
20     Sherin Abraham      Zigbee/Wi-Fi     Heating
       et al. 2014[22]                      semiconductor
                                            sensors -
                                            C[O.sub.2],[O.sub.3],VOC
                                            Chemical sensors-
                                            C[O.sub.2]
                                            Digital Sensors-
                                            Temperature and
                                            Humidity
21     James J.Q.YU et     Not mentioned    Not mentioned
       al. 2012[23]
22     Elias Yaacoub       Internet         Solid state gas
       et.al 2013[25]                       sensors

23     Joonhee Kang et     RF               Solid state gas
       al. 2012 [26]                        sensors
24     Tanuj Ahuja et      Zigbee/GSM/      Solid state gas
       al. 2016[27]        GPRS/ Wi-Fi/     sensors
                           Blue Tooth
25     Ruiyun Yu et al.    Not mentioned    Not mentioned
       2016[28]
26     Gyu-Sik Kim et      Internet         Temperature,
       al., 2016[29]                        humidity, P[M.sub.10], and
                                            C[O.sub.2]
27     Movva Pavani        Internet         Solid state gas
       et.al 2016[31]                       sensors C[O.sub.2],
                                            [O.sub.2]

S.No  Operating     Sensor Node                 Data
      Environment   & Algorithm                  Availability
                   Employed

 1    Outdoor       Linux based embedded PC      Web Apps
                    (266 MHz/128 MB/1 GB)
 2    Outdoor       Octopus II (1 MHz/10         Researchers only
                    KB/1 MB)
                    Lab view Program
 3    Outdoor       Arduino                      Web App, Mobile
                    (16 MHz/8 KB/2 GB)           App
 4    Outdoor       Arduino
                    (14 MHz/512 KB/2 GB)         Email, SMS, Web
                    Clustering Protocol of Air   App
                    Sensor
 5    Outdoor       Not mentioned                Not mentioned
                    Recursive Converging
                    Quartiles (RCQ)
 6    Indoor        JN5168                       None
                    (32MIPs/128KB/*)
 7    Indoor        IPu8930 (*/*/512KB)          Web page
 8    Outdoor       HTC HERO saxophone           Web App, Mobile
                                                 App
 9                  PRO200 Sanyo cellphone       Web App, Mobile
      Outdoor                                    App
10    Indoor        Arduino                      Web App, Mobile
                    (16 MHz/2 KB/32 KB)          App
11    Outdoor       LG VX980 smartphone          Not mentioned
12    Outdoor       NOKIA N95 cellphone          Web page, Mobile
                                                 App
13    Outdoor       Motorola Q phone             Web App, Mobile
14    Outdoor       8051 uC (*/4KB/2MB)          Web App
15                  Generic Ultra Violet         None
      Outdoor       Sensor Technologies
                    (GUSTO) sensors
16                  JN5139 (16 MHz/96            Web App
      Outdoor       KB/192 KB)
                    Renesas H8S                  Web App
17    Outdoor
18    Outdoor       Arduino (16 MHz/8 KB)        None
19    Outdoor       Arduino (16 MHz/8            Web App
                    KB/128 KB)
20    Indoor        Arduino (16 MHz/8            Web App
                    KB/128 KB)
21    Outdoor       Not mentioned                Web App
22    Outdoor       R software system            Web App
      Outdoor
23                  MCU                          Web App
                    ATmega 128
24    Outdoor       Microcontroller              Web App
25    Outdoor       Random forest method         Web App
                    for the air quality
                    (RAQ)
26    Outdoor       ATmega128                    Web App
27    Outdoor       Libelium Waspmote            Web App
                    & multihop data
                    aggregation algorithm

S.No  Sensor
       Network Type

 1    SSN
 2    SSN
 3    SSN
 4
      SSN
 5    SSN
 6    SSN
 7    SSN
 8    CSN
 9    CSN
10    CSN
11    CSN
12    CSN
13    CSN
14    VSN
15
      VSN
16    VSN
      VSN
17
18    VSN
19    VSN
20    SSN
21    VSN
22    SSN
23    SSN
24    SSN
25    SSN
26    SSN
27    SSN

Table 2. Characteristics of the Metal Oxide Sensors

Manufa       Sensor      Gas                    Gas               Power
cturer       type        Sensitivity            Concentration     consum
             TGS2442     CO                     50-1.000          ption
                                                                  [mw]

                                                                   14
Figaro       TGS2444     N[H.sub.3]              10-100            56
             TGS825      [H.sub.2]S               5-100           660
             TGS2611     C[H.sub.4]             500-10.000        280
             TGS4161     C[O.sub.2]             400-9.000         300
             705         NH3                     25-10.000        975
Synkera      706         N[O.SUB.X]             0,5-10            500
             707         VOCS                   do 100            400
             714         [H.sub.2]S               1-100           900
Sgx          711         C[H.sub.4],             90-10,000        900
                         [C.sub.3][H.sub.8]
             MiCS-2714   N[O.sub.2]               0.05-5           50
Sensortech   MiCS-5524   CO                       1-1.00           88
Sencera      MiCS-5914   N[H.sub.3]             0,1-10             88
             HS134       CO                     do 100            800
             HS811       C[O.sub.2]C            350-10.000      1,200
                         [H.sub.4],[C.SUB.3]
             HS131       [H.sub.8]                 1.000-5.000    800

Table 3. Air Quality Index (US Standards)

S.No   Air Quality Index   Levels of Health   Colors
       (AQI) Values        Concern
       When the AQI is     ... air quality    .... as symbolized
       in this range:      conditions are:    by this colour

1        0 to 50           Good                Green
2       51 to 100          Moderate            Yellow
                           Unhealthy for
3      101 to 150          Sensitive Groups    Orange
       151 to 200          Unhealthy           Red
5      201 to 300          Very Unhealthy      Purple
6      301 to 500          Hazardous           Maroon

Table 4. Air Quality Index (Indian Standards) Sub-index

           AQI       [O.sub.3]   CO      N[O.sub.2]   P[M.sub.10]
Colour     RANGE
Coding     INDEX     8h          8h      24h          24h
                     Avg         Avg     Avg          Avg
                                                      [micro]g/[m.sup.3]

Good         0-100     0-50        0-1.7    0-42         0-
                                                       100
Moderate   101-200    51-98        1.8-    43-         101-
                                  10.3     94          150
Poor
Unhealthy  201-300    99-118      10.4-    95-         151-
for                               14.7    295          350
sensitive
group      301- 400  119 -        14.8    296-         351-
Very Poor  401-      392         -30.2    667          420
Very                 393-         30.3-   668-         421-
Unhealthy  Above     Above       Above   Above        Above

           P[M.sub.2.5]
Colour
Coding     24h
           Avg
           [micro]g/[m.sup.3]

Good          0-60
Moderate     61-90
Poor
Unhealthy    91-
for         210
sensitive
group       211-
Very Poor   252
Very        253-
Unhealthy  Above

Table 5. The six standard pollutants and their health effects

S.No  Pollutant          Health Effects

1     Carbon             Reducing oxygen in the blood cells leads to
      Monoxide           decreased oxygen delivery to the body's organs
      (CO)               and tissues. The extremely high level can cause
                         death.
2     Nitrogen           The high-risk factor for emphysema, asthma and
      Dioxide            bronchitis diseases. Aggravate existing heart
      (N[O.sub.2])       disease and increase premature death.
3     Ozone ([O.sub.3])  Trigger chest pain, throat irritation, coughing
                         and congestion. Worsen bronchitis, emphysema
                         and asthma
      Sulphur
4     Dioxide            The high-risk factor of bronchoconstriction can
      (S[O.sub.2])       increase asthma symptoms.
5     Particulate        Cause premature death in people with heart and
      Matter             lung diseases. Aggravate asthma, decrease lung
      (P[M.sub.2.5]      function and increase respiratory symptoms like
      & P[M.sub.10])     coughing and difficulty breathing.
6     Lead (Pb)          Accumulate in bones and affect the nervous
                         system, kidney function, immune system,
                         reproductive systems, developmental systems and
                         cardiovascular system. Affect oxygen capacity
                         of blood cells.

Table 6. Comparison of SSN, CSN and VSN Networks

S.No   Type of Network   SSN         CSN         VSN

1      Mobility          Static      Very Good   Excellent
       Temporal          Excellent   Good        Very Good
2      Resolution
3      Cost Efficiency   Good        Excellent   Very Good
4      Maintenance       Very Good   Good        Excellent
5      Data Quality      Excellent   Good        Very Good

Table 7. Comparison of Methods and Instruments used in Existing Air
Quality Monitoring Systems

Pollutant         Measurement Method             Advantages

                  IR Absorption with Gas         Accuracy, range and
                                                 precision is
                  Filter Correlation Wheel       excellent
Carbon
Monoxide
(CO)              Electrochemical Sensor         Lowest, very l
                                                 ittle power
                                                 consumption
                                                 Low maintenance,
                                                 Response time < 50s
                  Cavity Attenuated Phase        Accuracy, range
Nitrogen          Shift Spectroscopy             and precision is
                                                 excellent
Dioxide
(N[O.sub.2])
                  Electrochemical Sensor         Lowest, very little
                                                 power consumption
                                                 Low maintenance,
                                                 Response time < 50s
                  Chemiluminescence              Accuracy, range and
                                                 precision is excellent
                  Detection
Ozone
([O.sub.3])
                  Solid-State Sensor             Low Maintenance,
                                                 Response time
                                                 20 s -90s, Life
                                                 expectancy is
                                                 10+ years
                  Electrochemical Sensor         Lowest, very
                                                 little power
                                                 consumption
                                                 Low maintenance,
                                                 Response time
                                                 < 50s
Sulfur Dioxide    UV Fluorescence                Accuracy, range
                                                 and precision is
(S[O.sub.2])                                     excellent
                  Electrochemical Sensor         Lowest, very
                                                 little power
                                                 consumption
                                                 Low maintenance,
                                                 Response time < 50s
                  Solid-State Sensor             Low Maintenance,
                                                 Response time
                                                 20 s - 90s, Life
                                                 expectancy is
                                                 10+ years
Particulate       Tapered Element                Provide real-time
                                                 (<1 h) data with
                                                 high
matter (PM)       Oscillating Microbalance       precision
                  analyzers
                                                 Provide real-time
                                                 (<1 h) data with
                  [beta]-attenuation analyzers   High precision.
                  (BAM)
                                                 Simple, robust and
                                                 inexpensive. Low
                  Black smoke method             and Easy to
                                                 maintain. Short
                                                 sample time
                                                 (in min).
                                                 Small, lightweight,
                                                 battery operated.
                  Optical analyzers              Short sample time,
                                                 Measure different
                                                 sizes of particles
                                                 simultaneously

Pollutant              Disadvantages                  Accuracy

                       Costly equipment is required.  [+ or -]0.5% of
                                                      reading
Carbon
Monoxide
(CO)                   Life expectancy is 1-2 years   [+ or -]0.5 ppm
                                                      at 0-5 ppm
                                                      [+ or -]10% at
                                                      5-25 ppm
                       Costly equipment is required.  [+ or -]0.5% of
                                                      reading
Nitrogen
Dioxide
(N[O.sub.2])
                       Life expectancy is 1-2 years   [+ or -]0.02 ppm
                                                      at 0-0.2 ppm
                                                      or [+ or -]10% at
                                                      0.2-1 ppm
                       Costly equipment is required.  [+ or -]0.5% of
                                                      reading
Ozone
([O.sub.3])
                       High power consumption is,     [+ or -]5 ppb
                       Require heating element
                       Life Expectancy is 1-2 years   Not Provided
Sulfur Dioxide         Costly equipment is required.  [+ or -]0.5% of
                                                      reading
(S[O.sub.2])
                       Life Expectancy is 1-2 years   [+ or -]0.05 ppm
                                                      at 0-0.5 ppm
                                                      or [+ or -]10% at
                                                      0.5-10 ppm
                       Power consumption is Large,    Not provided
                       Require heating element.
Particulate            Large size, heavy weight and   [+ or -]0.5
matter (PM)            high cost. Require heating     [micro]g/[m.sup.3]
                       element
                       Large size, heavy weight and
                       high cost. Require             [+ or -]1.0
                       radioactive source for         [micro]g/[m.sup.3]
                       heating
                       Darkness-mass factor may       [+ or -]2.0
                                                      [micro]g/[m.sup.3]
                                                      or higher
                       Vary from time to time and
                       location to location.
                       Particle characteristics       Not provided
                       differ from location to
                       location and time to time
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Author:Pavani, Movva; Rao, P. Trinatha
Publication:International Journal of Communication Networks and Information Security (IJCNIS)
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Date:Dec 1, 2017
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