Urban Air Pollution Monitoring Using Wireless Sensor Networks: A Comprehensive Review.
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 . 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 . 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 .Air pollution is monitored by conventional systems with stationary devices traditionally. 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 .It describes the implementation, simulation methods to monitor Air pollution
2. Related work
Wei Ying Yi et al. 2015  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 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  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  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  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  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  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  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  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  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  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  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 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  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 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  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 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 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 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 , 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 .
* 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  for the air pollution monitoring are as follows:
Energy Efficiency: Designing the energy efficient  software algorithms, protocols  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 . 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  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.
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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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  802.11a/b/g 2 Liu J.H. et al. GSM and CO sensor 2011 Zigbee 3 Kadri A et al. GPRS [O.sub.3], N[O.sub.2], CO and [H.sub.2]S 2013 4 Mansour S et al. ZigBee Electrochemical 2014 ([O.sub.3], CO, N[O.sub.2]) 5 Khedo K.K et al. Not Not mentioned 2010 mentioned 6 Jelicic V et al. ZigBee Solid-state (CO, 2013 VOCs) 7 Postolache O.A Wi-Fi Solid-state et al. 2009 (802.11 (CO, VOCs) b/g) 8 Hasenfratz D et Cellular Solid-state ([O.sub.3]) al. 2012 network 9 Mendez D et.al Cellular Solid-state 2011 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 Solid-state (CO, [O.sub.3]) 11 Honicky R et al. Not Not mentioned 2008 mentioned 12 Maisonneuve N Cellular Microphone et al. 2009  network 13 Tsow F et.al Cellular QTF (VOCs) 2009 network 14 Volgyesi P et al. Wi-Fi Solid state (CO, N[O.sub.2], 2008 [O.sub.3]) 15 Ma Y et.al Wi-Fi or DUVAS ([O.sub.3], 2008 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  network Solid-state (GPRS) (CO, N[O.sub.2], NO, VOCs) 18 Lo Re, G et al. ZigBee Solid-state (CO, 2014 N[O.sub.2],[O.sub.3], C[O.sub.2]) 19 Devarakonda S Cellular Solid-state (CO), et.al 2013  network Optical analyzer (PM) 20 Sherin Abraham Zigbee/Wi-Fi Heating et al. 2014 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 22 Elias Yaacoub Internet Solid state gas et.al 2013 sensors 23 Joonhee Kang et RF Solid state gas al. 2012  sensors 24 Tanuj Ahuja et Zigbee/GSM/ Solid state gas al. 2016 GPRS/ Wi-Fi/ sensors Blue Tooth 25 Ruiyun Yu et al. Not mentioned Not mentioned 2016 26 Gyu-Sik Kim et Internet Temperature, al., 2016 humidity, P[M.sub.10], and C[O.sub.2] 27 Movva Pavani Internet Solid state gas et.al 2016 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)|
|Date:||Dec 1, 2017|
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