In recent years, the Smart City concept is emerging as a way to increase efficiency, reduce costs, and improve the overall quality of citizen life. The rise of Smart City solutions is encouraged by the increasing availability of Internet of Things (IoT) devices and crowdsensing technologies. This paper presents an IoTCrowdSensingplatform that offers a set of services to citizens by exploiting a network of bicycles as IoT probes. Based on a survey conducted to identify the most interesting bike-enabled services, the SmartBike plat- form provides: real time remote geo-location of users’ bikes, anti-theft service, information about traveled route, and airpollutionmonitoring. The proposed SmartBikeplatform is composed of three main components: the SmartBike mobile sensors for data collection in- stalled on the bicycle; the end-user devices implementing the user interface for geo-location and anti-theft; and the SmartBike central servers for storing and processing detected data and providing a web interface for data visualization. The suitability of the platform was evaluated through the implementation of an initial prototype. Results demonstrate that the proposed SmartBikeplatform is able to provide the stated services, and, in addition, that the accuracy of the acquired air quality measurements is compatible with the one provided by the official environmental monitoring system of the city of Turin. The described platform will be adopted within a project promoted by the city of Turin, that aims at helping people making their mobility behavior more sustainable.
Modern cities are moving towards novel approaches for urban sustainability for improving citizenship’s life quality, thus aiming at the Smart City model. Environmental and mobility issues represent two key areas where policy makers address their interventions and, amongst them, noise pollution is one of the most significant causes of public concern. However, noise monitoring campaigns are expensive and require skilled personnel. A viable alternative is represented by Mobile CrowdSensing (MCS) paradigm, which exploits mobile devices as sensing platforms. In this paper, we propose a MCS- based platform that exploits noise measurements collected by citizens and offers a suggestion system to city managers about noise abatement measures (in terms of both estimated noise reduction and average installation costs). Several field tests demonstrated the feasibility of this approach as a suitable way to support city managers and to widen the possibilities of collaborative urban noise monitoring.
Baihaqi Siregar ; Ahmad Badril Azmi Nasution et.al, “Integrated pollutionmonitoring system for smart city”. This paper demonstrated the application based findings in the smart cities by getting the knowledge on how integration has taken place to monitor the pollution level for optimizing the control segment across the service regions. 
The goal of building a smart city is to improve quality of life by using technology to improve the efficiency of services and meet residents’ needs. Information and Communication Technology allows city officials to interact directly with the public to tell what is happening in the city, how the city is evolving, and how to enable a better quality of life. A Smart City is one with at least one initiative addressing one or more of the following six characteristics: Smart Governance, Smart People, Smart Living, Smart Mobility, Smart Economy and Smart Environment. In this system, an application was developed that is going to bear a hand in this campaign. An area that is being surveyed for estimating how much the area is affected by pollution. The constituents of air along with its proportion are calculated and if it is higher than normal then the officials are intimated about it. Then the people are evacuated to a safe place. The description about the integrated network architecture and the interconnecting mechanisms for the reliable measurement of parameters by smart sensors and transmission of data via internet is being presented. The longitudinal learning system could provide a self-control mechanism for better operation of the devices in monitoring stage. The framework of the monitoring system was based on a combination of pervasive distributed sensing units, information system for data aggregation, and reasoning and context awareness. Results are encouraging as the reliability of sensing information transmission through the proposed integrated network architecture is 97%. The prototype was tested to generate real-time graphical information rather than a test bed scenario .
are (i) air quality management for reduction of pollution and healthy environment [1-2] and (ii) automation of public buildings for reducing human effort and energy consumption . There have been numerous efforts on microclimate monitoring using Wireless Sensor Network (WSN). In [2-4] authors report indoor air quality monitoring by measuring pollution levels for indoor environments. In  author attaining energy autonomy for sensor node. In day to day life, humans interact with environmental parameters like temperature, humidity, light etc. and try to regulate them manually. Monitoring of these parameters through WSN, makes the system suitable without major modifications in the infrastructure. Such monitoring system for home automation, a part of building automation provides people comfort, security as well as option of energy saving by monitoring the daily energy consumption.
As discussed in this paper, recent technological developments in the miniaturization of electronics and wireless communication technology have led to the emergence of Environmental Pollution Sensor Network. Wireless AirPollutionMonitoring System provides real-time information about the level of airpollution in these regions, as well as provides alerts in cases of drastic change in quality of air. This information can then be used by the authorities to take prompt actions such as evacuating people or sending emergency response team. Node is designed for minimum power consumption with various strategies like sleep mode scheduling and time delay based design. Selection of low power modules also helps in improving power dissipation characteristics. It uses an Air Quality Index to categorize the various levels of airpollution. It also associates meaningful and very intuitive colors to the different categories, thus the state of airpollution can be communicated to the user very easily. The system also uses the AQI to evaluate the level of health concern for a specific area.
Above fig. shows that the vehicle pollutionmonitoring using IoT. This system is based on Raspberry Pi and Arduino. Here we are sending Gas sensors value to the raspberry Pi board using serial communication on python. Here Arduino Uno is continuously sending sensor data to the python script and python is comparing the value. If value crosses the set range or it will go on danger value the python script will mail to the given mail address from vehicle’s mail address in the python script.
pressure, temperature and humidity make it possible to perform higher precision measurements of gas concentrations. The precision achieved after the so called collocation calibration is comparable with the precision after calibration in laboratory conditions, which entails higher costs (in terms of materials and time) and that has been proven by other studies too [14, 15]. The co-location calibration can be considered as a valuable tool in the next generation of mobile air quality monitoring. This is what makes the present approach an interesting alternative for calibration of sensors to measure air parameters and will thus be the subject of our future studies. In this study we did not examine the long-term performance response characteristics (e.g., drift of signal over extended time periods, stability of response depending on sensor lifetime etc.). We envisage to make extended measurements and design a calibration model, which will take into consideration not only the changes in temperature, humidity and atmospheric pressure, but also the corrections for the temporal drift.
infrastructural frameworks creating environmental affairs like atmospheric changes, malfunctioning and pollution. Pollution is becoming serious issue so there is need to build such a flourishing system which overcomes the problems and monitor the parameters that affecting the environmental pollution. The solution includes the technology Internet of Things (IOT) which is a hook up of computer science and electronics. It can provide means to monitor the quality of environmental parameters like Air, Noise, Temperature, Humidity and light. To monitor pollution levels in industrial environment or particular area of interest, wireless embedded computing system is proposed. The system is using a prototype implementation consists of sensing devices, Arduino uno board, ESP8266 as wi-fi module. These sensing devices are interfacing with wireless embedded computing system to monitor the fluctuations of parameters levels from their normal levels. The aim is to build powerful system to monitor environmental parameters.
Currently, the application aggregates data from various sources, processes it, formats it appropriately, and visualizes it for the user. The application relies on data collected at specific points where a metering station or a specialized sensor is located. However, users often want to check the air quality in a location where measurements are not actually taken. Another important feature is a short-term forecast for air quality. This sets additional requirements for the developed application:
In our proposed model we are using gas sensors(MQ- 135,MQ-7 etc.), noise sensor, temperature and humidity sensor(DHT 11) to interface it with Node MCU by using relay as it provides to read more than one analog values which overcome the limitation of Node MCU of having only one analog pin. Here the cloud internet global server is used where the information is sent to the cloud server and is accessible by any user around the world using there smart mobiles through an app that we introduced in our model i.e Blynk-IOT for Android/IOS. The Device has to only be installed in the areas whose pollutionmonitoring has to be done and the people or authorities of that area can access the data and info about the quantity in there apps through notification. And the threshold values when crossed a alert notification is send to the authority and then a corresponding action can be taken for that issue. In this Model the sensor will sense the air quality and noise where the data is analyzed by the sensors and is send to the Node MCU i.e Microcontroller where an analog value for each sensor is generated with help of Relay , the ppm value will be generated for the gases and value in db will be generated for noise, also the degree Celsius for temperature and humidity is expressed in percentage. The information is then send to the cloud memory where the data is stored which then be accessed by the users through the app which is installed in there respective smart phones or computers.
Considering the ill effects of pollution on humans, in 2012, one in eight of total global deaths were caused by airpollution which was 7 million premature deaths globally . These deaths were a result of numerous diseases such as ischemic heart disease, chronic obstructive pulmonary disease, stroke, lung cancer and acute lower respiratory infections in children . The causes for all those diseases were associated with outdoor and indoor airpollution combined. Now, if one talks about water pollution, consuming contaminated water can lead to serious health issues in human beings and one might get affected by life threatening waterborne diseases caused by protozoans, viruses and bacteria’s such as amoebiasis, hepatitis A, E coli and dysentery. As per the WHO (world health organization) these diseases have a share of around 3.6% in the total daily global burden of diseases , and cause about 1.5 million human deaths annually. Similarly, noise pollution is also as harmful as the other two kinds of pollution as it may lead to hearing impairment, hypertension, ischemic heart disease, annoyance, and sleep disturbance . Many people especially in big cities are
Quality Monitoring System”. This paper describes to ensure the safe supply of drinking water the quality should be monitored in real time for that purpose new approach IOT (Internet of Things) based water quality monitoring has been proposed. In this paper, the design of IOT based water quality monitoring system that monitors the quality of water in real time. This system consists of some sensors which measure the water quality parameter such as pH, turbidity, conductivity, dissolved oxygen, temperature. The measured values from the sensors are processed by the micro- controller and these processed values are transmitted remotely to the core controller that is raspberry pi using Zigbee protocol. Finally, sensors data can view on internet browser application using cloud computing. 
In order to assess environmental threats of airpollution, complex chemical analysis of air samples might not be efficient enough due to complex composition of the mixture and cholinergic interactions between individual compounds . For obtaining reliable information about biological activity of pollutants, bacterial test for mutagenicity, e.g. the one proposed by Ames  are now commonly used. Studies by Rybak at al. and Rutkowski at al. [16, 17] proved successful application of spider webs in Ames mutagenicity assessment of both indoor and outdoor airpollution. Spiders dwell readily in human buildings, where adults of Pholcidae and Agelenidae family can be observed for whole year. As they naturally occur at homes or in outdoor localities such us road tunnels we can use them for cheap, long term monitoring of air pollutants, what is particularly utile in case of indoor monitoring. Study of Rybak at al.  has shown mutagenic activity of web samples collected from rooms exposed to pyrogenic and petrogenic emission (boiler room, garage, bedroom in a house nearby traffic road). Houses at sites without this potential threats did not display mutagenic activity.
Sensitive material of MQ-4 gas sensor is SnO2, which with lower conductivity in clean air. When the target combustible gas exist, the sensor’s conductivity is higher along with the gas concentration rising. MQ-4 gas sensor has high sensitivity to Methane, also to Propane and Butane. The sensor could be used to detect different combustible gas, especially Methane. The operating voltage is 5.0V±0.1V. It is with low cost and suitable for different application. Detecting range is 300ppm-10000ppm Methane.
Because of random communications, constrained convention institutionalization, security of information stockpiling and complex distinguishing proof frameworks to get to information, issues emerges in field of observing thus to conquer these issues we are planning, ' IOT based ecological contamination checking framework', to pick up contamination free future live. Change to be actualized Devices must be effectively coordinated with IOT stage Uniform information group over various stages Platform must be expandable and Fine-grained information deceivability display
Abstract: Air Quality (AQ) is a very topical issue for many cities and has a direct impact on citizen health. The AQ of a large UK city is being investigated using low-cost Particulate Matter (PM) sensors, and the results obtained by these sensors have been compared with government operated AQ stations. In the first pilot deployment, six AQ Internet of Things (IoT) devices have been designed and built, each with four different low-cost PM sensors, and they have been deployed at two locations within the city. These devices are equipped with LoRaWAN wireless network transceivers to test city scale Low-Power Wide Area Network (LPWAN) coverage. The study concludes that (i) the physical device developed can operate at a city scale; (ii) some low-cost PM sensors are viable for monitoring AQ and for detecting PM trends; (iii) LoRaWAN is suitable for city scale sensor coverage where connectivity is an issue. Based on the findings from this first pilot project, a larger LoRaWAN enabled AQ sensor network is being deployed across the city of Southampton in the UK.
Abstract: Airpollution has become a major issue in the modern world, the reason is industrial emissions and increasing urbanization along with traffic jams and heating/cooling of buildings. Monitoring urban air quality is therefore required by municipalities and by the civil society. Current monitoring systems rely on smoke and exhaust detection system that has been developed for monitoring exhaust gases using far infrared camera which is costly. In this paper, we focus on an alternative or complementary approach, with image processing aiming at obtaining the images from environment and monitoring the pollutants present in the environment using image processing method. In image processing the input may be image or video frames. The outputs are also images. Various tasks like classification, feature extraction, recognizing different patterns can be done using image processing method.
of such rule can be carried out in a similar way as that of 4 out of 7 procedure of Rule 2, which is studied in Appendix A, using the Markov chain embedding technique; for a related discussion of Markov chain embedding methodology the reader is referred to Balakrishnan and Koutras (2002). The mean and the standard deviation of the run length (RL) distribution are 147.22 and 143.29, respectively. This is in agreement with Frisen (2008), who states that, considering annual data, the usual ARL of the control procedures is set in the inter- val 120-240 containing the one third and the half of the year. We note that instead of using a Shewhart-type control chart supplemented with runs rules, a CUSUM or EWMA control chart may be proposed as an alternative. However, the choice of Shewhart type control chart and specifically the choice of a Shewhart control chart based only on the signs of the resid- uals, while supplemented with runs rules, is somehow straightforward. In particular, three key advantages are pointed out: (a) this procedure is easy to implement, which is necessary since the proposed framework is aimed at monitoringpollution and should be accessible to environmental scientists; (b) it is very robust to outliers (even conservative), which is critical since the nature of the application demands a very low level of false signals (just consider the case of a monitoring system providing a large number of false announcements of a hypothesised problem); and (c) this procedure is easily interpretable by the practitioner (see e.g. Koutras et al. 2007).
According to a report published earlier this year by the World Health Organization, airpollution now kills approximately seven million people annually, worldwide. This accounts for as much as one in eight deaths, and is by far the single biggest environmental health risk. In order to counteract this alarming statistic and take action to clean up air around the globe, it’s important to first understand where the pollution is most concentrated, how it occurs, what elements are involved and how we can neutralize them. In order to do this, comprehensive airmonitoring must be undertaken on a national and international scale.