Environment Pollution Analysis on Smart Cities Using Wireless Sensor Networks
DOI:
https://doi.org/10.13052/spee1048-5236.42112Keywords:
Air pollution, water pollution, noise pollution, smart city, environment monitoring system, cloud, wireless sensor networkAbstract
One of the numerous independent sensing devices in this sector, WSNs can monitor physical and environmental parameters and thousands of applications in other disciplines. A large environmental change has numerous harmful repercussions on human beings, such as air pollution. Using a cloud that communicates through wireless sensor networks (WSNs), environmental factors such as pollution can be monitored with greater precision. If cities are considered smart, they will have to address air pollution, a significant environmental problem in cities. It negatively impacts human health, discouraging individuals from relocating to cities, resulting in a lack of economic development. This means that WSN nodes could monitor the pollution levels in and around the city and along major thoroughfares. In this paper, wireless sensor network-based environmental pollution analysis (WSN-EPA) has been suggested to reduce air pollution in a smart city. WSN nodes have been installed to continuously monitor the city’s air quality levels and the movement of public transit vehicles. Passenger vehicles and public transit buses return to their original locations after passing through stationary nodes across the city with data on air pollution particles, such as gases, smoke, and other pollutants, collected by sensors onboard. Nodes on public transportation, buildings, and automobiles wirelessly gather data from stationary nodes. Once the nodes returned to the pollution monitoring system, the data would be processed. The findings show that the suggested system is a visually effective environmental monitoring system.
Downloads
References
Khan, S., Nazir, S., García-Magariño, I., and Hussain, A. (2021). Deep learning-based urban big data fusion in smart cities: Towards traffic monitoring and flow-preserving fusion. Computers & Electrical Engineering, 89, 106906.
Zhang, Y., Geng, P., Sivaparthipan, C. B., and Muthu, B. A. (2021). Big data and artificial intelligence-based early risk warning system of fire hazard for smart cities. Sustainable Energy Technologies and Assessments, 45, 100986.
Rubí, J. N. S., and de Lira Gondim, P. R. (2021). An IoT-based platform for environment data sharing in smart cities. International Journal of Communication Systems, 34(2), e4515.
Tekouabou, S. C. K. (2021). Intelligent management of bike-sharing in smart cities using machine learning and the Internet of Things. Sustainable Cities and Society, 67, 102702.
Kandt, J., and Batty, M. (2021). Smart cities, big data and urban policy: Towards urban analytics for the long run. Cities, 109, 102992.
Yigitcanlar, T., Kankanamge, N., and Vella, K. (2021). How are smart city concepts and technologies perceived and utilized? A systematic geo-Twitter analysis of smart cities in Australia. Journal of Urban Technology, 28(1–2), 135–154.
Abu-Rayash, A., and Dincer, I. (2021). Development of integrated sustainability performance indicators for better management of smart cities. Sustainable Cities and Society, 67, 102704.
Simonofski, A., Vallé, T., Serral, E., and Wautelet, Y. (2021). Investigating context factors in citizen participation strategies: A comparative analysis of Swedish and Belgian smart cities. International Journal of Information Management, 56, 102011.
Belhadi, A., Djenouri, Y., Srivastava, G., Djenouri, D., Lin, J. C. W., and Fortino, G. (2021). Deep learning for pedestrian collective behavior analysis in smart cities: A group trajectory outlier detection model. Information Fusion, 65, 13–20.
Arun, M., Barik, D., Sridhar, K.P. and Vignesh, G., 2022. Performance Analysis of Solar Water Heater Using Al2O3 NanoParticle with Plain-Dimple Tube Design. Experimental Techniques, pp. 1–14.
Kim, H., Choi, H., Kang, H., An, J., Yeom, S., and Hong, T. (2021). A systematic review of the smart energy conservation system: From smart homes to sustainable smart cities. Renewable and Sustainable Energy Reviews, 140, 110755.
Zekić-Sušac, M., Mitrović, S., and Has, A. (2021). Machine learning-based system for managing the energy efficiency of the public sector as an approach towards smart cities. International journal of information management, 58, 102074.
Bibri, S. E. (2021). A novel model for data-driven smart, sustainable cities of the future: the institutional transformations required for balancing and advancing the three sustainability goals. Energy Informatics, 4(1), 1–37.
Shen, M., Liu, A., Huang, G., Xiong, N. N., and Lu, H. (2021). ATTDC: An Active and Traceable Trust Data Collection Scheme for Industrial Security in Smart Cities. IEEE Internet of Things Journal, 8(8), 6437–6453.
Lv, Z., Chen, D., and Li, J. (2021). Novel system design and implementation for the smart city vertical market. IEEE Communications Magazine, 59(4), 126–131.
Drangert, J. O. (2021). Urban water and food security in this century and beyond: Resource-smart cities and residents. Ambio, 50(3), 679–692.
Cai, Z., Li, D., Deng, L., and Yao, X. (2021). Smart city framework based on intelligent sensor network and visual surveillance. Concurrency and Computation: Practice and Experience, 33(12), e5301.
Alazab, M., Lakshmanna, K., Reddy, T., Pham, Q. V., and Maddikunta, P. K. R. (2021). Multi-objective cluster head selection using fitness averaged rider optimization algorithm for IoT networks in smart cities. Sustainable Energy Technologies and Assessments, 43, 100973.
Cha, J., Singh, S. K., Kim, T. W., and Park, J. H. (2021). Blockchain-empowered cloud architecture based on secret sharing for a smart city. Journal of Information Security and Applications, 57, 102686.
Hajjaji, Y., Boulila, W., Farah, I. R., Romdhani, I., and Hussain, A. (2021). Big data and IoT-based applications in smart environments: A systematic review. Computer Science Review, 39, 100318.
Song, T., Cai, J., Chahine, T., and Li, L. (2021). Towards smart cities by the Internet of Things (IoT)—a silent revolution in China. Journal of the Knowledge Economy, 12(2), 1–17.
Chen, J., Ramanathan, L., and Alazab, M. (2021). Holistic, big data integrated artificial intelligent modeling to improve privacy and security in data management of smart cities. Microprocessors and Microsystems, 81, 103722.
Ageed, Z. S., Zeebaree, S. R., Sadeeq, M. M., Kak, S. F., Rashid, Z. N., Salih, A. A., and Abdullah, W. M. (2021). A survey of data mining implementation in smart city applications. Qubahan Academic Journal, 1(2), 91–99.
Bellini, E., Bellini, P., Cenni, D., Nesi, P., Pantaleo, G., Paoli, I., and Paolucci, M. (2021). An IoE and Big Multimedia Data Approach for Urban Transport System Resilience Management in Smart Cities. Sensors, 21(2), 435.
Garcia-Retuerta, D., Chamoso, P., Hernández, G., Guzmán, A. S. R., Yigitcanlar, T., and Corchado, J. M. (2021). An Efficient Management Platform for Developing Smart Cities: Solution for Real-Time and Future Crowd Detection. Electronics, 10(7), 765.
https://aeeeuropeenergy.com/
