The periodic time series analysis of different aspects of urban areas is essential owing to rise ... more The periodic time series analysis of different aspects of urban areas is essential owing to rise in population, stressed resources, and lack of technology-based solutions. In this regard, temporal analysis of water quality holds paramount importance, and for this purpose, the data from satellite remote sensing, geographic information system (GIS), and internet of things (IoT) are collected to perform water quality trend analysis. The study area is Rawal Dam, where data are processed to derive water quality parameters (WQPs) and thereafter water quality index (WQI). The monthly, yearly, seasonal, pre- and post-COVID-19 temporal analyses are performed to analyze the trends of different WQPs and overall WQI, using suitable machine learning (ML) models over the last eight years (2013-20). The water quality classification is performed using neural networks (NN) with an accuracy of 80%, and predictions are made using vector auto-regression (VAR) and long short-term memory (LSTM) networks with an average root mean squared error (RMSE) of 25.63 and 2.664, respectively.
Advances in logistics, operations, and management science book series, 2022
Remote sensing through satellites and internet of things (IoT) technology are two widespread tech... more Remote sensing through satellites and internet of things (IoT) technology are two widespread techniques to assess inland water quality. However, both these techniques have their limitations. IoT provides point data, which is insufficient to represent entire water body, especially if the water body has complex terrain and hydrology. Through remote sensing, we can sample data of a large area, but data acquisition is constrained by satellite. Revisit time and quality of estimates can be affected by image resolution. Moreover, non-optical properties that might affect water quality cannot be sensed through satellites. To complement this, GIS data from labs can be useful for providing higher resolution and accurate data and can be used as ground truth. Thus, in this chapter, the authors aim to integrate both these data collection techniques followed by estimation and prediction through machine learning models. The accumulated datasets are used to train machine learning (ML) models deployed at a server. The selected ML model is an artificial neural network with train accuracy of 97% and test accuracy of 95%.
Transitioning to green energy transport systems, notably electric vehicles, is crucial to both co... more Transitioning to green energy transport systems, notably electric vehicles, is crucial to both combat climate change and enhance urban air quality in developing nations. Urban air quality is pivotal, given its impact on health, necessitating accurate pollutant forecasting and emission reduction strategies to ensure overall well-being. This study forecasts the influence of green energy transport systems on the air quality in Lahore and Islamabad, Pakistan, while noting the projected surge in electric vehicle adoption from less than 1% to 10% within three years. Predicting the impact of this change involves analyzing data before, during, and after the COVID-19 pandemic. The lockdown led to minimal fossil fuel vehicle usage, resembling a green energy transportation scenario. The novelty of this work is twofold. Firstly, remote sensing data from the Sentinel-5P satellite were utilized to predict air quality index (AQI) trends before, during, and after COVID-19. Secondly, deep learning m...
Empowering Sustainable Industrial 4.0 Systems With Machine Intelligence
As of today, increased air pollution has disrupted the air quality levels, deeming the air unsafe... more As of today, increased air pollution has disrupted the air quality levels, deeming the air unsafe to breathe. Traditional systems are hefty, costly, sparsely distributed, and do not provide ubiquitous coverage. The interpolation used to supplement low spatial coverage induces uncertainty especially for pollutants whose concentrations vary significantly over small distances. This chapter proposes a solution that uses satellite images and machine/deep learning models to timely forecast air quality. For this study, Lahore is chosen as a study area. Sentinel 5-Precursor is used to gather data for Sulphur Dioxide (SO2), Nitrogen Dioxide (NO2), and Carbon Monoxide (CO) for years 2018-2021. The data is processed for several AI models, where convolutional neural networks (CNN) performed the best with mean squared error (MSE) 0.0003 for the pollutants. The air quality index (AQI) is calculated and is shown on web portal for data visualization. The trend of air quality during COVID-19 lockdow...
AI for Emerging Verticals: Human-robot computing, sensing and networking, 2020
Water is one of the basic resources required for human survival. However, pollution of water has ... more Water is one of the basic resources required for human survival. However, pollution of water has become a global problem. 2.4 billion people worldwide live without any form of water sanitation. This work focuses on case study of water pollution in Pakistan where only 20% of the population has an access to good-quality water. Drinking bad-quality water causes diseases such as hepatitis, diarrhea and typhoid. Moreover, people living close to the industrial areas are more prone to drinking polluted water and catching diseases as a result. Yet, there is no system that can monitor the quality of water or help in disease prevention. In this work, an Internet of Things (IoT)-enabled water quality monitoring system is developed that works as a stand-alone portable solution for monitoring water quality accurately and in real time. The real-time results are stored in a cloud database. The public web portal shows these results in the form of data sheets, maps and charts for analyzing data. Further, this data along with the collected data of past water quality is used to generate machine learning (ML) models for prediction of water quality. As a consequence, a model for prediction of water quality is trained and tested on a test set. The predictions on the test set resulted in a mean squared error (MSE) of 0.264.
2018 15th International Conference on Smart Cities: Improving Quality of Life Using ICT & IoT (HONET-ICT)
The ubiquitous use of IEEE 802.11 has aggravated the need to make efficient use of available band... more The ubiquitous use of IEEE 802.11 has aggravated the need to make efficient use of available bandwidth. Currently handoff decision in IEEE 802.11 is made based on the received signal strength but these results in poor connectivity specifically when an access point is overloaded. Overlapping regions where users can be connected to multiple access points, switching to less loaded access point can improve overall network capacity. In this article, we propose a decentralized approach for best access point selection which also prevents an access point to get overloaded. We propose an algorithm for handover strategy to improve network capacity via load balancing and it also minimizes switching overhead. We perform detail analysis on publically available dataset which consists of millions of Wi-Fi sessions with multiple access points.
The periodic time series analysis of different aspects of urban areas is essential owing to rise ... more The periodic time series analysis of different aspects of urban areas is essential owing to rise in population, stressed resources, and lack of technology-based solutions. In this regard, temporal analysis of water quality holds paramount importance, and for this purpose, the data from satellite remote sensing, geographic information system (GIS), and internet of things (IoT) are collected to perform water quality trend analysis. The study area is Rawal Dam, where data are processed to derive water quality parameters (WQPs) and thereafter water quality index (WQI). The monthly, yearly, seasonal, pre- and post-COVID-19 temporal analyses are performed to analyze the trends of different WQPs and overall WQI, using suitable machine learning (ML) models over the last eight years (2013-20). The water quality classification is performed using neural networks (NN) with an accuracy of 80%, and predictions are made using vector auto-regression (VAR) and long short-term memory (LSTM) networks with an average root mean squared error (RMSE) of 25.63 and 2.664, respectively.
Advances in logistics, operations, and management science book series, 2022
Remote sensing through satellites and internet of things (IoT) technology are two widespread tech... more Remote sensing through satellites and internet of things (IoT) technology are two widespread techniques to assess inland water quality. However, both these techniques have their limitations. IoT provides point data, which is insufficient to represent entire water body, especially if the water body has complex terrain and hydrology. Through remote sensing, we can sample data of a large area, but data acquisition is constrained by satellite. Revisit time and quality of estimates can be affected by image resolution. Moreover, non-optical properties that might affect water quality cannot be sensed through satellites. To complement this, GIS data from labs can be useful for providing higher resolution and accurate data and can be used as ground truth. Thus, in this chapter, the authors aim to integrate both these data collection techniques followed by estimation and prediction through machine learning models. The accumulated datasets are used to train machine learning (ML) models deployed at a server. The selected ML model is an artificial neural network with train accuracy of 97% and test accuracy of 95%.
Transitioning to green energy transport systems, notably electric vehicles, is crucial to both co... more Transitioning to green energy transport systems, notably electric vehicles, is crucial to both combat climate change and enhance urban air quality in developing nations. Urban air quality is pivotal, given its impact on health, necessitating accurate pollutant forecasting and emission reduction strategies to ensure overall well-being. This study forecasts the influence of green energy transport systems on the air quality in Lahore and Islamabad, Pakistan, while noting the projected surge in electric vehicle adoption from less than 1% to 10% within three years. Predicting the impact of this change involves analyzing data before, during, and after the COVID-19 pandemic. The lockdown led to minimal fossil fuel vehicle usage, resembling a green energy transportation scenario. The novelty of this work is twofold. Firstly, remote sensing data from the Sentinel-5P satellite were utilized to predict air quality index (AQI) trends before, during, and after COVID-19. Secondly, deep learning m...
Empowering Sustainable Industrial 4.0 Systems With Machine Intelligence
As of today, increased air pollution has disrupted the air quality levels, deeming the air unsafe... more As of today, increased air pollution has disrupted the air quality levels, deeming the air unsafe to breathe. Traditional systems are hefty, costly, sparsely distributed, and do not provide ubiquitous coverage. The interpolation used to supplement low spatial coverage induces uncertainty especially for pollutants whose concentrations vary significantly over small distances. This chapter proposes a solution that uses satellite images and machine/deep learning models to timely forecast air quality. For this study, Lahore is chosen as a study area. Sentinel 5-Precursor is used to gather data for Sulphur Dioxide (SO2), Nitrogen Dioxide (NO2), and Carbon Monoxide (CO) for years 2018-2021. The data is processed for several AI models, where convolutional neural networks (CNN) performed the best with mean squared error (MSE) 0.0003 for the pollutants. The air quality index (AQI) is calculated and is shown on web portal for data visualization. The trend of air quality during COVID-19 lockdow...
AI for Emerging Verticals: Human-robot computing, sensing and networking, 2020
Water is one of the basic resources required for human survival. However, pollution of water has ... more Water is one of the basic resources required for human survival. However, pollution of water has become a global problem. 2.4 billion people worldwide live without any form of water sanitation. This work focuses on case study of water pollution in Pakistan where only 20% of the population has an access to good-quality water. Drinking bad-quality water causes diseases such as hepatitis, diarrhea and typhoid. Moreover, people living close to the industrial areas are more prone to drinking polluted water and catching diseases as a result. Yet, there is no system that can monitor the quality of water or help in disease prevention. In this work, an Internet of Things (IoT)-enabled water quality monitoring system is developed that works as a stand-alone portable solution for monitoring water quality accurately and in real time. The real-time results are stored in a cloud database. The public web portal shows these results in the form of data sheets, maps and charts for analyzing data. Further, this data along with the collected data of past water quality is used to generate machine learning (ML) models for prediction of water quality. As a consequence, a model for prediction of water quality is trained and tested on a test set. The predictions on the test set resulted in a mean squared error (MSE) of 0.264.
2018 15th International Conference on Smart Cities: Improving Quality of Life Using ICT & IoT (HONET-ICT)
The ubiquitous use of IEEE 802.11 has aggravated the need to make efficient use of available band... more The ubiquitous use of IEEE 802.11 has aggravated the need to make efficient use of available bandwidth. Currently handoff decision in IEEE 802.11 is made based on the received signal strength but these results in poor connectivity specifically when an access point is overloaded. Overlapping regions where users can be connected to multiple access points, switching to less loaded access point can improve overall network capacity. In this article, we propose a decentralized approach for best access point selection which also prevents an access point to get overloaded. We propose an algorithm for handover strategy to improve network capacity via load balancing and it also minimizes switching overhead. We perform detail analysis on publically available dataset which consists of millions of Wi-Fi sessions with multiple access points.
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