Search

The growing market of cloud computing resulted in increased demand for cloud resources and it will become difficult for individual service providers (SPs) to fulfill all resource requests. That leads to a situation where two or more SPs may form a group and share the resources. Now, due to the formation of more than one federations by different cloud providers, it may be difficult for users to select a suitable federation who can deliver cloud services at a fair price. In this context, it is necessary to have a framework that will efficiently allocate resources of cloud federations to the users at a fair price. In this paper, we propose a multi-unit double auction mechanism called TARA that can be used to efficiently choose cloud federations for users from which they can get resources. Here, we consider a multi-seller and multi-buyer double auction mechanism for heterogeneous resources. TARA achieves some important properties like truthfulness, individual rationality and budget balance for both buyers and sellers. TARA is also computationally efficient and possess high system efficiency. The simulation results also show that total utility of buyer is more than some existing double auction mechanisms.

Air pollution has become a major environmental risk of the new civilized world due to its severe influence on public health and the environment. Eventually, understanding the spatiotemporal variability of air pollution at high granularity is necessary to make relevant public policies. To explore spatiotemporal variability of air pollution at high granularity we have utilized the power of IoT based participatory sensing and data science. In this paper, we propose a predictive model for spatiotemporal air pollution estimation technique called Multiview data Fusion model (MVDF) that can consider spatial as well as temporal dependencies of air pollutants. The proposed technique is evaluated based on real-world air pollution dataset collected by participants over a period of 1 year in an urban area of city Kolkata. The results show that MVDF dominates over some baselines like Simple Kriging (SK), Modified Shepard’s Method (MSM) and Nearest Neighbor (NN). Besides, in this paper, we attempt to perform visual analysis that consists of state-of-the-art visualization techniques to explore spatiotemporal variability at different granularities on the estimated pollution levels of MVDF.

In smartphone-based crowd/participatory sensing systems, it is necessary to identify the actual sensor data provider. In this context, this paper attempts to recognize the users’ identity based on their gait patterns (i.e. unique walking patterns). More specifically, a deep convolution neural network (CNN) model is proposed for the user identification with accelerometer data generated from users smartphone sensors. The proposed model is evaluated based on the real-world benchmark dataset (accelerometer biometric competition data) having a total of 387 users accelerometer sensor readings (60 million data samples). The performance of the proposed CNN-based approach is also compared with five baseline methods namely Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), and K-Nearest Neighbours (KNN). It is observed that the proposed model achieves better results (accuracy = 98.8%, precision = 0.94, recall = 0.97, and F1-score = 0.95) as compared to the baseline methods.

The imposition of strict restrictions by the Government of India to restrict the spread of the novel coronavirus has changed the socio-economic landscape like never before. The air quality due to such unprecedented events has undergone drastic changes especially in major metropolitan cities, which serve as important financial and industrial hubs of the country. This study investigates the influence lockdowns had on the pollution scenario of four key cities namely, Delhi, Kolkata, Chennai and Mumbai during both the first (2020) and the second (2021) waves . To evaluate the impact, detailed analysis of ground based pollutant concentration data of PM2.5, NOx, SO2 and O3 from various government set up monitoring stations in the period ranging from April'20 to June'21 is conducted along with the corresponding period during 2019 when business was as usual (BaU). Results show that although PM2.5 and NOx for all cities presented a decrease during the first wave, higher pollutant levels were observed during the second wave. For SO2 and O3, the trend did not show any consistency over all cities as in some cities, the second wave levels showed a significant increase with regard to their BaU counterparts. Out of all the meteorological factors studied over that period, relative humidity was found to have a strong correlation with respect to pollutant levels. Regarding spatial variation within different cities, although stations especially based in industrial areas showed a significant increase in the winter months of October'20 to January'21, second wave and first wave pollutant levels for different stations during the summer months for all cities except Chennai were found to be nearly identical.

Spatial distributions of data of natural phenomena can be estimated by using different spatial interpolation techniques. These techniques can be used for the purpose of developing urban noise pollution monitoring applications, so they can truly describe the actual urban noise pollution scenario of any region of interest to make effective and informed decisions. In this context, our aim is to use IoT-cloud based framework to generate dynamic (i.e., changes in terms of time and space) noise maps as a service with the help of spatial interpolation techniques. Noise map generation is an effective method for visualizing and assessing urban noise pollution. In this article, we have proposed three spatial interpolation techniques (GLIDW, I-GLIDW, GLIDW-OK) that work on participatory sensing-based noise pollution data collected using smartphones as IoT devices to generate dynamic noise maps. Proposed techniques can address diverse scenarios such as sparse datasets , high accuracy , better response time , and so on. Depending on the situation, we can choose an appropriate technique. We evaluate our proposed methods based on a real-world urban noise pollution dataset collected by participants over a period of two years in an urban area of the city Kolkata. The results are compared with inverse distance weighting (IDW) and Ordinary Kriging (OK) methods. The method GLIDW is proposed for a dense dataset. The results validate that in the case of a dense dataset GLIDW dominates over other methods. But, when the data sparsity level is medium, I-GLIDW performs well. However, if the dataset is very sparse, then GLIDW-OK dominates in terms of predictive accuracy. The results also show that Relative Improvement (RI) of I-GLIDW and GLIDW-OK is always positive compared to baseline methods IDW and OK.

Tackling air pollution has become of utmost importance since the last few decades. Different statistical as well as deep learning methods have been proposed till now, but seldom those have been used to forecast future long-term pollution trends. Forecasting long-term pollution trends into the future is highly important for government bodies around the globe as they help in the framing of efficient environmental policies. This paper presents a comparative study of various statistical and deep learning methods to forecast long-term pollution trends for the two most important categories of particulate matter (PM) which are PM2.5 and PM10. The study is based on Kolkata, a major city on the eastern side of India. The historical pollution data collected from government set-up monitoring stations in Kolkata are used to analyse the underlying patterns with the help of various time-series analysis techniques, which is then used to produce a forecast for the next two years using different statistical and deep learning methods. The findings reflect that statistical methods such as auto-regressive (AR), seasonal auto-regressive integrated moving average (SARIMA) and Holt–Winters outperform deep learning methods such as stacked, bi-directional, auto-encoder and convolution long short-term memory networks based on the limited data available.

Forecasting time series has acquired immense research importance and has vast applications in the area of air pollution monitoring. This work attempts to investigate the abilities of various existing techniques when applied for short term, high granular time series forecasting of PM2.5. More specifically, a comparative study has been provided, taking into account both popularly used models and lesser-used models in this area. The study has been carried out considering ten well defined models that are ARIMA (auto-regressive integrated moving average), SARIMA (seasonal ARIMA), SES (single exponential smoothing), DES (double exponential smoothing), TES (triple exponential smoothing), ANN (artificial neural network), DT (decision tree), kNN (k-nearest neighbor), LSTM (long short-term memory) and MCFO (markov chain first order). A framework has been built that categories the models, implements them under identical execution environment and forecasts succeeding values. Implementation has been carried out over five data sets of real-world air pollution time series, that are collected from five differently located government setup monitoring stations over a period of 1 year (July 2018-June 2019). Rigorous statistical analysis has been performed that yields an insight to the nature and variability of these time series data. Forecasting has been carried out on short term basis, focusing on high granularity whereas, three different lengths of forecast horizon (1 day, 1 week, and 1 month) have been tested. Eventually, the models have been compared in terms of their associated performance measuring units namely, RMSE (root mean of squared error), MAE (mean absolute error) and MAPE (mean absolute percentage error). The comparative results verified with multiple datasets show that all the models posses less error for a shorter forecast horizon, where LSTM providing the best performance. Superiority of machine learning and deep learning models are found in case of longer length of forecast horizon with kNN achieving best accuracy whereas, significant performance degradation of ARIMA is found for longer forecast horizon. Moreover, TES, DT, kNN, LSTM, MCFO are found to be well adopted in relation with shape and variability of the data. Note that the performance on various length of high granular forecast horizon have been studied over multiple datasets that give an added value to this work.

Participatory sensing has become an effective way of sensing urban dynamics due to the widespread availability of smartphones among citizens. Traditionally, separate urban sensing applications are designed to monitor different urban dynamics like environment, transportation, mobility, etc. However, combining these applications to aggregate information can lead to various new inferences. The main objective of this work is to improve urban sensing applications by overcoming their individual limitations. A unified framework called JUSense (Judicious Urban Sensing) is proposed that can derive benefits from these applications by combining their functionalities. JUSense provides the opportunity for applications to tackle the challenges associated with data collection, aggregation of data in cloud, calibration, data cleaning, and prediction. A multi-view fusion model is proposed for spatiotemporal urban air and noise pollution map generation. Further, a random forest classifier is built to classify the driving events. Here, large scale experiments are performed to evaluate the efficacy of JUSense on real-world dataset. Both the fusion model and the random forest classifier yield better accuracies compared to the baseline methods. Additionally, case studies are conducted to show the advantages that can arise out of the mutual interactions among the applications.

Poor air quality is becoming a critical environmental concern in different countries over the last several years. Most of the air pollutants have serious consequences on human health and wellbeing. In this context, efficient forecasting of air pollutants is currently crucial to predict future events with a view to taking corrective actions and framing effective environmental policies. Although deep learning (DL) as well as statistical forecasting models are investigated in the literature, they have rarely used in air pollutant-specific optimal model building for long-term forecasting. In this paper, our aim is to develop the pollutant-specific optimal forecasting models for the phases spanning from preprocessing to model building by investigating a set of predictive techniques. In this regard, this paper presents a methodology for long-term forecasting of some important air pollutants. More specifically, a total of eight best performing models such as stacked LSTM, LSTM auto-encoder, Bi-LSTM, convLSTM, Holt-Winters, auto-regressive (AR), SARIMA, and Prophet are investigated for developing pollutant-specific optimal forecasting models. The study is carried out based on the real-world data obtained from government-run air quality monitoring units in Kolkata over a period of 4 years. The models such as Holt-Winters, Bi-LSTM, and ConvLSTM achieve high forecasting accuracy with respect to MAE and RMSE values for majority of the pollutants.

Deep learning methods have become the state of the art for spatio-temporal predictive analysis in a wide range of fields, including environmental management, public health, urban planning, pollution monitoring, and so on. Despite the fact that a variety of powerful deep learning-based models can address various problem-specific issues in different research domain, it has been found that no single optimal model can outperform everywhere. Now, in the last two years, various deep learning-based studies have provided a variety of best-performing techniques for predicting COVID-19 health outcomes. In this context, this study attempts to perform a case study that investigates the spatio-temporal variation in the performance of deep-learning-based methods for predicting COVID-19 health outcomes in India. Various widely applied deep learning models namely CNN (convolutional neural network), RNN (recurrent neural network), Vanilla LSTM (long short-term memory), LSTM Autoencoder, and Bidirectional LSTM are considered to investigate their spatio-temporal performance variation. The effectiveness of the models is assessed using various metrics based on COVID-19 mortality time-series from 36 states and union territories of India.

Rising real estate prices along with expensive maintenance costs, and lack of spares during times of instrument failure have become major issues for statutory bodies when dealing with real-time pollution monitoring stations. As a possible solution to these problems, a novel class of hybrid spatio-temporal pollution forecasting networks which are a combination of various widely used temporal forecasting methods and spatial interpolation methods have been proposed in this paper. In addition, a novel multi-site Multi Layer Perception based Ensemble method, capable of improving accuracy by taking exogenous variables into account, has also been proposed. Experimental results based on the multi-site air pollution data of Beijing demonstrate that the proposed class of hybrid networks have been effective in predicting the pollution of unknown locations with great levels of accuracy. Moreover, the proposed novel MLP Ensemble method for spatial interpolation has also been empirically shown to perform equivalently in comparison to commonly used spatial interpolation methods.

Proper maintenance of roads is an extremely complex task and also an important issue all over the world. One of the most critical road monitoring and maintenance activities is the detection of road anomalies such as potholes. Identification of potholes is necessary to avoid road accidents, prevent damage of vehicles, enhance travelling comforts, etc. Although maintenance of roads is considered to be a serious issue by the authorities over the years, lack of proper detection and mapping of road potholes makes the issue more severe. To overcome this problem, an end-to-end system called PotSpot is built for real-time detection, monitoring, and spatial mapping of potholes across the city A Convolutional Neural Network (CNN) model is proposed and evaluated on real-world dataset for pothole detection. Additionally, real-time pothole-marked maps are generated with the help of Google Maps API (Application Programming Interface). To provide an end-to-end service through this system, both the pothole detection and pothole mapping are integrated through an android application. The proposed model is also compared with six baselines namely Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and three pre-trained CNN models InceptionV3, VGG19 and VGG16 in terms of performance metrics to verify its effectiveness. The proposed model achieves better accuracy (≈ 97.6 %) as compared to the above-mentioned baseline methods. It is also observed that the Area Under the Curve (AUC) value for the proposed pothole detection model (AUC= 0.97) is higher than the baseline methods.

COVID-19 is a global crisis where India is going to be one of the most heavily affected countries. The variability in the distribution of COVID-19-related health outcomes might be related to many underlying variables, including demographic, socioeconomic, or environmental pollution related factors. The global and local models can be utilized to explore such relations. In this study, ordinary least square (global) and geographically weighted regression (local) methods are employed to explore the geographical relationships between COVID-19 deaths and different driving factors. It is also investigated whether geographical heterogeneity exists in the relationships. More specifically, in this paper, the geographical pattern of COVID-19 deaths and its relationships with different potential driving factors in India are investigated and analysed. Here, better knowledge and insights into geographical targeting of intervention against the COVID-19 pandemic can be generated by investigating the heterogeneity of spatial relationships. The results show that the local method (geographically weighted regression) generates better performance ($$R^{2}=0.97$$ R 2 = 0.97 ) with smaller Akaike Information Criterion (AICc $$=-66.42$$ = - 66.42 ) as compared to the global method (ordinary least square). The GWR method also comes up with lower spatial autocorrelation (Moran’s $$I=-0.0395$$ I = - 0.0395 and $$p < 0.01$$ p < 0.01 ) in the residuals. It is found that more than 86% of local $$R^{2}$$ R 2 values are larger than 0.60 and almost 68% of $$R^{2}$$ R 2 values are within the range 0.80–0.97. Moreover, some interesting local variations in the relationships are also found.

The physiological and psychological well-being of humans depends on an extensive set of factors, of which pollution is the most ubiquitous. Its effects are often latent but extremely detrimental, especially in the case of noise pollution. In pollution monitoring and smart city systems, predicting noise from urban road traffic is of utmost significance. It is a difficult task due to the complexity of spatial correlations among the locations and temporal correlations among the timestamps, coupled with the diverse nature of these spatio-temporal correlations which are also dependent on the type of area in which the locations are situated. As a solution, we propose a participatory sensing-based solution to assess the road traffic noise by attaining a high level of granularity. We subsequently predict the noise values with a hybrid predictor that uses a regression model and ordinary kriging. Our solution outperforms the baseline regression and kriging methods and thus provides a novel method to gain a deeper insight into the levels of road traffic noise pollution. The effectiveness and strength of the proposed method are validated by extensive experiments with a real-world participatory sensing-based road traffic noise dataset.