An Efficient Participant’s Selection Algorithm for Crowdsensing

Ali, Tariq; Draz, Umar; Yasin, Sana; Noureen, Javeria; Shaf, Ahmad; Ali, Murtaza

doi:10.14569/ijacsa.2018.090154

Cited by 10 publications

(7 citation statements)

References 23 publications

(27 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…STING is a Density-based clustering algorithm and it supports the only spatial type of datasets. In addition, most of our previous work in which we adopt similar approaches to machine learning has been successfully simulated for different aspects like artificial intelligence [18], participant selection and ranking algorithms [19,20] and neural networks [21] provide support our words.…”

Section: F Cactusmentioning

confidence: 70%

Systematic Review: A State of Art ML Based Clustering Algorithms for Data Mining

Ali

Faheem

Waseem

et al. 2020

2020 IEEE 23rd International Multitopic Conference (INMIC)

Self Cite

View full text Add to dashboard Cite

Data mining is an unsupervised learning technique to extract the insights and hidden relationships among data. Data mining has more importance in data science and machine learning because through data mining all hidden information is shown to determine various aspects of the data set. Clustering is a data mining technique to group the data, on the basis of similarity measures. The objects or data points in a cluster are similar. Similarly, objects or data points in another cluster will also be similar. But when these clusters are compared, they are dissimilar to each other. Clustering is considered the most important unsupervised learning technique because it deals with finding a structure in a collection of unlabeled data. Clustering can be done by the different approaches like partitioning clustering, hierarchical clustering, density-based clustering, and grid-based clustering. These clustering approaches can be done by the numbers of algorithms, such as K-means clustering, Fuzzy C-means clustering, Hierarchical clustering, DBSCAN, OPTICS, STING, ROCK and CACTUS. This proposed paper contained reviews of the above techniques by using a more powerful programming language (Python) as a tool. Evidence attainable from this study is helpful for researchers to select an appropriate clustering approach based on their domain.

show abstract

Section: F Cactusmentioning

confidence: 70%

Systematic Review: A State of Art ML Based Clustering Algorithms for Data Mining

Ali

Faheem

Waseem

et al. 2020

2020 IEEE 23rd International Multitopic Conference (INMIC)

Self Cite

View full text Add to dashboard Cite

show abstract

“…The purposed system uses four sensors for getting the data to use it as input and based on the data collected from the sensors a rule based fuzzy logic is implemented. For SMDD MATLAB fuzzy Logic Toolbox capabilities are used for simulation work to provide an accurate picture and to also push the flexibility and scalability of system to next levels with respect to other systems [33].…”

Section: Methods and Equipment'smentioning

confidence: 99%

Smart Fire Detection and Deterrent System for Human Savior by Using Internet of Things (IoT)

et al. 2021

Self Cite

View full text Add to dashboard Cite

Fire monitoring systems have usually been based on a single sensor such as smoke or flame. These single sensor systems have been unable to distinguish between true and false presence of fire, such as a smoke from a cigarette which might cause the fire alarm to go off. Consuming energy all day long and being dependent on one sensor that might end with false alert is not efficient and environmentally friendly. We need a system that is efficient not only in sensing fire accurately, but we also need a solution which is smart. In order to improve upon the results of existing single sensor systems, our system uses a combination of three sensors to increase the efficiency. The result from the sensor is then analyzed by a specified rule-set using an AI-based fuzzy logic algorithm; defined in the purposed research, our system detects the presence of fire. Our system is designed to make smart decisions based on the situation; it provides feature updated alerts and hardware controls such as enabling a mechanism to start ventilation if the fire is causing suffocation, and also providing water support to minimize the damage. The purposed system keeps updating the management about the current severity of the environment by continually sensing any change in the environment during fire. The purposed system proved to provide accurate results in the entire 15 test performed around different intensities of a fire situation. The simulation work for the SMDD is done using MATLAB and the result of the experiments is satisfactory.

show abstract

“…The gas levels obtained in a particular time slot are time-critical and time-sensitive, so the manual extraction of the features increases the complexity of the task. Time-series data analysis is a complicated task that also affects the target selection of the features, resulting in the degraded performance of a model [43][44][45].…”

Section: Deep Learning Modelmentioning

confidence: 99%

Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach

et al. 2020

Self Cite

View full text Add to dashboard Cite

Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of waste have been investigated. Most of the existing work in the literature has focused on providing a cost-efficient solution for the monitoring of garbage collection system using the Internet of Things (IoT). Though an IoT-based solution provides the real-time monitoring of a garbage collection system, it is limited to control the spreading of overspill and bad odor blowout gasses. The poor and inadequate disposal of waste produces toxic gases, and radiation in the environment has adverse effects on human health, the greenhouse system, and global warming. While considering the importance of air pollutants, it is imperative to monitor and forecast the concentration of air pollutants in addition to the management of the waste. In this paper, we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. The smart bin is connected to an IoT-based server, the Google Cloud Server (GCP), which performs the computation necessary for predicting the status of the bin and for forecasting air quality based on real-time data. We experimented with a traditional model (k-nearest neighbors algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network-based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the real-time monitoring of garbage levels along with notifications from the alert mechanism. The proposed works provide improved accuracy by utilizing machine learning as compared to existing solutions based on simple approaches.

show abstract

An Efficient Participant’s Selection Algorithm for Crowdsensing

Cited by 10 publications

References 23 publications

Systematic Review: A State of Art ML Based Clustering Algorithms for Data Mining

Systematic Review: A State of Art ML Based Clustering Algorithms for Data Mining

Smart Fire Detection and Deterrent System for Human Savior by Using Internet of Things (IoT)

Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach

Contact Info

Product

Resources

About