Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities

Internet of Things security

The surge in global population is compelling a shift toward smart agriculture practices. This coupled with the diminishing natural resources, limited availability of arable land, increase in unpredictable weather conditions makes food security a major concern for most countries. As a result, the use of Internet of Things (IoT) and data analytics (DA) are employed to enhance the operational efficiency and productivity in the agriculture sector. There is a paradigm shift from use of wireless sensor network (WSN) as a major driver of smart agriculture to the use of IoT and DA. The IoT integrates several existing technologies, such as WSN, radio frequency identification, cloud computing, middleware systems, and end-user applications. In this paper, several benefits and challenges of IoT have been identified. We present the IoT ecosystem and how the combination of IoT and DA is enabling smart agriculture. Furthermore, we provide future trends and opportunities which are categorized into technological innovations, application scenarios, business, and marketability.

An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges

Internet of Things (IoT) technology has attracted much attention in recent years for its potential to alleviate the strain on healthcare systems caused by an aging population and a rise in chronic illness. Standardization is a key issue limiting progress in this area, and thus this paper proposes a standard model for application in future IoT healthcare systems. This survey paper then presents the state-of-the-art research relating to each area of the model, evaluating their strengths, weaknesses, and overall suitability for a wearable IoT healthcare system. Challenges that healthcare IoT faces including security, privacy, wearability, and low-power operation are presented, and recommendations are made for future research directions.

Internet of Things for Smart Healthcare: Technologies, Challenges, and Opportunities

https://digitalcommons.uri.edu/cgi/viewcontent.cgi?article=1069&context=ele_facpubs

Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare

Analyzing electroencephalography (EEG) signals with machine learning approaches has become an attractive research domain for linking the brain to the outside world to establish communication in the name of the Brain-Computer Interface (BCI). Many researchers have been working on developing successful motor imagery (MI)-based BCI systems. However, they still face challenges in producing better performance with them because of the irrelevant features and high computational complexity. Selecting discriminative and relevant features to overcome the existing issues is crucial. In our proposed work, different feature selection algorithms have been studied to reduce the dimension of multiband feature space to improve MI task classification performance. In the procedure, we first decomposed the MI-based EEG signal into four sets of the narrowband signal. Then a common spatial pattern (CSP) approach was employed for each narrowband to extract and combine effective features, producing a high-dimensional feature vector. Three feature selection approaches, named correlation-based feature selection (CFS), minimum redundancy and maximum relevance (mRMR), and multi-subspace randomization and collaboration-based unsupervised feature selection (SRCFS), were used in this study to select the relevant and effective features for improving classification accuracy. Among them, the SRCFS feature selection approach demonstrated outstanding performance for MI classification compared to other schemes. The SRCFS is based on the multiple k-nearest neighbour graphs method for learning feature weight based on the Laplacian score and then discarding the irrelevant features based on the weight value, reducing the feature dimension. Finally, the selected features are fed into the support vector machines (SVM), linear discriminative analysis (LDA), and multi-layer perceptron (MLP) for classification. The proposed model is evaluated with two benchmark datasets, namely BCI Competition III dataset IVA and dataset IIIB, which are publicly available and mainly used to recognize the MI tasks. The LDA classifier with the SRCFS feature selection algorithm exhibits better performance. It proves the superiority of our proposed study compared to the other state-of-the-art BCI-based MI task classification systems.

/pdf/investigating-feature-selection-techniques-to-enhance-the-9ioz29qa.pdf

Investigating Feature Selection Techniques to Enhance the Performance of EEG-Based Motor Imagery Tasks Classification

Home automation system has achieved an enormous reputation in recent years, as day-to-day life is getting simpler due to the speedy growth of modern technology. Various implemented systems have been offered in the sphere of Home Automation but have apparently failed to provide cost-effective solutions for the same. In this paper, we provide a low-cost smart Home Automation and Security System (HASS) using Wireless Fidelity (Wi-Fi), and Bluetooth. These materialize the concept of internetworking of smart devices. A Wireless Sensor Network (WSN) based on Wi-Fi is considered to monitor and control the smart environment, safety as well as electrical parameters of a smart interconnected home. The Android application-based Graphical User Interface (GUI) on a Smartphone is used to give facility to users for seamless control over the devices in a smart home. It may also consist of home security for example access control and alarm systems. When connected to the Internet, home devices are an important element of the Internet of Things (IoT). Home security includes both the security hardware placed on a property and individuals' personal security practices. Security devices consist of a door locking, alert alarm, lighting, motion detectors, and security camera system. Additionally, several special features include such as ensuring windows are closed, each door is locked, all alarms are activated, and extra keys are not secret outside.

/pdf/iot-based-low-cost-smart-home-automation-and-security-system-2c7k0uft.pdf

IoT Based Low-Cost Smart Home Automation and Security System Using Wireless Technology

Web Based Student Registration and Exam Form Fill-up Management System for Educational Institute

Analysis of Bit Error Rate of Rake Receiver for CDMA System

The name of individuals has a specific meaning and great significance. Individuals’ names generally have substantial gender differences, and explicitly, Bengali names usually have a solid sexual identity. We can determine if a stranger is a man or a woman based on their name with remarkably suitable precision. In this research, we primarily conducted a thorough investigation into gender prediction based on a person’s name using DL-based methods. While various techniques have been explored for the English language, there has been little progress in the Bengali language. We address this gap by presenting a large-scale experiment with 2030 Bangladeshi unique names. We used both convolutional neural network (CNN)- and recurrent neural network (RNN)-based deep learning methods to infer gender from the Bangladeshi names in the Bengali language. We presented the one-dimensional CNN (Conv1D), simple long short-term memory (LSTM), bidirectional LSTM, stacked LSTM, and combined Conv1D and stacked bidirectional LSTM-based models and evaluated the performance of each scheme using our own dataset. Experimental results are analyzed on the basis of accuracy, precision, recall, F1-score, ROC AUC score, and loss performance metrics. The performance evaluative results show that Conv1D outperforms with 91.18% accuracy, which is likely to improve as the size of the training data grows.

/pdf/gender-recognition-of-bangla-names-using-deep-learning-2zgwubwb.pdf

Md. Humaun Kabir

Papers

Investigating Feature Selection Techniques to Enhance the Performance of EEG-Based Motor Imagery Tasks Classification

IoT Based Low-Cost Smart Home Automation and Security System Using Wireless Technology

Web Based Student Registration and Exam Form Fill-up Management System for Educational Institute

Analysis of Bit Error Rate of Rake Receiver for CDMA System

Gender Recognition of Bangla Names Using Deep Learning Approaches