We propose an independence criterion based on the eigen-spectrum of covariance operators in reproducing kernel Hilbert spaces (RKHSs), consisting of an empirical estimate of the Hilbert-Schmidt norm of the cross-covariance operator (we term this a Hilbert-Schmidt Independence Criterion, or HSIC). This approach has several advantages, compared with previous kernel-based independence criteria. First, the empirical estimate is simpler than any other kernel dependence test, and requires no user-defined regularisation. Second, there is a clearly defined population quantity which the empirical estimate approaches in the large sample limit, with exponential convergence guaranteed between the two: this ensures that independence tests based on HSIC do not suffer from slow learning rates. Finally, we show in the context of independent component analysis (ICA) that the performance of HSIC is competitive with that of previously published kernel-based criteria, and of other recently published ICA methods.

Measuring statistical dependence with Hilbert-Schmidt norms

Smart city is a collective term for technologies and concepts that are directed toward making cities efficient, technologically more advanced, greener and more socially inclusive. These concepts include technical, economic and social innovations. This term has been tossed around by various actors in politics, business, administration and urban planning since the 2000s to establish tech-based changes and innovations in urban areas. The idea of the smart city is used in conjunction with the utilization of digital technologies and at the same time represents a reaction to the economic, social and political challenges that post-industrial societies are confronted with at the start of the new millennium. The key focus is on dealing with challenges faced by urban society, such as environmental pollution, demographic change, population growth, healthcare, the financial crisis or scarcity of resources. In a broader sense, the term also includes non-technical innovations that make urban life more sustainable. So far, the idea of using IoT-based sensor networks for healthcare applications is a promising one with the potential of minimizing inefficiencies in the existing infrastructure. A machine learning approach is key to successful implementation of the IoT-powered wireless sensor networks for this purpose since there is large amount of data to be handled intelligently. Throughout this paper, it will be discussed in detail how AI-powered IoT and WSNs are applied in the healthcare sector. This research will be a baseline study for understanding the role of the IoT in smart cities, in particular in the healthcare sector, for future research works.

IoT for Smart Cities: Machine Learning Approaches in Smart Healthcare—A Review

Security and privacy of electronic health records: Concerns and challenges

The Industrial Internet of things (IIoT) helps several applications that require power control and low cost to achieve long life. The progress of IIoT communications, mainly based on cognitive radio (CR), has been guided to the robust network connectivity. The low power communication is achieved for IIoT sensors applying the Low Power Wide Area Network (LPWAN) with the Sigfox, NBIoT, and LoRaWAN technologies. This paper aims to review the various technologies and protocols for industrial IoT applications. A depth of assessment has been achieved by comparing various technologies considering the key terms such as frequency, data rate, power, coverage, mobility, costing, and QoS. This paper provides an assessment of 64 articles published on electricity control problems of IIoT between 2007 and 2020. That prepares a qualitative technique of answering the research questions (RQ): RQ1: “How cognitive radio engage with the industrial IoT?”, RQ2: “What are the Proposed architectures that Support Cognitive Radio LPWAN based IIOT?”, and RQ3: What key success factors need to comply for reliable CIIoT support in the industry?”. With the systematic literature assessment approach, the effects displayed on the cognitive radio in LPWAN can significantly revolute the commercial IIoT. Thus, researchers are more focused in this regard. The study suggests that the essential factors of design need to be considered to conquer the critical research gaps of the existing LPWAN cognitive-enabled IIoT. A cognitive low energy architecture is brought to ensure efficient and stable communications in a heterogeneous IIoT. It will protect the network layer from offering the customers an efficient platform to rent AI, and various LPWAN technology were explored and investigated.

https://www.mdpi.com/2071-1050/13/1/338/pdf

A systematic review on cognitive radio in low power wide area network for industrial IoT applications

Recently, interest in Internet of Vehicles’ (IoV) technologies has significantly emerged due to the substantial development in the smart automobile industries. Internet of Vehicles’ technology enables vehicles to communicate with public networks and interact with the surrounding environment. It also allows vehicles to exchange and collect information about other vehicles and roads. IoV is introduced to enhance road users’ experience by reducing road congestion, improving traffic management, and ensuring the road safety. The promised applications of smart vehicles and IoV systems face many challenges, such as big data collection in IoV and distribution to attractive vehicles and humans. Another challenge is achieving fast and efficient communication between many different vehicles and smart devices called Vehicle-to-Everything (V2X). One of the vital questions that the researchers need to address is how to effectively handle the privacy of large groups of data and vehicles in IoV systems. Artificial Intelligence technology offers many smart solutions that may help IoV networks address all these questions and issues. Machine learning (ML) is one of the highest efficient AI tools that have been extensively used to resolve all mentioned problematic issues. For example, ML can be used to avoid road accidents by analyzing the driving behavior and environment by sensing data of the surrounding environment. Machine learning mechanisms are characterized by the time change and are critical to channel modeling in-vehicle network scenarios. This paper aims to provide theoretical foundations for machine learning and the leading models and algorithms to resolve IoV applications’ challenges. This paper has conducted a critical review with analytical modeling for offloading mobile edge-computing decisions based on machine learning and Deep Reinforcement Learning (DRL) approaches for the Internet of Vehicles (IoV). The paper has assumed a Secure IoV edge-computing offloading model with various data processing and traffic flow. The proposed analytical model considers the Markov decision process (MDP) and ML in offloading the decision process of different task flows of the IoV network control cycle. In the paper, we focused on buffer and energy aware in ML-enabled Quality of Experience (QoE) optimization, where many recent related research and methods were analyzed, compared, and discussed. The IoV edge computing and fog-based identity authentication and security mechanism were presented as well. Finally, future directions and potential solutions for secure ML IoV and V2X were highlighted.

/pdf/machine-learning-technologies-for-secure-vehicular-4np7kbx8py.pdf

Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications

Load forecasting is a vital part of smart grids for predicting the required electrical power using artificial intelligence (AI). Deep learning is broadly used for load forecasting in the smart grid using the artificial neural network (ANN). Generally, computing the deep learning in the smart grid requires massive data aggregation or centralization and significant computational time. This paper presents a survey of deep learning-based load forecasting techniques from 2015 to 2020. This survey discusses the studies based on their deep learning techniques, Distributed Deep Learning (DDL) techniques, Back Propagation (BP) based works, and non-BP based works in the load forecasting process. Consequent to the survey, it was determined that data aggregation dependency would be beneficial for reducing computational time in load forecasting. Therefore, a conceptual model of DDL for smart grids has been presented, where the HSIC (Hilbert-Schmidt Independence Criterion) Bottleneck technique has been incorporated to provide higher accuracy.

/pdf/hsic-bottleneck-based-distributed-deep-learning-model-for-2dc43gdryl.pdf

HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey

Protecting the digital medical data from cyber-attacks is essential in a healthcare communication system. Blockchain is a distributed as well as shared ledger-based technology which can more readily encourage information security. Blockchain approach is commonly applied for the digital crypto-currency; however, it is awaited to have future conducts in health care. Many healthcare systems today are still subordinated on the back-dated application for keeping healthcare records. Here, the main challenges faced to date are information security and interoperability. This study introduces a blockchain-based model for securing the Electronic Medical Records (EMR). Here, we have used SHA256 secure hash algorithm for generating a unique and identical 256-bit or 32-byte hash value for a particular medical record. In this manuscript, we have focused on five mechanisms (Digital Access Rules, Data Aggregation, Data Immutability, Data Liquidity, and Patient Identity) of data transition for securing the medical records at the proposed blockchain model.

Blockchain-Based Information Security of Electronic Medical Records (EMR) in a Healthcare Communication System

Many companies are relying on software to manage their businesses. Usually, the software, especially those used by smaller companies, is not secure against unauthorized or unethical data manipulation on the database level. This paper recommends and demonstrates the use of blockchain for securing small enterprises against hacking by alerting the management whenever a change is made to the data without using the authorized channels. This is done through blockchain technology’s inherent hash replication and mining algorithm. The paper shows an application where this idea has successfully been implemented for a desktop application built upon a remote database access (RDA) protocol-based relational database management system (RDBMS) such as remote MySQL and remote Oracle database.

Integration of Blockchain and Remote Database Access Protocol-Based Database

Corruption of price hike is common and one of the major issues in the third world as well as in developing countries. Corruption and price hike issues are correlated with one another. A number of recent studies have been examined to establish the relationship between corruption and poverty. The people of low and middle-income countries cannot take proper medical treatment and buy nutritional food due to price hike and corruption. As a result, every year, many children and poor people have been suffering and dying from malnutrition. Blockchain is a leading technology which is prohibitive to data manipulation and an open distributed computation system where data transactions occur between two parties efficiently. Price Intelligence technology is used for price monitoring at the market level. This study presents a technological model where we have used blockchain, price intelligence and IoT approaches for monitoring the price transaction in the medical, banking and educational areas. This proposed model represents a technological architecture where different people and organizations are connected to the cloud through the internet and money transactions happened through blockchain approach. As a result, people of differing abilities and resources can detect the price hike and corruption and poor people can get priority for better service. Finally, we have suggested the potential impact of the proposed model and have explained how poor people can get better service by using this model.

/pdf/a-combined-model-of-blockchain-price-intelligence-and-iot-78guu0n1gg.pdf

A Combined Model of Blockchain, Price Intelligence and IoT for Reducing the Corruption and Poverty

A market which provides an innovative way to allow customers to interact with each other called Customer-to-customer (C2C) market. In C2C communications, online communities play an important role in decision making to buy a product. This investigation develops a research model for online communities of Facebook commerce (F-Commerce) in Bangladesh region, which is based on Information Adoption Model (IAM). This study exhibits a model to influences of C2C communication on Bangladeshi consumers’ purchase decision in the online communities of F-Commerce. The proposed model used the Partial Least Squares (PLS) technique to test 120 effective survey data. This survey data has been taken from the Bangladesh Facebook users and strongly involved in product buy-sell at F-Commerce. The analyzed results show that Argument Quality (AQ), Source Credibility (SC) and Tie Strength (TS) positively influence Purchase Decision (PD) through Product Usefulness Evaluation (PUE). In addition, Tie Strength exhibits difference effect on Product Usefulness Evaluation between the contexts of consumers communicating with virtual consumers relationships. Theoretical and executive implications are discussed for constructing our proposed model.

/pdf/modeling-the-role-of-c2c-information-quality-on-purchase-17klqj8igy.pdf

S. Rayhan Kabir

Papers

HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey

Blockchain-Based Information Security of Electronic Medical Records (EMR) in a Healthcare Communication System

Integration of Blockchain and Remote Database Access Protocol-Based Database

A Combined Model of Blockchain, Price Intelligence and IoT for Reducing the Corruption and Poverty

Modeling the Role of C2C Information Quality on Purchase Decision in Facebook