With the development of the next generation of information technology, an increasing amount of attention is being paid to smart residential spaces, including smart cities, smart buildings, and smart homes. Building indoor safety intelligence is an important research topic. However, current indoor safety management methods cannot comprehensively analyse safety data, owing to a poor combination of safety management and building information. Additionally, the judgement of danger depends significantly on the experience of the safety management staff. In this study, digital twins (DTs) are introduced to building indoor safety management. A framework for an indoor safety management system based on DT is proposed which exploits the Internet of Things (IoT), building information modelling (BIM), the Internet, and support vector machines (SVMs) to improve the level of intelligence for building indoor safety management. A DT model (DTM) is developed using BIM integrated with operation information collected by IoT sensors. The trained SVM model is used to automatically obtain the types and levels of danger by processing the data in the DTM. The Internet is a medium for interactions between people and systems. A building in the bobsleigh and sled stadium for the Beijing Winter Olympics is considered as an example; the proposed system realises the functions of the scene display of the operation status, danger warning and positioning, danger classification and level assessment, and danger handling suggestions.

https://www.mdpi.com/1424-8220/20/20/5771/pdf

A Framework for an Indoor Safety Management System Based on Digital Twin.

Smart city and cyber-security; technologies used, leading challenges and future recommendations

Flow-based anomaly intrusion detection using machine learning model with software defined networking for OpenFlow network

Medical Information Retrieval Systems for e-Health Care Records using Fuzzy Based Machine Learning Model

In recent years, there has been significant focus on smart cities, on how they operate and develop, and on their technical and social challenges. The importance of infrastructure as a major pillar of support in cities, in addition to the rapid developments in smart city research, necessitate an up-to-date review of smart cities’ infrastructure issues and challenges. Traditionally, a majority of studies have focused on traffic control and management, transport network design, smart grid initiatives, IoT (Internet of Things) integration, big data, land use development, and how urbanization processes impact land use in the long run. The work presented herein proposes a novel review framework that analyzes how smart city infrastructure is related to the urbanization process while presenting developments in IoT sensor networks, big data analysis of the generated information, and green construction. A classification framework was proposed to give insights on new initiatives regarding smart city infrastructure through answering the following questions: (i) What are the various dimensions on which smart city infrastructure research focuses? (ii) What are the themes and classes associated with these dimensions? (iii) What are the main shortcomings in current approaches, and what would be a good research agenda for the future? A bibliometric analysis was conducted, presenting cluster maps that can be used to understand different research trends and refine further searches. A bibliographic analysis was then followed, presenting a review of the most relevant studies over the last five years. The method proposed serves to stress where future research into understanding smart systems, their implementation and functionality would be best directed. This research concluded that future research on the topic should conceptualize smart cities as an emergent socio-techno phenomenon.

https://www.mdpi.com/2075-5309/11/2/73/pdf

Multiple Dimensions of Smart Cities’ Infrastructure: A Review

Enhancing cyber–physical systems with hybrid smart city cyber security architecture for secure public data-smart network

In the last decade, numerous researches have been focused on Image Super-Resolution (SR); this recreation or improvement model is vital in different research areas. Recently, deep learning algorithm finds useful to advance in the resolution of the medical output. Here, we devise a novel Deep Convolutional Network model along with the optimal learning rate of the Rectified Linear Unit (ReLU) intended for Medical Image Super-Resolution (MISR). For getting the optimal values of Deep Learning AlexNet structure, Modified Crow Search (MCS) is utilized, which is mainly depends on the behavior of crow sets. The chosen Alexnet lacks in a sort of suitable supervision for upgrading execution of the proposed model that effectively aims to overfit. The proposed design, i.e., MISR, named Deep Optimal Convolutional AlexNet (DOCALN), derives the optimal values of learning rates of the ReLU activation function. Based on this optimal deep learning structure, the Low Resolution (LR) medical images can be applied. Experimentation results of our proposed model are compared with variants of Convolution Neural Networks (CNN) concerning different measures such as image quality assessment, SR efficiency analysis, and execution time.

Sudhakar Sengan

Papers

Enhancing cyber–physical systems with hybrid smart city cyber security architecture for secure public data-smart network

Images super-resolution by optimal deep AlexNet architecture for medical application: A novel DOCALN