Intelligent systems are wanting for cities to cope with limited spaces and resources across the world. As a result, smart cities emerged mainly as a result of highly innovative ICT industries and markets, and additionally, they have started to use novel solutions taking advantage of the Internet of Things (IoT), big data and cloud computing technologies to establish a profound connection between each component and layer of a city. Several key technologies congregate to build a working smart city considering human requirements. Even though the smart city concept is an advanced solution for today’s cities, recently, more living spaces should be discovered, and the concept of a smart city could be moved to these alternative living spaces, namely floating cities. The concept of a floating city emerged as a novel solution due to rising sea levels and land scarcity in order to provide alternative living spaces for humanity. In this article, our main research question is to raise awareness on the current state of smart city concepts across the world by understanding the key future trends, including floating cities, by motivating researchers and scientists through new IoT technologies and applications. Therefore, we present a survey of smart city initiatives and analyze their key concepts and different data management techniques. We performed a detailed literature survey and review by applying a complex literature matrix including terms, like smart people, smart economy, smart governance, smart mobility, smart environment, and smart living. We also discuss multiple perspectives of smart floating cities in detail. With the proposed approach, recent advances and practical future opportunities for smart cities can be revealed.

/pdf/future-trends-and-current-state-of-smart-city-concepts-a-21zcx4eie1.pdf

Future Trends and Current State of Smart City Concepts: A Survey

The COVID-19 is not mitigating without a vaccine, but its effect can reduce using some advance technology. Smart cities are one of the essential needs for reducing this pandemic. This study presents the role and implementation of smart city technology to minimise the COVID-19 risk. The smart city technology is used to maintain social distancing, which avoids the face-to-face interaction and physical contact. It has been observed that the position technology is used to trace the infected people. Drones and robot technology are used to work as medical staff and helpful for other essential services. It can be seen that smart healthcare is one of the advance smart city technologies, which is used to take care and treat infected people without any contact. It has also been observed that the smart delivery system is one of the contact-free technologies. It is used to deliver essential items, including groceries, food, and medicine, without any physical contact. The uses of all the above smart city technologies have been discussed widely to mitigate the COVID-19 risk.

Smart solution for reducing the COVID-19 risk using smart city technology

The growth in the number of devices connected to the Internet of Things (IoT) poses major challenges in security. The integrity and trustworthiness of data and data analytics are increasingly important concerns in IoT applications. These are compounded by the highly distributed nature of IoT devices, making it infeasible to prevent attacks and intrusions on all data sources. Adversaries may hijack devices and compromise their data. As a result, reactive countermeasures, such as intrusion detection and resilient analytics, become vital components of security. This article overviews algorithms for secure distributed inference in IoT.

The Internet of Things: Secure Distributed Inference

Sensors for internet of medical things: State-of-the-art, security and privacy issues, challenges and future directions

With the advent of the Internet of Things (IoT) concept and its integration with the smart city sensing, smart connected health systems have appeared as integral components of the smart city services. Hard sensing-based data acquisition through wearables or invasive probes, coupled with soft sensing-based acquisition such as crowd-sensing results in hidden patterns in the aggregated sensor data. Recent research aims to address this challenge through many hidden perceptron layers in the conventional artificial neural networks, namely by deep learning. In this article, we review deep learning techniques that can be applied to sensed data to improve prediction and decision making in smart health services. Furthermore, we present a comparison and taxonomy of these methodologies based on types of sensors and sensed data. We further provide thorough discussions on the open issues and research challenges in each category.

Big Sensed Data Meets Deep Learning for Smarter Health Care in Smart Cities

The Internet of Things (IoT) envisions to create a smart, connected city that is composed of ubiquitous environmental and user sensing along with distributed, low-capacity computing. This provides ample information regarding the citizens in various smart environments. We can leverage this people-centric information, provided by the smart city infrastructure, to improve “smart health” applications: user data from connected wearable devices can be accompanied with ubiquitous environmental sensing and versatile actuation. The state-of-the-art in smart health applications is black-box, end-to-end implementations which are neither intended for use with heterogeneous data nor adaptable to a changing set of sensing and actuation. In this paper, we apply our modular approach for IoT applications—the context engine—to smart health problems, enabling the ability to grow with available data, use general-purpose machine learning, and reduce compute redundancy and complexity. For smart health, this improves response times for critical situations, more efficient identification of health-related conditions and subsequent actuation in a smart city environment. We demonstrate the potential with three sets of interconnected context-aware applications, extracting health-related people-centric context, such as user presence, user activity, air quality, and location from IoT sensors.

Jaganathan Venkatesh

Papers

Modular and Personalized Smart Health Application Design in a Smart City Environment