A Unified Semantic Engine for Internet of Things and Smart Cities: From Sensor Data to End-Users Applications

This paper reports on the early-stage development of an analytics framework to support the semantic integration of dynamic surveillance data across multiple scales to inform decision making for malaria eradication. We propose using the Semantic Web of Things (SWoT), a combination of Internet of Things (IoT) and semantic web technologies, to support the evolution and integration of dynamic malaria data sources and improve interoperability between different datasets generated through relevant IoT assets (e.g. computers, sensors, persons, and other smart objects and devices).

Smart cities leverage advanced technologies such as the Internet of Things (IoT) to enhance the quality of life for residents, improve city infrastructure, and foster sustainability. The role of connectivity in urban development is central to the functioning of smart cities, as IoT-enabled devices provide real-time data and automated systems that optimize city operations, from traffic management to waste disposal. This article explores the role of IoT in smart cities, highlighting how connectivity and integrated technologies contribute to efficient urban living. The paper examines key applications of IoT in smart cities, including transportation, energy management, and public services, while also addressing challenges such as data privacy, security, and system interoperability. The future of smart cities and the growing influence of IoT technologies are also discussed in the context of urbanization and sustainable development.

The Internet of Things (IoT) is being applied for stovepipe solutions, since it presents a semantic description limited to a specific domain. IoT needs to be pushed towards a more open, interoperable and collaborative IoT. The first step has been the Web of Things (WoT). WoT evolves the IoT with a common stack based on web services. But, even when a homogeneous access is reached through web protocols, a common understanding is not yet acquired. For this purpose, the Semantic Web of Things (SWoT) is proposed for the integration of the semantic web on the WoT. This work analyses the SWoT, presenting its different levels to offer an IoT convergence. Specifically, we analyse the trends for capillary networks and for cellular networks with standards such as IPSO, ZigBee, OMA, and the oneM2M initiative. This work also analyses the impact of the semantic-annotations/metadata in the performance of the resources.

With rampant urbanization and industrialization, the quest to become a Smart City is in vogue. Elaborate designs, ideas, and solutions to existing problems are being devised. However, in this race, city planners often neglect the most critical factor related to one’s existence on the Earth, i.e., Sustainability. Designing smart and sustainable cities is a challenge whose solutions are not easy. Also, developing a proposed smart city is a time-consuming process as it involves its foundations, analyzing the needs, latest technology, infrastructure, and topography. This paper aims to analyze the patterns followed by cities worldwide in the quest to make smart cities. This shall also show how newer cities can become innovative based on the work already done by the existing cities through the concept of Inheritance. In order to do this, a dataset comprising 102 cities is analyzed based on six metrics. A combination of the Principal Component Analysis and K-means Clustering technique is used to assist with the analysis of this unlabeled dataset. It is thereby shown that these 102 cities can be identified as belonging to 4 clusters and the cluster properties can then be inherited by any new city poised to become a future smart city.

Internet of Things (IoT) is the growing invention in the current development of different domains like industries, e-health, and education, etc. Semantic web of things (SWoT) is an extension of IoT that enhance the communication by behaving intelligently. SWoT comprises 7 layered architecture. The perception layer is an important layer for collecting data from devices and to communicate with its associated layer. The data loss at the perception layer is very common due to inadequate resources, unpredictable link, noise, collision, and unexpected damage. To address this problem, we propose a method based on Compressive Sensing which recovers and estimates sensory data from a low-rank structure. The contribution of this paper is three folds. Firstly, we determine the problem of data acquisition and data loss at semantic sensory nodes in SWoT. Secondly, we introduce a compressive sensing based framework for SWoT that recovers the data accurately using low-rank features. Thirdly, the data estimation method is utilized to reduce the volume of the data. Proposed Compressive Sensing based Data Recoverability and Estimation (CS-RE) method is evaluated and compared with the existing reconstruction methods. The simulation results on real sensory datasets depict that the proposed method significantly outperforms existing methods in terms of error ratio and data recoverability accuracy.

In today’s modern society, video surveillance is a growing trend and it can revolutionize many aspects of technology, especially in future smart cities that will transform traditional surveillance systems into intelligent and interconnected networks. It may be difficult for even well-trained employees to process and respond immediately to monitored data. Moreover, IoT-enabled surveillance systems overcome the challenges and flaws of conventional passive monitoring techniques by offering real-time surveillance and automated notifications for suspicious activity, intrusions, or anomalies. Therefore, the objective of this article is to provide an Internet of Things (IoT)-based smart surveillance system that may successfully connect the ecosystem, resulting in enhanced monitoring for smart city services. The primary goal of this study is to provide a comprehensive review of several IoT-based surveillance techniques used in various smart city applications. The categorization of tasks for each section is as follows: to present the historical context overview; to examine the significance of IoT and its application in smart cities; to present a standardized architecture for IoT-based smart city surveillance; to highlight an authoritative and thorough review of current IoT-based smart surveillance systems; and to identify various research issues. The methods used are: statistical graph or chart approach, schematic, and timeline diagram. Conclusions. This article outlines numerous research challenges for future video surveillance that may be addressed by researchers. In summary, this comprehensive review provides a valuable and streamlined resource for future researchers exploring smart city surveillance through the IoT.

Small cells (SCs) have proven their marvelous capability, especially dealing with high density of user equipment (UE) in the configuration of Internet of Things (IoT). It is widely accepted that the SCs concept is one of the prospective solutions for 5G networks, specifically targeting IoT, to attain the high demand in the future smart city. SC deployment has attracted the attention of multiple research efforts, due to its importance and potential towards enhancing future networking for IoT. However, there is still a lack of works providing SC allocation with comprehensive optimization modeling, which provides essential infrastructural platform to SC functionalities and services (e.g. offloading). The analysis of the number and positions of SCs, towards the simultaneous optimization of both energy efficiency and data rates, is a highly required research direction. SC allocation facilitates the sustainability and reliability, to support exponentially increasing number of users, as well as yielding feasibility of achieving high traffic demand and low latency performance, in addition to longer sustainability based on enhanced energy efficiency. In this paper, a smart small cell allocation scheme is proposed for IoT in smart city. Integer programming multi-objective optimization problem is formulated, and a new algorithm, based on the fusion of Branch-and-Bound (BnB) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II), is developed. Energy efficiency optimization and data rate maximization are formulated, as mutual objectives to fulfill the key demands of IoT in smart city, based on the estimation of users' behaviors. Simulations are conducted, whose results show that the proposed Smart-SC allocation outperforms the different considered benchmarks, resulting in higher offered data rates, while achieving better energy efficiency.

Systems thinking for developing sustainable complex smart cities based on self-regulated agent systems and fog computing

The articles in this special section focus on Big Data as it impacts future smart cities. The world is experiencing a period of extreme urbanization. Moreover, this process will continue, and the global urban population is expected to double by 2050. Smart city has been proposed to improve the efficiency of services and meet residents’ needs for better quality of life. Essentially, smart city integrates the Internet of Things and emerging communication technologies such as fifth generation (5G) solutions to manage the citys’ assets, including transportation systems, hospitals, water supply networks, waste management, and so on. Therefore, smart city is driving innovation and new technologies, especially big data technologies for the big data era. In the future smart city, there is an urgent need to address the following issues: how to design algorithms to process mass data and how to utilize big data to improve the quality of service (QoS) for future smart cities.

Intelligent, autonomous agents are still not available in the Semantic Web at large scale today. Also the fields of Semantic Web and Multi-Agent Systems are not working together very closely although they could profit much from each other. Existing approaches merely use the Web as a transport layer and are not properly aligned to the architectural style of the Web. The Internet of Things, which would be very useful for agents to act upon Things in the real world, on the other side is very fragmented and not easily inter-operable. The Web of Things has emerged as an approach to use the (Semantic) Web as an application layer for Things. It is still unclear however, how agents on the WoT should look like. We propose that the Semantic Web is a suitable integration layer, both for agents and Things. We investigate how a Multi-Agent System in the Semantic Web of Things can be build by utilizing simple reflex agents and the communication paradigm of stigmergy. We map Things and artifacts to Web resources that are managed by a Web server and provide affordances to agents through hypermedia.KeywordsMulti-Agent SystemsWeb of ThingsSimple reflex agentsStigmergyRestHypermedia

South Korea has a long history of the planning, development, and management of smart cities to integrate emerging technological advances into complex physical infrastructure. This article explores lessons learned from smart city programs in South Korea to better understand the challenges and opportunities of future sustainable smart city innovation and development. This article conducted a comprehensive review and analysis of South Korea's smart city programs and conceptualized a sustainable smart city framework that will assist policymakers, planners, citizens, and other key stakeholders. This research proposed Governance, Policy, and Services (GPS) as the three pillars of a successful smart city framework, in addition to integrating physical and cyber infrastructures. This article argues that a smart city should function as a service platform that incubates and delivers long-term services to citizens and society. This article also emphasizes that a strong groundwork of the GPS framework will lead to the successful adaptation of innovative technologies and ideas for future smart city programs.

The concept of "smart cities" The Internet of Things (IoT) is a multi-layered framework that allows cities to employ urban networks to enhance economic performance, generate innovative technological solutions, and more. Smart Cities are the result of a new age in information technology and smart economy growth, based on Internet mesh networks, communication networks, transmission networks, wireless networks, and other end-to-end sensor networks. The Internet of Things (IoT) is at the heart of it. The Internet of Things incorporates sensors into common things and links them through particular protocols for sharing data and communications over the Internet. Smart detection leads, location, monitoring, and administration are all provided by this modular architecture. A smart city of excellence must overcome three main factors: instrumented, linked, and intelligent, with the help of IoT technology. At this point of IoT development, smart cities can only arise through the interconnectedness of all of these smart elements. The goal of this essay is to learn how the Internet and sensors may assist in the development of smart cities.
 The study's main goal is to figure out how the Internet of Things, or IoT, is being used in smart cities. We also want to establish the necessity for IoT systems in these smart cities, as well as the issues that they may pose to individuals and society, via the research.
 Review secondary research papers, reviews, dissertations, and online documents from the EBSCO, Scopus, and Emerald databases to analyse and comprehend the usage of IoT in smart cities and the possible problems that its implementation may provide in order to accomplish the following goals.
 Municipal requirements are getting more complicated, necessitating the adoption of new technology like the Internet of Things to ensure that all of these cities' duties are completed. Although more cities are becoming smart via the usage of IoT in a number of businesses, the study shows that this IoT system is also creating worries about the security of citizens' data.

Chapter 5 - Blockchain-based energy-efficient smart green city in IoT environments

In the future smart cities, billions of communicating Internet of Things (IoT) devices are expected which communicate wirelessly in the limited spectrum offered by 5G and long-range technologies. This means that a huge amount of interferences must be overcome by new agile technologies without wasting power resources in the IoT nodes. In this paper, these challenges are addressed by a neural-network-based machine learning system that is based on frequency-domain features extracted from the communication channel. This machine learning system predicts the needed transmit power to overcome the interferences by a predefined margin. Extensive system simulations have been performed on a real-world dataset that shows power savings in the range of 35-83% and a packet receive-ratio of at least 95%. Similarly, it has been found that the system converts after approximately 50 supervised samples, which supports efficient tracking of parameter variations in the communication channel.

Internet of Things (IoT) and Big Data have played a significant role in the development and sustainability of Smart Cities. They are the backbones of Smart Cities' efforts to connect human resources, social capital, and ICT infrastructure to address public challenges, achieve sustainable development and improve people's standard of life. It helps Smart Cities improve public services, such as water management, healthcare, education, security issues, transportation, energy usage, and other community to achieve a better life. Big Data helps to analyze, store and back-ups a massive amount of data generated in smart cities in seconds day by day. For this matter, different analytical tools are employed, such as data analytics, machine learning, statistics, and data mining. Even though there are many benefits offered to smart cities; the Smart Cities encounters challenges that need to be addressed. Many of these challenges focus on security and privacy, infrastructures and operational and handling massive data. This paper describes the overview of IoT, Big Data, and Smart Cities. The Applications of IoT and Smart Cities were also discussed; the features of Smart Cities and Big Data; Different challenges of IoT, Big Data, and Smart Cities are described. Also, the proposed research work has presented the relationship between IoT and Big Data to Smart Cities; the role of IoT and Big Data in smart cities was identified; finally, the future directions for IoT and Big Data regarding Smart Cities are discussed.