Knowledge graph-enhanced fault diagnosis: a bibliometric review of AI applications in sensor management (1998–2024)

Smart city systems are increasingly built with data analytics and machine learning techniques, basing on massive data sets. They have a heavy impact on human behavior and quality of life, and thus need to deliver a controllable and sufficiently transparent experience for the users.The aim of my work is to make smart city systems more interoperable and explainable, involving data visualization and communication techniques, sensor data processing, as well as the associated intelligent data value chain production and consumption. Here, the data and the information are shared employing Knowledge Graphs, that are becoming a key enabler for large-scale processing of massive collections of interrelated facts. Examples include the Google Knowledge Graph with dozens of billion facts, dataCommons, DBPedia, YAGO, and Knowledge Vault, a very large scale probabilistic knowledge graph created with information extraction methods for unstructured or semi-structured information. Specifically, Knowledge Graphs provide the means of development of the newest methods for data management, data fusion, data merging, and graph and network optimization and modeling, serving as a source of high quality data and a base for information integration.In particular, Knowledge Graphs help to infer new relationships out of existing facts, giving context and meaning to the content, and can be used in applications. For example, the data generated by a computer vision system could be semantically represented and shared across numerous systems, taking into account the needs and requirements of these systems, as well as the context, provenance, licensing and consent aspects of the generated data. I demonstrate Knowledge Graphs-based methods in advanced smart city applications from the domains such as automation and construction of buildings, energy efficiency, tourism, transport.

Building trustworthy knowledge graphs for cyber–physical social systems (CPSS) is a challenge. In particular, current approaches relying on human experts have limited scalability, while automated approaches are often not validated by users resulting in knowledge graphs of questionable quality. This paper introduces a novel pervasive knowledge graph builder for mobile devices that brings together automation, experts’ and crowdsourced citizens’ knowledge. The knowledge graph grows via automated link predictions using genetic programming that are validated by humans for improving transparency and calibrating accuracy. The knowledge graph builder is designed for pervasive devices such as smartphones and preserves privacy by localizing all computations. The accuracy, practicality, and usability of the knowledge graph builder is evaluated in a real-world social experiment that involves a smartphone implementation and a Smart City application scenario. The proposed methodology of knowledge graph building outperforms a baseline method in terms of accuracy while demonstrating its efficient calculations on smartphones and the feasibility of the pervasive human supervision process in terms of high interactions throughput. These findings promise new opportunities to crowdsource and operate pervasive reasoning systems for cyber–physical social systems in Smart Cities.

The ‘Smart City’ is a notion that has evolved in the latest period and encompasses a broad of aspects of urban life. Smart Cities will have to address several issues in the future, such as community security, transportation infrastructure, energy consumption, environmental sustainability, and cost-saving. Several elements of Smart Cities are intrinsically tied to the Internet of Things (IoT). The sensors on various IoT-enabled devices will produce heterogeneous data (data of temperature, moisture, heat, air pollution level, traffic, heath-related medical data, etc.) and that data should be sent to the server for taking a decision based on the received data. To send the data to the server an effective smart communication system should the integrated as a soul part of a Smart City. A key role is played by Wireless Sensor Networks (WSNs) in the construction of an effective smart communication network to interlink city centers using existing infrastructure with minimum investment. The WSN can be used as an alternative to building a smart network between IoT devices and the local server (Sink Node). The battery-powered sensor nodes of WSN collect, process, and communicate with the sink node using the limited battery stored energy. Because the WSNs operate unsupervised in a harsh atmosphere, it is not possible to recharge the sensors’ batteries. The power of WSN nodes may also be disrupted by several hazards, resulting in node failure. As a result, the most essential aspect to look for in a smart communication system in a Smart City is energy efficiency. Another major important challenge for the construction of an effective Wireless Sensor Network is to use the existing infrastructure of a city. Therefore, in this chapter, the challenges of constructing an energy-efficient WSN network using existing infrastructure have been considered.KeywordsWireless sensor network (WSN)Effective construction of WSN in Smart City (ECwSC)Genetic algorithm (GA)Ant colony optimization (ACO)Hybrid algorithmGlobal pheromone update (GPU) ruleLocal pheromone update (LPU) rule

The Weight of Surgical Knowledge: Navigating Information Overload.

Rule based complex event processing for an air quality monitoring system in smart city

This study presents basic concepts and applications of Artificial Intelligence System (AIS) for development of intelligent transport systems in smart cities in India. With growing urbanization the government has now realized the need for developing smart cities that can cope with the challenges of urban living and also be magnets for investment in India. Transport system in smart cities should be accessible, safe, environmentally friendly, faster, comfortable and affordable without compromising the future needs. The Indian cities largely lacks of Intelligent Transport System in India and there are various problems such as inefficient public transport system, severe congestion, increasing incidence of road accidents, inadequate parking spaces and a rapidly increasing energy cost etc. Therefore, development of Intelligent Transportation System is essential for smart cities due to concerns regarding the environmental, economic, and social equity. Artificial Intelligence is a key technology to resolve these issues. Therefore, there is an urgent need to adopt Artificial Intelligence system for development of Intelligent Transport System to better understand and control its operations in smart cities. Hence, the main objective of this study is to present some basic concepts of Artificial Intelligence and its applications for development of Intelligent Transport System in smart cities in India. This study concludes that Artificial Intelligence system needs to be adopted to develop smart public transport system, intelligent traffic management and control, smart traveller information system, smart parking management and safe mobility & emergency system in smart cities. It is expected that this study will pave the way for development of Intelligent Transport System in smart cities in India.

Purpose This study aims to explore the level of adoption of Construction 4.0 (C4.0) technologies and of Lean Construction (LC) ambidexterity capability in China’s construction industry. By incorporating C4.0 and LC ambidexterity into a sociotechnical systems (STS) framework, this research provides practical insights for construction practitioners and companies seeking C4.0 and LC. Design/methodology/approach The study adopts the questionnaire survey method. Data was obtained from Chinese construction professionals working on projects using C4.0 technologies. The data were analysed using mean score ranking and three nonparametric techniques, including the Wilcoxon signed-rank test and the Spearman rank correlation. Findings According to the ranking analysis, building information modelling and offsite construction have the highest adoption level, while additive manufacturing and blockchain rank lowest. Digital technology is more widely used in China’s construction industry than in physical technology. Many C4.0 technologies are significantly correlated with LC ambidexterity. Based on the STS perspective, we propose a holistic approach that incorporates C4.0 technologies and LC ambidexterity. Originality/value Conceptually, the study incorporates C4.0 and LC ambidexterity into the STS framework. Empirically, this study fills the gap in the current literature by exploring the level of C4.0 technology adoption in the Chinese construction industry, as well as the level of LC ambidexterity capabilities. Construction companies can use this framework to make strategic decisions regarding investments in LC practices and the adoption of C4.0 technologies, such as investing in staff skill training and new technology deployment to adapt to the ever-changing environment.

Wastewater management is a mechanism that is used to extract and refine pollutants from wastewater or drainage that can be recycled to the water supply with minimal environmental effects. New methods and techniques are required to ensure safe and smart wastewater management systems in smart cities because of the present deteriorating environmental state. Wireless sensor networks and the Internet of Things (IoT) represent promising wastewater treatment technologies. The elaborated literature survey formulates a conceptual framework with an Internet of Things (IoT)-based wastewater management system in smart cities (IoT-WMS) using blockchain technology. Blockchain technology is now being used to store information to develop an incentive model for encouraging the reuse of wastewater. Concerning the quality and quantity of recycled wastewater, tokens are issued to households/industries in smart cities. Nevertheless, this often encourages tampering with the information from which these tokens are awarded to include certain rewards. Anomaly detector algorithms are used to identify the possible IoT sensor data which has been tampered with by intruders. The model employs IoT sensors together with quality metrics to measure the amount of wastewater produced and reused. The simulation analysis shows that the proposed method achieves a high wastewater recycling rate of 96.3%, an efficiency ratio of 88.7%, a low moisture content ratio of 32.4%, an increased wastewater reuse of 90.8%, and a prediction ratio of 92.5%.

The process of extracting and refining contaminants from drainage or wastewater so they can be recycled back into the water supply with the least amount of negative environmental impact is known as wastewater management. The current declining environmental condition necessitates the development of new strategies and tactics to guarantee safe and intelligent wastewater management systems in smart cities. The Internet of Things (IoT) and wireless sensor networks are potential technologies for treating wastewater. The comprehensive literature review develops a conceptual framework utilizing blockchain technology for an Internet of Things (IoT)- based wastewater management system in smart cities (IoT- WMS). Information is now being stored using blockchain technology to create an incentive scheme that promotes the reuse of waste water. In smart cities, tokens related to the quantity and quality of recovered wastewater are distributed to homes and businesses. However, this frequently promotes manipulating the data used to award these tokens in order to incorporate particular benefits. To determine whether there is evidence of altered IoT sensor data, anomaly detector techniques are employed. In order to facilitate the most efficient distribution of water depending on user usage at the plot holding level, this study suggests a water distribution and integrated management system based on the Internet of Things (IoT) and data analytics (DA). In addition to preventing water waste, the suggested approach would assist in gathering consumption statistics for macro-level research and town planning

Making knowledge graphs work for smart manufacturing: Research topics, applications and prospects

KGVQL: A knowledge graph visual query language with bidirectional transformations

In the last few years, Wireless Sensor Networks (WSN) are gaining importance as a data collection mechanism for smart city systems. The development, deployment and operation of these networks involve a wide and heterogeneous set of technologies and participants. In many cases, city councils have outsourced the implementations of their WSNs to different external providers. This has resulted in a loss of control and visibility over the security of each individual WSN and, as well, over the entire system as a whole. In this article, we first describe the security problems related to the present model of WSN implementation within smart city systems. Then, we propose a non-intrusive architecture to recover part of the lost visibility, detect attacks on the WSNs operated by third parties, increase control over the providers and, in general, improve the security of the smart city from a holistic perspective.

The cognitive building provides promises if its social and technical requirements are considered holistically, but these have not been consciously explored, particularly within modular construction, which offers an unexploited opportunity. This study, therefore, aims to develop a framework for the modular construction of a cognitive building as a socio-technical system to ensure effective human-technology interaction, stakeholder acceptance and engagement. The framework is examined with a mock-up project of a student smart residential building in Hong Kong, similar to a cognitive building, which adopted a modular construction technique. The case demonstrated that the framework effectively ensures multi-perspectival involvement of key stakeholders at the design and manufacturing phase to produce modular units cleanly embedded with appropriate and operable cognitive building technologies for users and facility managers before transported to the site. The study is based on a single case study; hence, there could be a little scientific rigour providing little basis for the generalisation of the results to a wider space. As buildings are technologically advancing, future studies are encouraged to look at other possible case projects that utilise modular construction principles and evaluate them by adopting this study’s STS framework or its enhanced form.

This chapter emphasizes reducing energy consumption by the computing systems in smart cities by means of improving the efficiency of query auto-completion, thereby limiting the energy consumption and its impact on the environment. Ontology is created to understand the meaning of the terms in the query. Ontology comprises a collection of individual class instances; the various characteristics and attributes of the category are defined as the properties of a class. The semantic Web is the expansion of the present web, consisting of ontology as its main part. The knowledge graph is self-contained Semantic Web crawling through the entire web. This chapter discusses the performance improvement of auto-query completion using the combination of a knowledge graph and the long short-term memory model, for predicting the next word of the query. This chapter should enable the reader to understand the importance and contribution of an efficient semantic auto-query completion model for smart cities.

PurposeIrrigation System Management (ISM) poses many issues in terms of social and technical aspects. These can be further elaborated as the issues toward the efficiency of infrastructure, equity of water sharing, environmental integrity and economic acceptability. Water losses and the structural changes of the system caused many issues putting the irrigation system into distress. Hence, this urges the need for sustainable ISM to improve social and technical attributes in ISM. This paper aims to apply the Socio-Technical Systems (STSs) theory for sustainable ISM.Design/methodology/approachThe qualitative expert opinion survey method was selected as the research strategy to approach the research aim. Sixteen industry professionals in the ISM sector were selected through the purposive sampling method to conduct semi-structured interviews to collect data in the Sri Lankan context. Code-based content analysis through a directed approach was used to analyse the data.FindingsFour main categories of strategies were emerged from the research findings as “engineering strategies”, “operational strategies”, “management strategies” and “regulatory strategies” through STS approach for sustainable ISM. The STS framework was developed by combining engineering and operational strategies into the technical subsystem and the management and regulatory strategies into the social subsystem to approach the aim of achieving contemporary system management in irrigation in a sustainable manner.Originality/valueThe research revealed the links between the strategies emerged from various disciplines to minimise the issues in ISM from the perspectives of technical and social subsystems. The study contributes to knowledge by providing a framework for sustainable ISM by applying the socio-technical systems theory by integrating the concepts of sustainability.