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Smart city (SC) technologies can provide appropriate services according to citizens’ demands. One of the key enablers in a SC is the Internet of Things (IoT) technology, which enables a massive number of devices to connect with each other. However, these devices usually come from different manufacturers with different product standards, which confr...
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... whole process of semantic analysis is illustrated in Figure 6. When devices access, they will register to the platform and their basic information will be used for semantic model search. ...
Context 2
... whole process of semantic analysis is illustrated in Figure 6. When devices access, they will register to the platform and their basic information will be used for semantic model search. ...
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... In smart factories, systems in which physical mechanisms, machines or systems are controlled or monitored by computer-based algorithms are called cyber physical systems (SFS). As a result of R&D activities on SFS within the scope of Industry 4.0, the foundations of smart manufacturing methods were laid [189]. ...
Information technology applications are crucial to the proper utilization of manufacturing equipment in the new industrial age, i.e., Industry 4.0. There are certain fundamental conditions that users must meet to adapt the manufacturing processes to Industry 4.0. For this, as in the past, there is a major need for modeling and simulation tools in this industrial age. In the creation of industry-driven predictive models for machining processes, substantial progress has recently been made. This paper includes a comprehensive review of predictive performance models for machining (particularly analytical models), as well as a list of existing models' strengths and drawbacks. It contains a review of available modeling tools, as well as their usability and/or limits in the monitoring of industrial machining operations. The goal of process models is to forecast principal variables such as stress, strain, force, and temperature. These factors, however, should be connected to performance outcomes, i.e., product quality and manufacturing efficiency, to be valuable to the industry (dimensional accuracy, surface quality, surface integrity, tool life, energy consumption, etc.). Industry adoption of cutting models depends on a model's ability to make this connection and predict the performance of process outputs. Therefore, this review article organizes and summarizes a variety of critical research themes connected to well-established analytical models for machining processes.
... To enhance the quality of life for the growing urban population, it is believed that smart cities should incorporate distant, intelligent, and efficient healthcare systems (Agbali et al., 2019). IoT has revolutionised healthcare by allowing hospitals to detect and control things and monitor patients through sensors remotely (Guo et al., 2018). Several security and privacy concerns arise when new technologies for smart health and mobile health are implemented in smart cities. ...
The human population living in cities in the world is constantly increasing. As a result of the increasing population, the use of digital technologies has become widespread to facilitate city life. The importance of a smart city has increased mainly due to the integration of artificial intelligence and Internet of Things technologies into cities. Therefore, this study aims to examine the effect of smart city applications on life intention and how technology anxiety moderates this effect. For this purpose, data were collected from 386 people using a questionnaire. The data were analysed with a structural equation model. As a result of the analyses, it was determined that smart public service, smart transportation and smart environmentalism practices significantly positively affect the intention to live in smart cities. As a result of the moderator analysis, it was found that technology anxiety has a moderating role in the effect of smart city applications on the intention to live in smart cities. Suggestions were made according to the research findings.
... Furthermore, the adoption of data minimization approaches, where only indispensable information is collected and maintained, aids in curtailing the potential ramifications of a security breach. Figure 4. Automated Healthcare [15] Achieving equilibrium between the advantages of data exchange and safeguarding patient confidentiality represents a pivotal facet of data privacy in intelligent healthcare. Collaborative endeavors involving healthcare institutions, tech enterprises, and regulatory authorities become indispensable in formulating universally accepted protocols for data management and privacy procedures within the sector. ...
This research paper delves into the transformative role of Artificial Intelligence (AI) and the Internet of Things (IoT) in revolutionizing disease diagnosis within smart healthcare systems. AI, a cornerstone in computer science, is now pivotal in healthcare, offering sophisticated algorithms for analyzing medical data, thus aiding in decision-making and predictions. IoT extends this capability by enabling continuous data collection through web-enabled devices, including wearables and implanted sensors. The integration of AI and IoT in smart healthcare systems not only refines medical procedures but also enhances patient experiences and operational workflows. This paper explores how AI-driven routines in conjunction with IoT data streams facilitate rapid and accurate disease diagnosis, surpassing traditional methods often limited by human biases. The adoption of these technologies in healthcare promises to improve diagnostic accuracy, expedite processes, and broaden access to medical expertise, especially in remote areas. However, challenges such as ensuring data privacy, security, and ethical use of AI remain paramount.
... Both AI and IoT technologies have played a paramount role in this evolution, by acting as the conduits that facilitate comprehensive data collection and analysis. IoT devices, embedded throughout the property, provide real-time data on numerous variables, ranging from environmental parameters such as temperature and humidity to security features encompassing door locks and surveillance systems (Guo et al., 2018). The continuous flow of data allows property managers to analyze and draw actionable insights, thereby enabling them to respond promptly to emerging situations and ensure that the properties are well-maintained and secure. ...
This chapter delves into the transformative relationship between the metaverse and real estate development, exploring the potential impacts, opportunities, and challenges that arise from their seamless integration. Traditional real estate development models are becoming less and less reliable due to a myriad of challenges, including cost considerations, environmental concerns, stakeholder engagement, and market volatility; hence, the need for new approaches that would guarantee both the sellers and buyers some exciting experience. This is what the metaverse is deemed to have made possible and is expected to revolutionize the sector even more as it continues to gain prominence. However, like many other technologies and platforms, it is prone to issues, especially related to legal and ethical concerns that will require to be addressed if it is to fully transform the real estate development.
... Architecture of IoT enhancing the overall IoT value, while triggering capabilities are typically restricted and often require authorization [24]. ...
In response to the growing IoT device diversity, efforts are underway to better integrate data, applications, and services. The Semantic Web, known for its simplicity in integration, has the potential to improve data interpretation and interoperability. In this research, a pollution management model is used, combining the Semantic Web of Things (SWoT) and Artificial Intelligence (AI), to create smarter cities, providing real-time environmental information. The dataset has been sourced from Aarhus City, Denmark, and the study outlines Semantic Web Technologies (SWTs) in IoT frameworks, including common ontologies for IoT-based architecture. The dataset’s relationship between various gases/pollutants is analyzed using correlation matrix. Machine learning methods like Multi-Layer Perceptron (MLP) with Sigmoid, ReLU, Tanh, Maxout, Swish hybrid activation functions are employed, with results assessed using Root Mean Squared Error (RMSE) and Mean Squared Error (MSE). A comparison of errors for different activation functions is also performed and the findings reveal good results when comparing actual and predicted values in the proposed model.
... Due to the growing number of devices and the wide range of devices being used, it is hard to ensure security and secure routing in LLN. 8 The remaining chapters of the paper are organised as follows. Section on "Trust Management" describes work available related to it. ...
The number of internet-connected IoT devices is increasing at a rapid speed. Because of this exponential growth, there are now some serious security concerns and challenges. As most of these devices are part of our daily activities, they contain personal private data which is getting transferred to some other location. When many IoT devices are in a network, they transfer the data on a hop-by-hop basis, which raises concerns about the routing security and trustworthiness of other IoT devices on which they are dependent for transferring their data. The goal of this study is to develop a safe environment for routing with the help of a trust analysis of each node participating in the network. Routing nodes are prone to some routing attacks like the Rank attack, wormhole attack, and Sybil attack. In this study, a lightweight trust-based system has been developed to detect and remove these attacks. The proposed model consists an accuracy of 98%. Moreover, proposed model is compared with earlier studies, proposed model's performance stands remarkable.
... Internet of Things (IoT) framework allows for the creation of intelligent applications and infrastructures by bridging the gap between the real world and the internet through physical items [1]. The markets of IoT which have steadily evolved over the past few years, cover every facet of human life, encompassing energy, smart houses, smart towns and cities, healthcare, smart traffic management, etc. [2][3][4][5][6]. The Social Internet of Things (SIoT) concept has become increasingly popular in recent years as a result of the ☆ This article is a part of the "Security and Privacy in IoT Network" Special issue. ...
As nodes in Social Internet of Things (SIoT) become more intelligent, malicious information occurs more frequently and spreads more widely. This problem can severely affect the trustworthiness of services and applications in SIoT. Methods to effectively control malicious information spreading in SIoT are essential and necessary. Reputation mechanism provides a powerful tool to tackle this challenge. In this paper, we propose a reputation-based mechanism to activate the self-purification capacity of the SIoT network by balancing information conflicts triggered by reporters and supporters. In order to find the best rewarding and punishment strategy, a bilateral cumulative-prospect-based evolutionary game model of SIoT network information conflict is constructed. Using local stability analysis and numerical simulation, the evolutionary trends of the proposed game model under different theoretical application scenarios are analyzed. The findings indicate that the basic income and deposit of both sides, the popularity of information as well as the importance of the conformity effect all have a significant impact on the system's steady state and evolutionary path. The specific conditions that both participating sides of the game tend to treat conflicts relatively rationally are analyzed. Dynamic evolution analysis and sensitivity analysis of selected parameters show that basic income is positively related to smart object's feedback strategies, while deposit is negatively related to that. While weight of conformity effect or the information popularity goes up, the rising of feedback probability is observed. Based on the above results, suggestions on dynamic reward and punishment strategies are given. The proposed model is a helpful attempt to model the evolution of information spreading in SIoT networks, with the ability to simulate several well-known regularities of message dissemination. Proposed model and suggested quantitative strategies can be helpful to build feasible malicious information control facilities in SIoT networks.
... The results showed that the proposed method realized high-precision dynamic load forecasting in different time ranges. Guo et al. (2018) [18] studied the user-centered semantic IoT technology for smart cities and used a new algorithm of AI semantic IoT hybrid service architecture to integrate heterogeneous IoT devices to support intelligent services. The results showed that the proposed architecture was supported by semantics and AI technology, flexibly connecting heterogeneous devices. ...
... The results showed that the proposed method realized high-precision dynamic load forecasting in different time ranges. Guo et al. (2018) [18] studied the user-centered semantic IoT technology for smart cities and used a new algorithm of AI semantic IoT hybrid service architecture to integrate heterogeneous IoT devices to support intelligent services. The results showed that the proposed architecture was supported by semantics and AI technology, flexibly connecting heterogeneous devices. ...
In order to carry out a comprehensive design description of the specific architectural model of AI, the auxiliary model of AI and architectural spatial intelligence is deeply integrated, and flexible design is carried out according to the actual situation. AI assists in the generation of architectural intention and architectural form, mainly supporting academic and working theoretical models, promoting technological innovation, and thus improving the design efficiency of the architectural design industry. AI-aided architectural design enables every designer to achieve design freedom. At the same time, with the help of AI, architectural design can complete the corresponding work faster and more efficiently. With the help of AI technology, through the adjustment and optimization of keywords, AI automatically generates a batch of architectural space design schemes. Against this background, the auxiliary model of architectural space design is established through the literature research of the AI model, the architectural space intelligent auxiliary model, and the semantic network and the internal structure analysis of architectural space. Secondly, to ensure compliance with the three-dimensional characteristics of the architectural space from the data source, based on the analysis of the overall function and structure of space design, the intelligent design of the architectural space auxiliary by Deep Learning is carried out. Finally, it takes the 3D model selected in the UrbanScene3D data set as the research object, and the auxiliary performance of AI's architectural space intelligent model is tested. The research results show that with the increasing number of network nodes, the model fitting degree on the test data set and training data set is decreasing. The fitting curve of the comprehensive model shows that the intelligent design scheme of architectural space based on AI is superior to the traditional architectural design scheme. As the number of nodes in the network connection layer increases, the intelligent score of space temperature and humidity will continue to rise. The model can achieve the optimal intelligent auxiliary effect of architectural space. The research has practical application value for promoting the intelligent and digital transformation of architectural space design.
... An artificial intelligence-based semantic Internet of Things (AI-SIoT) algorithm was proposed by [16] to integrate heterogeneous IoT devices to support and manage intelligent services within the agricultural fields. This algorithm provides the flow function of each packet with the high-priority traffic which is based on the moves through the network environment in a series of hops with various parameters within the agricultural environment. ...
... The high-priority packets are regarded as unusual packets which are gathered by the SNs, and those packets are higher than a given traffic threshold within the agricultural environment. However, the algorithm proposed by [16] does not consider the packet delay with low priority in a network which remains a problem for network performance. This is because their algorithm focuses more on packets with high priority. ...
... In this paper, gaps and proposed methods based on WSNs challenges has been outlined that presented by different authors [16][17][18][19][20][21][22] such as delay of packets, highpriority traffic, network throughput, packet congestion and many others. The proposed solution provides different results as mentioned above in the related work. ...
Wireless Sensor Networks (WSNs) is an area that has attracted a lot of attention currently worldwide. WSNs are implemented to monitor temperature, humidity, and pressure, among others within the agricultural environment. This paper addresses the traffic congestion that occurs within WSNs in the agricultural environment during packet transmission that is normally caused by head-of-line blocking. As a result, packet loss, packet delay, and network performance impairment occurred during packet distribution in the network. This paper proposed an Intelligence Traffic Routing (ITR) algorithm to manage packet flow to avoid traffic congestion in WSNs within the agricultural environment while improving Quality of Service (QoS). The LBRM (Load Balancing Routing Management) and MLCC (Machine Learning Congestion Control) algorithms were integrated to develop the proposed ITR algorithm. Network Simulator 2 (NS-2) was used to test the effectiveness of the proposed ITR algorithm. The simulation results showed that the proposed ITR algorithm reduced packet loss by 27.3%, packet delay by 43.4%, and improved network throughput by 98.4% when compared with LBRM and MLCC algorithms.
... Artificial intelligence (AI) refers to programs, algorithms, systems, and machines that embody intelligence and show some similarity to certain characteristics of humans [1]. Due to its advantages in efficiency, precision, and ease of management [2], it is gradually being used in more and more fields, such as customer services, finance, unmanned vehicles, and mobile healthcare. The specific forms it appears in are mostly offline AI robots and online AI service systems. ...
In order to perpetuate service sustainability and promote sustainable growth in the service sector, it is important to resolve service failures. AI technology is being applied to service jobs in more and more industries, but AI will inevitably fail while providing service. How to carry out service recovery and obtain the understanding and forgiveness of customers is a problem that urgently needs solving in the practice and research of AI services. The purpose of this study was to explore the artificial intelligence remediation mechanism in the context of service failure and to explore the remedial utility of AI’s self-deprecating humor responses. The study conducted data collection through three experiments to test our hypotheses: study 1 verified the main effect of self-deprecating humor responses and the mediating effect of perceived sincerity and perceived intelligence; study 2 verified the moderated effect of the sense of power; and study 3 verified the moderated effect of failure experience. The experimental results show that, in the context of AI for service recovery, self-deprecating humor responses can improve customers’ willingness to tolerate failure, with perceived intelligence and perceived sincerity found to play a mediating role in this. The sense of power also plays a moderating role by affecting perceived sincerity, and failure experience has a moderate effect by affecting perceived intelligence. The theoretical contribution of the article is to introduce the perspective of AI’s self-deprecating humor service recovery, which complements theoretical research in the field of AI services. The management significance of the article is to provide new AI communication strategies and practical suggestions for enterprises and technical personnel.
