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The real world applications are more prone to difficulties of challenges due to fast growth of technologies and inclusion of artificial intelligence (AI) based logical solutions. The massive internet-of-things (IoT) devices are involved in number of Industry 5.0 applications like smart healthcare, smart manufacturing, smart agriculture, smart transportation. Advanced wireless techniques, customization of services and different technologies are experiencing a major transformation. The desire to increase the communication reliability without adding energy overhead is the major challenge for massive IoT enabled networks. To cope up with the above challenges, Industry 5.0 requirements needs to be monitored at the remote level which again adds on the communication challenge. Use of relays in 6G based wireless networks is denied due to high requirement of energy. Therefore in this paper, Intelligent reflecting surfaces (IRSs) assisted energy constrained 6G wireless networks are studied. To provide seamless connection between the communicating mobile nodes, IRS with an array of reflecting elements are configured in the system set up. A use-case scenario of IRS enabled network in Internet-of-Underwater things (IoUT) for smart ocean transportation is also provided. The IRS assisted wireless network is evaluated for target rates achieved. A power consumption model of the IRS supported system is also proposed to optimise the energy efficiency of the system. Further, the paper evaluates the impact of number of reflecting elements N on the IRS and the phase resolution b of each element on the system performance. The energy efficiency improves by 20% for IRS with N = 100 with b = 2 over IRS with b = 1 . [ABSTRACT FROM AUTHOR]

In recent years, driven by Industry 4.0 wave, academic research has focused on the science, engineering, and enabling technologies for intelligent and cyber manufacturing. Using a network science and data mining-based Keyword Co-occurrence Network (KCN) methodology, this work analyzes the trends in data science topics in the manufacturing literature over the past two decades to inform the researchers, educators, industry leaders of knowledge trends in intelligent manufacturing. It studies the evolution of research topics and methods in data science, Internet of Things (IoT), cloud computing, and cyber manufacturing. The KCN methodology is applied to systematically analyze the keywords collected from 84,041 papers published in top-tier manufacturing journals between 2000 and 2020. It is not practically feasible to review this large body of literature through tradition manual approaches like systematic review and scoping review to discover insights. The results of network modeling and data analysis reveal important knowledge components and structure of the intelligent and cyber manufacturing literature, implicit the research interests switch and provide the insights for industry development. This paper maps the high frequency keywords in the recent literature to nine pillars of Industry 4.0 to help manufacturing community identify research and education directions for emerging technologies in intelligent manufacturing. [ABSTRACT FROM AUTHOR]

The fog computing concept was proposed to help cloud computing for the data processing of Internet of Things (IoT) applications. However, fog computing faces several challenges such as security, privacy, and storage. One way to address these challenges is to integrate blockchain with fog computing. There are several applications of blockchain-fog computing integration that have been proposed, recently, due to their lucrative benefits such as enhancing security and privacy. There is a need to systematically review and synthesize the literature on this topic of blockchain-fog computing integration. The purposes of integrating blockchain and fog computing were determined using a systematic literature review approach and tailored search criteria established from the research questions. In this research, 181 relevant papers were found and reviewed. The results showed that the authors proposed the combination of blockchain and fog computing for several purposes such as security, privacy, access control, and trust management. A lack of standards and laws may make it difficult for blockchain and fog computing to be integrated in the future, particularly in light of newly developed technologies like quantum computing and artificial intelligence. The findings of this paper serve as a resource for researchers and practitioners of blockchain-fog computing integration for future research and designs. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence, big data, Internet of Things, and other technologies, digital twin has gained great attention and become a current research topic. Using digital twin technology, the digital twin model can be constructed in the cyber space that is fully equivalent to the physical entity. It is always consistent with the physical entity in the operation process, which greatly improves the dynamic perception and prediction ability of the real world. After the development in recent years, digital twin has gradually changed from the initial concept discussion to the study of model framework and implementation method. However, because the research objects in different industries have great differences in their own composition, service conditions, and application scenarios, they have personalized characteristics in modeling strategies and usage methods. Therefore, based on different industries, this paper reviews the current articles on digital twins and distinguishes the focus of digital twin modeling research; subsequently, the relevant supporting techniques and methods are summarized according to their different importance for digital twin modeling. Based on the review in this paper, future researchers can conduct targeted research on digital twin technology in term of the characteristics of the objects in their industry. [ABSTRACT FROM AUTHOR]

We are witnessing the digitalization era, where artificial intelligence (AI)/machine learning (ML) models are mandatory to transform this data deluge into actionable information. However, these models require large, high-quality datasets to predict high reliability/accuracy. Even with the maturity of Internet of Things (IoT) systems, there are still numerous scenarios where there is not enough quantity and quality of data to successfully develop AI/ML-based applications that can meet market expectations. One such scenario is precision agriculture, where operational data generation is costly and unreliable due to the extreme and remote conditions of numerous crops. In this paper, we investigated the generation of synthetic data as a method to improve predictions of AI/ML models in precision agriculture. We used generative adversarial networks (GANs) to generate synthetic temperature data for a greenhouse located in Murcia (Spain). The results reveal that the use of synthetic data significantly improves the accuracy of the AI/ML models targeted compared to using only ground truth data. [ABSTRACT FROM AUTHOR]

The emerging Space-Air-Ground, Artificial intelligence, blockchain and Vehicle-to-everything technology in Integrated Internet of Things (IoT) enables vehicles to communicate with other vehicles and roadside units (RSU), which improves communication efficiency and driving safety. Due to the rich data in the Integrated IoT, it is easy for illegal persons to steal data or deceive users. While reasonably allocating resources to complete the transmission task, it is still a challenge to ensure the communication security of vehicle users in Integrated IoT. In this paper, we study the cost of vehicle users transmitting tasks by Vehicle to Infrastructure and Vehicle to Vehicle (V2V) in Integrated IoT. In order to protect the information security, we establish the identity authentication and matching model to obtain a better channel environment of Integrated IoT. Moreover, we consider dynamic pricing based on bandwidth resource occupancy to ensure user experience and server load in Integrated IoT. Under the constraints of task tolerable delay, we fix the bandwidth on the V2V side and decouple the task decomposition and bandwidth allocation in Integrated IoT. Then, we propose algorithms Blockchain-based Vehicle Identity Authentication (BVIA) and Delay Weight Fairness Bandwidth Allocation (DWFBA). In this way, vehicle users and RSUs entering the Integrated IoT system are authenticated and matched with devices with good trust value, and the fairness of users resource allocation is guaranteed. Simulation results show that BVIA algorithm can reduce communication overhead, and DWFBA algorithm can control user costs and effectively reduce the number of task failures in Integrated IoT. [ABSTRACT FROM AUTHOR]

The Internet of things (IoT) is emerging as a prime area of research in the modern era. The significance of IoT in the daily life is increasing due to the increase in objects or things connected to the internet. In this paper, routing protocol for low power and lossy networks (RPL) is examined on the Contiki operating system. This paper used RPL attack framework to simulate three RPL attacks, namely hello-flood, decreased-rank and increased-version. These attacks are simulated in a separate and simultaneous manner. The focus remained on the detection of these attacks through artificial neural network (ANN)-based supervised machine learning approach. The accurate detection of the malicious nodes prevents the network from the severe effects of the attack. The accuracy of the proposed model is computed with hold-out approach and tenfold cross-validation technique. The hyperparameters have been optimized through parameter tuning. The model presented in this paper detected the aforesaid attacks simultaneously as well as individually with 100% accuracy. This work also investigated other performance measures like precision, recall, F1-score and Mathews correlation coefficient (MCC). [ABSTRACT FROM AUTHOR]

Due to digitalization, small and medium-sized enterprises (SMEs) have significantly enhanced their efficiency and productivity in the past few years. The process to automate SME transaction execution is getting highly multifaceted as the number of stakeholders of SMEs is connecting, accessing, exchanging, adding, and changing the transactional executions. The balanced lifecycle of SMEs requires partnership exchanges, financial management, manufacturing, and productivity stabilities, along with privacy and security. Interoperability platform issue is another critical challenging aspect while designing and managing a secure distributed Peer-to-Peer industrial development environment for SMEs. However, till now, it is hard to maintain operations of SMEs' integrity, transparency, reliability, provenance, availability, and trustworthiness between two different enterprises due to the current nature of centralized server-based infrastructure. This paper bridges these problems and proposes a novel and secure framework with a standardized process hierarchy/lifecycle for distributed SMEs using collaborative techniques of blockchain, the internet of things (IoT), and artificial intelligence (AI) with machine learning (ML). A blockchain with IoT-enabled permissionless network structure is designed called "B-SMEs" that provides solutions to cross-chain platforms. In this, B-SMEs address the lightweight stakeholder authentication problems as well. For that purpose, three different chain codes are deployed. It handles participating SMEs' registration, day-to-day information management and exchange between nodes, and analysis of partnership exchange-related transaction details before being preserved on the blockchain immutable storage. Whereas AI-enabled ML-based artificial neural networks are utilized, the aim is to handle and optimize day-to-day numbers of SME transactions; so that the proposed B-SMEs consume fewer resources in terms of computational power, network bandwidth, and preservation-related issues during the complete process of SMEs service deliverance. The simulation results present highlight the benefits of B-SMEs, increases the rate of ledger management and optimization while exchanging information between different chains, which is up to 17.3%, and reduces the consumption of the system's computational resources down to 9.13%. Thus, only 14.11% and 7.9% of B-SME's transactions use network bandwidth and storage capabilities compared to the current mechanism of SMEs, respectively. [ABSTRACT FROM AUTHOR]

In nursing homes using technologies such as IoT, big data, cloud computing, and machine learning, there is a constant risk of attacks such as Brute Force FTP, Brute Force SSH, Web Attack, Infiltration, and Botnet during data communication between individual terminals and the cloud server. Therefore, effectively identifying network communication data is essential to protect data communication security between individual terminals and the cloud server. Aiming at the data mentioned above regarding communication security issues, we propose an intelligent intrusion detection model NIDD (Network Intelligent Data Detection) model that combines deep convolution generation adversarial network (DCGAN) with Light Gradient Boosting Machine (LightGBM) and Shapley Additive exPlanations (SHAP). The NIDD model first generates new attack samples by learning the feature distribution of the existing attack sample data and effectively expands the rare attack samples. Secondly, we use the Light Gradient Boosting Machine (LightGBM) algorithm as the base classifier to train the dataset and start to build the intrusion detection model. Then use Shapley Additive exPlanations (SHAP) to analyze the contribution of the classification results, and adjust the model parameters according to the analysis results. Finally, we obtain the optimal model for the intelligent detection model of network intrusion. This paper conducts experimental tests on the NSL-KDD dataset. The experimental results show that the NIDD model built based on Light Gradient Boosting Machine can detect Brute Force FTP, Brute Force SSH, DoS, Heartbleed, Web Attack, Infiltration, Botnet, PROBE, R2L, and U2R attacks with an accuracy of 99.76%. Finally, we re-verified the NIDD model on the CIC-IDC-2018 dataset. The results once again proved that the NIDD model could solve the data communication security between the nursing robot and the cloud server and the data before the IoT terminal and the cloud server. Communication security provides a sufficient guarantee. [ABSTRACT FROM AUTHOR]

This article conducts a literature review of current and future challenges in the use of artificial intelligence (AI) in cyber physical systems. The literature review is focused on identifying a conceptual framework for increasing resilience with AI through automation supporting both, a technical and human level. The methodology applied resembled a literature review and taxonomic analysis of complex internet of things (IoT) interconnected and coupled cyber physical systems. There is an increased attention on propositions on models, infrastructures and frameworks of IoT in both academic and technical papers. These reports and publications frequently represent a juxtaposition of other related systems and technologies (e.g. Industrial Internet of Things, Cyber Physical Systems, Industry 4.0 etc.). We review academic and industry papers published between 2010 and 2020. The results determine a new hierarchical cascading conceptual framework for analysing the evolution of AI decision-making in cyber physical systems. We argue that such evolution is inevitable and autonomous because of the increased integration of connected devices (IoT) in cyber physical systems. To support this argument, taxonomic methodology is adapted and applied for transparency and justifications of concepts selection decisions through building summary maps that are applied for designing the hierarchical cascading conceptual framework. [ABSTRACT FROM AUTHOR]

In this paper, we present a new novel simple iTFET with overlapping gate on source-contact (SGO), Drain Schottky Contact, and intrinsic SiGe pocket (Pocket-SGO iTFET). The aim is to achieve steep subthreshold swing (S.S) and high ION current. By optimizing the gate and source-contact overlap, the tunneling efficiency is significantly enhanced, while the ambipolar effect is suppressed. Additionally, using a Schottky contact at the drain/source, instead of ion implantation drain/source, reduces leakage current and thermal budget. Moreover, the tunneling region is replaced by an intrinsic SiGe pocket posing a narrower bandgap, which increases the probability of band-to-band tunneling and enhances the ION current. Our simulations are based on the feasibility of the actual process, thorough Sentaurus TCAD simulations demonstrate that the Pocket-SGO iTFET exhibits an average and minimum subthreshold swing of S.Savg = 16.2 mV/Dec and S.Smin = 4.62 mV/Dec, respectively. At VD = 0.2 V, the ION current is 1.81 × 10–6 A/μm, and the ION/IOFF ratio is 1.34 × 109. The Pocket-SGO iTFET design shows great potential for ultra-low-power devices that are required for the Internet of Things (IoT) and AI applications. [ABSTRACT FROM AUTHOR]

With the fast growth of technologies like cloud computing, big data, the Internet of Things, artificial intelligence, and cyber-physical systems, the demand for data security and privacy in communication networks is growing by the day. Patient and doctor connect securely through the Internet utilizing the Internet of medical devices in cloud-healthcare infrastructure (CHI). In addition, the doctor offers to patients online treatment. Unfortunately, hackers are gaining access to data at an alarming pace. In 2019, 41.4 million times, healthcare systems were compromised by attackers. In this context, we provide a secure and lightweight authentication scheme (RAPCHI) for CHI employing Internet of medical Things (IoMT) during pandemic based on cryptographic primitives. The suggested framework is more secure than existing frameworks and is resistant to a wide range of security threats. The paper also explains the random oracle model (ROM) and uses two alternative approaches to validate the formal security analysis of RAPCHI. Further, the paper shows that RAPCHI is safe against man-in-the-middle and reply attacks using the simulation programme AVISPA. In addition, the paper compares RAPCHI to related frameworks and discovers that it is relatively light in terms of computation and communication. These findings demonstrate that the proposed paradigm is suitable for use in real-world scenarios. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet of Things (IoT) technology and the rising popularity of IoT devices, an increasing number of computing intensive IoT applications have been developed. However, due to the limited resources of IoT devices, cloud computing systems are required to compute intensive IoT applications. Furthermore, to be subject to a single cloud computing service provider, multi-cloud computing has become an IoT service cloud computing solution. As a result of the complexity of multi-cloud scheduling, the application of artificial intelligence is an important technology to solve IoT multi-cloud scheduling. The corresponding talent training plays an important role in the development and implementation of the IoT artificial intelligence multi-cloud scheduling. First, this paper studies the key influencing factors of IoT artificial intelligence multi-cloud scheduling applied talent training. Combined with the characteristics of the development of China's artificial intelligence industry, this paper summarizes the influencing factors from the four-dimensional training path of government departments, universities, enterprises and scientific research institutes. The purpose of artificial intelligence multi-cloud scheduling applied talent training is to build an artificial intelligence multi-cloud scheduling applied talent training influencing factor index system. Then, the DEMATEL method is used to establish multiple correlation matrices according to the direct influence correlation between the factors and calculate the degree of influence, the degree of being influenced, the center degree and the cause degree of the factors. Using the improved AISM method, based on the idea of game confrontation, from the two opposite extraction rules of result priority and cause priority, a group of confrontation level topological maps with comprehensive influence values reflecting the interacting factors are obtained, and relevant suggestions are presented to provide a reference for the training of artificial intelligence multi-cloud scheduling applied talent. [ABSTRACT FROM AUTHOR]

Potential benefits of precision medicine in cardiovascular disease (CVD) include more accurate phenotyping of individual patients with the same condition or presentation, using multiple clinical, imaging, molecular and other variables to guide diagnosis and treatment. An approach to realising this potential is the digital twin concept, whereby a virtual representation of a patient is constructed and receives real-time updates of a range of data variables in order to predict disease and optimise treatment selection for the real-life patient. We explored the term digital twin, its defining concepts, the challenges as an emerging field, and potentially important applications in CVD. A mapping review was undertaken using a systematic search of peer-reviewed literature. Industry-based participants and patent applications were identified through web-based sources. Searches of Compendex, EMBASE, Medline, ProQuest and Scopus databases yielded 88 papers related to cardiovascular conditions (28%, n = 25), non-cardiovascular conditions (41%, n = 36), and general aspects of the health digital twin (31%, n = 27). Fifteen companies with a commercial interest in health digital twin or simulation modelling had products focused on CVD. The patent search identified 18 applications from 11 applicants, of which 73% were companies and 27% were universities. Three applicants had cardiac-related inventions. For CVD, digital twin research within industry and academia is recent, interdisciplinary, and established globally. Overall, the applications were numerical simulation models, although precursor models exist for the real-time cyber-physical system characteristic of a true digital twin. Implementation challenges include ethical constraints and clinical barriers to the adoption of decision tools derived from artificial intelligence systems. [ABSTRACT FROM AUTHOR]

The emergence of a new generation of applications such as the Internet of Things, Artificial Intelligence, and 5G has brought fog computing as cloud development toward the edge network. It is done by adding an extra layer of resources to meet low latency, high performance, mobility, and interoperability challenges. The development of fog markets and new beneficial business paradigms such as fog services considerably affect the future of fogonomics (which is defined as the economics of fog computing). Thus, it is a challenging issue to design a market-oriented model to deal with the economics-based resource management in fog computing, which adjusts the resource supply of Fog Resource Providers (FRPs) and the demands of User Services (USs) based on users' requirements while satisfying all parties' utilities. This paper proposes the Fog-Marketing scheme consisting of a Resource Propagation Component (RPC) and a Contract-aware Market Selection Component (CMSC) for resource provisioning in multi-tier fog computing. The RPC is deployed in each fog node to perform hierarchical resource propagation of FRPs to auction-based Fog Decentralized Markets (FDMs) close to USs to increase revenue opportunities, and embedded CMSC in the user agent applies a matching strategy to assign USs and FDMs aims to promote USs' utility. The simulation results under extensive experiments confirmed that the proposed scheme succeeded in reducing the Round-Trip-Time, increasing the revenue of FRPs, and improving the utility of USs compared to existing models. [ABSTRACT FROM AUTHOR]

This paper presents a review of voltammetric and related methods implemented with low-cost, mainly lab-made instrumentation. In recent years, there has been a growing interest in developing low-cost electrochemical instrumentation. Advances in electronics and software have made it easier to access high-performance components for the construction of potentiostats. Currently, lab-made instrumentation for electrochemical research can be very simple, with minimal components to perturb the system and record the response signal. It can also be very advanced, using high-performance electronic components and software development. Several authors have presented the feasibility of designing and manufacturing electrochemical instruments for voltammetry, chronoamperometry, chronopotentiometry, and electrodeposition. This review presents some available options, ranging from basic to advanced. The aim is to provide readers with the full range of possibilities for using their own instruments, especially in cases where access to commercial apparatus is difficult or where specific applications are needed, for example in the Internet of Things or artificial intelligence. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) enables the improvement of the productivity and intelligent level of factory. The procedure of product quality inspection has generally adopted machine intelligence algorithms instead of manual operation to improve efficiency. In this paper, we propose a product quality inspection system scheme based on software-defined edge intelligent controller (SD-EIC). By adopting the software definition and resource virtualization technologies, the hardware platform of SD-EIC is designed to support the real-time control tasks and non-real-time edge computing tasks at the same time. To this end, we propose the scheme and architecture of product quality inspection system based on SD-EIC. Multiple virtual controllers and virtual edge computing nodes are constructed on a set of SD-EIC hardware platform to realize the integrated deployment of the real-time control for terminal devices and the AI model reasoning of product defect recognition algorithm based on machine vision respectively. In addition, the management and control scheme of product quality inspection system based on industrial information model is proposed. By constructing the semantic-based digital twin information model of terminal device, the flexible adjustment and parameter configuration of terminal device are realized to meet the demands of flexible production and manufacturing. The proposed product quality inspection system solution can effectively improve the utilization of hardware resources and the efficiency of product quality inspection, and reduce the overall deployment cost of the system. It can flexibly adapt to product diversity and different industrial scenarios. [ABSTRACT FROM AUTHOR]

There is a growing demand for intensive care units, but there is a relative shortage of medical staff. Intensive care work is heavy and stressful. Optimizing the working conditions and processes of the intensive care unit is of great significance for improving the work efficiency and the level of diagnosis and treatment in the intensive care unit. The intelligent intensive care unit is a new ward management model gradually developed on the basis of modern science and technology such as communication technology, internet of things, artificial intelligence, robots, and big data. Under this model, the potential risks caused by human factors are greatly reduced, and the monitoring and treatment of patients has been significantly improved. This paper reviews the progress in related fields. [ABSTRACT FROM AUTHOR]

The advances in Industry 4.0 provide both challenges and opportunities for digital manufacturing and assembly systems. This paper first addresses the state-of-the-art readiness for Industry 4.0 concerning assembly and manufacturing systems through a literature review of the relevant papers recently published. Then it assesses the challenges faced nowadays by assembly and manufacturing systems. Third, it focuses on the most promising future developments and evolution of such production systems as well as their digitalisation. Finally, this manuscript illustrates the content of the papers selected for this special issue. Through the study presented in this special issue, valuable contributions to both theory and application in this area have been achieved, and a useful reference for future research is given. [ABSTRACT FROM AUTHOR]

With the development and application of advanced technologies such as Cyber Physical System, Internet of Things, Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., more manufacturing enterprises are transforming to intelligent enterprises. Smart manufacturing systems (SMSs) have become the focus of attention of some countries and manufacturing enterprises. At present, there are some applications of SMSs in different industrial fields. However, there is still a lack of a unified definition of SMSs and a unified analysis of requirements. In order to have a comprehensive understanding of SMSs, this paper summarized the evolution, definition, objectives, functional requirements, business requirements, technical requirements, and components of SMSs. At the same time, it points out the current development status and level. Based on above, an autonomous SMSs model driven by dynamic demand and key performance indicators is proposed. Through the review of this paper, the reference can be provided for the transformation of more manufacturing enterprises from the traditional to the intellectualized ones. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence and Internet of Things (IoT) technology, increasingly edge devices have entered people's daily lives. However, due to the limited performance of edge devices, complex models can affect the response speed and efficiency of the whole system. Existing research still cannot simultaneously satisfy the demand for accuracy and response speed of edge devices. This paper proposes a lightweight and highly accurate object detection model that uses the Transformer to address edge devices' limited computational capacity and storage space. Specifically, the proposed model adopts the Swin Transformer for multi-scale feature extraction to achieve better global modeling capability. In addition, we propose the Neck module with path aggregation network (PAN), which is designed with a two-feature pyramid structure capable of combining semantic and localization information in order to improve the operational performance by exploiting the underlying location features. A lightweight detection head is then developed using group convolution, fusing the two localization branches and removes the additional decoupling operation. Finally, we conduct comparative experiments on three datasets: the Retail-cabinet dataset, the Roadsign dataset, and the Pascal VOC dataset. Experimental results show that compared with the baseline model, our model achieves an 11.8% improvement in mAP on the Retail-cabinet dataset while reducing Params and FLOPs by 23.19% and 71.50%, respectively. The proposed model effectively reduces the model's computational complexity and improves detection performance, thereby possessing high practical value. This code is released on https://github.com/ydlam/ST-YOLOX. [ABSTRACT FROM AUTHOR]

With the Internet's rapid development and the increasing amount of netizens, social contradictions frequently manifest over the Internet. Public emergencies develop and spread constantly online. Thus, it is of great significance to reasonably address the Online Public Sentiment (OPS) in the current critical stage of social transformation. The aim is to create a safe and credible network environment and realize the modern transformation of the dynamic evolution of OPS in public emergencies. Firstly, this paper expounds on the blocking process of the OPS evolution on public emergencies according to the Internet of Things-native big data. Then, it discusses the algorithm process of the Long Short-Term Memory (LSTM) Neural Network (NN) model. Further, it optimizes the LSTM NN model using the Adaptive Momentum Estimation (Adam). Finally, it simulates and predicts the OPS evolution using Artificial Intelligence technology and big data. The results show that the Adam-optimized LSTM NN model can predict the hotness of OPS in the dynamic evolution with high prediction accuracy. In predicting OPS evolution, the Mean Relative Errors (MRE) of the proposed Adam-LSTM, LSTM, and Backpropagation NN models are 0.06, 0.10, and 0.14, respectively. The proposed Adam-LSTM model presents the least MRE on the hotness of OPS. The relevant governments can refer to model-predicted OPS evolution to control public emergencies and OPS through the IoT. Therefore, the proposed Adam-LSTM model is feasible for predicting the OPS hotness. The finding has particular research significance for employing the LSTM model under the IoT in predicting the OPS evolution in public emergencies. Lastly, the OPS on public emergencies can be effectively guided thanks to the proposed Adam-LSTM prediction model and time nodes. [ABSTRACT FROM AUTHOR]

IoT is one of the fastest-growing technologies and it is estimated that more than a billion devices would be utilised across the globe by the end of 2030. To maximise the capability of these connected entities, trust and reputation among IoT entities is essential. Several trust management models have been proposed in the IoT environment; however, these schemes have not fully addressed the IoT devices' features, such as device's role, device type and its dynamic behavior in a smart environment. As a result, traditional trust and reputation models are insufficient to tackle these characteristics and uncertainty risks while connecting nodes to the network. Whilst continuous study has been carried out and various articles suggest promising solutions in constrained environments, research on trust and reputation is still at its infancy. In this paper, we carry out a comprehensive literature review on state-of-the-art research on the trust and reputation of IoT devices and systems. Specifically, we first propose a new structure, namely a new taxonomy, to organise the trust and reputation models based on the ways trust is managed. The proposed taxonomy comprises of traditional trust management-based systems and artificial intelligence-based systems, and combine both the classes which encourage the existing schemes to adapt these emerging concepts. This collaboration between the conventional mathematical and the advanced ML models result in design schemes that are more robust and efficient. Then we drill down to compare and analyse the methods and applications of these systems based on community-accepted performance metrics,e.g. scalability, delay, cooperativeness and efficiency. Finally, built upon the findings of the analysis, we identify and discuss open research issues and challenges, and further speculate and point out future research directions. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) promises to deliver innovative business models across multiple domains by providing ubiquitous connectivity, intelligent data, predictive analytics, and decision-making systems for improved market performance. However, traditional IIoT architectures are highly susceptible to many security vulnerabilities and network intrusions, which bring challenges such as lack of privacy, integrity, trust, and centralization. This research aims to implement an Artificial Intelligence-based Lightweight Blockchain Security Model (AILBSM) to ensure privacy and security of IIoT systems. This novel model is meant to address issues that can occur with security and privacy when dealing with Cloud-based IIoT systems that handle data in the Cloud or on the Edge of Networks (on-device). The novel contribution of this paper is that it combines the advantages of both lightweight blockchain and Convivial Optimized Sprinter Neural Network (COSNN) based AI mechanisms with simplified and improved security operations. Here, the significant impact of attacks is reduced by transforming features into encoded data using an Authentic Intrinsic Analysis (AIA) model. Extensive experiments are conducted to validate this system using various attack datasets. In addition, the results of privacy protection and AI mechanisms are evaluated separately and compared using various indicators. By using the proposed AILBSM framework, the execution time is minimized to 0.6 seconds, the overall classification accuracy is improved to 99.8%, and detection performance is increased to 99.7%. Due to the inclusion of auto-encoder based transformation and blockchain authentication, the anomaly detection performance of the proposed model is highly improved, when compared to other techniques. [ABSTRACT FROM AUTHOR]

This paper aims to probe the influence of innovation spillovers in the artificial intelligence (AI) and financial technology (Fin-tech) industries on the value of the internet of things (IoT) companies. Python was utilized to download public information from Yahoo Finance, and then the GARCH model was used to extract the fluctuations of cross-industry innovation spillovers. Next, the Fama–French three-factor model was used to explore the interactive changes between variables. The panel data regression analysis indicates that the more firms accept innovation spillovers from other industries, the better the excess return; however, this effect differs because of industrial attributes and the environmental changes induced by COVID-19. Additionally, this study finds that investing in large-cap growth stocks of IoT firms is more likely to yield excess returns. Finally, the study yields lessons for policy leverage to accelerate the upgrading and transformation of innovation-interactive industries by referring to the practices of Singapore and South Korea. [ABSTRACT FROM AUTHOR]

This paper is about characterizing intelligence in Smart Physical Objects, i.e., objects based on the tight and seamless integration of a physical and a digital counterpart. The design of these objects gives rise to new opportunities but requires taking into account a number of dimensions that contribute to smartness. In our view, supported by considerable literature on this subject, smart behavior is the result of proper combinations of several dimensions of intelligence. In the paper we analyze these dimensions, singling out different alternatives leading to different capabilities of smart objects. The contribution of the paper is to provide a framework to guide a designer in making decisions about smartness in the physical object being designed, starting from its requisites. At the same time the framework provides an effective guide to classify and compare smart physical objects according to the type and level of smartness they exhibit. We apply the approach to a set of smart physical objects showing its suitability and usefulness. [ABSTRACT FROM AUTHOR]

With advances in networks, Artificial Intelligence (AI), and the Internet of Things, humanoid robots are rising in many areas, including elderly care, companion, education, and services in public sectors. Given their sensing and communication functionality, information leakage and unauthorized access will be of big concern. Very often, authentication techniques for service robots, especially those related to behavioral identification, have been developed, in which behavior models are created using raw data from sensors. However, behavioral-based authentication and re-authentication is still an open area for research, including cold start problems, accuracy, and uncertainty. This paper proposes a hierarchical implicit authentication system by joint built-in sensors and trust evaluation, coined sAuth, which exploits sensor data-based sliding window trust model to identify the service robot and its expected users. In order to mitigate the fluctuations of identification results in the real world environment, the trust evaluation is computed via combining the weighted intermediate identification probability of various small sliding windows. The performance of sAuth is evaluated under different scenarios where we show that (i) approximately 5–7% higher accuracy and 2–18% lower equal error rate can be achieved by our method compared to other works; and (ii) the hierarchical scheme with joint sensors and trust sliding windows improves the authentication accuracy significantly by comparing it with only sensor-based authentication. [ABSTRACT FROM AUTHOR]

The article discusses the ways which advanced technologies shape and impact, potentially or actually, global systems stability with a particular emphasis on the environment and human society. Advances in intelligent automated applications intended at addressing environmental issues and understanding online social networks is examined.

Digital technologies have changed the way supply chain operations are structured. In this article, we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks. A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0, with a specific focus on the mitigation of cyber risks. An analytical framework is presented, based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies. This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning (AI/ML) and real-time intelligence for predictive cyber risk analytics. The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge. This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when AI/ML technologies are migrated to the periphery of IoT networks. [ABSTRACT FROM AUTHOR]

The industrial Internet of Things (IIoT) has grown to empower advantages of advanced manufacturing machinery and smarter control. The cloud-based technology of remote data collection, intelligent machine interconnectivity, and sensor monitoring provide the opportunity for a pattern modification across all manufacturing divisions including the latest and rapidly growing technology of additive manufacturing (AM) or 3D printing. AM is a type of direct manufacturing and revolutionary technology that enables complicated production and a formation which can shorten manufacturing processes and supply chain procedures. Data is a key factor in the age of big data, embedded IIoT solutions, and services in the new machinery and mechanism that bring an additional capability to integrate and manage data streams within the Internet of Things (IoT) cloud-based platform. Movement and merging conventional (legacy) manufacturing technology into the shared and modern machinery required for the state-of-the-art manufacturing technology such as AM is complex and challenging, but it needs to be organized and fixed resourcefully, while it remains linked, flexible, and scalable. AM is an advanced manufacturing system and technology involving the new era of complex machinery and operating systems. AM has been identified as a special value to the industry which has many applications in the different industries such as aerospace, medical and healthcare, energy, and automotive. Hence, high-performance computation and processing will be very important in AM. This research takes an overview of the cloud-based model and concept of cloud computing (CC), cloud manufacturing (CM), IoT, and their relations and influences in the AM industry 4.0 era. This study contributes as a theoretical basis and as a comprehensive framework for AM integration. Furthermore, this paper presents CM applications and integration with AM and proposes an integrated AM cloud platform. [ABSTRACT FROM AUTHOR]

A new generation of systems appeared with the birth of Cyber-Physical Systems (CPS) that integrated computational and physical capabilities. CPS represents an emerging domain as it has an important interest in the literature due to its interaction with Structural Health Monitoring System applications. In this paper, we present a deep investigation of SHM and wireless sensing technologies towards an efficient CPS. Structural Health Monitoring (SHM) based on wireless sensor networks (WSNs) has the potency to reduce the cost of installation and maintenance of public and private infrastructure. Many types of research are interested in SHM using WSN due to its application domain diversity and its importance in public safety. WSN networks can be a prominent candidate to solve many SHM problems thanks to its implementation simplicity and its significant cost reductions. We present a comprehensive survey about SHM based on new technologies and methods including the internet of things (IoT), Software-defined Networking (SDN), fog and cloud computing. SHM application domains are also highlighted with analytic research domains, projects, testbeds, and experimental works. Besides that, this investigation pinpoints SHM functionalities (damage detection, prognostic and risk assessment) and Artificial Intelligence (AI) contributions for SHM such as sensor placement and clustering and its benefits on energy optimization. The main challenges of WSN design, energy consumption, damage prediction, SHM mobility, SHM large scale were presented and discussed. [ABSTRACT FROM AUTHOR]

Recently, cloud computing has become popular for smart societies because it made dynamical network without building a physical network. Despite recent research on the cloud, it is necessary to study security functions for the identification of fake VNFs and the encryption of communication between entities. Also, the VNFs can not only be attacked, but also perform malicious behavior such as botnets to disable user service. In this paper, we propose a lightweight PKI mechanism that detects the fake VNFs and guarantees data security through mutual authentication between VNFs. To evaluate the proposal, we built a MANO environment to test the performance of authentication and key generation for data security. In addition, we tested the performance of the detection for the DDoS attack by using real attack data. The LW_PKI guaranteed the reliability of a smart service by enhancing the security of the cloud environment. [ABSTRACT FROM AUTHOR]

I Intelligent Design and Manufacturing i deals with intelligent product design with consideration of the manufacturing and production limitations, and intelligent manufacturing technologies to address the needs of advanced product development. The cyber-physical integration will also provide a key "platform" for the manufacturing technologies (such as additive manufacturing) and the advanced automation solutions (as collaborative robots, mobile robots, autonomous vehicles and drones, etc.). Keywords: Manufacturing digitalisation; Industry 4.0; Smart factory; Intelligent manufacturing; Smart manufacturing; Cyber-physical production systems; Internet of things; Digital twin; Artificial intelligence; Additive manufacturing EN Manufacturing digitalisation Industry 4.0 Smart factory Intelligent manufacturing Smart manufacturing Cyber-physical production systems Internet of things Digital twin Artificial intelligence Additive manufacturing 1793 1796 4 09/01/21 20211001 NES 211001 Introduction I Intelligent Manufacturing i is a key concept, developed during the past decades and nowadays gaining further momentum thanks to the potential brought by the Industry 4.0 vision. Manufacturing digitalisation, Industry 4.0, Smart factory, Intelligent manufacturing, Smart manufacturing, Cyber-physical production systems, Internet of things, Digital twin, Artificial intelligence, Additive manufacturing. [Extracted from the article]

In the era of the knowledge economy, one of the key issues is how to integrate and use intelligent information systems to collect data and make valuable predictions to support business decisions. Intelligent information systems use artificial intelligence to enhance system performance, giving the enterprise a competitive advantage. This paper uses ontology technology for user requirements analysis—defining the class, slot, and instance of the ontology—and then designs the system architecture based on that ontology. In accordance with the firm's requirements, we build a data warehouse system that is integrated with different data sources within the enterprise and that supports a web-based interface in the Internet of Things (IoT). The system also supports the standard queries, reports, summary tables, and datasets required for data mining. The proposed data mining method for manufacturing industries that use Bayesian network incorporates Bayesian theory and graphical models and can predict causal and probabilistic relationships among a set of variables. Our results bring information system functionality closer to satisfying the real-world needs of business. The proposed system can reduce production cycle times, increase the speed and accuracy with which production information is analyzed, and offer predictions that can be used for better business decisions. Data mining technology can improve the efficiency of manufacturing processes by using feedback data to tune the manufacturing parameters and improve the accuracy of yield rate predictions, giving the firm a greater competitive advantage. [ABSTRACT FROM AUTHOR]

At present, the security of the Internet of things (IoT) has aroused great concern in artificial intelligence area, Artificial Intelligence of Things (AIoT) are widely used in various intelligent surveillance scenarios. However, due to the weak interpretability of the model and high data security risks, developing a robust and explainable deep learning network framework for scene understanding under AIoT is extremely difficult. In addition, the fusion of IoT and AI also poses several challenges. To solve these difficulties, we develop a self-learning and explainable deep learning network toward the security of AIoT. The constructed system contains video collection, upload and display as well as data analysis and early warning operation at the embedded device end, and automatically recognizes the behaviors of scene by our developed visual recognition algorithms. In addition, the cloud computing platform can be controlled through our developed network. Our developed visual recognition algorithms contribute to three aspects. First, we propose a lightweight reinforcement learning network model by extracting spatial–temporal feature of different behavior characteristic. Then, we propose a self-paced learning framework through fusing the deep reinforcement learning and transfer learning. Finally, we propose a multi-perspective deep transfer learning model to solve the problem of weak explanation of model. The experimental results show that our proposed model is able to provide high interpretability of model and outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Cities, such as industry or the universities, are immersed in a process of digital transformation generated by the possibility and technological convergence of the Internet of Things (IoT), Big Data and Artificial Intelligence and its consequences: hyperconnectivity, datafication and algorithmization. A process of transformation towards what has come to be called as Smart Cities. The aim of this paper is to show the impacts and consequences of digital connectivity, algorithmization and the datafication of urban digital society to outline possible ways of resolving the underlying moral conflicts. [ABSTRACT FROM AUTHOR]

The Internet has immeasurably changed all aspects of life, from work to social relationships. The Internet of things (IoT) promises to add a new dimension by making possible not only communications with and among objects but also, thereby, the vision of anytime, anywhere, anything communications. The IoT allows sensing or control of objects remotely across network infrastructures. Its application, thus, is very extensive. The principal IoT applications are infrastructure management, smart manufacturing, smart agriculture, energy management, environment monitoring, building and home automation, metropolitan-scale deployments, medicine and health care, and smart transportation. Many IoT applications entail the collection and also forwarding of event data. To realize the IoT's potential, combining it with artificial intelligence (AI) technologies is necessary. The IoT collects data, which AI processes so as to make sense of it. In order to trigger an action in the IoT and in AI applications, knowledge of the time at which an event occurs can be very useful. Time information, in fact, is an essential infrastructural component of any distributed system. Indeed, in IoT and AI applications, time information and time synchronization are among the most fundamental components. The IoT and AI thus require a scheme for data's combination with time. This paper proposes a network clock model that enables the sharing, by IoT and AI devices, of a consistent notion of time. A proposed network clock model is implemented and evaluated in an actual test platform of MICAz-compatible sensor nodes operated in TinyOS 2.0 and Arduino Uno (R3) in order to verify its feasibility. The experimental results indicate that, for any application, IoT devices are capable of maintaining standard time and serving a standard timestamp. [ABSTRACT FROM AUTHOR]

As a growing up-and-coming branch of the Internet of Things and traditional vehicular ad hoc networks, the Internet of Vehicles (IoV) is intended to perform as a core information carrying and processing platform for Intelligent Transport Systems. However, owing to its dynamic topological structures, large network scale, and mobile limitation, IoV systems still struggle with many unresolved challenges, especially those concerning security and privacy. Therefore, researchers consider security a significant concern due to the variety of vulnerabilities. Recently, existing security researchers have worked extensively to guarantee IoV security. However, multiple challenges in terms of security and privacy are generated by various types of attacks, such as authentication and identification attacks, availability attacks, confidentiality attacks, and data authenticity attacks. In this context, we provide a systematic review of emerging security and privacy vulnerabilities IoV and identify current challenges and the remaining open issues. Furthermore, we will discuss the future of this area. The paper contributes by presenting significant issues and solutions in a clear, smooth, comprehensive, and integrated way. [ABSTRACT FROM AUTHOR]

Human evacuation is a critical process during disasters, whether arising from natural events, intentional acts of aggression, or other calamities. The incorporation of diverse computational approaches such as the Internet of Things (IoT) technology and Edge-empowered Cloud platforms has the capability to improve the effectiveness of route recommendation procedures significantly. Conspicuously, this research (i) proposes a sophisticated evacuation framework that integrates the IoT-Edge-Cloud (IEC) computing platform for human evacuation during a disaster; (ii) employs an Artificial Intelligence-based Support Vector Machine (SVM) to detect emergencies in real-time; (iii) facilitates the cloud-based evacuation by computing a safe and swift route using the proposed Markov Decision process. A simulated environment comprising 120,002 data segments is utilized to evaluate the proposed framework. Compared to existing state-of-the-art techniques, improvements in terms of Overall Temporal Delay (37.80 seconds), Energy Efficiency (0.13% per minute), Event Determination Analysis (Accuracy (94.32%)), Route Recommendation Performance (Precision (96.26%), Sensitivity (90.86%), Coverage (96.66%), and Specificity (93.00%)), and Reliability (94.46%) are registered. [ABSTRACT FROM AUTHOR]

Highlights: Typical structures/working mechanisms of gel-based triboelectric nanogenerators and performance advantages of gel materials reviewed. Optimization of hydrogels, organogels, and aerogels for triboelectric nanogenerators in flexible sensing summarized. Applications, challenges, and future development directions of gel-based triboelectric nanogenerators in flexible sensing are discussed. The rapid development of the Internet of Things and artificial intelligence technologies has increased the need for wearable, portable, and self-powered flexible sensing devices. Triboelectric nanogenerators (TENGs) based on gel materials (with excellent conductivity, mechanical tunability, environmental adaptability, and biocompatibility) are considered an advanced approach for developing a new generation of flexible sensors. This review comprehensively summarizes the recent advances in gel-based TENGs for flexible sensors, covering their principles, properties, and applications. Based on the development requirements for flexible sensors, the working mechanism of gel-based TENGs and the characteristic advantages of gels are introduced. Design strategies for the performance optimization of hydrogel-, organogel-, and aerogel-based TENGs are systematically summarized. In addition, the applications of gel-based TENGs in human motion sensing, tactile sensing, health monitoring, environmental monitoring, human–machine interaction, and other related fields are summarized. Finally, the challenges of gel-based TENGs for flexible sensing are discussed, and feasible strategies are proposed to guide future research. [ABSTRACT FROM AUTHOR]

Big cities and growing industrial areas bring high risk of different kinds of pollutions which would implicate to the quality of life of the society. Discovering and monitoring of polluted areas using autonomous mobile robots is nowadays a frequently considered solution concerning both environmental and human safety problems. Being part of a distributed control system, such robots can help to improve the efficiency of the existing conventional pollution prevention systems. On the other hand, during the last decade, wireless sensor networks (WSN) have provoked the interest of specialists from different areas by imposing a large number of theoretical and practical challenges related to their implementation. This paper attempts to deal with both issues by presenting a possible way for building robotized WSNs that can be suitable for solving different environmental monitoring tasks. The goal is to transform the WSN into an adaptive sensor system with intelligent behavior that can be used to discover and track spots or areas where given monitored environmental parameters violate defined thresholds. The inclusion of robotized agents into the WSN structure can provide additional flexibility with respect to the installation of the network sensors and allow reliable information gathering and transfer. The proposed algorithmic methods have been tested on a pre-built laboratory prototype of a robotized WSN. The robotized agents (mobile network nodes) have been built using iRobot Create mobile platforms that are additionally equipped with a single-chip computers Gumstix Verdex pro TM XL6P with various expansion modules. [ABSTRACT FROM AUTHOR]

In order to promote the integrated application of the Internet of Things and cloud platform computing technology in intelligent manufacturing system, a cloud platform of cutting database was established in this paper. With the help of multi-sensor and distributed storage technology, mathematics model and data mining algorithm, functions of fast accessing and storing cutting data, intelligent modeling and analysis, efficient mining and optimizing processing parameters can be realized in the system, solving the key problems of analyzing cutting force and controlling surface quality in turning difficult-to-cut materials. Data equipment and users in the system were connected as a whole through wireless network, which can provide intelligent service of data querying, modeling, and optimizing through the cloud server. The study made up for the drawbacks in current intelligent database system such as oversimplified platform architecture, low level of intelligence, and low utilization of historical data, providing an efficient method of managing and applying big data in cutting process for the production system. [ABSTRACT FROM AUTHOR]

More researchers are proposing artificial intelligence algorithms for Internet of Things (IoT) devices and applying them to themes such as smart cities and smart transportation. In recent years, relevant research has mainly focused on data processing and algorithm modeling, and most have shown good prediction results. However, many algorithmic models often adjust parameters for the corresponding datasets, so the robustness of the models is weak. When different types of data face other model parameters, the prediction performance often varies a lot. Thus, this work starts from the perspective of data processing and algorithm models. Taking traffic data as an example, we first propose a new data processing method that processes traffic data with different attributes and characteristics into a dataset that is more common for most models. Then we will compare different types of datasets from the perspective of multiple model parameters, and further analyze the precautions and changing trends of different traffic data in machine learning. Finally, different types of data and ranges of model parameters are explored, together with possible reasons for fluctuations in forecast results when data parameters change. [ABSTRACT FROM AUTHOR]

With the rapid advancement of the Internet of Things and the popularization of mobile Internet-based applications, the location-based service (LBS) has attracted much attention from commercial developers and researchers. Received signal strength indicator (RSSI)-based indoor localization technology has irreplaceable advantages for many LBS applications. However, due to multipath fading, noise, and the limited dynamic range of the RSSI measurements, precise localization based on a path-loss model and multiliterate becomes highly challenging. Therefore, this study proposes a machine learning (ML)-based improved RSSI-based indoor localization approach in which RSSI data is first augmented and then classified using ML algorithms. In addition, we implement an experimental testbed to collect the RSSI value based on Wi-Fi using various reference and target nodes. The received RSSI measurements undergo pre-processing using pseudo-linear solution techniques for closed-form solutions, approximating the original system of nonlinear RSSI measurement equations with a system of linear equations. Finally, the RSSI measurement are trained using ML models such as linear regression, polynomial regression, support vector regression, random forest regression, and decision tree regression. Consequently, the experimental results express in terms of root mean square error and coefficient of determinant compared with various machine learning models with hyper-parameter tuning. [ABSTRACT FROM AUTHOR]

All enterprises are concerned with employee turnover risk due to the significant impact on their effectiveness and competitiveness. Evaluation of the risk is a frequent topic in the literature. However, the majority of past work has not incorporated the advancement of modern information technology, particularly in the era of Internet of Things (IoT). In this paper, we propose to use an artificial intelligence method, case-based reasoning (CBR), to develop a multi-level employee turnover risk evaluation model. The proposed model adopts multiple CBR techniques including case representation, organization and management, and retrieval and matching to evaluate employee turnover risk. Specifically, we employ an object-oriented method in case knowledge expressing, utilize relational database in case organization and management, and follow a tree-hash algorithm to retrieve the best cases. Both theoretical and practical implications of the proposed model are discussed. [ABSTRACT FROM AUTHOR]

Intelligence augmentation was one of the original goals of computing. Artificial Intelligence (AI) inherits this project and is at the leading edge of computing today. Computing can be considered an extension of brain and body, with mathematical prowess and logic fundamental to the infrastructure of computing. Multimedia computing—sensing, analyzing, and translating data to and from visual images, animation, sound and music, touch and haptics, as well as smell—is based on our human senses and is now commonplace. We use data visualization and sonification, as well as data mining and analysis, to sort through the complexity and vast volume of data coming from the world inside and around us. It helps us 'see' in new ways. We can think of this capacity as a new kind of "digital glasses". The Internet of Living Things (IOLT) is potentially an even more profound extension of ourselves to the world: a network of electronic devices embedded into objects, but now with subcutaneous, ingestible devices, and embedded sensors that include people and other living things. Like the Internet of Things (IOT), living things are connected; we call those connections "ecology". As the IOT becomes increasingly synonymous with the IOLT, the question of ethics that is at the centre of aesthetics and the arts will move to the forefront of our experience of and regard for the world in and around us. [ABSTRACT FROM AUTHOR]

Networked service organisations are increasingly adopting a 'smarter networking' philosophy in their design of more agile and customer-focused supply models. Changing consumer behaviours and the emergence of transformative technologies—industry 4.0, artificial intelligence, big data analytics, the Internet of Things—are driving a series of innovations, in terms of 'products' and business models, with major implications for the industrial enterprise, in their design of more 'digitalised' supply chains. For B2B systems, emerging 'product-service' offerings are requiring greater visibility, alignment and integration across an increasingly complex network of multiple partners and collaborators, in order to deliver a better service and customer 'experience'. To support the design and operation of these multi-organisational service networks, we outline a concept of operations architecture here, underpinned by the literature and network theory, and demonstrate application using a series of exemplar case studies. Focusing on relational elements and the processes key to network integration within service supply networks, the cases inform a set of operating principles and protocols—applicable to all stakeholders 'cooperating', within a 'shared' environment. Equally critical is to understand how digital technologies may influence future operating philosophies. This article extends our theoretical understanding of network organisations, from a traditional 'product' perspective to that of 'services', and presents the case for developing a common, unified approach to designing diverse forms of multi-partner service networks. [ABSTRACT FROM AUTHOR]

Considering the variety of Internet of Things (IoT) device types and access methods, it remains necessary to address the security challenges we currently encounter. Physical layer security (PLS) can offer streamlined security solutions for the next generation of IoT networks. Presently, we are witnessing the application of intelligent technologies including machine learning (ML) and artificial intelligence (AI) for precise prevention or detection of security breaches. Active eavesdropping detection is a physical layer security-based method that can differentiate wireless signals between wireless devices through feature classification. However, the operation of numerous IoT devices operate in environments characterized by low signal-to-noise ratios (SNR), and active eavesdropping attack detection during communication is rarely studied. We assume that the wireless system comprising an access point (AP), K authorized users and a proactive eavesdropper (E), following the framework of transforming wireless signals at AP into organized datasets that this article proposes a BP neural network model based on deep learning as a classifier to distinguish eavesdropping and non-eavesdropping attack signals. By conducting experiments under SNRs, the numerical results show that the proposed model has stronger robustness and detection accuracy can significantly improve the up to 19.58% compared with the reference approach, which show the superiority of our proposed method. [ABSTRACT FROM AUTHOR]

Energy theft is a significant problem that needs to be addressed for effective energy management in smart cities. Smart meters are highly utilized in smart cities that help in monitoring the energy utilization level and provide information to the users. However, it is not able to detect energy theft or over-usage. Therefore, we have proposed a multi-objective diagnosing structure named an Energy Theft Prevention System (ETPS) to detect energy theft. The proposed system utilizes a combination of machine learning techniques Gated Recurrent Unit (GRU), Grey Wolf Optimization (GWO), Deep Recurrent Convolutional Neural Network (DDRCNN), and Long Short-Term Memory (LSTM). The statistical validation has been performed using the simple moving average (SMA) method. The results obtained from the simulation have been compared with the existing technique in terms of delivery ratio, throughput, delay, overhead, energy conversation, and network lifetime. The result shows that the proposed system is more effective than existing systems. [ABSTRACT FROM AUTHOR]

In complex industrial environments such as the Internet of Things in coal mines, large mechanical and electrical equipment can generate powerful impulsive noise, which can cause sudden errors. Because it is difficult to establish an accurate channel model, the performance of current error control techniques is limited. To enhance the reliability of information recovery in the Internet of Things in coal mines, the traditional method of shortening the communication distance between sensors is often utilized, but this can be costly. Therefore, this article proposes an intelligent signal processing method against impulsive noise interference that draws on the concept of the Artificial Intelligence of Things (AIoT) and incorporates deep learning technology. This method replaces the traditional sensor signal processing module with a Convolutional Neural Network (CNN), which learns the intricate mapping relationship between transmitted information and sensor signals in impulsive noise environments. Simulation results demonstrate that the proposed method outperforms the traditional sensor signal processing method in three impulsive noise environments by achieving a lower Bit Error Rate (BER). Moreover, this method adopts an improved lightweight neural network, which is more conducive to the deployment of mobile terminals in the Internet of Things. [ABSTRACT FROM AUTHOR]