Smart Internet Of Things Devices Research Articles

Meta-inventory management decisions: A theoretical model

The manufacturing industry leverages the traceability and visibility of Industrial 4.0 to integrate digital and physical twins for solving the intricacy of a production system. Physical entities can be converted into digital twins with smart IoT (Internet of Things) devices and computational techniques. Research efforts have generally focused on the development of digital twin models for basic practical applications. Advanced applications of digital twins have not been widely reported. An innovative use of digital twins for inventory management has only been analysed theoretically and reported as meta-inventory management for the first time by Wang and Huang (2023). This paper extends the novel concept of meta-inventory by using a theoretical Newsvendor model for original equipment manufacturing (OEM) and own brand manufacturing (OBM) factories. The impacts of meta-inventory on both supply chain members, including the factory and its downstream retailer, are investigated through two kinds of response models (e.g., hybrid and separate response models). Analytical and numerical results show that a factory achieves better performance by using a separate model since it clarifies the responsibility of digital twins. The hybrid response model holds a higher proportion of digital inventory, but its final profits are less than that in the separate model due to fewer orders and higher prices and costs of uncertainty. OBM can better leverage the advantage of digital twins than OEM. Also, both supply chain members benefit from the implementation of meta-inventory out of profit increase, price reduction, and risk hedging. This research provides guidance for manufacturing sites to implement digital twins and reduce their concerns on investing in meta-inventory.

Policy-Based Contextual Access Control Mechanism for Smart IoT Devices

Access control remained an important aspect of computer security, and it has been the focus of extensive research over the past several decades. Access control mechanisms generally composed of two fundamental components: authentication and authorization. Authentication refers to verifying the identity of an entity, and authorization guarantees that only authenticated entity or devices can access the permitted devices or other resources. Various traditional access control schemes, such as Access Control Lists (ACLs), Role-Based Access Control (RBAC), and Attribute-Based Access Control (ABAC) exist, but these have certain limitations that hinder their direct implementation in the Internet of Things (IoT). For instance, ACLs maintain user-specific access privilege lists, which are feasible for environments with limited users and devices, but impractical for large scale systems like IoT. RBAC assigns devices access through roles associated with permissions, however role management in dynamic IoT environments poses significant challenges. ABAC grants access based on user and devices attributes that requires certain attribute criteria for authorization. We advocate that IoT environments are dynamic in nature and consist of very large volumes of smart IoT devices (such as smart sensors, smart phones and gadgets) that which introduce unique access control challenges. One significant challenge is providing dynamic access to smart IoT devices, as opposed to relying on static rules, roles, or attributes. Considering these challenges, this research advocates for a novel access control scheme tailored for accessing smart IoT devices in internet of things environments. The prototype implementation of the proposed approach is carried out along with conducting the usability study to evaluate the performance and suitability of the proposed system for real world internet of things (IoT) scenarios.

Lightweight Privacy Preserving Scheme for IoT based Smart Home

Background: The Internet of Things (IoT) is the interconnection of physical devices, controllers, sensors and actuators that monitor and share data to another end. In a smart home network, users can remotely access and control home appliances/devices via wireless channels. Due to the increasing demand for smart IoT devices, secure communication also becomes the biggest challenge. Hence, a lightweight authentication scheme is required to secure these devices and maintain user privacy. The protocol proposed is secure against different kinds of attacks and as well as is efficient. Methods: The proposed protocol offers mutual authentication using shared session key establishment. The shared session key is established between the smart device and the home gateway, ensuring that the communication between the smart devices, home gateway, and the user is secure and no third party can access the information shared. Results: Informal and formal analysis of the proposed scheme is done using the AVISPA tool. Finally, the results of the proposed scheme also compare with existing security schemes in terms of computation and communication performance cost. The results show that the proposed scheme is more efficient and robust against different types of attacks than the existing protocols. Conclusion: In the upcoming years, there will be a dedicated network system built inside the home so that the user can have access to the home from anywhere. The proposed scheme offers secure communication between the user, the smart home, and different smart devices. The proposed protocol makes sure that security and privacy are maintained since the smart devices lack computation power which makes them vulnerable to different attacks.

A lightweight group-based SDN-driven encryption protocol for smart home IoT devices

With the increasing adoption rate of Internet of Things (IoT) devices in smart home applications, it is vital to safeguard the privacy and security of information, communications among IoT devices and the underlying infrastructure. In open communication scenarios, an adversary can easily tamper with data transmitted by IoT communication devices. This paper proposes a softwarized lightweight authentication key agreement scheme for smart home applications in Software-Defined IoT (SDIoT) environment. The proposed scheme comprises registration, authentication, and key selection phases. Devices are registered in the registration phase after the successful completion of validation checks, while sessions’ keys are granted to the registered devices in the authentication phase. In the final phase, controllers classify IoT devices based on pre-defined parameters and impose class-specific access control based on classification. The security of the proposed scheme is validated using the widely accepted AVISPAs verification tool against a strong adversary. Simulation results demonstrate that our proposed scheme outperforms existing schemes in terms of running time, computation complexity, and energy consumption. It is found that the proposed scheme has significant cost-effectiveness.

Adaptive TS-ANFIS neuro-fuzzy controller based single phase shunt active power filter to mitigate sensitive power quality issues in IoT devices

The emergence of Internet of Things (IoT) offers numerous functions, such as intelligent sensor integration, remote sensing, and high-speed data transmission, which have found widespread applications in the smart industry and commercial applications. The associative nonlinear effects of a variety of undesirable power quality concerns were resolved by using harmonic mitigation in nonlinear loads and high-performance converters were built on power electronics in conventional systems. Among other performance objectives, total harmonic (THD) distortion analysis and higher order harmonics mitigation is given main concern in smart electronics equipment. This paper proposes an approach to minimize higher-order harmonics due to nonlinear load disturbances in smart IoT devices using the Takagi–Sugeno (TS) Neuro Fuzzy (TS-ANFIS) supervised shunt APF. The proposed work elicits the novel adaptive harmonic mitigation technique in hybrid IoT embedded systems to protect from malfunctioning and to deal with the uncertainty of harmonic signal stimuli in sensitive sensors-based IoT systems. Hysteresis current control is used for the ignition of reference signal under uncertainty of harmonic stimuli. The single phase shunt APF is used to mitigate higher-order harmonics from supply mains, while the implementation of TS-ANFIS supervises the controller action to generate a trigger signal for adequate single phase APF gate excitation. The higher order harmonic current data set is used for error deviation for training neural networks and adaptive control. The estimated adoption of the neuron's empirical weight reduces the total THD to a significant reduction rate of 72.8 % to 0.78 %. The control mechanism is feasible for a range of smart IoT systems to adhere to the standard of 519 (IEEE).

Intrusion Detection System based on Chaotic Opposition for IoT Network

The rapid advancement of network technologies and protocols has fueled the widespread endorsement of the Internet of Things (IoT) in numerous domains, including everyday life, healthcare, industries, agriculture, and more. However, this rapid growth has also given rise to numerous security concerns within IoT systems. Consequently, privacy and security have become paramount issues in the IoT framework. Due to the heterogeneous data produced by smart IoT devices, traditional intrusion detection system doesn't work well with IoT system. The massive volume of heterogeneous data has several irrelevant, redundant, and unnecessary features which lead to high computation time and low accuracy of IDS. Therefore, to tackle these challenges, this paper presents a novel metaheuristic-based IDS model for the IoT systems. The chaotic opposition-based Harris Hawk optimization (CO-IHHO) algorithm is used to perform the feature selection of data traffic. The chosen features are subsequently inputted into a machine learning (ML) classifier to detect network traffic intrusions. The performance of the CO-IHHO based IDS model is verified against the BoT-IoT dataset. Experimental findings reveal that CO-IHHO-DT achieves the maximal accuracy of 99.65% for multiclass classification and 100% for binary classification, and minimal computation time of 31.34 sec for multiclass classification and 133.54 sec for binary classification.

Computation‐efficiency‐oriented resource optimization in NOMA‐assisted multi‐UAV‐enabled MEC networks

SummaryUnmanned aerial vehicles (UAVs) can serve as aerial mobile edge computing (MEC) servers to provide computing services to Internet of Things smart devices (ISDs) with insufficient computing capacity on the ground. However, how to provide energy‐efficient computing services by designing appropriate resource optimization strategy is still a challenge issue in UAV‐enabled MEC networks. To this end, this paper proposes a computation efficiency (CE)‐oriented partial offloading framework for UAV‐enabled MEC networks, where the ISDs' computation bits and energy consumption are taken into account simultaneously. Specifically, we firstly divide the ISDs into multiple clusters and determine the deployment of UAVs by k‐means method. Meanwhile, ISDs occupying the same subchannel in the same cluster can offload data through non‐orthogonal multiple access (NOMA) technology. Then, the problem of maximizing the CE of the system is formulated by optimizing subchannel allocation, transmit power and computation resources of ISDs. To solve it, we propose a staged optimization approach by using matching theory, Dinkelbach and sequential convex programming (SCP) methods. Numerical results demonstrate the proposed scheme can achieve higher CE compared with other baseline schemes.

Dynamic and Distributed Intelligence over Smart Devices, Internet of Things Edges, and Cloud Computing for Human Activity Recognition Using Wearable Sensors

A wide range of applications, including sports and healthcare, use human activity recognition (HAR). The Internet of Things (IoT), using cloud systems, offers enormous resources but produces high delays and huge amounts of traffic. This study proposes a distributed intelligence and dynamic HAR architecture using smart IoT devices, edge devices, and cloud computing. These systems were used to train models, store results, and process real-time predictions. Wearable sensors and smartphones were deployed on the human body to detect activities from three positions; accelerometer and gyroscope parameters were utilized to recognize activities. A dynamic selection of models was used, depending on the availability of the data and the mobility of the users. The results showed that this system could handle different scenarios dynamically according to the available features; its prediction accuracy was 99.23% using the LightGBM algorithm during the training stage, when 18 features were used. The prediction time was around 6.4 milliseconds per prediction on the smart end device and 1.6 milliseconds on the Raspberry Pi edge, which can serve more than 30 end devices simultaneously and reduce the need for the cloud. The cloud was used for storing users’ profiles and can be used for real-time prediction in 391 milliseconds per request.

Low hysteresis zwitterionic supramolecular polymer ion-conductive elastomers with anti-freezing properties, high stretchability, and self-adhesion for flexible electronic devices.

The fabrication of stretchable ionic conductors with low hysteresis and anti-freezing properties to enhance the durability and reliability of flexible electronics even at low temperatures remains an unmet challenge. Here, we report a facile strategy to fabricate low hysteresis, high stretchability, self-adhesion and anti-freezing zwitterionic supramolecular polymer ion-conductive elastomers (ICEs) by photoinitiated polymerization of aqueous precursor solutions containing a newly designed zwitterionic monomer carboxybetaine ureido acrylate (CBUIA) followed by solvent evaporation. The resultant poly(carboxybetaine ureido acrylate) (PCBUIA) ICEs are highly stretchable and self-adhesive owing to the presence of strong hydrogen bonds between ureido groups and dipole-dipole interactions of zwitterions. The zwitterion groups on the polymer side chains and loaded-lithium chloride endow PCBUIA ICEs with excellent anti-freezing properties, demonstrating mechanical flexibility and ionic transport properties even at a low temperature (-20 °C). Remarkably, the PCBUIA ICEs demonstrate a low hysteresis (≈10%) during cyclic mechanical loading-unloading (≤500%), and are successfully applied as wearable strain sensors and triboelectric nanogenerators (TENGs) for energy harvesting and human motion monitoring. In addition, the PCBUIA ICE-based TENG was used as a wireless sensing terminal for Internet of Things smart devices to enable wireless sensing of finger motion state detection.

Environmental Impact Assessment of IoT Devices: A Graph-based Approach

The proliferation of the Internet of Things (IoT) has enriched modern life, but their increasing ubiquity raises concerns about environmental impact. To address this, comprehensive Life Cycle Assessments (LCAs) of IoT products, which have historically been manual, costly, and time-consuming, are vital. Noting the recurring nature of core components in IoT devices, such as CPUs and sensors, we propose to use graphs and machine learning to simplify and scale LCA estimations for IoT products. This paper introduces a novel approach to representing IoT devices as graphs with specific component characteristics and interconnections. Applied to a preliminary dataset of smart home IoT devices, the methodology unveils insights into structural similarities using a composite kernel approach. This initial phase lays the groundwork for the machine learning component. The integration of machine learning planned as part of ongoing research, provides a pathway for efficient and timely ecological assessments, ensuring that the rapid growth of IoT aligns with sustainable practices.

A network communication speedup model using multiple fungible and non-fungible paths

The advent of industry 5.0 has seen technologies such as cyber-physical systems, sensors, mobile devices and a host of others, connected to the Internet of Things (IoT). The sheer growth of the IoT and the data transmitted across it calls for faster speeds (speedup), which in turn are facilitated by more bandwidth and better use of existing bandwidth. Speedup itself is not new and has been extensively studied in the literature in high performance computing and related fields. In this paper, some methods to achieve theoretical speedup in network communications are explored and tested using simulations and extrapolations. We also explored the theoretical limits of speedup. Some theoretical limits of speedup are also proposed, along with a model to estimate the speedup that can be achieved using larger container sizes over the existing bandwidth. We have also proposed a way to estimate the speedup that can be achieved when more bandwidth is provided using multiple fungible and non-fungible paths. It is anticipated that our proposed speedup model will assist the manufacturers of IoT sensors and smart devices, as well as network administrators and software developers in determining what theoretical speedup can be achieved with a certain network configuration, and in building devices and software capable to achieve practical speedups as close as possible to the predicted theoretical speedups. This in turn will lead to faster response and data transmission times in IoT and other network applications that leverage the speedup achieved.

GDE model: A variable intrusion detection model for few-shot attack

With the formation and popularity of the Internet of Things(IoT), the difficulty of protecting IoT infrastructure and smart devices from a few-shot of ever-changing malicious attacks has increased significantly. Traditional intrusion detection models in static mode cannot defend against intelligent attacks that change in real time and are good at reconnaissance, and it is difficult to achieve effective detection of few-shot attacks. Therefore, to solve the above problems, this paper proposes a variable few-shot intrusion detection model GDE Model for IoT, which contains a data processing module consisting of an improved Gramian Angular Fields(GAF), a data augmentation module consisting of an improved Denoising Diffusion Probabilistic Models(DDPM), and an image classification module consisting of a variable network ETNet V2 obtained from a neural architecture search based on efficient net v2. The one-dimensional network traffic data collected from a real network environment is converted into two-dimensional images and fed into the above model for learning and training, so as to construct a variable intrusion detection model that combines accuracy and inference speed. To evaluate the work of this paper, different categories of evaluation metrics were selected and experiments were conducted on each module and the overall model separately. The experimental results show that the proposed intrusion detection model achieves variable functionality with an accuracy of up to 99.20% for few-shot detection and an average inference time as low as 23ms, which can be used not only for IoT but also for different network environments and attack types.

Enhancing Smart Home Security: Anomaly Detection and Face Recognition in Smart Home IoT Devices Using Logit-Boosted CNN Models.

Internet of Things (IoT) devices for the home have made a lot of people's lives better, but their popularity has also raised privacy and safety concerns. This study explores the application of deep learning models for anomaly detection and face recognition in IoT devices within the context of smart homes. Six models, namely, LR-XGB-CNN, LR-GBC-CNN, LR-CBC-CNN, LR-HGBC-CNN, LR-ABC-CNN, and LR-LGBM-CNN, were proposed and evaluated for their performance. The models were trained and tested on labeled datasets of sensor readings and face images, using a range of performance metrics to assess their effectiveness. Performance evaluations were conducted for each of the proposed models, revealing their strengths and areas for improvement. Comparative analysis of the models showed that the LR-HGBC-CNN model consistently outperformed the others in both anomaly detection and face recognition tasks, achieving high accuracy, precision, recall, F1 score, and AUC-ROC values. For anomaly detection, the LR-HGBC-CNN model achieved an accuracy of 94%, a precision of 91%, a recall of 96%, an F1 score of 93%, and an AUC-ROC of 0.96. In face recognition, the LR-HGBC-CNN model demonstrated an accuracy of 88%, precision of 86%, recall of 90%, F1 score of 88%, and an AUC-ROC of 0.92. The models exhibited promising capabilities in detecting anomalies, recognizing faces, and integrating these functionalities within smart home IoT devices. The study's findings underscore the potential of deep learning approaches for enhancing security and privacy in smart homes. However, further research is warranted to evaluate the models' generalizability, explore advanced techniques such as transfer learning and hybrid methods, investigate privacy-preserving mechanisms, and address deployment challenges.

Federated learning‐based private medical knowledge graph for epidemic surveillance in internet of things

AbstractWith the explosive development of the Internet of Things (IoT), it is convenient and important to collect health data from medical sensors and smart devices and construct medical knowledge graph. The knowledge graph contributes to investigating the connection between patient and disease, especially for epidemic surveillance. However, it is possible to cause the leakage of sensitive health information due to the untrusted data collector or various malicious attackers. In this paper, we attempt to utilise federated learning to construct a special knowledge graph, that is, individual‐symptom relationship diagram with local differential privacy (LDP‐ISRD), for epidemic risk surveillance, which presents the underlying infectious relationship among individuals. At first, we propose a federated learning‐based framework of LDP‐ISRD by utilising individuals' smart devices in IoT. Then, we leverage locations to determine the connection among individuals in terms of physical contact. Next, we propose a randomised algorithm PrivISRD to implement federated learning‐based LDP‐ISRD, which consists of symptom perturbation and aggregation. Finally, extensive experiments evaluate the impact of various parameters and results demonstrate that LDP‐ISRD has good performance.

Meta-inventory

In an Industry 4.0 Factory, physical entities such as humans, machines and materials are digitized into digital twins (DT) with smart IoT (Internet of Things) devices resulting in Cyber-Physical Production Systems (CPPS). Real-time data analytics builds up traceability and visibility, not only in the physical domain but also cyber space. This paper adds a new concept of cyber-physical inventory or simply meta-inventory to Industry 4.0 CPPS. In addition to physical items, their digital twins are considered as part of production inventory. Traceability and visibility enabled by digital twin can significantly reduce complexity and uncertainties (e.g. lead times and variability) while achieving resilience in case of major disturbances. The CPPS factory hedges the risks through meta-inventory without incurring cost for holding inventory digitally. After reflecting upon the developments of production inventory management corresponding to the evolutionary history of manufacturing systems to Industry 4.0, the paper presents the meta-inventory paradigm within a simple Industry 4.0 compliant supply chain. The factory, the supplier, and the transport implement a VMI (vendor-managed inventory) strategy. Two well-known basic EOQ (Economic Order Quantity) and EPQ (Economic Production Quantity or production Lot Sizing) problems are extended to demonstrate and quantify the impacts of using meta-inventory on the supply chain and the member enterprise. The analyses allow us to unfold key perspectives in more complex production and supply chain systems for further research.

Harmonic Distortions in Smart Devices: A Comprehensive Discussion on Challenges of Conventional and Future Smart IoT Devices

The extensive utility of sensor technology and M2M networking are the future of industries for the production of commercial and non-commercial applications. One such application, the Internet of Things (IoT), was the universally accepted technology of sensors and wide-area networking. This causes severe effects of low power quality and results in the device malfunctioning. The increase in the use of smart devices will be a future challenge for power quality and its effect on associative devices. The main contribution of this survey is to analyze harmonic distortions due to non-linear load characteristics in the power management unit of traditional systems & advanced smart IoT devices. It helps to understand the utility of conventional harmonic mitigation techniques and the need for adaptive features for the latest technology. The conventional compensation techniques are incorporated with machine learning approaches to deal with the uncertainty of load variation on real-time signals. This survey gives a brief insight into the present scope of conventional compensation techniques and futuristic soft computing techniques to mitigate harmonic distortions. Additionally, the survey is intended to call the attention of the researchers to the future perspective of smart devices and to make the academic community aware of the existing practices.

A Survey on Zero-Knowledge Authentication for Internet of Things

The Internet of Things (IoT) is ubiquitous in our lives. However, the inherent vulnerability of IoT smart devices can lead to the destruction of networks in untrustworthy environments. Therefore, authentication is a necessary tool to ensure the legitimacy of nodes and protect data security. Naturally, the authentication factors always include various sensitive users’ information, such as passwords, ID cards, even biological information, etc. How to prevent privacy leakage has always been a problem faced by the IoT. Zero-knowledge authentication is a crucial cryptographic technology that uses authenticates nodes on the networks without revealing identity or any other data entered by users. However, zero-knowledge proof (ZKP) requires more complex data exchange protocols and more data transmission compared to traditional cryptography technologies. To understand how zero-knowledge authentication works in IoT, we produce a survey on zero-knowledge authentication in privacy-preserving IoT in the paper. First, we overview the IoT architecture and privacy, including security challenges and open question in different IoT layers. Next, we overview zero-knowledge authentication and provide a comprehensive analysis of designing zero-knowledge authentication protocols in various IoT networks. We summarize the advantages of ZKP-based authentication in IoT. Finally, it summarizes the potential problems and future directions of ZKP in IoT.

Blockchain Integration for IoT-Enabled V2X Communications: A Comprehensive Survey, Security Issues and Challenges

In V2X (vehicle-to-everything) communication, there is a two-way communication among the vehicle(s) and other Internet of Things (IoT)-enabled smart devices around it that may change how we need to drive. Due to the advancement of Information and Communications Technology (ICT) and the rapid development of IoT in transportation, traditional applications are converted to intelligent applications. In V2X communications, the collected information from the IoT smart devices and other sources passes through low-latency, high-bandwidth, high-reliability links. With the future adoption of the 5th generation mobile network (5G) and beyond networks, V2X continues to produce a huge volume of data. However, collecting and storing data securely in blockchain-based storage are extremely needed for immutability and transparency. In this survey article, the convergence of IoT, V2X and blockchain technologies, and various security challenges and their countermeasures are discussed. Next, we discuss various V2X applications and their respective services. Moreover, IoT-V2X architecture and its enabling technologies are discussed in this article. In addition, we also provide a comprehensive analysis of various security mechanisms. Finally, we provide some important challenges and issues of Blockchain for Intelligent Transportation System (BITS).

Methodology to Control IoT Smart Devices Driven by Digital TV Video Scene

This paper presents a novel methodology to control Internet of Things (IoT) devices synchronized with digital TV (DTV) signal dynamic video flow. Through this approach, broadcasters can send commands to digital TV receivers and produce multiples physical effects on IoT devices paired with TV sets. Among IoT devices examples, are: smart lamps, robotic seats, smart watches, wearable devices, etc. Those devices will synchronously react with the video scene, according to the IoT command type received by TV set. With this methodology, broadcasters may multiplex multiple IoT commands and be able to produce several effects at same time, through different IoT devices, creating a home multi-dimensional environment. In case there is any command that does not correspond to any paired device on receiver side, or the command is corrupted, the same is simply discarded. The validation of this methodology is based on the development of a receiver embedded system to detect signalization of IoT commands, Data filtering and IoT device control through Smart TV. The IoT devices control is sync with video scene and is based at time stamps added to the DTV signal. Furthermore, to check transceiver channel behavior, a transport stream benchmark, composed by IoT commands data flow multiplexed with audio and video medias in multiple complexity levels, was generated. Lab experiments with transmission tests and IoT devices control results shown technical feasibility to use this methodology in a real digital TV environment.

Forecasting the Status of Municipal Waste in Smart Bins Using Deep Learning

The immense growth of the population generates a polluted environment that must be managed to ensure environmental sustainability, versatility and efficiency in our everyday lives. Particularly, the municipality is unable to cope with the increase in garbage, and many urban areas are becoming increasingly difficult to manage. The advancement of technology allows researchers to transmit data from municipal bins using smart IoT (Internet of Things) devices. These bin data can contribute to a compelling analysis of waste management instead of depending on the historical dataset. Thus, this study proposes forecasting models comprising of 1D CNN (Convolutional Neural Networks) long short-term memory (LSTM), gated recurrent units (GRU) and bidirectional long short-term memory (Bi-LSTM) for time series prediction of public bins. The execution of the models is evaluated by Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Coefficient determination (R2) and Root Mean Squared Error (RMSE). For different numbers of epochs, hidden layers, dense layers, and different units in hidden layers, the RSME values measured for 1D CNN, LSTM, GRU and Bi-LSTM models are 1.12, 1.57, 1.69 and 1.54, respectively. The best MAPE value is 1.855, which is found for the LSTM model. Therefore, our findings indicate that LSTM can be used for bin emptiness or fullness prediction for improved planning and management due to its proven resilience and increased forecast accuracy.