Node Devices Research Articles

Encoder decoder-based Virtual Physically Unclonable Function for Internet of Things device authentication using split-learning

Internet of Things (IoT) networks have been deployed widely making device authentication a crucial requirement that poses challenges related to security vulnerabilities, power consumption, and maintenance overheads. While current cryptographic techniques secure device communication; storing keys in Non-Volatile Memory (NVM) poses challenges for edge devices. Physically Unclonable Functions (PUFs) offer robust hardware-based authentication but introduce complexities such as hardware production and conservation expenses and susceptibility to aging effects. This paper’s main contribution is a novel scheme based on split learning, utilizing an encoder–decoder architecture at the device and server nodes, to first create a Virtual PUF (VPUF) that addresses the shortcomings of the hardware PUF and secondly perform device authentication. The proposed VPUF reduces maintenance and power demands compared to the hardware PUF while enhancing security by transmitting latent space representations of responses between the node and the server. Also, since the encoder is placed on the node, while the decoder is on the server, this approach further reduces the computational load and processing time on the resource-constrained node. The obtained results demonstrate the effectiveness of the proposed VPUF scheme in modeling the behavior of the hardware-based PUF. Additionally, we investigate the impact of Gaussian noise in the communication channel between the server and the node on the system performance. The obtained results further reveal that the achieved authentication accuracy of the proposed scheme is 100%, as measured by the validation rate of the legitimate nodes. This highlights the superior performance of the proposed scheme in emulating the capabilities of a hardware-based PUF while providing secure and efficient authentication in IoT networks.

Security Enhancement Scheduling Model for IoT‐Based Smart Cities Through Machine Learning Method

ABSTRACTThe Internet of Things (IoT) is rapidly evolving, and this has supported the adoption of a new computing paradigm that moves processing power to the network's edge. The job must be assigned to the computer nodes, where their associated data is available, to minimize overheads generated by data transmissions in the network, because the edge nodes have limited processing power and are vulnerable to security. Hence, the paper introduces a novel security‐enhanced scheduling model for IoT‐based smart cities utilizing machine learning techniques. Initially, nodes are initialized using the LHK‐Means algorithm. Subsequently, tasks, representing requests from multiple users to access IoT data, are scheduled. Anomaly detection tasks are then identified using an L2‐Norm‐based fuzzy model. Normal tasks are processed by the BN‐CNN model, which schedules data collection tasks through the initialized IoT device nodes. Comparative analysis with existing models illustrates the effectiveness of the proposed approach in terms of accuracy, precision, recall, sensitivity, specificity, and f‐measure.

Differential Hall Effect Metrology for Electrical Characterization of Advanced Semiconductor Layers

Semiconductor layers employed in fabricating advanced node devices are becoming thinner and their electrical properties are diverging from those established for highly crystalline standards. Since these properties also change as a function of depth within the film, accurate carrier profiling solutions are required. The Differential Hall Effect (DHE) technique has the unique capability of measuring mobility and carrier concentration (active carriers) through the depth of a semiconductor film. It comprises making successive sheet resistance and sheet Hall coefficient measurements as the thickness of the electrically active layer at a test region is reduced through successive material removal steps. Difference equations are then used to process the data and plot the desired depth profiles. The fundamentals of DHE were established in 1960s. Recently, the adaption of electrochemical processing for the material removal steps, and the integration of all other functionalities in a Differential Hall Effect Metrology (DHEM) tool, has made this technique more practical and accurate and improved its depth resolution to a sub-nm range. In this contribution, we review the development history of this important technique and present data from recent characterization work carried out on Si, Ge and SiGe layers.

Inner External DQN LoRa SF Allocation Scheme for Complex Environments

In recent years, with the development of Internet of Things technology, the demand for low-power wireless communication technology has been growing, giving rise to LoRa technology. A LoRa network mainly consists of terminal nodes, gateways, and LoRa network servers. As LoRa networks often deploy many terminal node devices for environmental sensing, the limited resources of LoRa technology, the explosive growth in the number of nodes, and the ever-changing complex environment pose unprecedented challenges for the performance of the LoRa network. Although some research has already addressed the challenges by allocating channels to the LoRa network, the impact of complex and changing environmental factors on the LoRa network has yet to be considered. Reasonable channel allocation should be tailored to the situation and should face different environments and network distribution conditions through continuous adaptive learning to obtain the corresponding allocation strategy. Secondly, most of the current research only focuses on the channel adjustment of the LoRa node itself. Still, it does not consider the indirect impact of the node’s allocation on the entire network. The Inner External DQN SF allocation method (IEDQN) proposed in this paper improves the packet reception rate of the whole system by using reinforcement learning methods for adaptive learning of the environment. It considers the impact on the entire network of the current node parameter configuration through nested reinforcement learning for further optimization to optimize the whole network’s performance. Finally, this paper evaluates the performance of IEDQN through simulation. The experimental results show that the IEDQN method optimizes network performance.

Perancangan Prototipe Sistem Pemanggil Perawat Nirkabel menggunakan Keypad dan Transceiver NRF24L01

Hospital health services require effective communication tools between patients and nurses for patient comfort. Along with the development of wireless technology, many nurse call systems have been developed by researchers, application developers, and electronics activists to produce a more effective system and ease of use. The purpose of this study is to design a prototype of a wireless nurse caller system using NRF24L01 communication, and a keypad as a button that can be used by patients in the inpatient room to call patients, as well as an audio system in the nurse's room to receive calls from patients. By using the System Development Life Cycle (SDLC) method, the prototype of the nurse caller system was successfully realized and tested at the range and delay without obstacles and with obstacles. Based on the results of the distance and delay test in the unobstructed area, the caller node device and the call receiving node device can communicate at a distance of <100 meters with an average delay of 70.4 ms. Meanwhile, in the area where there is an obstacle (in the building), it shows the maximum communication ability between devices at a distance of 60 m with a delay of 74 ms and an average delay of 71 ms, Thus this prototype can be applied in hospitals.

Miniaturized Active-Frequency Selective Surfaces for Low-Power Internet of Things Devices.

With the proliferation of smart devices, the Internet of Things (IoT) is rapidly expanding. This study proposes a miniaturized controllable metamaterial with low control voltage for achieving low-power and compact designs in IoT node devices. Operating at a target frequency of 2.4 GHz, the proposed metamaterial requires only a 3.3 V control voltage and occupies approximately one-third of the wavelength in size. Experimental validation demonstrates its excellent reflective control performance, positioning it as an ideal choice for low-power IoT systems, particularly in the context of miniaturized and low-power IoT node applications.

RN‐Net: Reservoir Nodes‐Enabled Neuromorphic Vision Sensing Network

Neuromorphic computing systems promise high energy efficiency and low latency. In particular, when integrated with neuromorphic sensors, they can be used to produce intelligent systems for a broad range of applications. An event‐based camera is such a neuromorphic sensor, inspired by the sparse and asynchronous spike representation of the biological visual system. However, processing the event data requires either using expensive feature descriptors to transform spikes into frames, or using spiking neural networks (SNNs) that are expensive to train. In this work, a neural network architecture is proposed, reservoir nodes‐enabled neuromorphic vision sensing network (RN‐Net), based on dynamic temporal encoding by on‐sensor reservoirs and simple deep neural network (DNN) blocks. The reservoir nodes enable efficient temporal processing of asynchronous events by leveraging the native dynamics of the node devices, while the DNN blocks enable spatial feature processing. Combining these blocks in a hierarchical structure, the RN‐Net offers efficient processing for both local and global spatiotemporal features. RN‐Net executes dynamic vision tasks created by event‐based cameras at the highest accuracy reported to date at one order of magnitude smaller network size. The use of simple DNN and standard backpropagation‐based training rules further reduces implementation and training costs.

Realization of Authenticated One-Pass Key Establishment on RISC-V Micro-Controller for IoT Applications

Internet-of-things networks consist of multiple sensor devices spread over a wide area. In order to protect the data from unauthorized access and tampering, it is essential to ensure secure communication between the sensor devices and the central server. This security measure aims to guarantee authenticity, confidentiality, and data integrity. Unlike traditional computing systems, sensor node devices are often limited regarding memory and computing power. Lightweight communication protocols, such as LoRaWAN, were introduced to overcome these limitations. However, despite the lightweight feature, the protocol is vulnerable to different types of attacks. This proposal presents a highly secure key establishment protocol that combines two cryptography schemes: Elliptic Curve Qu–Vanstone and signcryption key encapsulation. The protocol provides a method to establish a secure channel that inherits the security properties of the two schemes. Also, it allows for fast rekeying with only one exchange message, significantly reducing the handshake complexity in low-bandwidth communication. In addition, the selected schemes complement each other and share the same mathematical operations in elliptic curve cryptography. Moreover, with the rise of a community-friendly platform like RISC-V, we implemented the protocol on a RISC-V system to evaluate its overheads regarding the cycle count and execution time.

Enhanced radioactive waste drum monitoring: A sensorized LoRa-based network for identification and integrity assessment

A reliable monitoring and oversight system is essential to guarantee the safe operation and eventual closure of radioactive waste disposal facilities. With recent technological advancements, there is now the capability to deploy distributed wireless sensor networks that are cost-effective and low in power consumption. Such networks are versatile, finding applications in several civil, environmental, and industrial sectors. This work presents a novel system and method for identifying and continuously monitoring the integrity of stored radioactive waste packages. The proposed approach, developed by the University of Pisa in the context of the H2020 European Project PREDIS, employs a network of wireless node devices, provided with solid-state micro-power radiation detectors tasked with the measurement of gamma-rays and thermal neutrons coming from within cemented waste drums. Collected data are automatically transmitted across the storage site via Long Range radio technology and forwarded to cloud-based or offline platforms, allowing for further visualization, data processing, and safe storage. The system highlights the feasibility of autonomously and passively gathering data from radioactive waste drums over extended distances (more than 250 m in non-line-of-sight conditions), decreasing the reliance on portable or automated scanning mechanisms. In line with its capability for autonomous, long-range data collection, the system satisfies the requirement for structural integrity, containment, and confinement of packaging throughout both interim and long-term disposal of radioactive wastes, thereby playing a key role in reinforcing the safe management of radioactive waste.

Secure and efficient communications with fine-grained access control in underwater wireless sensor networks

As UWSN have received attention, a need for an efficient and secure communication protocol has arisen to overcome the low device performance, node failure, and high propagation latency. However, existing works are either specialized for one-to-one communication or cannot satisfy low latency constraints. Therefore, this work proposes a secure communication protocol with fine-grained access control for UWSN that support secure and efficient one-to-many communication and considers potential internal attackers for high security level. Specifically, we adopt lightweight ABE to achieve fine-grained access control at low cost, and introduce outsourced decryption to further alleviate the computational load of underwater sensors.

Semi-supervised learning model synergistically utilizing labeled and unlabeled data for failure detection in optical networks

In optical networks, reliable failure detection is essential for maintaining quality of service. The methodology has evolved from traditional performance threshold-driven approaches to contemporary data-driven AI algorithms, predominantly employing supervised and unsupervised learning. However, with the advent of second-level telemetry, optical transport networks have amassed a wealth of unlabeled performance data, while labeled data remains limited due to the intensive effort required for annotation. In this scenario, to address the challenges of scarce labeled data in supervised learning and the accuracy issues in unsupervised methods, we propose an OpenFE-VIME semi-supervised model. This model synergizes the robustness of supervised approaches with the flexibility of unsupervised approaches. It not only leverages the abundant reservoir of unlabeled data but also addresses the challenges posed by the limited availability of labeled data, enabling reliable and efficient failure detection. Upon evaluation using performance data from OTN node devices in the operator’s optical backbone network, the OpenFE-VIME model demonstrates remarkable performance, achieving an F1-score of 0.947 and accuracy of 0.946, while significantly reducing false negative and false positive rates to 0.073 and 0.035, respectively. Moreover, our research explores the model’s capabilities in utilizing both labeled and unlabeled data and investigates the threshold for training convergence across various data ratios. Additionally, the model’s internal mechanisms and decision-making processes are interpreted using t-SNE visualization, offering enhanced insights into its operational efficacy.

Enhancing Traffic Routing Inside a Network through IoT Technology & Network Clustering by Selecting Smart Leader Nodes

. IoT networking uses real items as stationary or mobile nodes. Mobile nodes complicate networking. Internet of Things (IoT) networks have a lot of control overhead messages because devices are mobile. These signals are generated by the constant flow of control data as such device identity, geographical positioning, node mobility, device configuration, and others. Network clustering is a popular overhead communication management method. Many cluster-based routing methods have been developed to address system restrictions. Node clustering based on the Internet of Things (IoT) protocol, may be used to cluster all network nodes according to predefined criteria. Each cluster will have a Smart Designated Node. SDN cluster management is efficient. Many intelligent nodes remain in the network. The network design spreads these signals. This paper presents an intelligent and responsive routing approach for clustered nodes in IoT networks. An existing method builds a new sub-area clustered topology. The Nodes Clustering Based on the Internet of Things (NCIoT) method improves message transmission between any two nodes. This will facilitate the secure and reliable interchange of healthcare data between professionals and patients. NCIoT is a system that organizes nodes in the Internet of Things (IoT) by grouping them together based on their proximity. It also picks SDN routes for these nodes. This approach involves selecting one option from a range of choices and preparing for likely outcomes problem addressing limitations on activities is a primary focus during the review process. Predictive inquiry employs the process of analyzing data to forecast and anticipate future events. This document provides an explanation of compact units. The Predictive Inquiry Small Packets (PISP) improved its backup system and partnered with SDN to establish a routing information table for each intelligent node, resulting in higher routing performance. Both principal and secondary roads are available for use. The simulation findings indicate that NCIoT algorithms outperform CBR protocols. Enhancements lead to a substantial 78% boost in network performance. In addition, the end-to-end latency dropped by 12.5%. The PISP methodology produces 5.9% more inquiry packets compared to alternative approaches. The algorithms are constructed and evaluated against academic ones.

Distributed resilient fault-tolerant cooperative automatic generation control and multi-event triggering mechanism co-design for wind energy conversion power system

In wind energy conversion power system, some sensor and actuator fall into abnormal condition which may lead to lower wind energy utilization rate and poor system performance or even system collapse. Meanwhile, with the increasing complexity of wind energy conversion power system, more device nodes compete for limited network communication resources, there are time-delays and packets dropout in data transmission, and these may reduce the efficiency of wind energy conversion. Accordingtoaboveconsideration, the multi-event triggering mechanism based distributed resilient fault-tolerant cooperative control scheme for wind energy conversion power system is put forward. A novel model of the wind energy conversion power system is established when the sensor and actuator faults simultaneously. According to Lyapunov stability theory, the sufficient condition for system exponential mean-square stable is obtained. Different from previous fault-tolerant control method, the proposed control strategy considered the wind energy conversion power system with data transmission management under simultaneous stochastic sensor and actuator faults. The simulation demonstrates that the method proposed in this paper is effective and the multi-event triggering mechanism based distributed resilient fault-tolerant controller provides stronger reliability and higher wind energy utilization efficiency.

DATA POISONING ATTACKS ON FEDERATED MACHINE LEARNING

Federated machine learning which enables resource constrained node devices (e.g., mobile phones and IoT devices) to learn a shared model while keeping the training data local, can provide privacy, security and economic benefits by designing an effective communication protocol. However, the communication protocol amongst different nodes could be exploited by attackers to launch data poisoning attacks, which has been demonstrated as a big threat to most machine learning models. In this paper, we attempt to explore the vulnerability of federated machine learning. More specifically, we focus on attacking a federated multi-task learning framework, which is a federated learning framework via adopting a general multi-task learning framework to handle statistical challenges. We formulate the problem of computing optimal poisoning attacks on federated multitask learning as a bilevel program that is adaptive to arbitrary choice of target nodes and source attacking nodes. Then we propose a novel systems-aware optimization method, ATTack on Federated Learning (AT2FL), which is efficiency to derive the implicit gradients for poisoned data, and further compute optimal attack strategies in the federated machine learning. Our work is an earlier study that considers issues of data poisoning attack for federated learning. To the end, experimental results on real-world datasets show that federated multi-task learning model is very sensitive to poisoning attacks, when the attackers either directly poison the target nodes or indirectly poison the related nodes by exploiting the communication protocol.

AUTOMATIC ADJUSTMENT OF REACTIVE POWER BY FACTS DEVICES UNDER CONDITIONS OF VOLTAGE INSTABILITY IN THE ELECTRIC NETWORK

This article describes the problem of automatic regulation of reactive power using electronic devices FACTS (Flexible AC Transmission Systems): static synchronous compensator (STATCOM) and unified power flow controller (UPFC). With the help of a complex writing form, the following are determined: voltages at the installation nodes of the FACTS device and loads, currents of loads, power sources and electronic compensators in case of voltage instability at the load node of the electrical network. Voltages and currents are determined using the node-voltage method. The task of STATCOM is partial or full compensation of reactive power. During the reduction of the voltage at the load node, the reactive power generated by the power source decreases. The STATCOM should partially or fully compensate for the reactive power imbalance as quickly as possible. However, at the same time, it is not possible to fully compensate for the voltage reduction. A series-parallel or parallel-series UPFC can be used to solve this problem. As a result of using the UPFC, it is possible to automatically raise the voltage level to acceptable values with the help of the UPFC series compensator. The analysis shows that the parallel-serial UPFC is characterized by the stability of operation. In the case of using a series-parallel UPFC, there are restrictions on the ability to adjust the imaginary voltage component of the series compensator, since the angle of the voltage vector changes, which causes a failure in the operation of the regulator of the parallel compensator UPFC.

A novel blockchain-based digital forensics framework for preserving evidence and enabling investigation in industrial Internet of Things

To address challenges in digital evidence collection and responsibility determination for industrial safety accidents involving industrial Internet of Things (IIoT) device nodes, this paper proposes a blockchain-based digital forensic scheme within the IIoT communication architecture. The scheme utilizes a decentralized blockchain storage mechanism to enable remote storage of digital forensic data. Additionally, it leverages smart contract mechanisms to facilitate efficient retrieval and tracing of related evidence chains. To enhance data security of IIoT device nodes, a token mechanism is implemented for access control. Moreover, to meet real-time evidence acquisition requirements in IIoT, an efficient batch consensus mechanism is proposed. Experimental simulations demonstrate the superiority of the novel consensus algorithm compared to the traditional Delegated Proof-of-Stake (DPOS) consensus in the proposed scheme for the IIoT environment. It meets speed requirements for evidence collection, ensuring tamper-proof, non-repudiable, and permanent storage of digital forensic data. Consequently, the application of blockchain technology for judicial access and evidence storage has made significant contributions to digital forensics within the IIoT context.

A Digitalization Algorithm Based on the Voltage Waveform of the Multifunction Vehicle Bus

The MVB is a kind of widely used vehicle-level bus, which is crucial for the normal operation of trains. However, the MVB bus contains device node terminals and the topology is complex, which makes fault location difficult. The voltage waveform of the MVB physical layer can reflect the electrical characteristics and fault characteristics of a network in real time and has the value of assisting the diagnosis of MVB network faults. Based on the characteristics of the MVB physical-layer voltage waveform, this paper studies the optimal sampling rate and feature-extraction algorithm of the MVB voltage waveform and the combination of the data of the data-link layer to assign accurate timestamps to waveform data, and finally presents a design for an MVB dual-mode data-acquisition platform with multiple sampling rates/test conditions. Experimental results show that the proposed algorithm can accurately extract waveform feature information under the optimal sampling rate of 62.5 MHz, which makes the collection of MVB voltage waveform data more reliable and practical.

CHESSIoT: A model-driven approach for engineering multi-layered IoT systems

Context:The current technology revolution, which places the highest value on people’s welfare, is frequently seen as being mainly supported by Internet of Things (IoT) technologies. IoT is regarded as a powerful multi-layered network of systems that integrates several heterogeneous, independently networked (sub-)systems working together to achieve a shared purpose. Objective:In this article, we present CHESSIoT, a model-driven engineering environment that integrates high-level visual design languages, software development, safety analysis, and deployment approaches for engineering multi-layered IoT systems. With CHESSIoT, users may conduct different engineering tasks on system and software models under development to enable earlier decision-making and take prospective measures, all supported by a unique environment. Methodology:This is achieved through multi-staged designs, most notably the physical, functional, and deployment architectures. The physical model specification is used to perform both qualitative and quantitative safety analysis by employing logical Fault-Trees models (FTs). The functional model specifies the system’s functional behavior and is later used to generate platform-specific code that can be deployed on low-level IoT device nodes. Additionally, the framework supports modeling the system’s deployment plan and run-time service provisioning, which would ultimately be transformed into deployment configuration artifacts ready for execution on remote servers. Results:To showcase the effectiveness of our proposed approach, as well as the capability of the supporting tool, a multi-layered Home Automation system (HAS) scenario has been developed covering all its design, development, analysis, and deployment aspects. Furthermore, we present the results from different evaluation mechanisms which include a comparative analysis and a qualitative assessment. The evaluation mechanisms target mainly completeness of CHESSIoT by addressing specific research questions.

Understanding electromigration failure behaviors of narrow cobalt lines and the mechanism of reliability enhancement for extremely dilute alloying of manganese oxide

Self-forming barriers for copper (Cu) interconnect metallization are well developed. However, the self-forming-barrier process and associated electromigration behaviors of narrow lines of cobalt (Co), a new interconnect wiring material for sub-10-nm semiconductor device nodes, are yet to be explored. In this study, we present an all-wet electroless trench-filling process to fabricate narrow (100 nm) lines of Co and MnO-added Co [Co(MnO)], then thermally annealed to some extent (400 °C/30 min) prior to electromigration testing. Empirical data obtained from accelerated electromigration testing, including failure lifetimes, current-density scaling factors, activation energies and physical failure profiles, consistently show that electromigration reliability of the Co lines is markedly enhanced by 0.1% added MnO. Transmission electron microscopy and focused ion beam imaging, along with dynamic adhesion strength data, reveal that the enhancement is related to annealing-induced segregation and dispersion of MnO particles at the interface with SiO2, ensuring adhesion of Co on SiO2, preventing interfacial interdiffusion and stuffing electromigration pathways. The adhesion strength, microstructures and Joule-heating data of Co and Co(MnO) are provided for the discussion of the differences in their electromigration performance and failure mechanism. Comments on electroless-plated Co as a new interconnect material are also given.

Design of an Autonomous IoT Node Powered by a Perovskite-Based Wave Energy Converter

Abstract This paper presents the results of experimental research focused on wave energy harvesting and its conversion to power Internet of Things (IoT) devices. The harvesting and conversion process was performed using a wave energy converter (WEC) consisting of a lead zirconate titanate piezoelectric ceramic perovskite material and a prototype power electronic circuit. The designed WEC was considered as a power supply for an end node device (END) of an IoT network. The END consisted of a long-range radio module and an electronic paper display. A set of physical experiments were carried out, and the results confirmed that an energy surplus was supplied by WEC compared to the energy consumed by the END. Hence, the proposed scheme was experimentally validated as a convenient solution that could enable the autonomous operation of an IoT device. The use case presented here for the proposed WEC was analysed for selected sea areas on the basis of wave statistics. The novelty of this paper arises from an investigation that confirms that WECs can significantly contribute to the development of wireless and mobile IoT communication powered by freely available sea wave energy.