Internet Of Things Nodes Research Articles

IoT enabled smart solar water heater system using real time ThingSpeak IoT platform

AbstractThis dissertation aims to reuse the cold water which will initially present inside the water heater and reduce its wastage by sending it back to the water tank. The water heater temperature could be customized based on the number of users either manually or in an automated way using Internet of Things (IoT) techniques. The system is designed and operated using open‐source hardware and an IoT platform. The smart solar water heater with IoT capabilities continuously monitors the tank level, temperature, and pH level, and the data collected by the IoT node is uploaded/published to ThingSpeak. The IoT cloud analyses the sensor value and instructs the control unit of the smart solar system. The IoT enabled solar water heating system efficiency is high for smart homes and Industry 4.0. The main advantages of this model include handling multiple users, reducing the wastage of cold water, and purifying the water based on TDS level and this model aims to make it possible to control the proposed system from anywhere and at any time. The highest efficiency of the respective solar water heater could be well utilized.

SamurAI: A Versatile IoT Node With Event-Driven Wake-Up and Embedded ML Acceleration

Increased capabilities such as recognition and self-adaptability are now required from IoT applications. While IoT node power consumption is a major concern for these applications, cloud-based processing is becoming unsustainable due to continuous sensor or image data transmission over the wireless network. Thus optimized ML capabilities and data transfers should be integrated in the IoT node. Moreover, IoT applications are torn between sporadic data-logging and energy-hungry data processing (e.g. image classification). Thus, the versatility of the node is key in addressing this wide diversity of energy and processing needs. This paper presents SamurAI, a versatile IoT node bridging this gap in processing and in energy by leveraging two on-chip sub-systems: a low power, clock-less, event-driven Always-Responsive (AR) part and an energy-efficient On-Demand (OD) part. AR contains a 1.7MOPS event-driven, asynchronous Wake-up Controller (WuC) with a 207ns wake-up time optimized for sporadic computing, while OD combines a deep-sleep RISC-V CPU and 1.3TOPS/W Machine Learning (ML) for more complex tasks up to 36GOPS. This architecture partitioning achieves best in class versatility metrics such as peak performance to idle power ratio. On an applicative classification scenario, it demonstrates system power gains, up to 3.5x compared to cloud-based processing, and thus extended battery lifetime.

CorrFL: Correlation-Based Neural Network Architecture for Unavailability Concerns in a Heterogeneous IoT Environment

The Federated Learning (FL) paradigm faces several challenges that limit its application in real-world environments. These challenges include the local models’ architecture heterogeneity and the unavailability of distributed Internet of Things (IoT) nodes due to connectivity problems. These factors posit the question of “how can the available models fill the training gap of the unavailable models?". This question is referred to as the “Oblique Federated Learning" problem. This problem is encountered in the studied environment that includes distributed IoT nodes responsible for predicting CO2 concentrations. This paper proposes the Correlation-based FL (CorrFL) approach influenced by the representational learning field to address this problem. CorrFL projects the various model weights to a common latent space to address the model heterogeneity. Its loss function minimizes the reconstruction loss when models are absent and maximizes the correlation between the generated models. The latter factor is critical because of the intersection of the feature spaces of the IoT devices. CorrFL is evaluated on a realistic use case, involving the unavailability of one IoT device and heightened activity levels that reflect occupancy. The generated CorrFL models for the unavailable IoT device from the available ones trained on the new environment are compared against models trained on different use cases, referred to as the benchmark model. The evaluation criteria combine the mean absolute error (MAE) of predictions and the impact of the amount of exchanged data on the prediction performance improvement. Through a comprehensive experimental procedure, the CorrFL model outperformed the benchmark model in every criterion.

An IoT-Enabled Intelligent and Secure Manufacturing Model Using Blockchain in Hybrid Cloud Communication System

The smart manufacturing system can become a linked network with the help of the Internet of Things (IoT). Devices connected to the IoT are susceptible to various attacks and assaults. An effective protection plan is needed to ensure that the billions of IoT nodes are protected from these hazards. The security mechanisms on IoT devices are ineffective due to resource limitations. As a result, the academic community has recently paid attention to the cloud-, fog-, and edge-based IoT systems. A robust cloud provider is in the cloud or fog to perform computationally demanding activities, including safety, data analysis, decision-making process, and monitoring. Hash identities and upgraded Rivest–Shamir–Adleman (RSA) have been used to secure the IoT device’s data. A four-prime integer of 512 bits makes up the proposed security algorithm. A hash signature is used to provide device authentication. An effective clustering method for sensing devices based on the node level, separation from the clusters, remaining energy, and fitness has been presented for long network life. The suggested swarm-based method determines the sensor nodes’ fitness. A deep neural network- (DNN-) based resource scheduling algorithm (DNN-RSM) is meant to reduce the delay and communications overhead for IoT components in the hybrid cloud system. For optimum resource allocation, all queries originating from the cluster head are categorised using DNN based on their storage, processing, and bandwidth needs. The suggested structure delivers better outcomes, particularly regarding energy use, delay, and safety level. The results of the simulation provide credence to the concept that the proposed strategy is superior to the current system. The suggested scheme includes stringent security, decreased energy usage, decreased latency, and efficient resource utilization.

Light-Weight ML Aided Autonomous IoT Networks

A common challenge in Internet-of-Things (IoT) networks is managing and connecting a large number of field deployed IoT nodes. With the massive growth of IoT applications, any human intervention is even more impractical. Hence, node- and network-level coordination for low-cost sensing, energy sustainability, self-organized fault remediation capability, auto-calibration of field nodes are some of the desirable features. Aiming at autonomous IoT, this article first presents the recent progress on light-weight machine learning aided strategies for context-aware IoT applications, wherein, depending on the application context and adaptation requirement, the data-driven intelligence operates at the field nodes or at the nearby edge node or at the cloud storage. A few motivating results towards operational autonomy and network scalability on the chosen use cases are presented. Next, the requirements towards fully autonomous and self healing IoT networks are presented, highlighting several future research directions and challenges.

IoT Security Detection Method Based on Multifeature and Multineural Network Fusion

IoT security detection plays an important role in securing the IoT ecosystem. The current detection systems suffer from poor fault tolerance and inefficient detection results. To address the IoT security vulnerability, the paper designs a multifeature fusion-based IoT security detection model to simulate an attacker sending test commands to IoT nodes. Firstly, the data collection algorithm is introduced, and the collected dataset is analyzed by three neural network models, namely, RNN, LSTM, and GRU, respectively. The best scoring model is selected as the classifier to identify vulnerabilities and achieve IoT security detection.

The Cryptographic Key Distribution System for IoT Systems in the MQTT Environment.

The Internet of Things (IoT) is a very abundant source of data, as well as a source of many vulnerabilities. A significant challenge is preparing security solutions to protect IoT nodes' resources and the data exchanged. The difficulty usually stems from the insufficient resources of these nodes in terms of computing power, memory size, range energy resource, and wireless link performance. The paper presents the design and demonstrator of a system for symmetric cryptographic Key Generating, Renewing, and Distributing (KGRD). The system uses the TPM 2.0 hardware module to support cryptographic procedures, including creating trust structures, key generation, and securing the node's exchange of data and resources. Clusters of sensor nodes and traditional systems can use the KGRD system to secure data exchange in the federated cooperation of systems with IoT-derived data sources. The transmission medium for exchanging data between KGRD system nodes is the Message Queuing Telemetry Transport (MQTT) service, which is commonly used in IoT networks.

An artificial intelligence approach for energy‐aware intrusion detection and secure routing in internet of things‐enabled wireless sensor networks

SummaryIn today's world, the Internet of things (IoT) and wireless sensor networks (WSNs) have been integrated into various applications to support reliable communication systems. The IoT nodes collect the sensed data with the aid of WSN and transfer them to the cloud environment. Nevertheless, these nodes become vulnerable when Denial of Service attacks occurred in the IoT‐enabled WSN (IWSN). The insecure route also affects the energy efficiency of IWSN. Therefore, robust intrusion detection along with secure routing is necessitated to protect the IWSN from various security threats. In this paper, a novel Artificial Intelligence‐based Energy‐aware Intrusion Detection and Secure Routing model has been proposed to develop a secured IWSN. Initially, the proposed model executes the intrusion detection system to identify the various attacks. Afterward, a game strategy‐based decision mechanism is integrated with the proposed intrusion detection model to decide whether protection is needed or not. In the last phase, an energy‐aware ad‐hoc on‐demand distance vector algorithm has been established to offer secured routing among multiple nodes. The performance results show that the proposed model achieves superior detection accuracy of 95% and reduced energy utilization of 38% as compared with the existing intrusion detection models.

A new concatenated Multigraph Neural Network for IoT intrusion detection

The last few years have seen a high volume of sophisticated cyber-attacks leading to financial instability and privacy breaches. This reveals the need for a Network Intrusion Detection System (NIDS) that is more robust, effective, and capable of learning complex relations of network flows. Existing machine learning approaches are barely effective for picking new attacks in IoT traffic, primarily due to treating flows independently. Recently, Graph Neural Networks (GNNs) have gained popularity due to their ability to model underlying topology in the form of nodes and edges. We propose a GNN-based NIDS that maximizes the possibility of involving structural characteristics of normal and malicious network traffic. We construct a multi-edged graph structure that captures full communication between any pair of IoT nodes at the edge level. This novel GNN model combines the benefits of spectral and spatial GNNs tailored to process and learn from complex multigraph-structured information. The proposed GNN model is adjusted to work on multi-edges and multi-dimensional edge features. Extensive experimental analysis using evaluation metrics is carried out on four benchmark datasets, demonstrating the competence of our proposed scheme compared to the state-of-the-art GNN models. Our proposed model shows about 2% to 5% improvement in accuracy, precision, F1 and recall offering less training time and model size in the considered datasets. The proposed model has achieved high accuracy ranging from 98.91% to 99.96%, with precision ranging from 98.86 to 99.96, recall ranging from 98.91% to 99.96%, and F1-score ranging from 98.74% to 99.95% in the four benchmark datasets.

Performance optimization of the nano-scale carry-skip adder based on quantum dots and its application in the upcoming Internet of Things

Internet of Things (IoT) is a network of linked computers, electronic and mechanical equipment, objects, living things, and people that can exchange data without human-to-human or human-to-computer interaction. Real objects with sensors, computing power, software, and other technologies are called IoT when they connect to and exchange data with other systems and devices over the Internet or other communication networks. On the other hand, Quantum-dot Cellular Automata (QCA) is a highly appealing alternative to transistor-based technologies for future circuit design, thanks to its excellent performance and low power consumption characteristics. Therefore, researchers are trying to implement IoT-related circuits in this technology and take advantage of it. In IoT nodes, the circuits were previously designed based on transistors and used extensively are adder circuits and their various forms, such as carry-look-ahead adder, ripple carry adder, and Carry-Skip Adder (CSA). The proposed CSA is part of the bypass adder family, and its major goal in IoT devices is to reduce latency and power consumption. Designing CSA is a fascinating area of research in the QCA and IoT domains. In this research, we will offer nano-scale CSA designs based on logic gate structures. This CSA aids in improving system performance in terms of average power dissipation, area, and delay. This design has performed admirably in terms of physical qualities, particularly power usage. As a result, an ultra-low-power CSA is proposed, which employs a reduced number of cells and a smaller area while achieving the desired highly polarized output. All the circuits provided in the simulator software have been tested, and their correctness has been confirmed. 36 quantum cells were employed, and the adder's area with propagating signal is around 0.04 µm2. The latency is about 2 clock phases. Additionally, the CSA for IoT devices is roughly 0.4 µm2, which results in a delay of 17 clock phases and the need for 374 quantum cells. The suggested CSA reduces cell consumption by 64 %, improves latency by 15 %, and increases area by 85 %. Compared to a carry-look-ahead adder, the CSA is the only way to reduce the latency of a ripple carry adder with low effort.

A Micro-Acoustic Enhanced Low-Impedance Antenna System for IoT Wake-Up Receivers

In this work, we propose a passive radio frequency (RF) front-end tailored for wake-up receivers (WuRx) to be deployed in cellular IoT devices and wearables networks, featuring a low radiation resistance antenna and a high-Q matching network implemented with microacoustic resonators integrated to obtain a systematic higher node’s sensitivity at no cost in terms of power consumption. We show how these components can be co-designed to obtain high passive voltage gain, hardwarelevel blocker immunity, and increased resilience to integration parasitics, relaxing link budget for low-power IoT nodes. We report experimental validation of a PCB antenna with 2 dBi gain measured on an 11Ω input resistance at resonance, and an in-house fabricated Micro-Electro-Mechanical System (MEMS) thin-film aluminum nitride bulk acoustic resonator with a quality factor Q = 550 and a piezoelectric coupling coefficient k2 t =7%, hybridly integrated with a commercial off-the-shelf low-power WuRx circuit to benchmark the proposed RF front-end design at 850MHz. We demonstrate a passive voltage gain of 12 dB due to the MEMS resonator, and an additional 11 dB due to the proposed antenna design (for a total of an unprecedented 23 dB passive gain in this frequency range) leading to an over the air.61dBm minimum detectable input power and 23 dB blocker rejection.

Effective injection of adversarial botnet attacks in IoT ecosystem using evolutionary computing

AbstractWith the widespread adoption of Internet of Things (IoT) technologies, botnet attacks have become the most prevalent cyberattack. In order to combat botnet attacks, there has been a considerable amount of research on botnet attacks in IoT ecosystems by graph‐based machine learning (GML). The majority of GML models are vulnerable to adversarial attacks (ADAs). These ADAs were created to assess the robustness of existing ML‐based security solutions. In this letter, we present a novel adversarial botnet attack (ADBA) that modifies the graph data structure using genetic algorithms (GAs) to trick the graph‐based botnet attack detection system. According to the experiment results and comparative analysis, the proposed ADBA can be executed on resource‐constrained IoT nodes. It offers a substantial performance gain of 2.15 s, 52 kb, 92 817 mJ, 97.8%, and 27.74%–41.82% over other approaches in term of Computing Time (CT), Memory Usage (MU), Energy Usage (EU), Attack Success Rate (ASR) and Accuracy (ACC) metrics, respectively.

Throughput analysis of frequency-domain contention under realistic conditions for hyperautomation

ABSTRACT Internet of Things (IoT) has widely been deployed in various industrial fields. To achieve hyperautomation in IoT-based applications, it calls for high-efficient wireless transmission technologies to improve the response speed of IoT nodes. Frequency-domain contention (FDC) is a promising contention-based channel access mechanism to meet this requirement. However, the nature of FDC’s multiple-transmission-multiple-reception and the limit of IoT chip’s synchronization accuracy will lead to carrier frequency offset (CFO) and hence frequent transmission collisions and null transmissions. Considering integer and fractional CFO, this paper theoretically analyzes the FDC throughput and verifies the accuracy of our model via extensive simulations.

Formula omitted]: Energy-efficient intelligent edge–cloud collaboration for remote IoT services

Existing centralized cloud-based solution is challenging to cope with the explosive growth of data generated from massive IoT devices to meet the requirements of critical services, especially for remote IoT services provided in remote/rural areas where the resource and energy capacity of local data processing infrastructure is limited. To solve this issue, we propose ELECT, an energy-efficient intelligent edge-cloud collaboration scheme that achieves satisfactory data processing performance. ELECT includes a platform that utilizes edge computing node as a core element that locates close to the IoT nodes for coordinating the data processing between the cloud and the IoT devices. Specifically, based on the importance of a node’s collected data in the overall data processing performance, a dynamic IoT node management algorithm is developed to manage each node’s active/inactive status to reduce energy consumption. Moreover, a deep Q-network (DQN)-based workflow scheduling algorithm that fully utilizes the data-centric device–edge–cloud continuum is introduced to reduce makespan and energy consumption for obtaining a compromising solution. For verification, we develop an experimental environment simulating structural health monitoring (SHM) services in remote areas. Extensive experiments verify the effectiveness of ELECT in terms of various service requirements, including makespan, energy consumption and communication cost.

Joint Active and Passive Beamforming Design for RIS-Aided IBFD IoT Communications: QoS and Power Efficiency Considerations

In a reconfigurable intelligent surface (RIS) aided communication network, a RIS equipped with multiple passive reflecting elements assists in extending the coverage of wireless networks by overcoming the effects of non-line-of-sight propagation. Accordingly, to present the case for utilizing RIS panels in future consumer internet of things (IoT) networks, in this paper, we focus on a RIS-aided IoT network operating in in-band full-duplex (IBFD) mode. Since all the IoT node pairs communicate simultaneously through individual IBFD links at the same time-frequency resources, the nodes suffer from strong self-interference (SI) and inter-node interference. Consequently, in this paper, we investigate a joint active and passive beamforming design at each IoT node and the RIS, respectively, by minimizing the total transmission power of the network, subject to a minimum rate constraint for each IoT node and a constraint that guarantees a unit modulus phase shift at the RIS. As the original joint active and passive beamforming design problem is non-convex and intractable, we decompose the problem into two sub-problems. For a given passive beamformer design, we solve the original problem to obtain the optimal beamforming vectors at each IoT node. Next, for the obtained active beamformer, we propose two algorithms based on i) gradient descent (GD) search and ii) Riemannian conjugate gradient (RCG) to obtain the optimal passive beamformer at the RIS. Through extensive numerical analysis, we verify the effectiveness of the proposed algorithms and also show the effects of using RIS and transmitting in IBFD mode on the average rate and power of the considered consumer IoT network. We investigate the impact of several system parameters such as RIS elements, node pairs, level of hardware impairments at the transmitter and receiver, etc. We also highlight the trade-off between performance improvement and complexity due to elements used at RIS.

A System Design With Deep Learning and IoT to Ensure Education Continuity for Post-COVID

In this paper, an IoT and deep learning-based comprehensive study to reduce the effects of COVID-19 on the education system is presented. The proposed system consists of an edge device, IoT nodes, and a neural network that runs on a server. The purpose of the proposed system is to protect students and staff against infectious diseases and increase the students performance during classes by monitoring the environmental conditions via an IoT-based sensor network, during the current pandemic to ensure the use of masks in closed areas by training a customized deep learning model, and to monitor the student attendance data by deep learning and IoT-based solution. Furthermore, effective heating and cooling can be done to save energy by transmitting the environmental conditions of the indoor environment to the relevant destinations. The experiment is conducted with five different networks to classify the faces in the images as masked or unmasked, and their performances were examined. The networks were trained on the Face Mask Detection Dataset which contains a total of 7553 masked and unmasked images. The best results were obtained as 99.5% for the F1 Score and 99% for MCC by the model trained on the InceptionV3 network.

Cognitive Lightweight Logistic Regression-Based IDS for IoT-Enabled FANET to Detect Cyberattacks

In recent few years, flying ad hoc networks are utilized more for interconnectivity. In the topological scenario of FANETs, IoT nodes are available on ground where UAVs collect information. Due to high mobility patterns of UAVs cause disruption where intruders easily deploy cyberattacks like DoS/DDoS. Flying ad hoc networks use to have UAVs, satellite, and base station in the physical structure. IoT-based UAV networks are having many applications which include agriculture, rescue operations, tracking, and surveillance. However, DoS/DDoS attacks disturb the behaviour of entire FANET which lead to unbalance energy, end-to-end delay, and packet loss. This research study is focused about the detail study of machine learning-based IDS. Also, cognitive lightweight-LR approach is modeled using UNSW-NB 15 dataset. IoT-based UAV network is introduced using machine learning to detect possible security attacks. The queuing and data traffic model is utilized to implement DT, RF, XGBoost, AdaBoost, Bagging and logistic regression in the environment of IoT-based UAV network. Logistic regression is the proposed approach which is used to estimate statistical possibility. Overall, experimentation is based on binomial distribution. There exists linear association approach in logistic regression. In comparison with other techniques, logistic regression behaviour is lightweight and low cost. The simulation results presents logistic regression better results in contrast with other techniques. Also, high accuracy is balanced well in optimal way.

Decentralized and scalable hybrid scheduling-clustering method for real-time applications in volatile and dynamic Fog-Cloud Environments

A major challenge in Cloud-Fog settings is the scheduling of workflow applications with time constraints as the environment is highly volatile and dynamic. Furthermore, adding the complexities of handling IoT nodes, as the major owners of the workflow requests, renders the problem space even harder to address. This paper presents a hybrid scheduling-clustering method for addressing this challenge. The proposed lightweight, decentralized, and dynamic clustering algorithm is based on fuzzy inference with intrinsic support for mobility to form stable and well-sized clusters of IoT nodes while avoiding global clustering and recurrent re-clustering. The proposed distributed method uses Cloud resources along with clusters of mobile and inert Fog nodes to schedule time-constrained workflow applications with considering a proper balance between contradicting criteria and promoting scalability and adaptability. The Velociraptor simulator (version 0.6.7) has been used to throughtly examine and compare the proposed method in real workloads with two contemporary and noteworthy methods. The evaluation results show the superiority of the proposed method as the resource utilization is about 20% better and the schedule success rate is almost 21% better compared with the two other methods. Also, other parameters such as throughput and energy consumption have been studied and reported.

Flamingo-Optimization-Based Deep Convolutional Neural Network for IoT-Based Arrhythmia Classification.

Cardiac arrhythmia is a deadly disease that threatens the lives of millions of people, which shows the need for earlier detection and classification. An abnormal signal in the heart causing arrhythmia can be detected at an earlier stage when the health data from the patient are monitored using IoT technology. Arrhythmias may suddenly lead to death and the classification of arrhythmias is considered a complicated process. In this research, an effective classification model for the classification of heart disease is developed using flamingo optimization. Initially, the ECG signal from the heart is collected and then it is subjected to the preprocessing stage; to detect and control the electrical activity of the heart, the electrocardiogram (ECG) is used. The input signals collected using IoT nodes are collectively presented in the base station for the classification using flamingo-optimization-based deep convolutional networks, which effectively predict the disease. With the aid of communication technologies and the contribution of IoT, medical professionals can easily monitor the health condition of patients. The performance is analyzed in terms of accuracy, sensitivity, and specificity.

The importance of nature‐inspired metaheuristic algorithms in the data routing and path finding problem in the internet of things

SummaryOver the last decade, the Internet of Things (IoT) has become ever more popular, as is evident from its role in changing the human lifestyle and conferring remarkable privileges for them. It has a significant presence in various crucial areas, including smart cities, smart factories, manufacturing, transportation, and healthcare. Massive amounts of data generated by IoT devices have the potential to endanger the lifetime of nodes in IoT‐based networks due to increased communication power consumption. It has become crucial to propose solutions for network‐based issues, such as quality of service, security, network heterogeneity, congestion avoidance, reliable routing, and energy conservation. To address the mentioned problems, routing protocols play a critical role in data transmission among heterogeneous items. In such environments, routing refers to constructing routes between mobile nodes. Since identifying optimal routes among IoT nodes and establishing an effective routing protocol in an IoT network are an NP‐hard issue, employing metaheuristic algorithms may be a viable solution to overcome this problem. Various IoT routing protocols based on metaheuristic algorithms have been presented in recent years, but there is still a lack of systematic study for reviewing the existing works. The current study emphasizes the impact of metaheuristic algorithms in the IoT routing problem, discusses the optimization models, presents a comprehensive comparison of protocols based on critical parameters, and eventually suggests some hints for future studies.