Internet Of Things Edge Devices Research Articles

A novel trusted hardware-based scalable security framework for IoT edge devices

The Internet of Things (IoT) devices are pervasively deployed and embedded into our daily lives. Over several years, the massive assimilation of IoT devices has given rise to smart cities, smart factories, smart farms, etc. While IoTs can collect immense amounts of user and environmental information, the lack of adequate security measures is among the major concerns about information security and privacy. With the ever-increasing presence of IoTs, information flow between IoT devices in a single or multi-layered network is becoming increasingly vulnerable to attacks. Although several software-based solutions have been developed over the years that improve the security of resource-constrained IoT devices, there are limited efforts on hardware-oriented IoT security solutions. This paper proposes a Trusted HArdware-based Scalable Secure Architecture (THASSA) for IoT Edge systems. Leveraging standard cryptographic algorithms, THASSA provides a secure mechanism for data transfer and off-loads cryptography operations to trusted hardware. The modularized THASSA architecture allows devices to be added or removed without user intervention. Also, THASSA could be scaled to multiple subsystems using multiple trusted hardware to form IoT clusters with secure data path(s) for mission-critical applications. An experimental study has verified the correctness and effectiveness of THASSA.

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.

Inverse chi-square-based flamingo search optimization with machine learning-based security solution for Internet of Things edge devices

<abstract> <p>Internet of Things (IoT) edge devices are becoming extremely popular because of their ability to process data locally, conserve bandwidth, and reduce latency. However, with the developing count of IoT devices, threat detection, and security are becoming major concerns. IoT edge devices must avoid cyber threats and protect user data. These devices frequently take limited resources and can run on lightweight operating systems, which makes them vulnerable to security attacks. Intrusion detection systems (IDS) can be run on edge devices to recognize suspicious actions and possible risks. These systems monitor traffic patterns, and behavior, and identify attack signatures to detect and report on possible attacks. This study presents a design for an inverse chi square-based flamingo search optimization algorithm with machine learning (ICSFSO-ML) as a security solution for Internet of Things edge devices. The goal of the ICSFSO-ML technique is to apply ML and metaheuristics for threat recognition in IoT edge devices. To reduce the high dimensionality problem, the ICSFSO-ML technique uses the ICSFSO algorithm for feature selection purposes. Further, the ICSFSO-ML technique exploits the stacked bidirectional long short-term memory (SBiLSTM) model for the threat detection process. To enhance the efficacy of the SBiLSTM model, an arithmetic optimization algorithm (AOA) is applied for the hyperparameter selection process. The simulation performance of the ICSFSO-ML technique can be tested on a benchmark threat database. The performance analysis showed the benefits of the ICSFSO-ML methodology compared to existing methodologies with a maximum accuracy of 98.22%.</p> </abstract>

Research on IoT Edge Data Mining Techniques Based on Cloud Computing Models

Abstract With the continuous development of the Internet of Things (IoT) and the needs of the times, the number of IoT edge devices is rapidly increasing, and to be able to provide users with more reliable information services and decision-making, this paper constructs an IoT system based on cloud-edge collaboration. Based on cloud-edge collaboration, data mining technology is used to categorize data, and regression models and branch neural networks are combined to further improve the IoT system based on cloud-edge collaboration. Finally, the particle swarm algorithm is used to optimize reinforcement learning, and the framework of an IoT system built on cloud-edge partnership is established. Through the data processing experiments on IoT edge computing, the data mining performance of the IoT edge computing-based data mining method is improved by 0.0295, 0.1870, and 0.2059 on the large datasets of Dataset4 and Dataset5, respectively. the average turnaround time is lower than 6.5 for different edge computing power levels. The system rewards are gradually improved from 5.3 to 8.2, and the number of packet loss of the algorithm is less than 5. The feasibility and effectiveness of the IoT data mining method and system based on cloud-edge collaboration are confirmed by this study, and a feasible method for data mining can be applied to IoT systems under cloud-edge collaboration.

TinyML Olive Fruit Variety Classification by Means of Convolutional Neural Networks on IoT Edge Devices

Machine learning (ML) within the edge internet of things (IoT) is instrumental in making significant shifts in various industrial domains, including smart farming. To increase the efficiency of farming operations and ensure ML accessibility for both small and large-scale farming, the need for a low-cost ML-enabled framework is more pressing. In this paper, we present an end-to-end solution that utilizes tiny ML (TinyML) for the low-cost adoption of ML in classification tasks with a focus on the post-harvest process of olive fruits. We performed dataset collection to build a dataset that consists of several varieties of olive fruits, with the aim of automating the classification and sorting of these fruits. We employed simple image segmentation techniques by means of morphological segmentation to create a dataset that consists of more than 16,500 individually labeled fruits. Then, a convolutional neural network (CNN) was trained on this dataset to classify the quality and category of the fruits, thereby enhancing the efficiency of the olive post-harvesting process. The goal of this study is to show the feasibility of compressing ML models into low-cost edge devices with computationally constrained settings for tasks like olive fruit classification. The trained CNN was efficiently compressed to fit into a low-cost edge controller, maintaining a small model size suitable for edge computing. The performance of this CNN model on the edge device, focusing on metrics like inference time and memory requirements, demonstrated its feasibility with an accuracy of classification of more than 97.0% and minimal edge inference delays ranging from 6 to 55 inferences per second. In summary, the results of this study present a framework that is feasible and efficient for compressing CNN models on edge devices, which can be utilized and expanded in many agricultural applications and also show the practical insights for implementing the used CNN architectures into edge IoT devices and show the trade-offs for employing them using TinyML.

Tiny-PPG: A lightweight deep neural network for real-time detection of motion artifacts in photoplethysmogram signals on edge devices

Photoplethysmogram (PPG) signals are easily contaminated by motion artifacts in real-world settings, despite their widespread use in Internet-of-Things (IoT) based wearable and smart health devices for cardiovascular health monitoring. This study proposed a lightweight deep neural network, called Tiny-PPG, for accurate and real-time PPG artifact segmentation on IoT edge devices. The model was trained and tested on a public dataset, PPG DaLiA, which featured complex artifacts with diverse lengths and morphologies during various daily activities of 15 subjects using a watch-type device (Empatica E4). The model structure, training method and loss function were specifically designed to balance detection accuracy and speed for real-time PPG artifact detection in resource-constrained embedded devices. To optimize the model size and capability in multi-scale feature representation, the model employed depth-wise separable convolution and atrous spatial pyramid pooling modules, respectively. Additionally, the contrastive loss was also utilized to further optimize the feature embeddings. With additional model pruning, Tiny-PPG achieved state-of-the-art detection accuracy of 87.4% while only having 19,726 model parameters (0.15 megabytes), and was successfully deployed on an STM32 embedded system for real-time PPG artifact detection. Therefore, this study provides an effective solution for resource-constraint IoT smart health devices in PPG artifact detection.

A system for monitoring the microclimate parameters of premises based on the Internet of Things and edge devices

Recent years have been characterized by the rapid development of Internet of Things (IoT) and edge device technologies. Systems of various purposes with IoT elements and edge devices are increasingly finding practical use in people’s lives, the IoT element base is expanding and improving, which makes it possible to develop effective measuring systems, including those with feedback control. An essential role in ensuring people’s lives is played by the microclimate of the premises where people live, work, and study. As you know, the excess or decrease of the environmental microclimate relative to the norm negatively affects the physiological state of a person, his performance, and concentration and reduces the efficiency of work and training. Therefore, in this work, the problem of round-the-clock monitoring of the microclimate of classrooms is solved by developing an autonomous IoT system using edge devices to measure climatic parameters such as temperature, relative humidity, carbon dioxide level in the air, and the concentration of light air ions with data recording on a smartphone and saving on a remote server. The principles of building microclimate monitoring systems are presented, the requirements for the system are set, the criteria for choosing the elemental base and the technical characteristics of each component are given. The structure of the air ion concentration sensor developed by the author and the method of measuring the air ion concentration in the room are also described. The structural diagram of the developed microclimate parameters monitoring system is also presented. The development is part of a system for studying the influence of microclimate parameters on the physiological state of applicants for education. The results obtained in the work will allow development measures to ensure the necessary normal conditions for training in confined spaces. Research conducted using the developed system will allow better formation of student learning conditions in order to achieve maximum performance indicators.

A cognitive security framework for detecting intrusions in IoT and 5G utilizing deep learning

The fast growth of Internet of Things (IoT) gadgets and 5G networks has increased linkage and accessibility. However, growing interconnectivity poses new threat levels in these environments, making intrusion detection critical. In this article, we introduce a novel security framework that centres on deep learning and is tailored to support the particular risks posed by IoT channels and 5G networks. Deep neural networks are used in our proposed framework to effectively analyze network activity patterns and recognize any possible breaches in real-time communication. Deep learning autonomously learns complicated features and patterns, facilitating the proposed model's adaptability to changing threat vectors and traffic features. This research proposed a Hybrid model using enhanced light-weight CNNs architecture (MobileNetV3-SVM) and Transfer learning (TL) for intrusion detection in 5G communication. The proposed model utilizes the advantages of a multi-layered structure, which enables it to acquire knowledge from raw network information hierarchically. It provides the ability to distinguish between authentic and malicious behaviour efficiently. We have implemented several cutting-edge strategies to maximize the effectiveness of intrusion detection in environments characterized by limited availability of resources, such as those associated with the IoT and high-speed 5G networks. The proposed hybrid model processes network packets in real-time using light-weight MobileNet, reducing the computational overhead and making it suitable for IoT and 5G edge devices. In the proposed model, a MobileNetV3-SVM auto-classifies the network's intrusion images, enhancing the overall accuracy. In addition, to address the issue of limited labelled data in dynamic and constantly changing systems, we use a transfer learning strategy to deal with this issue. The proposed hybrid model and existing CNN- architectures, i.e., VGG-16, VGG-19, Efficient-Net and Inception-Net, are tested on CICIDS-2017, 2018 and UNSW-NB15 IoT 5G security datasets. During the experimental assessment, we demonstrated the strength of the proposed model by simulating a wide range of network settings and intrusion scenarios. Experimental findings show considerable improvements by the proposed hybrid model in accuracy, precision, false positive rates, Matthew's Correlation Coefficient (MCC) and AUC-ROC over existing approaches.

Two-Stage Deep Single-Image Super-Resolution With Multiple Blur Kernels for Internet of Things

Single image super-resolution (SISR) aims to reconstruct a high-resolution image from a single low-resolution (LR) image. Although convolutional neural network (CNN)-based SISR methods greatly enhance image restoration, they face critical challenges. First, SISR models using CNNs process image patches uniformly regardless of importance, causing spatial inefficiency in computation and representation. However, due to resource constraints, edge devices in the Internet of Things (IoT) cannot bear heavy computations or large memory storage. Second, most of the existing SISR methods are designed only for the widely adopted bicubic degradation and cannot handle LR images with arbitrary blur kernels, resulting in poor recovery performance. To address these issues, in this paper we propose a two-stage semantic and spatial deep super-resolution (SSDSR) model suitable for the IoT environment. The proposed SSDSR model is capable of handling a variety of blur kernels (e.g., isotropic Gaussian, motion and disk blur) by effectively using their prior information. Moreover, the Semantic Feature Extraction (SFE) module enables the proposed model to focus on key areas of LR images rather than treating all pixels equally, which significantly reduces the computational load. The semantic information from the SFE module and the spatial information from the Spatial Attention module are fused adaptively, allowing the proposed model to extract key information in LR images, thereby increasing the representation capacity of the CNN and improving image recovery. Compared with state-of-the-art SISR methods on benchmark datasets, the proposed SSDSR model demonstrates superior performance. When run in real time on an IoT edge device, our model exhibits high computational efficiency and excellent image quality.

Resilient Edge: Building an Adaptive and Resilient Multi-Communication Network for IoT Edge Using LPWAN and WiFi

Edge computing has gained attention in recent years due to the adoption of many Internet of Things (IoT) applications in domestic, industrial and wild settings. The resiliency and reliability requirements of these applications vary from noncritical (best delivery efforts) to safety-critical with time-bounded guarantees. The network connectivity of IoT edge devices remains the central critical component that needs to meet the timebounded Quality of Service (QoS) and fault-tolerance guarantees of the applications. Therefore, in this work, we systematically investigate how to meet IoT applications mixed-criticality QoS requirements in multi-communication networks. We (i) present the network resiliency requirements of IoT applications by defining a system model (ii) analyse and evaluate the bandwidth, latency, throughput, maximum packet size of many state-of-theart LPWAN technologies, such as Sigfox, LoRa, and LTE (CATM1/ NB-IoT) and Wi-Fi, (iii) implement and evaluate an adaptive system Resilient Edge and Criticality-Aware Best Fit (CABF) resource allocation algorithm to meet the application resiliency requirements using Raspberry Pi 4 and Pycom FiPy development board having five multi-communication networks.We present our findings on how to achieve 100% of the best-effort high criticality level message delivery using multi-communication networks.

Fog‐Sec: Secure end‐to‐end communication in fog‐enabled IoT network using permissioned blockchain system

AbstractThe technological integration of the Internet of Things (IoT)‐Cloud paradigm has enabled intelligent linkages of things, data, processes, and people for efficient decision making without human intervention. However, it poses various challenges for IoT networks that cannot handle large amounts of operation technology (OT) data due to physical storage shortages, excessive latency, higher transfer costs, a lack of context awareness, impractical resiliency, and so on. As a result, the fog network emerged as a new computing model for providing computing capacity closer to IoT edge devices. The IoT‐Fog‐Cloud network, on the other hand, is more vulnerable to multiple security flaws, such as missing key management problems, inappropriate access control, inadequate software update mechanism, insecure configuration files and default passwords, missing communication security, and secure key exchange algorithms over unsecured channels. Therefore, these networks cannot make good security decisions, which are significantly easier to hack than to defend the fog‐enabled IoT environment. This paper proposes the cooperative flow for securing edge devices in fog‐enabled IoT networks using a permissioned blockchain system (pBCS). The proposed fog‐enabled IoT network provides efficient security solutions for key management issues, communication security, and secure key exchange mechanism using a blockchain system. To secure the fog‐based IoT network, we proposed a mechanism for identification and authentication among fog, gateway, and edge nodes that should register with the blockchain network. The fog nodes maintain the blockchain system and hold a shared smart contract for validating edge devices. The participating fog nodes serve as validators and maintain a distributed ledger/blockchain to authenticate and validate the request of the edge nodes. The network services can only be accessed by nodes that have been authenticated against the blockchain system. We implemented the proposed pBCS network using the private Ethereum 2.0 that enables secure device‐to‐device communication and demonstrated performance metrics such as throughput, transaction delay, block creation response time, communication, and computation overhead using state‐of‐the‐art techniques. Finally, we conducted a security analysis of the communication network to protect the IoT edge devices from unauthorized malicious nodes without data loss.

Secure Video Streaming Using Dedicated Hardware

The purpose of this article is to present a system that enhances the security, efficiency, and reconfigurability of an Internet-of-Things (IoT) system used for surveillance and monitoring. A Multi-Processor System-On-Chip (MPSoC) composed of Central Processor Unit (CPU) and Field-Programmable Gate Array (FPGA) is proposed for increasing the security and the frame rate of a smart IoT edge device. The private encryption key is safely embedded in the FPGA unit to avoid being exposed in the Random Access Memory (RAM). This allows the edge device to securely store and authenticate the key, protecting the data transmitted from the same Integrated Circuit (IC). Additionally, the edge device can simultaneously publish and route a camera stream using a lightweight communication protocol, achieving a frame rate of 14 frames per Second (fps). The performance of the MPSoC is compared to a NVIDIA Jetson Nano (NJN) and a Raspberry Pi 4 (RPI4) and it is found that the RPI4 is the most cost-effective solution but with lower frame rate, the NJN is the fastest because it can achieve higher frame-rate but it is not secure, and the MPSoC is the optimal solution because it offers a balanced frame rate and it is secure because it never exposes the secure key into the memory. The proposed system successfully addresses the challenges of security, scalability, and efficiency in an IoT system used for surveillance and monitoring. The Rivest-Shamir-Adleman (RSA) encryption key is securely stored and authenticated, and the edge device is able to simultaneously publish and route a camera stream feed high-definition images at 14 fps. The proposed system enhances the security, efficiency, and reconfigurability of an IoT system used for surveillance and monitoring. The RSA encryption key is effectively protected by the implementation of an MPSoC with Programmable Logic (PL), which also allows for faster processing and data transmission.

Earthquake Detection with tinyML

Abstract Earthquake detection is the critical first step in earthquake early warning (EEW) systems. For robust EEW systems, detection accuracy, detection latency, and sensor density are critical to providing real-time earthquake alerts. Traditional EEW systems use fixed sensor networks or, more recently, networks of mobile phones equipped with microelectromechanical systems (MEMS) accelerometers. Internet of things edge devices, with built-in tiny machine learning (tinyML) capable microcontrollers, and always-on, internet-connected, stationary MEMS accelerometers provide the opportunity to deploy ML-based earthquake detection and warning using a single-station approach at a global scale. Here, I test and evaluate tinyML deep learning algorithms for earthquake detection on a microcontroller. I show that the tinyML earthquake detection models can generalize to earthquakes outside the training set.

Dynamic Reliability Management of Multigateway IoT Edge Computing Systems

The emerging paradigm of edge computing envisions to overcome the shortcomings of cloud-centric Internet of Things (IoT) by providing data processing and storage capabilities closer to the source of data. Accordingly, IoT edge devices, with the increasing demand of computation workloads on them, are prone to failures more than ever. Hard failures in hardware due to aging and reliability degradation are particularly important since they are irrecoverable, requiring maintenance for the replacement of defective parts, at high costs. In this article, we propose a novel dynamic reliability management (DRM) technique for multigateway IoT edge computing systems to mitigate degradation and defer early hard failures. Taking advantage of the edge computing architecture, we utilize gateways for computation offloading with the primary goal of maximizing the battery lifetime of edge devices, while satisfying the Quality of Service (QoS) and reliability requirements. We present a two-level management scheme, which work together to 1) choose the offloading rates of edge devices; 2) assign edge devices to gateways; and 3) decide multihop data flow routes and rates in the network. The offloading rates are selected by a hierarchical multitimescale distributed controller. We assign edge devices by solving a bottleneck generalized assignment problem (BGAP) and compute optimal flows in a fully distributed fashion, leveraging the subgradient method. Our results, based on real measurements and trace-driven simulation, demonstrate that the proposed scheme can achieve a similar battery lifetime and better QoS compared to the state-of-the-art approaches while satisfying reliability requirements, where other approaches fail by a large margin.

NEAT: A Resilient Deep Representational Learning for Fault Detection Using Acoustic Signals in IIoT Environment

Fault diagnostics involving the Internet-of-Things (IoT) sensors and edge devices is a challenging task due to their limited energy and computational capabilities. Another challenge concerning IoT sensors or devices is the incursion of noise when used in an industrial environment. The noisy samples affect the decision support system that could lead to financial and operational losses. This article proposes a noisy encoder using artificial intelligence of things (NEAT) architecture for fault diagnosis in IoT edge devices. NEAT combines autoencoders and Inception module to co-train the clean and noisy samples for solving the said problem. Experimental results on benchmark data sets reveal that the NEAT architecture is noise resilient in comparison to the existing works. Furthermore, we also show that the NEAT architecture has lightweight characteristics as it yields a lower number of parameters, weight storage, training, and testing times that support its real-life applicability in an Industrial IoT environment.

Word-Based Processor Structure for Montgomery Modular Multiplier Suitable for Compact IoT Edge Devices

The Internet of Things (IoT) is an emerging technology that forms a huge network of different objects and intelligent devices. IoT Security is becoming more important due to the exchange of sensitive sensor data and the potential for incorporating the virtual and real worlds. IoT edge devices create serious security threats to network systems. Due to their limited resources, it is challenging to implement cryptographic protocols on these devices to secure them. To address this problem, we should perform compact implementation of cryptographic algorithms on these devices. At the heart of most cryptographic algorithms is the modular multiplication operation. Therefore, efficient implementation of this operation will have a great impact on the implementation of the whole cryptographic protocol. In this paper, we will focus on the resource and energy efficient hardware implementation of the adopted Montgomery modular multiplication algorithm over GF(2m). The main building block of the proposed word-based processor structure is a processor array that has a modular structure with local connectivity between its processing elements. The ability to manage the saving amounts of area, delay, and consumed energy is the main benefit of the suggested hardware structure. We used ASIC technology to implement the suggested word-based processor structure. The final results show an average reduction in the area of 86.3% when compared with the competitive word-based multiplier structures. Additionally, the recommended design achieves significant average savings in area-time product, power, and consumed energy of 53.7%, 83.2%, and 72.6%, receptively, over the competitive ones. The obtained results show that the provided processor structure is best suited for application in compact IoT edge devices with limited resources.

Securely Offloading Computation to the Edge with the Tangle Cache

The rising area of the Internet of Things (IoT) faces various issues related to the scalability and security of the data produced. Most IoT devices cannot perform complex computations on their own due to resource constraints, which require them to outsource their jobs. Due to the abundance of devices and the IoT network’s heterogeneous nature, there is a large attack surface against IoT systems. During outsourcing jobs, a major factor that affects performance is the delay in data transmission. However, the security issues such as data manipulation and leakage for edge caching have not been fully addressed in existing literature. This paper proposes a Tangle based caching model for edge services, as well as an efficient and effective scheme that will aid the offloading process between IoT devices and edge devices. The proposed model utilizes reversible integer transformation in the context of edge computing to build a secure data transfer technique that distorts the original data so that an adversary cannot access it. Experimental studies and theoretical evaluation demonstrate that our proposed scheme is promising for adoption in future IoT systems with its efficiency and security features.

A Novel Light-Weight DCNN Model for Classifying Plant Diseases on Internet of Things Edge Devices

One of the essential aspects of smart farming and precision agriculture is quickly and accurately identifying diseases. Utilizing plant imaging and recently developed machine learning algorithms, the timely detection of diseases provides many benefits to farmers regarding crop and product quality. Specifically, for farmers in remote areas, disease diagnostics on edge devices is the most effective and optimal method to handle crop damage as quickly as possible. However, the limitations posed by the equipment’s limited resources have reduced the accuracy of disease detection. Consequently, adopting an efficient machine-learning model and decreasing the model size to fit the edge device is an exciting problem that receives significant attention from researchers and developers. This work takes advantage of previous research on deep learning model performance evaluation to present a model that applies to both the Plant-Village laboratory dataset and the Plant-Doc natural-type dataset. The evaluation results indicate that the proposed model is as effective as the current state-of-the-art model. Moreover, due to the quantization technique, the system performance stays the same when the model size is reduced to accommodate the edge device.

RIHANN: Remote IoT Hardware Authentication With Intrinsic Identifiers

The heterogeneous array of edge devices in an Internet of Things (IoT) infrastructure is increasingly vulnerable to physical in-field tampering attacks. These devices can significantly benefit from a difficult-to-clone and tamper-immune intrinsic identifier that can verify the authenticity or integrity of the physical components. In this article, we develop an intrinsic device identifier, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RIHANN</monospace> , that captures the state of the electronic hardware in an IoT device. This state can adequately reflect any physical tampering of the hardware components by transforming the intrinsic delay variations in the electronic components of an edge device into unique and robust signatures. Our proposed authentication approach utilizes the boundary scan architecture (BSA) in printed circuit boards (PCBs). BSA is a prevalent design for test (DFT) structure used in most PCBs in IoT edge devices. This technique supports an extensive array of heterogeneous devices and can seamlessly operate during the device’s runtime. We measure the boundary scan path delays using the parallel scan delay-measurement (PSDM) technique for commercially available ICs. We perform practical experiments on 20 devices, generate signatures, and evaluate their uniqueness, robustness, randomness, and resistance to aging. We also introduce a security protocol for the cloud server, owner/verifier, or other IoT devices connected to a network to verify their identity remotely. The policy prevents attacks from extracting the device’s secret keys using an efficient moving target defense mechanism that periodically updates and evolves the challenge–response database.

Analysis of the NTRU Post-Quantum Cryptographic Scheme in Constrained IoT Edge Devices

Research on post-quantum cryptography aims to solve the problematic of modern public-key cryptography being broken by attacks coming from quantum computers in the future and, moreover, by using classical electronics. This task is so critical that the National Institute of Standards and Technology (NIST) is in the final process of standardizing post-quantum schemes for the future protection of embedded applications. Though there are some research work done on embedded systems, it is important to study the impact of these proposals in realistic environments for the Internet of Things (IoT), where the limited computational resources and the strict requirements for power consumption can become incompatible with the usage of cryptographic schemes. In this work, the performance of one of the finalists of the standardization process called NTRU is studied and implemented in a custom wireless sensor node designed for applications in the extreme edge of the IoT. The cryptosystem is implemented and evaluated within the processes of the Contiki-NG operating system. Furthermore, additional experiments are performed to check if commonly integrated hardware peripherals for cryptography inside modern microcontrollers can be used to achieve better performance with NTRU, not only at the single node level but also at the network level, where the NTRU key encapsulation mechanism is tested in a real communication process. The results derived from these experiments show that NTRU is suitable for modern microcontrollers targeting wireless sensor networks design, while old devices present in popular platforms might not afford the cost of its implementation.