GENERATION OF PSEUDORANDOM SEQUENCES ON MICROCONTROLLERS WITH LIMITED COMPUTATIONAL RESOURCES, ENTROPY SOURCES, AND STATISTICAL PROPERTIES TESTING

The proliferation of Internet of Things (IoT) devices in personal and household environments has led to a significant increase in security vulnerabilities. These devices, due to their limited computational resources, often struggle to support conventional security solutions, making them prime targets for cyberattacks. This paper explores the potential of lightweight machine learning (ML) models to enhance the security of personal IoT devices. By leveraging the power of AI, lightweight models can offer real-time threat detection and anomaly identification without compromising the device's performance or requiring extensive computational resources. The paper investigates various ML techniques that are well-suited for IoT environments, such as decision trees, k-nearest neighbours (KNN), and support vector machines (SVM), focusing on their ability to detect intrusions, unauthorized access, and other malicious activities while maintaining efficiency. Additionally, the study highlights the trade-offs between model complexity, accuracy, and resource consumption, offering practical insights for deploying ML solutions in resource-constrained IoT systems. Key challenges, including data privacy, model generalization, and the adaptability of models to diverse IoT ecosystems, are addressed. Finally, the paper discusses future directions for the integration of more advanced lightweight models, such as federated learning and edge computing, which could further enhance security capabilities while ensuring minimal impact on IoT device performance. Through this review, the paper advocates for the adoption of lightweight ML models as a feasible and scalable solution to securing personal IoT devices in an increasingly connected world.

Integrating the Internet of Things (IoT) in smart grids has revolutionized the energy sector, enabling real-time data collection and efficient energy distribution. However, this integration also introduces significant security challenges, particularly data encryption. Traditional encryption algorithms used in IoT are vulnerable to various attacks, and the advent of quantum computing exacerbates these vulnerabilities. To address the above challenges, this paper proposes a novel encryption mechanism, the Quantum-Resistant Hybrid Encryption for IoT (QRHE-IoT), designed to enhance the security of communications in IoT-enabled smart grids. QRHE-IoT combines the strengths of symmetric and asymmetric encryption algorithms and incorporates quantum-resistant algorithms to provide robust security. This paper explains the QRHE-IoT mechanism, its theoretical basis, and how it addresses the security challenges in smart grid communications. It also presents the results of tests conducted to evaluate the effectiveness of QRHE-IoT in a simulated smart grid environment. The proposed QRHE-IoT mechanism presents a promising solution to these challenges, offering robust security for smart grid communications in the face of emerging threats, including quantum computing.

The explosive growth of Internet of Things (IoT) devices has demanded the urgency of robust data security to protect private information. The random numbers generated by pseudo‐random number generators (PRNGs) play a crucial role in cryptographic algorithms. However, these sequences have become increasingly predictable due to advances in computing power and machine learning. Therefore, the development of true random number generators (TRNGs), which exploit the intrinsic hardware‐level randomness as an entropy source, is important for future data security. In this study, we present TRNG circuits that harness the inherent stochasticity of a multilayer hBN volatile memristor. The fabricated device exhibits highly stable threshold switching with low set/hold voltages and a significantly high on/off ratio. We implement a spike generator by integrating the threshold switching memristor with passive components. Digitization with a fixed‐reference comparator directly converts output spikes into bitstreams that successfully pass NIST randomness tests without postprocessing. The practical utility of the TRNG is demonstrated through XOR‐based encryption and decryption of black‐and‐white and grayscale images, resulting in noise‐like encrypted data and perfect recovery of the originals using identical keys. These results establish 2D hBN‐based threshold switching memristors as compelling hardware entropy sources for secure, next‐generation electronic systems.

With ubiquitous computing and penetration of high-speed data networks, the Internet of Medical Things (IoMT) has found widespread application. Digital healthcare helps medical professionals monitor patients and provide services remotely. With the increased adoption of IoMT comes an increased risk profile. Private and confidential medical data is gathered across various IoMT devices and transmitted to medical servers. Privacy breach or unauthorized access to personal medical data has far-reaching consequences. However, heterogeneity, limited computational resources, and lack of standardization in authentication schemes prevent a robust IoMT security framework. This paper introduces a secure lightweight authentication and authorization scheme. The use of the Physical Unclonable Function (PUF) reduces pressure on computational resources and establishes the authenticity of the IoMT. The use of OAuth 2.0 open standard for authorization allows interoperability between different vendors. The resilience of the model to impersonation and replay attacks is analyzed.

In this paper, a novel method for obtaining the random numbers utilizing astronomical data is proposed. The method uses two different algorithms for generation of random numbers sequence. Astronomical data collected from the scientific study of the universe, especially of the relative motions, relative positions of astronomical objects are utilized in our algorithms. The first algorithm uses a particular astronomical object in a fixed position for the random number generation. The random sequence is obtained from the relative positions of other astronomical objects with reference to the selected object. The second algorithm selects any diverse astronomical object as a reference in a varying mode for computation of the relative positions of different objects with that reference to generate the random number stream. Both algorithms use mathematical equations for computing the next jump or hop to the other astronomical object. The generated random numbers obtained from the two algorithms are tested with a standard statistical test suite including, frequency test, run test, random binary matrix rank test, complexity test, universal test and entropy test. The results obtained from the statistical tests of the two algorithms are compared with the other publicly available random number generation techniques, like, linear congruential and modular exponentiation. The preliminary results show that the algorithms perform well. The random numbers generated by our method has sufficient period and unpredictability that makes them suitable for consideration as encryption keys in symmetric cryptography.

With the advancement in artificial intelligence (AI) and microchips, we have been witnessing growing use of AI in object detection from video frames captured from Internet of thing (IoT) devices (e.g., IP cameras). From the video capturing sources to Cloud, generally, the packet forwarding delay could reach up to several hundreds of milliseconds, thereby making Cloud based objection detection as an impractical solution when the latency requirement for object detection from video frames is stringent. Embedded edge devices which are situated at the network access segment are considered to be a promising solution as they perform object detection locally and impose a negligible amount of packet forwarding delay. Nevertheless, due to lack of computation resources on such embedded edge devices, processing delay is likely to be increased as the frame rates of incoming images increase. To solve this issue, we design an adaptive queue management-based object detecting (QMOD) scheme for achieving low latency AI services with limited computation resources. We introduce a target delay requirement of AI services, and map such requirement when deciding optimal queue size so as to contain frames prior to object detection procedures. The optimal queue size, which represents a desired value at the embedded devices, is dynamically determined by the target delay requirement and weighted-average of processing time. Thus, maximum queue size is adaptively controlled, and some redundant frames are automatically dropped due to overflow of the queue. We demonstrate through experiments that the proposed QMOD scheme achieves 80%-90% reduction of latency for queuing and object detection in the commercial edge devices such as NVIDIA Xavier, Jetson TX2, and Nano, compared to the various benchmark schemes (i.e., static queue size).

The rapid proliferation of Internet of Things (IoT) devices has led to a surge in energy consumption, necessitating innovative strategies to enhance energy efficiency. Machine Learning (ML) has emerged as a promising approach to optimize energy utilization in IoT networks by enabling intelligent data processing, adaptive resource allocation, and predictive maintenance. This paper explores various ML techniques, including supervised, unsupervised, and reinforcement learning, for optimizing energy consumption in IoT ecosystems. It highlights key challenges such as data heterogeneity, real-time processing constraints, and limited computational resources while discussing potential solutions like federated learning, edge computing, and energy-aware neural networks. The study also presents recent advancements and case studies that demonstrate the effectiveness of ML-driven energy-efficient IoT frameworks. By integrating ML-based approaches, IoT networks can achieve enhanced sustainability, prolonged device lifespan, and improved operational efficiency. Future research directions focus on lightweight ML models, decentralized learning paradigms, and AI-driven energy harvesting techniques to further advance energy-efficient IoT networks.

Research Article| March 01, 2016 The Internet of Geophysical Things: Raspberry Pi Enhanced REF TEK (RaPiER) System Integration and Evaluation Frank Sepulveda; Frank Sepulveda aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Search for other works by this author on: GSW Google Scholar Jay Pulliam Jay Pulliam aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Search for other works by this author on: GSW Google Scholar Author and Article Information Frank Sepulveda aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Jay Pulliam aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Publisher: Seismological Society of America First Online: 14 Jul 2017 Online Issn: 1938-2057 Print Issn: 0895-0695 © 2016 by the Seismological Society of America Seismological Research Letters (2016) 87 (2A): 345–357. https://doi.org/10.1785/0220150234 Article history First Online: 14 Jul 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Frank Sepulveda, Jay Pulliam; The Internet of Geophysical Things: Raspberry Pi Enhanced REF TEK (RaPiER) System Integration and Evaluation. Seismological Research Letters 2016;; 87 (2A): 345–357. doi: https://doi.org/10.1785/0220150234 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search ABSTRACT The proliferation of commercial Internet of Things (IoT) devices is raising consumers’ awareness of the benefits of enhancing everyday objects with the ability to communicate, sense, and process information. Commercial‐off‐the‐shelf (COTS) versions of the embedded technologies responsible for the rise of the IoT are easy‐to‐use, inexpensive, and relatively powerful. IoT Makers, technological do‐it‐yourself enthusiasts, utilize these technologies and the IoT ecosystem to create IoT devices that range from casual hobbyist to entrepreneurial in nature.In an effort to develop a “no‐engineer‐needed” Internet of Geophysical Things (IoGT) device, we integrated a COTS embedded computer with a geoscience‐related COTS data acquisition system (DAS). Using skills common to geoscientists, we integrated the Raspberry Pi System‐On‐Chip with the REF TEK (a Trimble brand) 130‐01 DAS. This Raspberry Pi enhanced REF TEK (RaPiER) provides IoGT Makers a platform for the development of geoscientific sensor node or network enhancements. IoGT Makers can use the RaPiER to tinker with IoT capabilities while simultaneously acquiring research‐quality data. Geoscientific (i.e., seismic) application areas that may benefit from RaPiER, particularly RaPiER nodal processing, include earthquake and engineering seismology.In this article, we discuss the emerging technologies that allow IoGT Makers to build their own IoGT device. We review our selection of the Raspberry Pi and REF TEK 130‐01 for IoGT device integration. We provide a RaPiER system integration guide and REF TEK interface software compiled for the Raspberry Pi ( available in the electronic supplement to this article). We present methods to expand RaPiER capabilities using the Raspberry Pi’s ecosystem. Last, we discuss our evaluation of RaPiER’s performance and its suitability for realistic field deployments. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

The Internet of Things (IoT) has begun transforming our digital space, in which everyday objects are interconnected and capable of communicating with each other. This transformation not only simplifies our lives but also creates unprecedented opportunities and enhances efficiency in areas such as smart homes, healthcare, industrial manufacturing, and urban management. However, as with any technological breakthrough, IoT requires a careful and well-planned implementation. The large-scale and unregulated deployment of IoT devices raises significant security concerns that must be mitigated.At the core of IoT devices lies the ability to autonomously collect, process, and transmit information independently of human intervention. While this autonomy is the primary innovation, it also introduces numerous security vulnerabilities. For instance, IoT devices often operate on private and public networks, increasinf the attack surface for malicious actors capable of compromising data confidentiality and integrity. However, security is not the sole concern. The reliability and availability of the critical functions these devices provide are equally crucial. From thermostats and smartwatches to autonomous vehicles and urban infrastructure, compromised IoT devices can cause anything from minor inconveniences to catastrophic failures affecting millions.IoT devices typically have limited computational power and memory. These constraints often render the implementation of traditional security measures, such as complex encryption algorithms, impractical. Given these limitations and the explosive growth in the number of IoT devices, the potential for unauthorized interference with systems also increases. According to Cybersecurity Ventures, the number of IoT devices worldwide is expected to reach 25.1 billion by 2025. Each device represents a potential entry point for attackers. This widespread proliferation creates a vast, often poorly secured, network of interconnected devices susceptible to exploitation by malicious actors for data theft, DDoS attacks, and other cyber threats.The infrastructure surrounding IoT devices can be divided into four levels: perception (node capture, node impersonation, replay attack, timing attack, sleep deprivation attack), network (eavesdropping, DoS/DDoS attacks, Man-in-the-Middle attacks), data processing (resource exhaustion, exploits), and application (exploits). Therefore, key security challenges in IoT include unauthorized access to devices and data, communication interception, data manipulation, and depletion of system and device resources.The purpose of this article is to describe a comprehensive proposed solution for securing IoT systems. There is an urgent need for scalable and reliable solutions. Our approach considers the unique characteristics of IoT ecosystems, particularly their diversity, limited computational resources, and the physical nature of the devices.

The exponential growth of the Internet of Things (IoT) is being witnessed nowadays in different sectors. This makes IoT data communications more complex and harder to manage. Addressing such a challenge using a centralized model is an ineffective approach and would result in security and privacy difficulties. Technologies such as blockchain provide a potential solution to enable secure and effective management of IoT data communication in a distributed and trustless manner. In this paper, a novel lightweight blockchain-centric IoT architecture is proposed to address effective IoT data communication management. It is based on an event-driven smart contract that enables manageable and trustless IoT data exchange using a simple publish/subscribe model. To maintain system complexity and overhead at a minimum, the design of the proposed system relies on a single smart contract. All the system operations that enable effective IoT data communication among the different parties of the system are defined in the smart contract. There is no direct blockchain–IoT-device interaction, making the system more useable in wide IoT deployments incorporating IoT devices with limited computing and energy resources. A practical Ethereum-based implementation of the system was developed with the ability to simulate different IoT setups. The evaluation results demonstrated the feasibility and effectiveness of the proposed architecture. Considering varying-scale and varying-density experimental setups, reliable and secure data communications were achieved with little latency and resource consumption.

Ensuring safe and private data sharing has become a crucial concern due to the quick spread of Internet of Things (IoT) devices and their integration into cloud environments. Because of the limited computational and energy resources of IoT devices, traditional cryptographic solutions frequently fall short. In order to overcome these obstacles, we suggest a dependable and safe blockchain-based data exchange system that makes use of low-power cryptographic techniques and is designed for multi-level privacy protection in IoT-cloud systems. While lightweight encryption algorithms like PRESENT and ECC allow for effective security on devices with limited resources, the suggested approach makes use of the decentralized and immutable characteristics of blockchain to guarantee data integrity and transparency.Smart contracts with attribute-based encryption provide multi-level privacy by allowing for fine-grained access control according to user roles and permissions. Blockchain hashes serve as the foundation for off-chain storage in the cloud, which guarantees scalability without sacrificing security. Our plan offers a strong foundation that strikes a balance between security, privacy, and performance, which makes it a good fit for next-generation IoT-cloud settings in fields like smart cities, healthcare, and industrial monitoring.

WITHDRAWN: A novel RSS based light weight digital certificate and key generation scheme for coherent communication of IoT devices

Lightweight devices are widely used in the Internet of Things (IoT), Due to the limited computation and communication resources, lightweight devices can only conduct simple actions and will thereby hinder the intelligent cooperation of the devices in IoT. Such situation will last long since currently it is hard to replace all lightweight devices with intelligent ones. To address this issue and improve the integrated intelligence of IoT, we propose an augmented digital twin empowered intelligence for lightweight devices in IoT. The augmented digital twin, or shorten as augmented twin (AT), is a virtual entity built for a given lightweight device, which can be constructed on some resource-rich devices such as a gateway or an edge server. Based on the resources in its carrier, AT can not only clone all functions of the lightweight device, but also extend some intelligent analysis and cooperation abilities for this device. We first propose the architecture of AT for lightweight device in IoT. Then, to achieve the most effective AT carrier selection of the lightweight device, we formulate an optimization problem and present a self-initialized genetic algorithm to solve this problem. Simulations are conducted to evaluate the cost in constructing the ATs of the lightweight devices, as well as the performance promotion when AT is used in an IoT network. Compared to the benchmark schemes, AT can decrease the scheduling latency and energy consumption by 60.38% and 32.16% respectively than normal digital twin with reasonable constructing cost.

Delivering information from Internet of Things (IoT) devices to a cloud server possesses several security issues, e.g. information eavesdropping, modification, and theft. Therefore, communication between IoT devices and the cloud server should be protected by encryption methods. However, there are few encryption techniques options that are suitable for the need for lightweight communication as demanded by the IoT devices. Due to these circumstances, the NSA launched an encryption algorithm for IoT named Simon and Speck, which are maximally efficient while still providing the advertised level of security, as determined by the key size. This study aims to test and compare the Simon-Speck and AES encryption algorithms and their effect on networking performance on IoT devices. The parameters in this test are delay, throughput, the efficiency of memory usage from the encryption algorithm, and the value of the avalanche effect. Experimental results show that the Speck algorithm outperforms the Simon and the AES algorithms in terms of communication delay and memory usage. Regarding the avalanche effect values, the Simon algorithm possesses the highest avalanche effect value on average against the Speck and the AES algorithms.

The Internet of Things (IoT) is one of the emerging fields of technology, which allows the appliance of Smart Home to connect via the internet for communicating and sharing data between them. However, ensuring the security of IoT devices is challenging for their processing and power consumption. IoT devices are resource-constrained, and traditional encryption algorithms are computationally expensive for these small-size and low-powered devices. In this research, an efficient and effective lightweight cryptographic scheme is presented for securing communication between IoT Home devices based on Elliptic Curve Cryptography (ECC). It ensures complete protection against security risks such as authentication, confidentiality, integrity, and key agreement and protects against common cyber attacks. We have conducted an experimental evaluation to demonstrate that the proposed method can outperform the state-of-the-art cryptographic techniques, which significantly improves the security and reduces the storage and communication cost.KeywordsIoTSmart HomeConfidentialityECCSecurityLightweight cryptographyAuthenticationIntegrity