Low-power Internet Of Things Devices Research Articles

DATA SECURITY IN IOT DEVICES AND SENSOR NETWORKS FOR ROBUST THREAT DETECTION AND PRIVACY PROTECTION

The rapid proliferation of Internet of Things (IoT) devices and sensor networks has revolutionized various industries by enhancing automation, connectivity, and operational efficiency. However, these advancements have also introduced significant security challenges due to the resource constraints and decentralized nature of IoT environments. This paper provides a systematic review of IoT security solutions, focusing on encryption techniques, authentication protocols, and machine learning-based anomaly detection methods. A total of 55 peer-reviewed articles were analyzed following the PRISMA guidelines. The findings reveal that while lightweight cryptographic algorithms, such as elliptic curve cryptography (ECC), offer robust security with low energy consumption, scalability across large IoT networks remains a challenge. Blockchain-based authentication has emerged as a promising decentralized solution, but issues related to energy consumption and latency hinder its widespread adoption. Machine learning techniques have shown high accuracy in detecting threats in real-time, but their resource-intensive nature limits their application in low-power IoT devices. This review underscores the need for multi-layered, integrated security frameworks and highlights gaps in research on quantum-resistant cryptography and interoperable security standards. Future research must focus on developing scalable, energy-efficient security solutions to ensure data integrity and privacy in expanding IoT ecosystems.

A new energy-efficient design for quantum-based multiplier for nano-scale devices in internet of things

An enormous variety of items and things are connected via wired or wireless connections and specific addressing schemes, which is known as the Internet of Things (IoT). However, IoT devices that adopt aggressive duty-cycling for high power efficiency and prolonged lifespan necessitate the incorporation of ultra-low power consumption always-on blocks. The multiplier plays a crucial role in enhancing the capabilities of low-power IoT devices, particularly those operating with energy-efficient batteries that offer extended battery life. The previous multipliers have a struggling speed, enormous occupied area, and high energy consumption; therefore, all prior flaws must be fixed by implementing it in a suitable technology, like the quantum computing. Therefore, this paper examines the ultra-low power circuit for nano-scale IoT platforms. It also suggests novel quantum-based adders for multiplier structure. The proposed designs are simulated using the QCADesignerE 2.2 tool by focusing on energy-efficient and occupied areas for miniaturizing IoT systems.

Dynamic Decision Tree Ensembles for Energy-Efficient Inference on IoT Edge Nodes

With the increasing popularity of Internet of Things (IoT) devices, there is a growing need for energy-efficient Machine Learning (ML) models that can run on constrained edge nodes. Decision tree ensembles, such as Random Forests (RFs) and Gradient Boosting (GBTs), are particularly suited for this task, given their relatively low complexity compared to other alternatives. However, their inference time and energy costs are still significant for edge hardware. Given that said costs grow linearly with the ensemble size, this paper proposes the use of dynamic ensembles, that adjust the number of executed trees based both on a latency/energy target and on the complexity of the processed input, to trade-off computational cost and accuracy. We focus on deploying these algorithms on multi-core low-power IoT devices, designing a tool that automatically converts a Python ensemble into optimized C code, and exploring several optimizations that account for the available parallelism and memory hierarchy. We extensively benchmark both static and dynamic RFs and GBTs on three state-of-the-art IoT-relevant datasets, using an 8-core ultra-lowpower System-on-Chip (SoC), GAP8, as the target platform. Thanks to the proposed early-stopping mechanisms, we achieve an energy reduction of up to 37.9% with respect to static GBTs (8.82 uJ vs 14.20 uJ per inference) and 41.7% with respect to static RFs (2.86 uJ vs 4.90 uJ per inference), without losing accuracy compared to the static model.

PLS-IoT Enhancement Against Eavesdropping via Spatially Distributed Constellation Obfuscation

Wireless communication has become a ubiquitous facet of modern-day life. With the advent of Internet of Things (IoT) and massive machine-type connectivity, the number of devices is expected to increase even more. However, increased dependence on wireless connectivity is marred with risks related to user privacy and data confidentiality, especially for low-power IoT devices. Therefore, it is imperative to provide an extremely reliable secure algorithm with minimal complexity and computational expense in IoT paradigm. With that in mind, this letter presents asymmetric multi-level physical layer security (PLS) scheme, in which each transmitted symbol undergoes two types of distortion: channel-based phase distortion and multi-reception amplitude randomization. The proposed scheme offers a substantial security advantage for legitimate links, while also streamlining receiver design. Numerical results indicate that the scheme effectively confounds eavesdroppers without compromising the bit error rate of the intended receiver. Additionally, the proposed scheme provides another level of security by concealing the modulation information, requiring the receiver to detect the modulation scheme before detecting transmitted symbols.

Securing Low-Power Blockchain-enabled IoT Devices against Energy Depletion Attack

Blockchain-enabled Internet of Things (IoT) envisions a world with rapid development and implementations to change our everyday lives based on smart devices. These devices are attached to the internet that can communicate with each other without human interference. A well-known wireless network in blockchain-enabled IoT frameworks is the Low Power and Lossy Network (LLN) that uses a novel protocol known as Routing protocol for low power and lossy networks (RPL) to provide effective and energy-efficient routing. LLNs that run on RPL are inherently prone to multiple Denial of Service (DoS) attacks due to the low cost, shared medium, and resource-constrained nature of blockchain-enabled IoT devices. A Spam DODAG Information Solicitation (DIS) attack is one of the novel attacks that drains the energy source of legitimate nodes and ends up causing the legitimate nodes to suffer from DoS. To address this problem, a mitigation scheme named DIS Spam Attack Mitigation (DISAM) is proposed. The proposed scheme effectively mitigates the effects of the Spam DIS attack on the network’s performance. The experimental results show that DISAM detects and mitigates the attack quickly and efficiently.

Joint optimization of UAV-IRS placement and resource allocation for wireless powered mobile edge computing networks

The rapid evolution of communication systems towards the next generation has led to an increased deployment of Internet of Things (IoT) devices for various real-time applications. However, these devices often face limitations in terms of processing power and battery life, which can hinder overall system performance. Additionally, applications such as augmented reality and surveillance require intensive computations within tight timeframes. This research focuses on investigating a mobile edge computing (MEC) network empowered by unmanned aerial vehicle intelligent reflecting surfaces (UAV-IRS) to enhance the computational energy efficiency of the system through optimized resource allocation. The MEC infrastructure incorporates the energy transfer circuit (ETC) and edge server (ES), co-located with the intelligent access point (AP). To eliminate interference between energy transfer and data transmission, a time-division multiple access method is utilized. In the first phase, the ETC wirelessly transfers power to low-power IoT devices, which efficiently harvest and store the received energy in their batteries. In the second phase, IoT devices employ the stored energy for local computing or offloading tasks. Furthermore, the presence of tall buildings may obstruct communication routes, impacting system functionality. To address these challenges, we propose an optimization framework that simultaneously considers time, power, phase shift design, and local computational resources. This joint optimization problem is non-convex and non-linear, making it NP-hard. To tackle this complexity, we decompose the problem into subproblems and solve them iteratively using a convex optimization toolbox like CVX. Through simulations, we demonstrate that our proposed optimization framework significantly improves 40.7% system performance compared to alternative approaches.

Fingerprinting IoT Devices Using Latent Physical Side-Channels

The proliferation of low-end low-power internet-of-things (IoT) devices in "smart" environments necessitates secure identification and authentication of these devices via low-overhead fingerprinting methods. Previous work typically utilizes characteristics of the device's wireless modulation (WiFi, BLE, etc.) in the spectrum, or more recently, electromagnetic emanations from the device's DRAM to perform fingerprinting. The problem is that many devices, especially low-end IoT/embedded systems, may not have transmitter modules, DRAM, or other complex components, therefore making fingerprinting infeasible or challenging. To address this concern, we utilize electromagnetic emanations derived from the processor's clock to fingerprint. We present Digitus, an emanations-based fingerprinting system that can authenticate IoT devices at range. The advantage of Digitus is that we can authenticate low-power IoT devices using features intrinsic to their normal operation without the need for additional transmitters and/or other complex components such as DRAM. Our experiments demonstrate that we achieve ≥ 95% accuracy on average, applicability in a wide range of IoT scenarios (range ≥ 5m, non-line-of-sight, etc.), as well as support for IoT applications such as finding hidden devices. Digitus represents a low-overhead solution for the authentication of low-end IoT devices.

Flexible Solution-Processed Electron-Transport-Layer-Free Organic Photovoltaics for Indoor Application

Organic photovoltaics (OPVs) have unique advantages of low weight, mechanical flexibility, and solution processability, which make them exceptionally suitable for integrating low-power Internet of Things devices. However, achieving improved operational stability together with solution processes that are applicable to large-scale fabrication remains challenging. Their major limitation arises due to the instable factors that occur both inside the thick active film and from the ambient environment, which cannot be completely resolved via the current encapsulation techniques used for flexible OPVs. Additionally, thin active layers are highly vulnerable to point defects, which result in low yield rates and impede the laboratory-to-industry translation. In this study, flexible fully solution-processed OPVs with improved indoor efficiency and long-term operational stability than that of conventional OPVs with evaporated electrodes are achieved. Benefiting from the oxygen and water vapor permeation barrier of the spontaneously formed gallium oxide layers on the exposed eutectic gallium-indium surface, fast degradation of the OPVs with thick active layers is prevented, maintaining 93% of its initial Pmax after 5000 min of indoor operation under 1000 lx light-emitting diode (LED) illumination. Additionally, by using the thick active layer, spin-coated silver nanowires could be directly used as bottom electrodes without complicated flattening processes, thereby substantially simplifying the fabrication process and proposing a promising manufacturing technique for devices with high-throughput energy demands.

Simple Authentication Method for Vehicle Monitoring IoT Device With Verifiable Data Integrity

This article proposes a new authentication method for vehicular monitoring Internet of Things (IoT) systems. Based on parallel hash chains, the proposed method is suitable for low-power and low-cost IoT devices. Encryption keys are continuously generated on identical parallel hash chains on both the IoT device and the server, removing the need to transmit secret keys over the network. The proposed method only needs two transmission handshakes for each authentication and data transmission. It does not need random number generation on the IoT device, reducing hardware requirements and potential security vulnerability. It minimizes power consumption and data transmission cost while providing mutual authentication of both the server and the device. Other merits of our proposed methods include: it provides device anonymity, allows message authentication, provides database integrity verification, is resistant to transmission loss, and is dynamically adaptable to disturbances in transmission network quality.

Blocking probability of massive MIMO: What is the capacity of a massive MIMO IoT system?

In this paper, we provide a methodology to evaluate the capacity of a Massive multiple-input multiple-output (MIMO) supported Internet of Things (IoT) system in which a large number of low cost low power IoT devices transmit and receive sporadic data. Numerous IoT devices are supported by a single cell Massive MIMO base station (BS) with maximum-ratio (MR) processing. Orthogonal reference signals (RSs) or pilots are assigned randomly to all the IoT devices for channel estimation purpose. The number of simultaneously active IoT devices follows Poisson distribution. Due to the tremendous number of IoT devices, orthogonal RSs are heavily reused, which severely degrades the receiver signal quality. One of the most important performance criteria for this kind of system is the blocking probability which shows the percentage of the outage IoT devices, and how we maintain the low blocking probability while supporting all the IoT devices simultaneously is particularly important. Due to RS reuse, we can divide IoT devices into two groups based on their interference levels. We provide detailed theoretical analyses, and show that the blocking primarily happens to the group with higher interference level. Increasing the number of service antennas and/or reducing the number of IoT devices can help to improve the performance of the blocking probability, however there is a regime in which the parameter adjustment helps little to improve the performance. Based on these factors, we provide a useful algorithm to improve the performance of blocking probability. A number of simulation results are also provided to validate the theoretical analysis.

Grace: Low-cost time-synchronized GPIO tracing for IoT testbeds

Testbeds have become a vital tool for evaluating and benchmarking applications and algorithms in the Internet of Things (IoT). IoT testbeds commonly consist of low-power IoT devices augmented with observer nodes providing control, debugging, logging, and often also power-profiling capabilities. Today, the research community operates numerous testbeds, sometimes with hundreds of IoT nodes, to allow for detailed and large-scale evaluation. Most testbeds, however, lack opportunities for tracing distributed program execution with high accuracy in time, for example, via minimally invasive, distributed GPIO tracing. And the ones that do, like Flocklab, are built from custom hardware, which is often too complex, inflexible, or expensive to use for other research groups.This paper closes this gap and introduces Grace, a low-cost, retrofittable, distributed, and time-synchronized GPIO tracing system built from off-the-shelf components, costing less than €20 per node. Grace extends observer nodes in a testbed with (1) time-synchronization via wireless sub-GHz transceivers and (2) logic analyzers for GPIO tracing and logging, enabling time-synchronized GPIO tracing at a frequency of up to 8 MHz. We deploy Grace in a testbed and evaluate it, showing that it achieves an average time synchronization error between nodes of 1.53 μs using a single time source, and 15.3 μs between nodes using different time sources, sufficient for most IoT applications.

Remote Interference Discrimination Testbed Employing AI Ensemble Algorithms for 6G TDD Networks.

The Internet-of-Things (IoT) massive access is a significant scenario for sixth-generation (6G) communications. However, low-power IoT devices easily suffer from remote interference caused by the atmospheric duct under the 6G time-division duplex (TDD) mode. It causes distant downlink wireless signals to propagate beyond the designed protection distance and interfere with local uplink signals, leading to a large outage probability. In this paper, a remote interference discrimination testbed is originally proposed to detect interference, which supports the comparison of different types of algorithms on the testbed. Specifically, 5,520,000 TDD network-side data collected by real sensors are used to validate the interference discrimination capabilities of nine promising AI algorithms. Moreover, a consistent comparison of the testbed shows that the ensemble algorithm achieves an average accuracy of 12% higher than the single model algorithm.

Attention-Based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices

Due to great success of transformers in many applications, such as natural language processing and computer vision, transformers have been successfully applied in automatic modulation classification. We have shown that transformer-based radio signal classification is vulnerable to imperceptible and carefully crafted attacks called adversarial examples. Therefore, we propose a defense system against adversarial examples in transformer-based modulation classifications. Considering the need for computationally efficient architecture particularly for Internet of Things (IoT)-based applications or operation of devices in an environment where power supply is limited, we propose a compact transformer for modulation classification. The advantages of robust training such as adversarial training in transformers may not be attainable in compact transformers. By demonstrating this, we propose a novel compact transformer that can enhance robustness in the presence of adversarial attacks. The new method is aimed at transferring the adversarial attention map from the robustly trained large transformer to a compact transformer. The proposed method outperforms the state-of-the-art techniques for the considered white-box scenarios, including the fast gradient method and projected gradient descent attacks. We have provided reasoning of the underlying working mechanisms and investigated the transferability of the adversarial examples between different architectures. The proposed method has the potential to protect the transformer from the transferability of adversarial examples.

Security and Privacy for Low Power IoT Devices on 5G and Beyond Networks: Challenges and Future Directions

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">The growth in the use of small sensor devices, commonly known as the Internet of Things (IoT), has resulted in unprecedented amounts of data being generated and captured. With the rapidly growing popularity of personal IoT devices, the collection of personal data through such devices has also increased exponentially. To accommodate the anticipated growth in connected devices, researchers are now investigating futuristic network technologies that are capable of processing large volumes of information at much faster speeds. However, the introduction of innovative network technologies coupled with existing vulnerabilities of personal IoT devices and insufficient device security standards is resulting in new challenges for the security of data collected on these devices. While existing research has focused on the technical aspects of security vulnerabilities and solutions in either network or IoT technologies separately, this paper thoroughly investigates common aspects impacting IoT security on existing and futuristic networks, including human-centric issues and the mechanisms that can lead to loss of confidentiality. By undertaking a comprehensive literature review of existing research, this article has identified five key areas that impact IoT security for futuristic next generation networks. Furthermore, by extensively analysing each area, the article reports on conclusive findings and future research opportunities for IoT privacy and security for the next generation of network technologies</i> .

Pattern Classification Using Quantized Neural Networks for FPGA-Based Low-Power IoT Devices.

With the recent growth of the Internet of Things (IoT) and the demand for faster computation, quantized neural networks (QNNs) or QNN-enabled IoT can offer better performance than conventional convolution neural networks (CNNs). With the aim of reducing memory access costs and increasing the computation efficiency, QNN-enabled devices are expected to transform numerous industrial applications with lower processing latency and power consumption. Another form of QNN is the binarized neural network (BNN), which has 2 bits of quantized levels. In this paper, CNN-, QNN-, and BNN-based pattern recognition techniques are implemented and analyzed on an FPGA. The FPGA hardware acts as an IoT device due to connectivity with the cloud, and QNN and BNN are considered to offer better performance in terms of low power and low resource use on hardware platforms. The CNN and QNN implementation and their comparative analysis are analyzed based on their accuracy, weight bit error, RoC curve, and execution speed. The paper also discusses various approaches that can be deployed for optimizing various CNN and QNN models with additionally available tools. The work is performed on the Xilinx Zynq 7020 series Pynq Z2 board, which serves as our FPGA-based low-power IoT device. The MNIST and CIFAR-10 databases are considered for simulation and experimentation. The work shows that the accuracy is 95.5% and 79.22% for the MNIST and CIFAR-10 databases, respectively, for full precision (32-bit), and the execution time is 5.8 ms and 18 ms for the MNIST and CIFAR-10 databases, respectively, for full precision (32-bit).

Link-Aware Frame Selection for Efficient License Plate Recognition in Dynamic Edge Networks

With the booming development of Internet of Things (IoT) and computer vision technology, running vision-based applications on IoT devices becomes an overwhelming tide. In vision-based applications, the Automatic License Plate Recognition (ALPR) is one of the fundamental services for smart-city applications such as traffic control, auto-drive and safety monitoring. However, existing works about ALPR usually assume that IoT devices have sufficient power to transmit the whole captured stream to edge servers via stable network links. Considering the limited resources of IoT devices and high-dynamic wireless links, this assumption is not suitable for realizing an efficient ALPR service on low-power IoT devices in real wireless edge networks. In this paper, we propose a link-aware frame selection scheme for ALPR service in dynamic edge networks aiming to reduce the transmission energy consumption of IoT devices. Specifically, we tend to select a few key frames instead of the whole stream and transmit them under good links. We propose a two-layer recognition frame selection algorithm to optimize the frame selection by exploiting both the video content variation and real-time link quality. The extensive results show that, by carefully selecting the offloaded frames to edge servers, our algorithm can significantly reduce the energy consumption of devices by 46.71% and achieve 97.95% recognition accuracy in the high-dynamic wireless link of the edge network.

Blockchain Tree Powered Green Communication for Efficient and Sustainable Connected Autonomous Vehicles

Connected and Autonomous Vehicles (CAV) is a system of inter-connectivity and communication between smart automated vehicles. Blockchain has emerged as a technology focusing on data security with a decentralized architecture and has been successful to a very high degree. Unfortunately, this technology was not originally meant for low-power IoT devices and is very computationally expensive with high power requirements and a tendency to introduce delays. With the explosion of the Internet of Things (IoT) enabled autonomous vehicles, boosted further by the advent of smart cities, energy requirements have become an issue that can no longer be ignored. As a consequence, research into green technologies has been gaining popularity. With this area of focus, blockchain with its exponential power consumption levels is heavily flawed and thus presents itself as a prime candidate for improvement. In this paper, a framework is proposed to further evolve blockchain technology to integrate it with a semi-centralized data storage mechanism, while keeping network control decentralized. The proposed Blockchain Tree (BCT) approach entails a combination of a tree structure network composed of blockchains, based on a time-based upward data propagation mechanism that optimizes the architecture to reduce communication delay, cut down its high power consumption levels and ensure sustainability in the long run. As is evident in the comparative study, such a blockchain-based network structure offers drastic improvements in terms of speed, energy savings, storage and computation. The proposed model reduces the storage requirement of the blockchain by a factor of 0.17. The energy requirements and time complexity have been optimized by approximately 195% in the early stages of optimization. All the parameters get further optimized by orders of magnitude with more transactions and users.

Synthesis‐free backscatter directional modulation communication using Van‐Atta arrays

Backscatter communication, an emerging ultra-low-power wireless communication paradigm, has been studied as a promising solution for wireless communications in some Internet-of-Things (IoT) applications. The required ubiquitous data transmission in the IoT world is demanding information security. Different from mathematical encryption at higher protocol layers, the directional modulation (DM) technology can provide physical-layer wireless security, which does not require digital and computation resources that are otherwise scarce in low-power IoT devices. In this paper, a synthesis-free Van-Atta backscatter array is constructed and studied. The authors show that a Van-Atta array can be made to project orthogonal interference upon the backscattered signals in the radio frequency domain. The design details of the Van-Atta DM array are elaborated, and its efficacy is validated via bit error rate simulations.

Real-Time Sound Source Localization for Low-Power IoT Devices Based on Multi-Stream CNN.

Voice-activated artificial intelligence (AI) technology has advanced rapidly and is being adopted in various devices such as smart speakers and display products, which enable users to multitask without touching the devices. However, most devices equipped with cameras and displays lack mobility; therefore, users cannot avoid touching them for face-to-face interactions, which contradicts the voice-activated AI philosophy. In this paper, we propose a deep neural network-based real-time sound source localization (SSL) model for low-power internet of things (IoT) devices based on microphone arrays and present a prototype implemented on actual IoT devices. The proposed SSL model delivers multi-channel acoustic data to parallel convolutional neural network layers in the form of multiple streams to capture the unique delay patterns for the low-, mid-, and high-frequency ranges, and estimates the fine and coarse location of voices. The model adapted in this study achieved an accuracy of 91.41% on fine location estimation and a direction of arrival error of 7.43° on noisy data. It achieved a processing time of 7.811 ms per 40 ms samples on the Raspberry Pi 4B. The proposed model can be applied to a camera-based humanoid robot that mimics the manner in which humans react to trigger voices in crowded environments.

Softwarized management of 6G network for green Internet of Things

In this paper, we propose 6G-SDI, a Software-Defined Network (SDN)-based green communication scheme for 6G-enabled Internet of Things (IoT) to control real-time actuation and flow-table configuration. 6G-SDI considers collisions during uplink transmissions and allocates a downlink slot to send critical alerts and commands such as CO2 level in a region. The SDN controller uses probe messages to periodically update the Remote Radio Head (RRH) for synchronizing channel access and power-saving of mobile nodes among different cells. The required node address and flow-rules are transferred to an RRH as per the location of a mobile node. We adopt a Markov Chain-based approach to predict the collision in the uplink channel and location of a mobile node. RRH determines the different sleeping schedules for itself and other low-power IoT devices, as per the current demands collected from the controller and stations. We show a use case of 6G-SDI, where the SDN controller predicts CO2 emissions of a vehicular network and send a set of critical alerts caring information such as CO2 level and the number of vehicles. The proposed scheme helps in taking adequate actions for sending commands or alerts with improved network energy consumption, latency, and throughput. 6G-SDI reduces critical traffic latency and energy consumption up to 55% and 62%, respectively, compared to the existing state-of-the-art.