Low-cost Internet Of Things Devices Research Articles

Enhancing Trust and Security in IoT Architecture for Low-Cost Microcontroller Devices using Elliptic Curve Cryptography

Abstract—In the rapidly expanding universe of the Internet of Things (IoT), low-cost microcontroller devices, such as the NodeMCU, have become ubiquitous. While these devices have transformed how we interact with the physical world, they also introduce significant security vulnerabilities. Chief among these is susceptibility to man-in-the-middle (MITM) attacks, which pose a serious risk to the confidentiality and integrity of data transmitted across these networks. This study addresses this critical issue by proposing the adoption of Elliptic Curve Cryptography (ECC), a cryptographic method known for its efficiency and strong security, despite the computational limitations of low-cost IoT devices. We conducted an experiment demonstrating the ease with which an MITM attack can intercept data from a NodeMCU device. Following this, we implemented ECC to secure data transmission, showcasing its viability as a lightweight yet robust security solution. Our research not only highlights the pressing need for enhanced security measures in the IoT ecosystem but also provides a practical framework for securing low-cost microcontroller devices against sophisticated cyber threats. Through our findings, we contribute to the development of more secure, trustworthy IoT architectures, ensuring that these devices can continue to safely serve as integral components of our digital lives. Keywords—NodeMCU, ESP8266, Data theft, Elliptic Curve Cryptography, Man-in-the-middle attack

An AI-Enabled Framework to Defend Ingenious MDT-based Attacks on the Emerging Zero Touch Cellular Networks

Deep automation provided by self-organizing network (SON) features and their emerging variants such as zero touch automation solutions is a key enabler for increasingly dense wireless networks and pervasive Internet of Things (IoT). To realize their objectives, most automation functionalities rely on the Minimization of Drive Test (MDT) reports. The MDT reports are used to generate inferences about network state and performance, thus dynamically change network parameters accordingly. However, the collection of MDT reports from commodity user devices, particularly low cost IoT devices, make them a vulnerable entry point to launch an adversarial attack on emerging deeply automated wireless networks. This adds a new dimension to the security threats in the IoT and cellular networks. Existing literature on IoT, SON, or zero touch automation does not address this important problem. In this paper, we investigate an impactful, first of its kind adversarial attack that can be launched by exploiting the malicious MDT reports from the compromised user equipment (UE). We highlight the detrimental repercussions of this attack on the performance of common network automation functions. We also propose a novel Malicious MDT Reports Identification framework (MRIF) as a countermeasure to detect and eliminate the malicious MDT reports using Machine Learning and verify it through a use-case. Thus, the defense mechanism can provide the resilience and robustness for zero touch automation SON engines against the adversarial MDT attacks.

A Model-agnostic XAI Approach for Developing Low-cost IoT Intrusion Detection Dataset

This study tackles the significant challenge of generating low-cost intrusion detection datasets for Internet of Things (IoT) camera devices, particularly for financially limited organizations. Traditional datasets often depend on costly cameras, posing accessibility issues. Addressing this, a new dataset was developed, tailored for low-cost IoT devices, focusing on essential features. The research employed an Entry/Exit IoT Network at CKT-UTAS, Navrongo, a Ghanaian University, showcasing a feasible model for similar organizations. The study gathered location and other vital features from low-cost cameras and a standard dataset. Using the XGBoost machine learning algorithm, the effectiveness of this approach for cybersecurity enhancement was demonstrated. The implementation included a model-agnostic eXplainable AI (XAI) technique, employing Shapley Additive Explanations (SHAP) values to interpret the XGBoost model's predictions. This highlighted the significance of cost-effective features like Flow Duration, Total Forward Packets, and Total Length Forward Packet, in addition to location data. These features were crucial for intrusion detection using the new IoT dataset. Training a deep-learning model with only these features maintained comparable accuracy to using the full dataset, validating the practicality and efficiency of the approach in real-world scenarios.

Software-Based Security Approach for Networked Embedded Devices

As the Internet of Things (IoT) continues to expand, data security has become increasingly important for ensuring privacy and safety, especially given the sensitive and, sometimes, critical nature of the data handled by IoT devices. There exist hardware-based trusted execution environments used to protect data, but they are not compatible with low-cost devices that lack hardware-assisted security features. The research in this paper presents software-based protection and encryption mechanisms explicitly designed for embedded devices. The proposed architecture consists of two parts: the Agent, which is designed to work with low-cost, low-end devices without requiring modifications to the underlying hardware, and the Computing Module, which is designed for slightly more computationally powerful devices. The Computing Module enables devices to write data in protected memory and continuously verifies its integrity to provide protection. Additionally, it utilizes the Agents located on the device to safeguard device applications against attacks by requesting the Agent to generate an application code signature and validating it. The proposed solution is an alternative data security approach for low-cost IoT devices without compromising performance or functionality. Our work underscores the importance of developing secure and cost-effective solutions for protecting data in the context of IoT.

Arduino IOT Based Inventory Management System Using Load Cell and NodeMCU

Nowadays, everything is made simpler with information and communication technological advancements. It is preferable to track and monitor using devices rather than perform it manually. This resulted in the rapid growth of Internet of Things (IoT) technology and relevant markets. Low cost IoT products has made access to IoT much easier and desirable. These low cost IoT devices and related technologies are widely used in areas such as educational, transportation, tracking, inventory management and many more. The use of Arduino and RFID in the inventory management system lacks in some areas including hardware limitations. In conjunction to the limitation of using an Arduino and RFID technology, this project aims to develop an IoT based inventory management system that incorporates the uses of a NodeMCU and a load cell. In comparison of the NodeMCU to an Arduino, the NodeMCU stands out with the built in Wi-Fi module with much higher processor and additional properties of it being much smaller. While the use of a load cell is much more convenient as to suit all kinds of inventory management needs compared to the use of RFID that suits better to larger scale businesses with larger inventory and massive stocks. Towards the end, this project is expected to ease inventory management by the implementation of IoT with IoT Based Inventory Management System using Load Cell and NodeMCU. The project will generate the inventory count and automatically stores data in the cloud platform. These data can be accessed with internet connection. The project also alerts users when the inventory is low or high in balance. The output of the project is that the project’s working prototype was successfully developed. Overall, the project is a success as all the objectives of the project was successfully achieved.

A Design of Low-Projection SCMA Codebooks for Ultra-Low Decoding Complexity in Downlink IoT Networks

This paper conceives a novel sparse code multiple access (SCMA) codebook design which is motivated by the strong need for providing ultra-low decoding complexity and good error performance in downlink Internet-of-things (IoT) networks, in which a massive number of low-end and low-cost IoT communication devices are served. By focusing on the typical Rician fading channels, we analyze the pair-wise error probability of superimposed SCMA codewords and then deduce the design metrics for multi-dimensional constellation construction and sparse codebook optimization. For significant reduction of the decoding complexity, we advocate the key idea of projecting the multi-dimensional constellation elements to a few overlapped complex numbers in each dimension, called low projection (LP). An emerging modulation scheme, called golden angle modulation (GAM), is considered for multi-stage LP optimization, where the resultant multi-dimensional constellation is called LP-GAM. Our analysis and simulation results show the superiority of the proposed LP codebooks (LPCBs) including one-shot decoding convergence and excellent error rate performance. In particular, the proposed LPCBs lead to decoding complexity reduction by at least 97% compared to that of the conventional codebooks, whilst owning large minimum Euclidean distance. Some examples of the proposed LPCBs are available at https://github.com/ethanlq/SCMA-codebook.

Internet of things (IoT) for smart agriculture: Assembling and assessment of a low-cost IoT system for polytunnels.

Internet of things (IoT) applications in smart agricultural systems vary from monitoring climate conditions, automating irrigation systems, greenhouse automation, crop monitoring and management, and crop prediction, up to end-to-end autonomous farm management systems. One of the main challenges to the advancement of IoT systems for the agricultural domain is the lack of training data under operational environmental conditions. Most of the current designs are based on simulations and artificially generated data. Therefore, the essential first step is studying and understanding the finely tuned and highly sensitive mechanism plants have developed to sense, respond, and adapt to changes in their environment, and their behavior under field and controlled systems. Therefore, this study was designed to achieve two specific objectives; to develop low-cost IoT components from basic building blocks, and to study the performance of the developed systems, and generate real-time experimental data, with and without plants. Low-cost IoT devices developed locally were used to convert existing basic polytunnels to semi-controlled and monitoring-only polytunnels. Their performances were analyzed and compared with each other based on several matrices while maintaining the planted tomato variety and agronomic practices similar. The developed system performed as expected suggesting the possibility of commercial applications and research purposes.

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.

Intelligent energy and ecosystem for real-time monitoring of glaciers

The United Nations is deeply concerned about global warming and its impacts on natural resources. Simultaneously, it has been recommended that cutting-edge technologies be employed to predict the impact of climate change on natural reservoirs such as glaciers. With the motivation of the above facts, this study investigates the impact and significance of emerging and cutting-edge technologies like Remote Sensing, the Internet of Things (IoT), Artificial Intelligence (AI), Unmanned Aerial Vehicles (UAVs), and robots implementation for the digitalization in glaciers. The study identified that convergence of AI, Machine Learning (ML), and Deep Learning approaches with Spatio-temporal data empowers to detect non-linear characteristics, especially in high mountainous regions due to their diversity and unpredictable nature. The article suggested valuable recommendations such as establishing an intelligent eco-system in glaciers, low-cost intelligent IoT devices with intelligent energy algorithms, ML empowered edge devices, glacier-resistant rescue robots, and Wearable IoT-based safety guide devices.

A Hybrid Model for Intrusion Detection in IoT Applications

Internet of Things (IoT) networks has recently become an important component of smart cities, smart buildings, health care, and other applications. It finds it beneficial due to the inherent characteristics of low cost, compact, and low-powered IoT devices. At the same time, security remains a challenging issue in the design of IoT networks. Intrusion detection systems (IDS) can be used to identify the occurrence of intrusions in the network, i.e., abnormal activities in the network. The latest advances in machine learning (ML) and metaheuristics can be employed to design effective IDS models for IoT networks. This article develops a novel political optimizer with cascade forward neural network (PO-CFNN-)-based IDS in the IoT environment. The major intention of the PO-CFNN technique is to determine the occurrence of intrusions from the IoT environment. The PO-CFNN technique follows three major processes, namely, preprocessing, classification, and parameter optimization. Initially, the networking data is preprocessed to transform it into a useful format. Following that, the CFNN technique is employed for the identification and classification of intrusions in the IoT environment. In the final stage, the PO algorithm is applied for the optimal adjustment of the parameters involved in the CFNN model. The experimental validation of the PO-CFNN technique on a benchmark dataset stated the better outcomes of the PO-CFNN technique over recent approaches.

Collision-free optimal packet scheduling algorithm for multi-hop wireless IoT networks

One of the key challenges for the IoT (Internet of Things) evolution is the network connectivity provision to a large number of resource-limited low-cost IoT devices. To meet this challenge, various efforts have been made in today’s wireless network technologies, such as LTE eMTC, 5G NB-IoT, IEEE 802.11ah, BLE, etc. Meanwhile, most of these technologies consider multi-hop wireless relaying to extend the wireless coverage and to enhance the network transmission capacity. Despite the benefits, however, the usage of multi-hop relaying has been limited in the real world, because of the performance degradation caused by the self-interference between adjacent wireless links. In this paper, we tackle the problem of alleviating the performance degradation in the multi-hop wireless network for IoT devices. In particular, we target low cost IoT devices that are equipped with inexpensive wireless links such as WiFi, IEEE 802.15.4, and Bluetooth. Our approach is to avoid self-interference by controlling the transmission timing at each hop. More specifically, our design goal is to minimize the data delivery delay for fast IoT sensing by preventing packet collisions (i.e., collision-free transmission). Since the retransmission at the IoT devices is eliminated, buffering at the intermediate nodes is not necessary so that no extra storage is consumed at the IoT devices to store the data being relayed. We present an algorithm to produce the packet transmission schedule at each hop. We prove that our algorithm is optimal in terms of data delivery delay (i.e., minimal delay is guaranteed) while collision between transmissions is completely avoided.

The Use of Blockchain to Support Distributed AI Implementation in IoT Systems

This article presents a distributed and decentralized architecture for the implementation of distributed artificial intelligence (DAI) using hardware platforms provided by the Internet of Things (IoT). A trained DAI system has been implemented over IoT, where each IoT device acts as one or more of the neurons within the DAI layers. This is accomplished by the utilization of decentralized, self-managed blockchain technologies that allow trusted interactions and information to be exchanged between distributed neurons. The platform was built and customized to be used within the IoT system, and it is capable of handling DAI-related tasks. A new consensus mechanism based on Proof of Authority (PoA) and Proof of Work (PoW) has been designed and implemented, along with bespoke block and transaction formats. The proposed architecture was analyzed, implemented, and tested using a dedicated testbed with low-cost IoT devices. A quantitative measurement and performance evaluation of the system based on a real-world IoT application was conducted. The implemented DAI is found to have an accuracy of 92%–98%, with an energy cost of 0.12 joules (J) when utilizing a Raspberry Pi to run one neuron. The measured hash per joule (h/J) when using a Raspberry Pi for mining is 13.8 Kh/J compared to 54 Kh/J using an ESP32. The results showed that it is feasible to implement a DAI system utilizing the IoT hardware platform while maintaining the system’s accuracy. The integration of the blockchain has added an element of security and trust to the data and the interaction between system components.

I-Talk: Reliable and Practical Superimposed Signal Decoding Without Power Control

Internet-of-Things (IoT) is emerging, while the spectrum is at a premium. To enhance spectrum efficiency, a promising solution is Non-Orthogonal Multiple Access (NOMA) that enables users to communicate with the same resource at the same time, while decoding the superimposed signal at the receiver. Existing NOMA technologies, however, rely on strict power control to decode the superimposed signal, infeasible for heterogeneous and low-cost IoT devices. In contrast, we propose I-Talk, a new NOMA scheme that is designed for IoT and can decode the superimposed signals from two transmitters without power control. Importantly, considering the IoT systems in the wild, both the hardware imperfections and mobility are unavoidable, which can cause severe signal variations, resulting in an unreliable decoding performance. To solve this problem, we design a synthesis channel coefficient to track all signal offsets caused by the hardware imperfection. Meanwhile, we propose a diversity transmission and smart combining scheme to achieve high reliable decoding performance. To demonstrate the feasibility of this new NOMA approach in practical systems, we implement I-Talk with USRP devices and the experimental results illustrate that I-Talk achieves a one-order lower bit-error-rate and a 1.47× higher throughput gain than the state-of-the-art superimposed signal decoding scheme.

IoT for Global Development to Achieve the United Nations Sustainable Development Goals: The New Scenario After the COVID-19 Pandemic

COVID-19 has not affected all countries equally: developing countries have been more disadvantaged by the pandemic. Regarding global development, the COVID-19 pandemic has forced a step back in the path to attaining the Sustainable Development Goals (SDGs). The SDGs most negatively affected by the pandemic are identified here: education, health, and work. Then using the SDGs as a reference, this research explores the new challenges faced by developing countries and the impact of the Internet of Things (IoT) after COVID-19's emergence. IoT solutions carried out in developing countries during the pandemic have been identified and reviewed. Successful Internet of Things for Development (IoT4D) projects, in relation to the SDGs, are highlighted. New social and technical challenges that have emerged for the IoT4D as a consequence of the pandemic are then studied. This work concludes that the future of IoT4D in the wake of COVID-19 should focus on the use of low-cost IoT devices for the SDGs most affected by the pandemic. After an exhaustive study, the Intelligent Internet of Things (IIoT) has been determined to be a key actor in the pandemic's wake, with a leading role in the health sector. The proposed approach includes an extensive study of the new role of the IoT4D for achieving the SDGs in our forever changed world.

Deep Reinforcement Learning (DRL): Another Perspective for Unsupervised Wireless Localization

Location is key to spatialize internet-of-things (IoT) data. However, it is challenging to use low-cost IoT devices for robust unsupervised localization (i.e., localization without training data that have known location labels). Thus, this paper proposes a deep reinforcement learning (DRL) based unsupervised wireless-localization method. The main contributions are as follows. (1) This paper proposes an approach to model a continuous wireless-localization process as a Markov decision process (MDP) and process it within a DRL framework. (2) To alleviate the challenge of obtaining rewards when using unlabeled data (e.g., daily-life crowdsourced data), this paper presents a reward-setting mechanism, which extracts robust landmark data from unlabeled wireless received signal strengths (RSS). (3) To ease requirements for model re-training when using DRL for localization, this paper uses RSS measurements together with agent location to construct DRL inputs. The proposed method was tested by using field testing data from multiple Bluetooth 5 smart ear tags in a pasture. Meanwhile, the experimental verification process reflected the advantages and challenges for using DRL in wireless localization.

New collision paths for round-reduced SKINNY-Hash

In recent years, with the rapid development of the Internet of Things (IoT), RFID tags, industrial controllers, sensor nodes, smart cards and other small computing devices are increasingly widely deployed. In order to help protect low-power, low-cost Internet of things devices, lightweight cryptography came into being. In order to launch the standard of cryptographic algorithm suitable for constrained environment, NIST started the process of lightweight cryptography standardization in 2016, and published the second round of candidate cryptographic algorithms in August 2019. SKINNY-Hash in the sponge construction is one of the second round candidates, as well as SKINNY-AEAD. The tweakable block cipher SKINNY is the basic component for both of them. Although cryptanalysts have proposed several cryptanalysis results on SKINNY and SKINNY-AEAD, there is no cryptanalysis results on SKINNY-Hash. Based on the differential cryptanalysis and the method of mixed integer programming (MILP), we perform differential cryptanalysis on SKINNY-Hash. The core is to set up the inequations of the MILP model. Actually, it is hard to obtain the inequations of the substitution (i.e. S-box) obeying the previous method. By a careful study of the permutation, we partition the substitution into a nonlinear part and a linear part, then a series of inequations in the MILP model is obtained to describe the differentials with high possibilities. As a result, we propose a differential hash collision path of 3-round SKINNY-tk3-Hash. By adjusting the bit rate of SKINNY-tk3-Hash, we propose a 7-round collision path for the simplified algorithm. The cryptanalysis in this paper will help to promote the NIST Lightweight Crypto Standardization process.

Compressed-Sensing-Based Pilot Contamination Attack Detection for NOMA-IoT Communications

Nonorthogonal multiple access (NOMA) technology can significantly promote Internet-of-Things (IoT) networks on spectral efficiency and massive connectivity. However, NOMA-IoT communications are vulnerable to pilot contamination attacks, where the attacker can send the same pilot signals as legitimate IoT users. Most existing countermeasures to this physical-layer threat struggle to adapt to NOMA-IoT networks, in which superimposed signals appear and low-cost IoT devices exist. In this article, we propose a compressed-sensing-based detection scheme to defend against pilot contamination attacks in NOMA-IoT networks. In particular, we present a multiple measurement vector (MMV) compressed sensing model and a security spreading code generation (SSCG) framework to prevent pilot contamination attacks from spoofing base station (BS) in NOMA-IoT networks. Furthermore, to efficiently reconstruct the superimposed signals based on the SSCG framework, a matching pursuit (MP) multiple response sparse Bayesian learning (MSBL) algorithm (MP-MSBL) is proposed. The security analysis and algorithm complexity of the proposed algorithms are provided. The simulation results evaluate and confirm the effectiveness of the proposed detection schemes. The reconstruction and detection accuracy of pilots can be higher than 99% under different scenarios.

A three-factor anonymous user authentication scheme for Internet of Things environments

To accelerate the deployment of fifth-generation (5G) cellular networks, millions of devices are being connected to massive Internet of Things (IoT) networks. However, advances in the scale of connectivity on 5G networks may increase the attack surface of these devices, thereby increasing the number of attack opportunities. To address the potential security risks in IoT systems, one feasible security practice involves the development of secure and efficient user authentication schemes. In 2017, Dhillon and Kalra proposed a three-factor user authentication scheme for IoT. We noted that their scheme suffers from several security weaknesses. In this study, we specifically demonstrate that the scheme proposed by Dhillon and Kalra (1) is not secured from a stolen mobile device attack; (2) does not prevent a user impersonation attack; (3) does not provide a session key agreement; (4) does not have a contingency plan (e.g., a revocation phase) for situations where a user’s private key is compromised, or a mobile device is stolen or lost. We propose an improved three-factor user authentication scheme to resolve these security issues. Furthermore, we demonstrate that the proposed scheme provides desirable attributes for IoT environments and that its computation and communication costs are suitable for extremely low-cost IoT devices.

A Millimeter Wave Network for Billions of Things

With the advent of the Internet of Things (IoT), billions of new connected devices will come online, placing a huge strain on today's Wi-Fi and cellular spectrum. This problem will be further exacerbated by the fact that many of these IoT devices are low-power devices that use low-rate modulation schemes and therefore do not use the spectrum efficiently. Millimeter wave (mmWave) technology promises to revolutionize wireless networks and solve the spectrum shortage problem through the usage of massive chunks of high-frequency spectrum. However, existing mmWave networks are power-hungry and expensive, which make them unsuitable for low-power, low-cost IoT devices. In this paper, we present mmX, a system which significantly reduces cost and power consumption of a mmWave network, enabling its use in IoT applications.

Approximate CPU Design for IoT End-Devices with Learning Capabilities

With the rise of Internet of Things (IoT), low-cost resource-constrained devices have to be more capable than traditional embedded systems, which operate on stringent power budgets. In order to add new capabilities such as learning, the power consumption planning has to be revised. Approximate computing is a promising paradigm for reducing power consumption at the expense of inaccuracy introduced to the computations. In this paper, we set forth approximate computing features of a processor that will exist in the next generation low-cost resource-constrained learning IoT devices. Based on these features, we design an approximate IoT processor which benefits from RISC-V ISA. Targeting machine learning applications such as classification and clustering, we have demonstrated that our processor reinforced with approximate operations can save power up to 23% for ASIC implementation while at least 90% top-1 accuracy is achieved on the trained models and test data set.