Constrained Internet Of Things Devices Research Articles

Resource Allocation Based on Task Priority and Resource Consumption in Edge Computing

The computational power of Internet of Things (IoT) devices is usually low, which makes it necessary to process data and extract relevant information on devices with higher processing capacity. Edge Computing emerged as a complementary solution to cloud computing, providing devices at the network edge with computational resources to handle the data processing and analysis that constrained IoT devices eventually cannot perform. This solution allows data processing closer to the IoT devices, reducing latency for IoT applications. However, the resource constraints of edge nodes, which have lower computational power than the cloud nodes, make resource allocation and processing massive requests challenging. This study proposes an edge resource allocation mechanism based on task priority and machine learning. The proposed approach efficiently allocates resources for IoT requests based on their task priorities while monitoring the resource consumption of edge nodes. This study evaluates the performance of different classification algorithms by using well-known metrics for classifying models. The most efficient classifier achieved an accuracy of 92% and a precision of 90%. The results indicate good performance when using this classifier in the evaluated approach. The proposed mechanism demonstrated that resource management can be done more efficiently with significantly lower resource utilization when compared to an allocation method based only on distance. The study tested different scenarios regarding the number of requests, edge nodes, and a proposed failure mechanism to schedule failed node tasks to functional nodes. This failure control mechanism is a significant contribution of the proposal. Therefore, the proposed method in this study can become a valuable tool for efficient resource management with reduced computational cost and efficient resource allocation.

Wireless IoT Networks Security and Lightweight Encryption Schemes- Survey

— IoT networks suffered from different kinds of attacks. The main issue of IoT security is the limitation of its node’s energy ability to perform big tasks such as encryption. This paper provides a comprehensive overview of the dynamic landscape of lightweight cryptography research, particularly in the context of real-time traffic security and Internet of Things (IoT) networks. The aim of this research is to identify the gaps in IoT research efforts and draw up ways to address it. The objectives are highlighting the global collaboration and standardization initiatives that have shaped its development. Also, it emphasizes the importance of balancing cryptographic strength with practical considerations such as encryption latency, energy efficiency, and memory constraints, particularly crucial in the context of resource constrained IoT devices. Furthermore, it highlights innovative approaches proposed by researchers to enhance the security of cryptographic operations while minimizing overhead and ensuring compatibility with evolving wireless environments. It calls for sustained research efforts to address these challenges and foster the development of robust cryptographic solutions capable of meeting the stringent security requirements of modern digital ecosystems.

Wireless IoT Networks Security and Lightweight Encryption Schemes- Survey

— IoT networks suffered from different kinds of attacks. The main issue of IoT security is the limitation of its node’s energy ability to perform big tasks such as encryption. This paper provides a comprehensive overview of the dynamic landscape of lightweight cryptography research, particularly in the context of real-time traffic security and Internet of Things (IoT) networks. The aim of this research is to identify the gaps in IoT research efforts and draw up ways to address it. The objectives are highlighting the global collaboration and standardization initiatives that have shaped its development. Also, it emphasizes the importance of balancing cryptographic strength with practical considerations such as encryption latency, energy efficiency, and memory constraints, particularly crucial in the context of resource constrained IoT devices. Furthermore, it highlights innovative approaches proposed by researchers to enhance the security of cryptographic operations while minimizing overhead and ensuring compatibility with evolving wireless environments. It calls for sustained research efforts to address these challenges and foster the development of robust cryptographic solutions capable of meeting the stringent security requirements of modern digital ecosystems.

Constrained IoT-Based Machine Learning for Accurate Glycemia Forecasting in Type 1 Diabetes Patients.

Individuals with diabetes mellitus type 1 (DM1) tend to check their blood sugar levels multiple times daily and utilize this information to predict their future glycemic levels. Based on these predictions, patients decide on the best approach to regulate their glucose levels with considerations such as insulin dosage and other related factors. Nevertheless, modern developments in Internet of Things (IoT) technology and innovative biomedical sensors have enabled the constant gathering of glucose level data using continuous glucose monitoring (CGM) in addition to other biomedical signals. With the use of machine learning (ML) algorithms, glycemic level patterns can be modeled, enabling accurate forecasting of this variable. Constrained devices have limited computational power, making it challenging to run complex machine learning algorithms directly on these devices. However, by leveraging edge computing, using lightweight machine learning algorithms, and performing preprocessing and feature extraction, it is possible to run machine learning algorithms on constrained devices despite these limitations. In this paper we test the burdens of some constrained IoT devices, probing that it is feasible to locally predict glycemia using a smartphone, up to 45 min in advance and with acceptable accuracy using random forest.

Combining Blockchain and IoT for Decentralized Healthcare Data Management

The emergence of the Internet of Things (IoT) has resulted in a significant increase in research on e-health. As the amount of patient data grows, it has become increasingly challenging to protect patients' privacy. Patient data is commonly stored in the cloud, making it difficult for users to control and protect their information. Moreover, the recent rise in security and surveillance breaches in the healthcare industry has highlighted the need for a better approach to data storage and protection. Traditional models that rely on third-party control over patients' healthcare data are no longer reliable, as they have proven vulnerable to security breaches. To address these issues, blockchain technology has emerged as a promising solution. Blockchain-based protocols have the potential to provide a secure and efficient system for e-health applications that does not require trust in third-party intermediaries. The proposed protocol outlined in this paper uses a blockchain-based approach to manage patient data securely and efficiently. Unlike Bitcoin, which is primarily used for financial transactions, the protocol described here is designed specifically for e-health applications. It employs a consensus mechanism that is more suitable for resource constrained IoT devices, thereby reducing network costs and increasing efficiency. The proposed protocol also provides a privacy-preserving access control mechanism that enables patients to have more control over their healthcare data. By leveraging blockchain technology, the protocol ensures that only authorized individuals can access the patient's data, which helps prevent data breaches and other security issues. Finally, the security and privacy of the proposed protocol are analysed to ensure that it meets the necessary standards for data protection. The protocol's effectiveness and efficiency are tested under different scenarios to ensure that it can perform reliably and consistently. Finally, the protocol proposed in this paper shows that how blockchain can be used to provide a secure and efficient system that empowers patients to take control of their healthcare data.

A Blockchain-Based Cross-Domain and Autonomous Access Control Scheme for Internet of Things

The volume, variety and value of data generated by Internet of Things (IoT) devices are expected to increase significantly in foreseeable future, hence, reinforcing the importance of secure and efficient access control solutions for these devices and their networks. However, existing access control solutions are not generally lightweight or scalable, particularly for geographically disperse, inexpensive resource constrained IoT devices. To tackle above challenges, we propose a lightweight consortium blockchain based architecture to enable intelligent autonomous access control for IoT devices. In our architecture, intelligent blockchain facilitates the storing of access policies, provision of authentication services for data access control, and trust evaluation for access request nodes through token accumulation mechanism. Specifically, the user's access request is approved only after it is confirmed by the blockchain network. To ensure the reliability of authenticity, a compromised resistant consensus algorithm is adapted and implemented to defend against at most <inline-formula><tex-math notation="LaTeX">$1/3$</tex-math></inline-formula> compromised authenticators. In addition, a cross-domain and flexible access control model is not only used to support data sharing among various users but can also be used for access control for exceptional blockchain situations. We explain how our system meets our design goals of reliability, availability, confidentiality, integrity, lightweight, security and scalability. In addition, we also analyze the proposed system's performance from computational, storage and network overheads (e.g., running cryptographic algorithms on a Raspberry Pi 4B), and the findings suggest that the time to run typical cryptographic algorithms is in the microsecond range.

LightCert4IoTs: Blockchain-Based Lightweight Certificates Authentication for IoT Applications

These in recent days, the proliferation of the internet of things (IoT) within the emergence of five-generation (5G) networks has received a huge attention in both industrial and academic domains. A 5G network is a cornerstone of realizing the full potential of the IoT, which interconnects billions of devices wirelessly. However, wireless communication in IoT devices reveals tremendous security risks in different dimensions and precisely in the distribution of the user certificates. The existing X.509 PKI, or the proposed decentralized PKI based on blockchain solutions have lacked practicality, and continue to have security flaws, or have not yet gained widespread acceptance owing to complexity and deployment issues. We present a lightweight certificate in size (LightCert4IoTs) that is not issued by Certification Authorities (CAs) due to the cost and complexity of the assignment of a signed certificate. In LightCert4IoTs first, an end-user (i.e., mobile and IoT devices) issues a self-signed certificate and lets Local Registration Authorities (LRAs)/EDGE nodes to verify and validate the binding identity-self signed certificate of the users through the Ethereum blockchain where The Ethereum network is used as the global notary for the IoT light certificates by saving them in the blockchain immutable ledger. The LightCert4IoTs leverages the advantages of blockchain technology and smart contracts to address the existing challenges of PKI certificates in IoT devices, which neatly achieve certificate issuance, update, and revocation more securely and efficiently. Finally, the LightCert4IoTs experimental results show that LightCert, as compared to relevant solutions/baselines, achieves reasonable overheads and is suitable for use in low- constrained IoT devices where the memory and processor power are optimized.

PANCODE: Multilevel Partitioning of Neural Networks for Constrained Internet-of-Things Devices

The increasing number of Internet-of-Things (IoT) devices will generate unprecedented data in the upcoming years. Fog computing may prevent the saturation of the network infrastructure by processing data at the edge or within these devices. Consequently, the machine intelligence built almost exclusively on the cloud can be scattered to the edge devices. While deep learning techniques can adequately process IoT-massive data volumes, their high resource-demanding nature poses a trade-off for execution on resource-constrained devices. This paper proposes and evaluates the performance of the PArtitioning Networks for COnstrained DEvices (PANCODE), a novel algorithm that employs a multilevel approach to partition large convolutional neural networks for distributed execution on constrained IoT devices. Experimental results with the LeNet and AlexNet models show that our algorithm can produce partitionings that achieve up to 2173.53 times more inferences per second than the Best Fit algorithm and up to 1.37 times less communication than the second-best approach. We also show that the METIS state-of-the-art framework only produces invalid partitionings in more constrained setups. The results indicate that our algorithm achieves higher inference rates and low communication costs in convolutional neural networks distributed among constrained and exceptionally very constrained devices.

Blockchain Enabled Architecture with Selective Consensus Mechanisms for IoT Based Saffron-Agri Value Chain

The Internet of Things (IoT) is the backbone behind numerous smart and automated applications in the modern era by providing seamless connectivity and information retrieval among the physical and virtual objects. IoT networks are resource constraint platforms hence prone to security and privacy challenges. Blockchain technology comes to the forefront to improvise the security, privacy and less dependency on the third party centralized servers. There exists a rich amount of work with numerous practical applications by fusing IoT and blockchain. In blockchain technology, the consensus mechanisms are considered to be the driving force in its implementation. In this paper, we propose a simplified blockchain based internet of things (BIoT) architecture for resource constrained IoT devices with selective consensus mechanisms based on the scale of IoT networks. We have selectively highlighted some of the important consensus algorithms which are favourable for the IoT networks. We have tailored the blockchain framework in a manner that suits to the resource constrained IoT networks. To evaluate our design, we implemented a prototype leveraging the blockchain and IoT network. The preliminary results suggest that the proposed system incorporating supply chain management of Saffron agri-value chain outperforms the existing systems. Furthermore, we have carried out a detailed case study on the cultivation and marketing strategies for maintaining the originality and transparency starting from farmer-to-consumer as saffron-Agri value chain.&#x0D;

Exploring Lightweight Deep Learning Solution for Malware Detection in IoT Constraint Environment

The present era is facing the industrial revolution. Machine-to-Machine (M2M) communication paradigm is becoming prevalent. Resultantly, the computational capabilities are being embedded in everyday objects called things. When connected to the internet, these things create an Internet of Things (IoT). However, the things are resource-constrained devices that have limited computational power. The connectivity of the things with the internet raises the challenges of the security. The user sensitive information processed by the things is also susceptible to the trusability issues. Therefore, the proliferation of cybersecurity risks and malware threat increases the need for enhanced security integration. This demands augmenting the things with state-of-the-art deep learning models for enhanced detection and protection of the user data. Existingly, the deep learning solutions are overly complex, and often overfitted for the given problem. In this research, our primary objective is to investigate a lightweight deep-learning approach maximizes the accuracy scores with lower computational costs to ensure the applicability of real-time malware monitoring in constrained IoT devices. We used state-of-the-art Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM deep learning algorithm on a vanilla configuration trained on a standard malware dataset. The results of the proposed approach show that the simple deep neural models having single dense layer and a few hundred trainable parameters can eliminate the model overfitting and achieve up to 99.45% accuracy, outperforming the overly complex deep learning models.

Over-the-Air Firmware Updates for Constrained NB-IoT Devices.

The Internet of Things (IoT) is being deployed to provide smart solutions for buildings, logistics, hospitals, and many more. It is growing with billions of connected devices. However, with such tremendous growth, maintenance and support are the hidden burdens. The devices deployed for IoT generally have a light microcontroller, low-power, low memory, and lightweight software. The software, which includes firmware and applications, can be managed remotely via a wireless connection. This improves flexibility, installation time, accessibility, effectiveness, and cost. The firmware can be updated constantly to remove known bugs and improve the functionality of the device. This work presents an approach to update firmware over-the-air (OTA) for constrained IoT devices. We used Narrowband IoT (NB-IoT) as the wireless communication standard to communicate between the managing server and devices. NB-IoT is one of the most promising low power wide area (LPWA) network protocols that supports more than 50k devices within a cell using a licensed spectrum. This work is a proof of concept demonstrating the usage of NB-IoT to update firmware for constrained devices. We also calculated the overall power consumption and latency for different sizes of the firmware.

Neural Network-Based OFDM Receiver for Resource Constrained IoT Devices

Orthogonal Frequency Division Multiplexing (OFDM)-based waveforms are used for communication links in many current and emerging Internet of Things (IoT) applications, including the latest WiFi standards. For such OFDM-based transceivers, many core physical layer functions related to channel estimation, demapping, and decoding are implemented for specific choices of channel types and modulation schemes, among others. To decouple hard-wired choices from the receiver chain and thereby enhance the flexibility of IoT deployment in many novel scenarios without changing the underlying hardware, we explore a novel, modular Machine Learning (ML)-based receiver chain design. Here, ML blocks replace the individual processing blocks of an OFDM receiver, and we specifically describe this swapping for the legacy channel estimation, symbol demapping, and decoding blocks with Neural Networks (NNs). A unique aspect of this modular design is providing flexible allocation of processing functions to the legacy or ML blocks, allowing them to interchangeably coexist. Furthermore, we study the implementation cost-benefits of the proposed NNs in resource-constrained IoT devices through pruning and quantization, as well as emulation of these compressed NNs within Field Programmable Gate Arrays (FPGAs). Our evaluations demonstrate that the proposed modular NN-based receiver improves bit error rate of the traditional non-ML receiver by averagely 61 percent and 10 percent for the simulated and over-the-air datasets, respectively. We further show complexity-performance tradeoffs by presenting computational complexity comparisons between the traditional algorithms and the proposed compressed NNs.

Performance Evaluation of Group OSCORE for Secure Group Communication in the Internet of Things

The Constrained Application Protocol (CoAP) is a major application-layer protocol for the Internet of Things (IoT). The recently standardized security protocol Object Security for Constrained RESTful Environments (OSCORE) efficiently provides end-to-end security of CoAP messages at the application layer, also in the presence of untrusted intermediaries. At the same time, CoAP supports one-to-many communication, targeting use cases such as smart lighting and building automation, firmware update, or emergency broadcast. Securing group communication for CoAP has additional challenges. It can be done using the novel Group Object Security for Constrained RESTful Environments (Group OSCORE) security protocol, which fulfills the same security requirements of OSCORE in group communication environments. While evaluations of OSCORE are available, no studies exist on the performance of Group OSCORE on resource-constrained IoT devices. This article presents the results of our extensive performance evaluation of Group OSCORE over two popular constrained IoT platforms, namely Zolertia Zoul and TI Simplelink. We have implemented Group OSCORE for the Contiki-NG operating system and made our implementation available as open source software. We compared Group OSCORE against unprotected CoAP as well as OSCORE. To the best of our knowledge, this is the first comprehensive and experimental evaluation of Group OSCORE over real constrained IoT devices.

Over-the-air firmware update for IoT devices on the wild

Internet of Things (IoT) has been gaining a lot of attention in the last few years and despite the large amount of investments, there is still much to be developed and refined. Software and firmware updates over-the-air (OTA) are a crucial part for IoT solutions longevity. This paper presents the design and implementation of an OTA firmware update method based on the standard architecture recently proposed by the Internet Engineering Task Force (IETF). The proposed solution is evaluated through a testbed using 20 constrained IoT devices and an open source IoT cloud platform, as well as on a set of devices deployed in a real world application. Results show that the proposed solution is suitable for constrained devices and has little impact on the network traffic.

Efficient PPA Nano AES for IoT Applications

Abstract: Due to the fast-growing number of connected tiny devices to the Internet of Things (IoT), providing end-to-end security is vital. Therefore, it is essential to design the cryptosystem based on the requirement of resource constrained IoT devices. This article presents a lightweight advanced encryption standard (AES), a high-secure symmetric cryptography algorithm, implementation on field-programmable gate array (FPGA) and 65-nm technology for resource-constrained IoT devices. The proposed architecture includes 8-bit data path and five main blocks. We design two specified register banks, Key-Register and State-Register, for storing the plain text, keys, and intermediate data. To reduce the area, Shift-Rows is embedded inside the State-Register. To adapt the Mix-Column to 8-bit data path, we design an optimized 8-bit block for Mix-Columns with four internal registers, which accept 8-bit and send back 8-bit. Also, a shared optimized Sub-Bytes is employed for the key expansion phase and encryption phase. To optimize Sub-Bytes, we merge and simplify some parts of the Sub-Bytes. To reduce power consumption, we apply the clock gating technique to the design. Application-specific integrated circuit (ASIC) implementation results show a respective improvement in the area over the previous similar works from 35% to 2.4%. Based on the results, the proposed design is a suitable cryptosystem for tiny IoT devices. Keywords: IOT, Clock gating technique, ASIC

SmartABAC: Enabling Constrained IoT Devices to Make Complex Policy-Based Access Control Decisions

While Attribute-Based Access Control (ABAC) is a promising technique to govern interactions in the Internet of Things (IoT), most existing ABAC models are designed to run on remote servers or gateway devices. This scenario is misaligned with recent trends towards IoT decentralization, such as the Swarm, which expects devices to autonomously share resources, making their own access decisions for enhanced privacy and reliability. In this paper, we propose SmartABAC: a fast, concise, and expressive ABAC model that can be executed in constrained IoT devices. It combines the performance of policies based on attribute enumeration, with techniques that enhance policy expressiveness, such as typed and hierarchical attributes. We specified SmartABAC using first-order logic, designed a use case, and evaluated it in both constrained and non-constrained IoT environments. Results show that our model can represent a variety of access policies, including nested multi-attribute rules, while using less than 100 bytes per policy, on average, for a smart home use case. Our C-based SmartABAC implementation is at least 255 times faster than existing models and can evaluate 3000 policies under 5 milliseconds on a 32 MHz MCU.

Performance evaluation of publish-subscribe systems in IoT using energy-efficient and context-aware secure messages

BackgroundThe Internet of Things (IoT) enables the development of innovative applications in various domains such as healthcare, transportation, and Industry 4.0. Publish-subscribe systems enable IoT devices to communicate with the cloud platform. However, IoT applications need context-aware messages to translate the data into contextual information, allowing the applications to act cognitively. Besides, end-to-end security of publish-subscribe messages on both ends (devices and cloud) is essential. However, achieving security on constrained IoT devices with memory, payload, and energy restrictions is a challenge.ContributionMessages in IoT need to achieve both energy efficiency and secure delivery. Thus, the main contribution of this paper refers to a performance evaluation of a message structure that standardizes the publish-subscribe topic and payload used by the cloud platform and the IoT devices. We also propose a standardization for the topic and payload for publish-subscribe systems.ConclusionThe messages promote energy efficiency, enabling ultra-low-power and high-capacity devices and reducing the bytes transmitted in the IoT domain. The performance evaluation demonstrates that publish-subscribe systems (namely, AMQP, DDS, and MQTT) can use our proposed energy-efficient message structure on IoT. Additionally, the message system provides end-to-end confidentiality, integrity, and authenticity between IoT devices and the cloud platform.

PANDA: Lightweight non-interactive privacy-preserving data aggregation for constrained devices

Privacy-preserving data aggregation is becoming a demanding necessity for many promising scenarios, e.g., health care analysis. Sensitive data are collected and aggregated in a privacy-preserving approach using current Internet of Things (IoT) technology, leading to immense challenge and consequent interest in developing secure computing algorithms for individual and organizational data. However, most existing solutions focus on specific evaluations (e.g., SUM), and they use heavy cryptographic techniques, which are far from practice for constrained IoT devices. The Trusted Execution Environment (TEE, implemented with Intel SGX) can assist in computing arbitrary functions and avoiding resource-consuming operations. Nevertheless, TEE is subject to several challenges because TEE is vulnerable to limited resource and even function violations, e.g., the attacker may bypass the boundary of TEE. In this paper, we propose a lightweight non-interactive privacy-preserving data aggregation scheme for resource-constrained devices, named PANDA, where TEE is introduced to bypass the trusted entities requirement and heavy overhead. Additionally, PANDA explores the certificate-aided function authorization to prevent leakage from unauthorized functions, and designs the public verifiable certificate management to detect the abnormal behaviors of the host to mitigate the external host compromise. We formalize PANDA with rigorous security analysis to indicate privacy protection against the compromised aggregator and analyst. The evaluation results show that PANDA has constant online communication cost and lightweight computation overhead for constrained devices, which is suitable for IoT applications.

A Lightweight Scheme for Mitigating RPL Version Number Attacks in IoT Networks

Internet of Things (IoT) systems incorporate a multitude of resource-limited devices typically interconnected over Low Power and Lossy Networks (LLNs). Robust IP-based network routing among such constrained IoT devices can be effectively realized using the IPv6 Routing Protocol for LLN (RPL) which is an IETF-standardized protocol. The RPL design features a topology maintenance mechanism based on a version numbering system. However, such a design property makes it easy to initiate Version Number (VN) attacks targeting the stability, lifetime, and performance of RPL networks. Thus the wide deployment of RPL-based IoT networks would be hindered significantly unless internal routing attacks such as the VN attacks are efficiently addressed. In this research work, a lightweight and effective detection and mitigation solution against RPL VN attacks is introduced. With simple modifications to the RPL functionality, a collaborative and distributed security scheme is incorporated into the protocol design (referred to as CDRPL). As the experimental results indicated, it provides a secure and scalable solution enhancing the resilience of the protocol against simple and composite VN attacks in different experimental setups. CDRPL guaranteed fast and accurate attack detection as well as quick topology convergence upon any attack attempt. It also efficiently maintained network stability, control traffic overhead, QoS performance, and energy consumption during different scenarios of the VN attack. Compared to other similar approaches, CDRPL yields better performance results with lightweight node-local processing, no additional entities, and less communication overhead.

Lightweight and Privacy-Preserving Remote User Authentication for Smart Homes

The rapid proliferation of embedded devices has led to the growth of the Internet of Things (IoT) with applications in numerous domains such as home automation, healthcare, education and agriculture. However, many of the connected devices particularly in smart homes are the target of attacks that try to exploit security vulnerabilities such as hard-coded passwords and insecure data transfer. Recent studies show that there is a considerable surge in the number of phishing attacks targeting smart homes during the COVID-19 pandemic. Moreover, many of the existing user authentication protocols in the literature incur additional computational overhead and need to be made more resilient to smart home targeted attacks. In this paper, we propose a novel lightweight and privacy-preserving remote user authentication protocol for securing smart home applications. Our approach is based on Photo Response Non-Uniformity (PRNU) to make our protocol resilient to smart home attacks such as smartphone capture attacks and phishing attacks. In addition, the lightweight nature of our solution is suitable for deployment on heterogeneous and resource constrained IoT devices. Besides, we leverage geometric secret sharing for establishing mutual authentication among the participating entities. We validate the security of the proposed protocol using the AVISPA formal verification tool and prototype it on a Raspberry Pi to analyze the power consumption. Finally, a comparison with existing schemes reveals that our scheme incurs a 20&#x0025; reduction in communication overhead on smart devices. Furthermore, our proposed scheme is usable as it absolves users from memorizing passwords and carrying smart cards.