Low-end Internet Of Things Devices Research Articles

ChamelIoT: a tightly- and loosely-coupled hardware-assisted OS framework for low-end IoT devices

The evergrowing Internet of Things (IoT) ecosystem continues to impose new requirements and constraints on every device. At the edge, low-end devices are getting pressured by increasing workloads and stricter timing deadlines while simultaneously are desired to minimize their power consumption, form factor, and memory footprint. Field-Programmable Gate Arrays (FPGAs) emerge as a possible solution for the increasing demands of the IoT. Reconfigurable IoT platforms enable the offloading of software tasks to hardware, enhancing their performance and determinism. This paper presents ChamelIoT, an agnostic hardware operating systems (OSes) framework for reconfigurable IoT devices. The framework provides hardware acceleration for kernel services of different IoT OSes by leveraging the RISC-V open-source instruction set architecture (ISA). The ChamelIoT hardware accelerator can be deployed in a tightly- or loosely-coupled approach and implements the following kernel services: thread management, scheduling, synchronization mechanisms, and inter-process communication (IPC). ChamelIoT allows developers to run unmodified applications of three well-established OSes, RIOT, Zephyr, and FreeRTOS. The experiments conducted on both coupling approaches consisted of microbenchmarks to measure the API latency, the Thread Metric benchmark suite to evaluated the system performance, and tests to the FPGA resource consumption. The results show that the latency can be reduced up to 92.65% and 89.14% for the tightly- and loosely-coupled approaches, respectively, the jitter removed, and the execution performance increased by 199.49% and 184.85% for both approaches.

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.

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.

IoT Device Friendly and Communication-Efficient Federated Learning via Joint Model Pruning and Quantization

Federated learning (FL) through its novel applications and services has enhanced its presence as a promising tool in the Internet of Things (IoT) domain. Specifically, in a multiaccess edge computing setup with a host of IoT devices, FL is most suitable since it leverages distributed client data to train high-performance deep learning (DL) models while keeping the data private. However, the underlying deep neural networks (DNNs) are huge, preventing its direct deployment onto resource-constrained computing and memory-limited IoT devices. Besides, frequent exchange of model updates between the central server and clients in FL could result in a communication bottleneck. To address these challenges, in this article, we introduce GWEP, a model compression-based FL method. It utilizes joint quantization and model pruning to reap the benefits of DNNs while meeting the capabilities of resource-constrained devices. Consequently, by reducing the computational, memory, and network footprint of FL, the low-end IoT devices may be able to participate in the FL process. In addition, we provide theoretical guarantees of FL convergence. Through empirical evaluations, we demonstrate that our approach significantly outperforms the baseline algorithms by being up to 10.23 times faster with 11 times lesser communication rounds, while achieving high-model compression, energy efficiency, and learning performance.

An Extended Software-Defined Approach for Reprogramming Low-end IoT Devices

The usage of the Software-Defined Sensor Networks (SD-WSNs) as support for applications of the Internet of Things (IoT) has been the focus of recent research to reduce the complexity of configuration and management. This simplification allows greater flexibility for the configuration of these networks in operating time. However, although more flexible than traditional approaches, the existing SD-WSN architectures found in the literature have limitations mainly with regard to the reprogramming of devices in operating time. In this article, we propose a novel approach called Extended Software-Defined Wireless Sensor Network (ESD-WSN), in which the concept of the SDNs is extended to the application layer allowing the reprogramming of low-end devices in operation time without the need for firmware update. The proposed architecture was validated by simulation and an experimental study using very constrained low-end devices. The simulation/experimental results showed the effectiveness, flexibility, and efficiency of the proposal.

A Secure Platform Model Based on ARM Platform Security Architecture for IoT Devices

The proliferation of Internet of Things (IoT) devices comes with many challenges among which security is one of the most serious issues. In order to address the security issue for low-end IoT devices, ARM recently proposed the platform security architecture (PSA), which provides execution isolation to safely manage and protect the computing resources of low-end IoT devices. However, developers implementing IoT services for PSA-based IoT devices need to follow complex development procedures and understand the PSA hardware, which dramatically increases the development time and cost of PSA-based IoT devices. This article analyzes vulnerabilities that may arise from general-purpose low-end IoT devices to derive the security requirements and essential security services for PSA-based IoT devices, and proposes a secure platform model based on the analysis results. The proposed secure platform model consists of System Security Services and Application Security Services based on the basic PSA model and essential trusted subsystems, and it is designed to be flexible and applicable to various types of PSA-based IoT devices. In addition, it provides secure platform services APIs to enable easy and fast development of IoT services. To evaluate the proposed secure platform model, two proof-of-concept implementations are provided by using both the basic PSA model with secure element (SE) and a reference device for ARM’s PSA. Finally, a case study shows that the development of IoT services can be done easily and quickly using the proposed security platform model.

Masked Implementation of Format Preserving Encryption on Low-End AVR Microcontrollers and High-End ARM Processors

Format-Preserving Encryption (FPE) for Internet of Things (IoT) enables the data encryption while preserving the format and length of original data. With these advantages, FPE can be utilized in many IoT applications. However, FPE requires complicated computations and these are high overheads on IoT embedded devices. In this paper, we proposed an efficient implementation of Format-preserving Encryption Algorithm (FEA), which is the Korean standard of FPE, and the first-order masked implementation of FEA on both low-end (i.e., AVR microcontroller) and high-end (i.e., ARM processor) IoT devices. Firstly, we show the vulnerability of FEA when it comes to the Correlation Power Analysis (CPA) approach. Afterward, we propose an efficient implementation method and the masking technique for both low-end IoT device and high-end IoT device. The proposed method is secure against power analysis attacks but the performance degradation of masked measure is only 2.53∼3.77% than the naïve FEA implementation.

Designing a CHAM Block Cipher on Low-End Microcontrollers for Internet of Things

As the technology of Internet of Things (IoT) evolves, abundant data is generated from sensor nodes and exchanged between them. For this reason, efficient encryption is required to keep data in secret. Since low-end IoT devices have limited computation power, it is difficult to operate expensive ciphers on them. Lightweight block ciphers reduce computation overheads, which are suitable for low-end IoT platforms. In this paper, we implemented the optimized CHAM block cipher in the counter mode of operation, on 8-bit AVR microcontrollers (i.e., representative sensor nodes). There are four new techniques applied. First, the execution time is drastically reduced, by skipping eight rounds through pre-calculation and look-up table access. Second, the encryption with a variable-key scenario is optimized with the on-the-fly table calculation. Third, the encryption in a parallel way makes multiple blocks computed in online for CHAM-64/128 case. Fourth, the state-of-art engineering technique is fully utilized in terms of the instruction level and register level. With these optimization methods, proposed optimized CHAM implementations for counter mode of operation outperformed the state-of-art implementations by 12.8%, 8.9%, and 9.6% for CHAM-64/128, CHAM-128/128, and CHAM-128/256, respectively.

A Secure Fog-Based Architecture for Industrial Internet of Things and Industry 4.0

The advent of Industrial Internet of Things (IIoT) along with cloud computing has brought a huge paradigm shift in manufacturing industries resulting in yet another industrial revolution, Industry 4.0. Huge amounts of delay-sensitive data of diverse nature are being generated, which need to be locally processed and secured because of their sensitivity. However, the low-end Internet of Things devices are unable to handle huge computational overheads. In addition, the semi-trusted nature of cloud introduces several security concerns. To address these issues, this article proposes a secure fog-based IIoT architecture by suitably plugging a number of security features into it and by offloading some of the tasks judiciously to fog nodes. These features secure the system alongside reducing the trust and burden on the cloud and resource-constrained devices, respectively. We validate our proposed architecture through both theoretical overhead analysis and practical experimentation, including simulation study and testbed implementation.

TREVERSE: TRial-and-Error Lightweight Secure ReVERSE Authentication With Simulatable PUFs

A physical unclonable function (PUF) generates hardware intrinsic volatile secrets by exploiting uncontrollable manufacturing randomness. Although PUFs provide the potential for lightweight and secure authentication for increasing numbers of low-end Internet of Things devices, practical and secure mechanisms remain elusive. Our work aims to explore simulatable PUFs (SimPUFs) that are physically unclonable but efficiently modeled mathematically through privileged one-time PUF access to address the problem of authentication for resource limited devices. Given a random challenge, a securely stored SimPUF in possession of a trusted server computes the corresponding response and its bit-specific reliability. Consequently, naturally noisy PUF responses generated by a resource-limited prover is immediately processed by a one-way function (OWF) and transmitted to a server, because the resourceful server can exploit the SimPUF to perform a trial-and-error search over likely error patterns to recover the noisy response to authenticate the prover. TREVERSE security is guaranteed by the hardness of inverting the OWF under the random oracle model. We formally evaluate the authentication capability of our trial-and-error reverse (TREVERSE) authentication under two SimPUFs experimentally derived from popular silicon PUFs.

Leveraging Electromagnetic Side-Channel Analysis for the Investigation of IoT Devices

Internet of Things (IoT) devices have expanded the horizon of digital forensic investigations by providing a rich set of new evidence sources. IoT devices includes health implants, sports wearables, smart burglary alarms, smart thermostats, smart electrical appliances, and many more. Digital evidence from these IoT devices is often extracted from third party sources, e.g., paired smartphone applications or the devices' back-end cloud services. However vital digital evidence can still reside solely on the IoT device itself. The specifics of the IoT device's hardware is a black-box in many cases due to the lack of proven, established techniques to inspect IoT devices. This paper presents a novel methodology to inspect the internal software activities of IoT devices through their electromagnetic radiation emissions during live device investigation. When a running IoT device is identified at a crime scene, forensically important software activities can be revealed through an electromagnetic side-channel analysis (EM-SCA) attack. By using two representative IoT hardware platforms, this work demonstrates that cryptographic algorithms running on high-end IoT devices can be detected with over 82% accuracy, while minor software code differences in low-end IoT devices could be detected over 90% accuracy using a neural network-based classifier. Furthermore, it was experimentally demonstrated that malicious modification of the stock firmware of an IoT device can be detected through machine learning-assisted EM-SCA techniques. These techniques provide a new investigative vector for digital forensic investigators to inspect IoT devices.

Guest Editorial Emerging Computing Offloading for IoTs: Architectures, Technologies, and Applications

Billions of Internet of Things (IoT) devices, e.g., sensors and RFIDs, are arising around us providing not only computing-intensive, but also delay-sensitive services, ranging from augmented/virtual realities to distributed data analysis and artificial intelligence. Notably, the low response latency for IoT services is achieved at the cost of computing complexity that far exceeds the capabilities of IoT devices. To feed this trend, multiple computing paradigms are emerging, such as mobile transparent computing (TC), edge computing, and fog computing. These paradigms employ more resourceful edge devices, e.g., small-scale servers, smart phones, and laptops, to assist the low-end IoT devices. By offloading the computing-intensive tasks to the edge devices, it is expected to converge the data collection at IoT devices and the data processing at edge devices to provision computing-intensive and delay-sensitive services. However, many issues remain in the application of computing offloading which impede its flourishing in IoTs. To name a few, what are the killer APPs that need computing offloading for performance boost? How to partition an encapsulated APP into offloadable code blocks for remote loading? How to determine which code blocks or computing tasks should be offloaded to edge servers? How to customize the communication protocol to guarantee the coherence of computation offloading?

A Security Framework for the Internet of Things in the Future Internet Architecture

The Internet of Things (IoT) is a recent trend that extends the boundary of the Internet to include a wide variety of computing devices. Connecting many stand-alone IoT systems through the Internet introduces many challenges, with security being front-and-center since much of the collected information will be exposed to a wide and often unknown audience. Unfortunately, due to the intrinsic capability limits of low-end IoT devices, which account for a majority of the IoT end hosts, many traditional security methods cannot be applied to secure IoT systems, which open a door for attacks and exploits directed both against IoT services and the broader Internet. This paper addresses this issue by introducing a unified IoT framework based on the MobilityFirst future Internet architecture that explicitly focuses on supporting security for the IoT. Our design integrates local IoT systems into the global Internet without losing usability, interoperability and security protection. Specifically, we introduced an IoT middleware layer that connects heterogeneous hardware in local IoT systems to the global MobilityFirst network. We propose an IoT name resolution service (IoT-NRS) as a core component of the middleware layer, and develop a lightweight keying protocol that establishes trust between an IoT device and the IoT-NRS.

Operating Systems for Low-End Devices in the Internet of Things: A Survey

The Internet of Things (IoT) is projected to soon interconnect tens of billions of new devices, in large part also connected to the Internet. IoT devices include both high-end devices which can use traditional go-to operating systems (OSs) such as Linux, and low-end devices which cannot, due to stringent resource constraints, e.g., very limited memory, computational power, and power supply. However, large-scale IoT software development, deployment, and maintenance requires an appropriate OS to build upon. In this paper, we thus analyze in detail the specific requirements that an OS should satisfy to run on low-end IoT devices, and we survey applicable OSs, focusing on candidates that could become an equivalent of Linux for such devices, i.e., a one-size-fits-most, open source OS for low-end IoT devices.