SCCTS Journal of Embedded Systems Design and Applications
Aim: The aim of the "SCCTS Journal of Embedded Systems Design and Applications" ISSN : 3048-8753 is to serve as a leading international forum for researchers, engineers, and practitioners to disseminate their latest findings, innovations, and advancements in embedded systems design and applications. The journal aims to facilitate interdisciplinary collaboration, foster technological innovation, and address real-world challenges in the development, deployment, and utilization of embedded systems across various domains. Subject Covered: The journal covers a broad spectrum of topics related to embedded systems design and applications, including hardware design, software development, system integration, prototyping, applications in diverse domains, security, low-power design, testing and validation, and exploration of emerging trends and technologies. It welcomes original research articles, review papers, case studies, and technical notes that contribute to the theoretical understanding, practical implementation, and advancement of embedded systems technology. Through rigorous peer review and editorial excellence, the journal strives to maintain high standards of scholarly integrity and to serve as a valuable resource for researchers, academics, engineers, and industry professionals worldwide. Frequency of Publication - Bi Annual - It has Two issue per year Language - English Subject - Engineering Year of Starting - 2024 ISSN : 3048-8753 Format of Publication - Online Only
Society for Communication and Computer Technologies
A Review of Fault-Tolerant Reconfigurable Architectures for Autonomous Systems Based on Redundant Logic Mapping
Mission-critical applications (aerospace, automotive, and defense) have autonomous systems that must have very high reliability to allow continued operation in varied environmental structures and fault conditions. The medium of flexibility and adaptability of the computer hardware is represented by reconfigurable computing platforms and, especially, by Field-Programmable Gate Arrays (FPGAs) and Coarse-Grained Reconfigurable Architectures (CGRAs). The review will provide an orderly research into the fault-tolerant reconfigurable architecture with an interest in redundant logic mapping which encompasses spatial, temporal, and hybrid redundancy model. We study important analysis metrics including Mean Time to Failure (MTTF), fault coverage, resources overhead and reconfiguration latency. Recent literature shows comparative results that hybrids are able to provide the MTTF with up to 3.5x scaling without a drastically high resource overhead (~2.1x LUTs, +25% power). The relevance also points to the real use in self-governing frameworks e.g. UAV, self-governing automobiles, and smart robotics, where fault cloaking, configuration abrasive, and flawless cycles are fundamental. Lastly, it presents open research issues in scalable fault diagnosis, AI-based reconfiguration as well as cross layer fault resilience, and states a guideline towards the design of next generation self-sustainable systems comprising of both economic and fault tolerant systems.
Fault-Tolerant Computing, Reconfigurable Architecture, Redundant Logic Mapping, Autonomous Systems, Dynamic Partial Reconfiguration, FPGA-Based Reliability
Quantum-Inspired Reconfigurable Computing for Accelerated Matrix Computations in High-Performance Embedded Systems
The emergence of data-hungry systems in the form of artificial intelligence, real-time signal processing, and scientific computing systems inside embedded systems has set extremely high requirements to the computational capabilities, electrical energy efficiency and flexibility. The computational backbone of many of these applications is matrix computations, that typically face bottlenecks in traditional processor architectures because of individual resources having no parallelism, and a lack of dynamism in resource allocation. In order to overcome such limitations, this paper introduces a new reconfigurable computing implementation, called Quantum-Inspired Matrix Engine (QIME) that emulates all three of these important quantum computing concepts of superposition, parallelism, and entanglement in a classical hardware setting. The system of the proposal will be applied to the Field-Programmable Gate Arrays (FPGAs), and here, the system will be used to incorporate a pseudo-quantum state encoding process that enables the representation and processing of multiple matrix states simultaneously. QIME also has dynamic partial reconfiguration (DPR) so as to further have flexibility and efficiency in matching the architecture in real time with the nature and the complexity of the operations performed in the matrix. Also, there is approximate computing, where computational precision is traded off against performance where the tradeoff is allowed to increase energy profiles under workload variability. Experimental analysis of some typical benchmarks on matrices, including multiplication, decomposition, and convolution, shows that for representative operations QIME provides up to 6.3x speedup and 4.1x energy efficiency improvements over typical matrix co-processors and embedded GPUs. These findings will support the viability of quantum-inspired design paradigms in moving forward the performance and scalability of high-performance embedded systems, especially in the edge, and constrained circumstances where conventional quantum hardware is infeasible.
Quantum-inspired computing, matrix computation, reconfigurable architecture, embedded systems, FPGA acceleration, dynamic partial reconfiguration, pseudo-superposition, parallelism.
Secure Runtime Reconfiguration Framework for FPGA-Based Embedded Systems in Mission-Critical Applications
Field-Programmable Gate Arrays (FPGAs) have become a popular platform to implement high performance and flexible mission-critical embedded systems with capabilities of configuration at run-time. Nevertheless, although dynamic partial reconfiguration (DPR) is the way to achieve flexibility and resource utilization, this introduces unacceptable security risks, especially in domains where the integrity of system, along with confidentiality and availability are some of the components that cannot be compromised. The theme of the paper is the design of a Secure Runtime Reconfiguration Framework (SRRF) targeted to meet the needs of FPGA-based embedded systems in a mission-critical setting like aerospace, defense, and medical instrumentation. The suggested structure is a combination of a cryptographically safe bitstream management component, a runtime verification and monitoring system, as well as a hardware-assisted tamper risk recognition system that enables secure loading, authentication and legislation of the reconfigurable modules. Confidentiality and integrity Our architecture leverages authenticated encryption (AES-GCM) to provide confidentiality and integrity of bitstreams and an authenticated and cryptographically secured boot chain and reconfiguration controllers (e.g., ICAP/PCAP interfaces). Here, the threat model is carefully examined through the use of the STRIDE and the threat vectors include bitstream spoofing, fault injection, and side-channel exploit. We deploy and test the framework on a Xilinx Zynq-7000 platform in different scenarios of working and attack operation. Cryptographic overhead is minimal (~3.1% latency overhead) and area is spared (< 8 percent LUTs allocated to security interventions) as indicated by experimental results, and it is robust to runtime attacks. The SRRF not only increases the trustworthiness of the system, but also does not come at the expense of performance, therefore, appearing as an attractive solution to high-assurance, reconfigurable embedded implementations. This work contributes towards the state-of-the-art on how to support secure FPGA reconfiguration by presenting a scalable, lightweight and standards-compliant solution in the safety- and security-sensitive fields.
Dynamic Partial Reconfiguration (DPR), FPGA Security, Mission-Critical Embedded Systems, Bitstream Authentication, Runtime Hardware Reconfiguration, Trusted Reconfigurable Computing
Hardware-Assisted Intrusion Detection System for Automotive Embedded ECUs
Modern automotive systems progressively rely on Electronic Control Units (ECUs) to take control of vital vehicle functionality and are heavily interlinked on in-vehicle networks, e.g. the Controller Area Network (CAN). With the increased vehicle sophistication into a complex cyber-physical system, the car is more exposed to cyber-related attacks such as message injection, spoofing, and denial-of-service (DoS) attacks that have the potential to affect safety and performance. Implementations of Intrusion Detection Systems (IDS) using software, can be not very effective when it comes to real-time detection, because of the computational overhead imposed by them and the shared CPU usage. The paper proposes a new hardware-assisted intrusion detection system (H-IDS) designed to work on the automotive platforms and embedded devices (ECUs) in particular but is focused on the low-latency and real-time nature of the system and its capability to deploy on resource-constrained devices. The described architecture uses a light on-chip co-processor in the ECU as a special security engine to observe CAN traffic and observe abnormal behavior based on temporal and statistical characteristics. The H-IDS performs detection tasks on a separate processor-offloading payload so it does not interfere with other processes or even stop when the vehicle does not perform its tasks. This was implemented on an ECU platform based on the ARM Cortex-R5 in a hardware prototyping system with FPGA and then tested in a range of simulated attack conditions. The input of the experiment shows that there is a significant reduction in the detection latency that the H-IDS displays an average latency of 14 microseconds that is much faster than software-only solutions. Also, the design has less than 2 percent CPU overhead and its power and memory requirements fit squarely within the power and memory budget of most automotive embedded systems. The provided proposed H-IDS is aligned with ISO/SAE 21434 standards of cybersecurity, and it is scalable to be deployed on various heterogeneous ECU systems. Through the increased detection responsiveness and negligible resource consumption, the work is a step towards realistic hardware-enforced security measures, which can be incorporated in the next generation of automotive architectures to guarantee resilient and trustful in-vehicle networks.
Intrusion Detection System, ECU, Automotive Security, CAN Bus, Hardware Security, ISO/SAE 21434, Real-Time Monitoring, Embedded System, ARM Cortex-R5
Secure Over-the-Air (OTA) Firmware Update Mechanism for Resource-Constrained Embedded Devices
Software embedded design systems are also penetrating more and more important areas of application: the Internet of Things (IoT), remote health monitoring, industrial automation, and smart infrastructure, current safe efficient and secure firmware update mechanisms are paramount. The ability associated with over-the-air (OTA) updating of firmware is strictly required in preserving the functioning of devices, exploiting such gaps, and empowering the features after release. But in highly constrained systems (those with limited memory, processing capacity and available energy), conventional secure update interfaces can incur unacceptable overheads or they may not provide complete protection against sophisticated cyber-attacks. In this paper the authors introduce a simple yet lightweight and robust OTA update mechanism built specifically to be used on such constrained platforms. This suggested framework combines the familiar AES-GCM-authenticated encryption to guarantee data confidentiality and integrity, a source authentication technique based on checking digital signatures secured using ECDSA, and a versioning protocol providing the anti-rollback protection using monotonic counters. As part of validating its practicality, the solution is applied and tested with ARM Cortex-M microcontrollers, i.e., STM32 and nRF52840, where communication is performed over low-power wireless networks like LoRaWAN and BLE. A signature of firmware packages via custom-built update server (TLS) will provide secure transfer of updated packages. Experimental scrutinisms indicate that the secure OTA system has very limited overhead in resources (about 14 KB extra flash usage and 3 KB of RAM) and the average update speed is 3.8 seconds with a 256 KB binary image using LoRaWAN. In addition, the system is well adapted to identify and discard a tainted or old firmware image, hence ensuring device immunity against attacks including firmware manipulation, replay, and man-in-the-middle intrusion. The performance highly conforms to the contention that the framework provides a robust degree of security and performance coupled with resource usability hence being ideal to use in real fully embedded applications that have strict limitations. This contribution consists of a scalable, secure OTA approach that builds resilience to the operational integrity and trustworthiness of the distributed embedded system in dynamic and security critical settings.
Secure OTA Update, Firmware Integrity, Embedded Security, ARM Cortex-M, AES-GCM, ECDSA, LoRaWAN, Anti-Rollback, Resource-Constrained Devices
Design and Evaluation of Lightweight Cryptographic Primitives for Secure Communication in Resource-Constrained LPWAN Devices
The fast growth of Low-Power Wide-Area Networks (LPWANs) within the scope of Internet of Things (IoT) brought up the urgent questions of secure communications in highly resource-limited settings. As a robust AES and RSA conventional cryptographic scheme, it applied a heavy burden to the embedded LPWAN nodes with limited memory capacity, computing ability, and power supplies. With this in mind, to fill this gap we suggest a set of lightweight cryptographic primitives that can be utilized in secure communication in LPWAN protocols including LoRaWAN and NB-IoT. The suite contains optimized versions of block ciphers (e.g. PRESENT, Speck), stream ciphers (e.g. Trivium), and hash functions (e.g. PHOTON, SPONGENT), benchmarked on microcontrollers with ultra-low power including STM32L0 and nRF52840. Security tests are done on differential, linear, and replay attacks and the performance tests on flash/RAM usage, latency, and energy consumption. Findings show that the PRESENT cipher implemented together with the SPONGENT hash-function decreases the memory demand by 52 percent, and energy utilization by 37 percent as contrasted to AES-CCM, with no foundational security promises damaged. These results highlight the ability to implement lightweight cryptography in the IoT system based on the LPWAN and maintain the data confidentiality, integrity, and authentication and at the same time support the efficiency of the devices. The work gives a scalable basis of secure embedded communication especially in areas like smart metering, environmental monitoring, industrials telemetry.
Lightweight Cryptography, Low-Power Wide-Area Networks (LPWAN), Secure Embedded Communication, Resource-Constrained IoT Devices, LoRaWAN Security, PRESENT Cipher, SPONGENT Hash Function, Energy-Efficient Encryption, STM32L0 / nRF52840 Microcontrollers, Authentication in LPWAN, Cryptographic Primitives Benchmarking, IoT Security, Hardware-Aware Cryptography, Secure MAC for Constrained Devices, Post-Quantum Ready Lightweight Ciphers.
Next-Generation Embedded Systems: Advanced Design Methodologies, Real-World Applications, and Emerging Technology Trends
The evolution of embedded systems is more rapid and is bringing about a transformation across all systems of healthcare, automotive, industrial automation, and consumer electronics where intelligent, energy efficient and application specific solutions are becoming more important. The paper seeks to describe the complete analysis of next generation embedded systems especially in designing new approaches, practical implementations of the systems, and technological trends. The paper uses a structured review methodology; it would therefore pull together results of recently published peer-reviewed sources, industry reports, and case studies. Analysed methodologies are hw/sw co-design, power/energy conscious architecture, AI-driven optimisations, security-aware design and model-based development processes. The review findings also underscore the fact that a combination of edge AI accelerators, heterogeneous computing systems and secure, reconfigurable systems both maximizes performance per-watt and operational resiliency. The analysis also shows that new enablers like 5G/6G connectivity, neuromorphic computing and quantum-inspired algorithms will redefine embedded capabilities in application areas that generate particular latency and mission-critical applications. The discussion explains some of the major issues that the dialogue addresses such as security weaknesses, interoperability gaps, and sustainable design prerequisites. Every conclusion re-iterates the need to focus future research on cross-disciplinary research to come up with more scalable, secure, and eco-friendly embedded systems. The synthesis provides guidance to academic researchers, engineers and practitioners in the industry to utilize the current state of art developments in embedded systems, into the real world.
Embedded systems design; hardware/software co-design; low-power architecture; edge AI; Internet of Things (IoT); reconfigurable computing; model-based design; secure embedded systems; AI-driven optimization; heterogeneous computing; neuromorphic computing; 5G/6G-enabled embedded platforms; real-time applications; emerging technology trends
A RISC-V Based Embedded Security Architecture for Trusted Execution in Industrial Control Systems
Industrial Control Systems (ICS) have become more vulnerable to cyber threats as they have emerged in the digital world, in particular, embedded environments that face limits to power, cost, and latency. A new embedded security architecture has been presented in this paper based on the open-source RISC-V instruction set architecture (ISA) to support trusted execution within the ICS setting. The first goal is to be able to create a low-overhead, transparent, and scalable system, which is enforcing hardware-based trust but without affecting the real-time performance. The architecture proposed incorporates a lightweight hardware Root of Trust (RoT), secure bootloader, memory protection units (MPUs) and minimal Trusted Execution Environment (TEE) as part of a bespoke RISC-V core. Important architectural updates are register-level TEE isolation, crypto-integrity verification, and hardware-accelerated AES-GCM and SHA-256. The system is run on a 32-bit RV32IM RISC-V and tested against real-time industrial protocols (Modbus, OPC-UA) and code injection, denial-of-service emulations. Through experimental evaluations, it is shown how the architecture is able to attain a security enhancement score of 92.4 % and blocks more than 91 % of the injected threats but without much overhead (execution time overload of 3.9-4.7 %). These results attest the feasibility of code-attestable, hardware-based security assimilation into ICS embedded systems, which makes a potential gateway to open and trusted, scale-able industrial automation.
RISC-V, Trusted Execution, Embedded Security, Industrial Control Systems, Root of Trust, Secure Boot, Hardware Security
Lightweight Authentication Protocols for Secure Embedded Communication in LPWAN
Low-Power Wide Area Networks (LPWANs) have become an essential communication infrastructure enabler of embedded devices with limited resources in use-cases including Industrial Internet of Things (IIoT), precision agriculture, environmental monitoring, and smart cities. Although they are energy-efficient and offer long-range communication services, the LPWANs have fundamental security shortcomings that arise out of the nature of restrictive end devices, which are usually powered over a microcontroller, with limited processing, storage, and energy consumption limits. The conventional authentication techniques like the Transport Layer Security (TLS) or the Internet Protocol Security (IPsec) cannot be used in these cases due to the overhead they add in terms of computational resources as well as the memory space. The paper outlines a new set of light techniques of authentication originally targeted to secure embedded communication in low-power, wide-area networking (LPWAN) system with a specific reference to LoRaWAN and NB-IoT technologies. The suggested architecture is based on the combination of hash-based message authentication codes (HMAC), elliptic curve cryptography (ECC) used to conduct public-key operations, and a secure and efficient mutual identification, at the same time allowing maintaining low power consumption. The system targets gadgets using ARM Cortex-M category of microcontrollers and will make sure that it complies with the LPWAN transmission limitations. It includes a strong session key derivation scheme that gives freshness to keys and replay attacks protection. The given protocols were firstly verified with the help of Contiki OS and Cooja simulation and then tested on real devices such as STM32L476RG and nRF52840 embedded substrates. The results of experiments show that the energy efficiency is increased by 35.7 percent and authentication latency is decreased by 47.2 percent over that of the baseline LoRaWAN security. Moreover, the protocol shows a high level of robustness against man-in-the-middle (MITM), impersonation and replaying types of attacks and a compact memory profile useable in embedded devices. The paper proposes an application that provides a realistic and safe method of large-scale deployment of an LPWAN and forms the groundwork on how it can be improved in the future with post-quantum cryptography and distributed trust frameworks like blockchain.
LPWAN, Lightweight Authentication, LoRaWAN, Secure Embedded Communication, ECC, IoT Security, Low-Power Devices
Energy-Aware Hardware/Software Co-Design for Deep Neural Networks on Reconfigurable Platforms
This paper defines an energy-aware hardware/software co-design system that is optimized to achieve deep neural networks (DNNs) executions on flexible hardware, like field-programmable gate arrays (FPGAs). The framework could optimize power consumption and computational performance by coupling fine-grained runtime energy monitoring, adaptive quantization strategy, and dynamic layer-wise hardware mapping. Conventional FPGA-based solutions tend to be lacking a co-ordinated adaptation between a hardware and software level, thus making the overall energy efficiency less than ideal. The proposed method, by contrast, allows smart trade-offs with the energy consumption and model accuracy due to close coupling of the hardware/software optimisation. To validate the framework, a great amount of experimentation is run on widely used DNN models, including ResNet-18 and MobileNetV2 achieving up to 52 decrease in energy consumption and 1.8\times increase in inference throughput, relative to conventional baseline architectures that have not been co-optimized. It gives device portability that successfully supports a wide range of neural network topologies and hardware configurations, supporting it to be particularly fitted to deployment in edge-AI systems, systems with energy needs, and embedded systems, where effectiveness is of the essence.
Energy-Aware Computing, Hardware/Software Co-Design, Deep Neural Networks (DNNs), Reconfigurable Platforms (FPGAs), Edge AI Acceleration, Low-Power Embedded Systems
Graph Signal Processing-Driven Anomaly Detection Framework for Secure Smart Grid Communication Networks
The trend of the implementation of smart grid communication infrastructures with intelligent sensors, edge controllers, and other distributed energy resources have brought in improved competence and increased exposure to the risks associated with cyber-physical anomalies. Traditional procedures of anomaly detection usually fail to preserve underlying topological and time-related consequences that are unique to smart grid architectures. This paper introduces a framework of anomaly detection based on Graph Signal Processing (GSP) and is suitable to a resource-limited communication system in smart grids. The real-time power flow and communication measures can be modeled by the graph signals continuously changing in the graph domain (network topology) so the proposed framework can make it possible to detect anomalous behavior using the spectral analysis and graph-based denoising. The basic GSP processor carries out Graph Fourier Transforms, adaptive spectral filtering and residual signal reconstruction and is developed to run on embedded environments like STM32 and ESP32 microcontrollers. Experimental validation of the IEEE 14-bus and 57-bus benchmark systems establishes that the framework has detection accuracy of 94.6 percent, false positive rate of less than 2.3 percent, and the framework obtains sub-20 milliseconds latency on constrained memory (no more than 512-MB sum) and computational resources (no more than 12-cores). Real-time, edge-based GSP-based anomaly detection is possible;thus these results validate the use of GSP as a scalable and efficient anomaly detector to enhance situational awareness and resilience in next-generation smart grids.
Smart Grid Security, Graph Signal Processing, Anomaly Detection, Embedded Signal Processing, Communication Networks, Edge Computing, Spectral Filtering, IoT Security, STM32, IEEE Test Systems
Ultra-Low-Power Embedded Processor for Wearable Healthcare Monitoring
Given that more and more wearable devices focus on continuous real-time health monitoring, energy-efficient embedded processor design is needed to ensure that systems meet consumers expectations in areas such as reliability and power and performance requirements. This article is an implementation, design, and demonstration of an ultra-low-power embedded processor optimally designed towards biomedical signal acquisition and analysis of wearable healthcare apps. The recommended architecture combines with sub-threshold logic sets, on-die non-volatile ferroelectric RAM (FRAM), dynamic-clock gating, and event-driven wake-up engine, so very high energy efficiency can be achieved without losing real time responsiveness. To authenticate the processor performance, prototype system was created and connected with physiological sensors with low power consumption comprising of ECG electrodes, thermistors, and pulse oximetry modules. The processor carries out very lightweight machine learning inference including arrhythmia detection and temperature anomaly classification on quantized neural networks and decision trees through CMSIS-NN and uBoost libraries respectively. The trials were performed on both the MIT-BIH arrhythmia database and hypothetical and novel-gathered vital data incorporated into actual operational execution situations. The findings show that the suggested system has as much as 65 per cent fewer active power consumption than that of ARM Cortex-M4 or MSP430FR platform, with an average inference time of 7.8 milliseconds and overall classification accuracy of over 95 per cent on all the observed parameters. Also, the battery life was increased to more than 130 hours on a typical 240 mAh Li-ion cell, proving its applicability to long, maintenance-free wearable applications. The architecture is effective with respect to balancing computing performance and energy savings hence demonstrating the possibility of integrating the intelligent health monitoring abilities in systems that require small size and power. This paper has made a step forward in achieving low-power edge intelligence within a digital health application since it presents a highly-flexible processor architecture upon which future components that are currently envisioned to be required in biometric sensing and safe communications can be added allowing next-generation self-sustainable wearable digital health technologies to emerge in the future.
Ultra-Low-Power Processor; Wearable Devices; Embedded Systems; Healthcare Monitoring; FRAM; Sub-Threshold Logic; Event-Driven Architecture; Biomedical Signal Processing; Edge AI; Battery Life Optimization.
AI-Augmented Dynamic Partial Reconfiguration for Adaptive Edge Intelligence in FPGA-Based Systems
Edge computing systems need to support dynamic workloads with low-latency and energy-efficient, flexible processing to be able to operate in real time. A hardware platform, such as Field-Programmable Gate Arrays (FPGAs) can offer a promising platform to satisfy these requirements due to its in-built reconfigurability. Conventional static strategies of reconfiguration though, are not much efficient in adapting to variations at run time leading to poor performance. This article suggests an AI-aided Dynamic Partial Reconfiguration (DPR) system to support intelligent handling, and scheduling of partial reconfigurable regions (PRRs) in FPGAs via reinforcement learning (RL). The RL agent will dynamically choose configuration bitstreams depending on the workload profiles and environmental context in order to achieve maximum system efficiencies. On a Xilinx Zynq-7000 platform experimental validation gains are up to 42% improvement in energy efficiency and 2.1X reduction in reconfiguration latency over static and heuristic approaches. The presented solution promotes flexibility and payload delivery, which forms a scalable base of real-time intelligence delivery in the field of autonomous vehicles, smart infrastructure, and Industry 5.0.
Dynamic Partial Reconfiguration, FPGA, Edge Intelligence, Reinforcement Learning, Adaptive Computing, AI-Augmented Reconfiguration, Runtime Adaptation
A Secure Boot and Over-the-Air Firmware Update Framework for Resource-Constrained Embedded IoT Devices
The fact that embedded Internet of Things (IoT) is deployed at a very high rate in some of the most critical areas including industrial automation, healthcare, and smart infrastructure has contributed to an increase in secure and reliable firmware management needs. Nevertheless, the standard encryption procedures are not always practicable to use with ultra-low-power microcontroller as they have severe limitations in memory, computation, and power. The paper proposes a small and adaptable Secure Boot and Over-the-Air (OTA) Firmware Update Framework that is adapted to limited resource-intensive embedded IoT machines. The proposed design provides 2 stage boot loader architecture to provide secure firmware verification and delivery over the boot loader. Digital signature verification is realized using Elliptic Curve Cryptography (ECC) and Cryptographically Secure firmware is implemented using AES-GCM both to encrypt and authenticate. OTA firmware flow is over MQTT/TLS and the rollback protection is done using the version counters. It was prototyped on ARM Cortex-M series microcontrollers, and an implementation of mbedTLS and TinyCrypt libraries. Performance benchmarks demonstrate the system has just 45 ms of boot time overhead and authenticates OTA updates in 70 ms and requires little memory (Flash: +16 KB, RAM: +3 KB). High tampering, spoofing, and replay resistance is ensured by security validation. The framework is a practical, safe, and energy-effective model of end-to-end control over the firmware lifecycle on contemporary embedded IoT devices.
Secure Boot, OTA Firmware Update, Embedded IoT, ARM Cortex-M, ECC, AES-GCM, Resource-Constrained Devices, Firmware Integrity, Cryptographic Bootloader, IoT Security
Recent Advances in Low-Power Embedded System Design for Smart Healthcare Applications
The fast pace of evolution of the embedded systems along with the enhancement in the technologies of digital health have brought up the improvement of smart healthcare applications that are small, energy-efficient, and can constantly track and monitor in real-time. With the trend towards patient centric, wearable, and at-a-distance health diagnostic solutions, the need to provide systems with ultra-low power embedded platforms becomes paramount specifically where battery capacity is limited or cultures of energy independency exist. This paper provides a general overview of the latest development in the designing of low-power embedded systems that apply in smart healthcare systems where there is a pressing need to balance between the efficiency of power consumption and real-time performance and data fidelity. The dynamic voltage frequency scaling (DVFS), near-threshold computing (NTC), event-driven processing, and integration into energy harvesting are crucial methods that are highly examined relative to wearable and implantable medical gadgets. Simultaneously, the introduction of edge artificial intelligence, notably by TinyML frameworks, has provided the possibility of on-device (CPU) biosignal processing, creating the possibility of overcoming transmission overheads, providing greater privacy, and lower latency. This document will examine the hardware-software co-optimization techniques which enable effective adoption of AI models on resource-limited micro-controllers. Some practical applications of them have been discussed based on several real-life case studies: ECG monitoring with low-power MSP430, glucose sensing with BLE enabled SoCs, and fall detection based on self-powered Cortex-M platforms. Measurements of power use, inference speed, memory overhead and battery life are cross-platform compared and bench-marked. The paper also mentions important trade-offs of the design, including accuracy and energy, as well as security of data transmission, interoperability, and form factor limitations to users. Having carried this out, the paper is not only able to point out state-of-the-art solutions, also pinpoints important limitations and areas that can be researched in the future, such as designing bio-inspired neuromorphic processors, battery-less energy harvesting architectures, privacy preserving embedded AI models. The proposed work will be a useful reference guide to researchers, developers and practitioners in terms of smart healthcare design and embedded systems.
Low-power design, embedded healthcare, TinyML, wearable medical devices, energy-efficient systems, edge AI, smart health monitoring.
The Role of Embedded Systems in the Development of Smart Cities: A Review
Smart cities is a catch phrase that has gathered a lot of momentum over past few years and being used by urban planners and city government officials as tool to solve the problems of fast growing cities and to enhance living conditions for people living in the cities. Embedded systems are at the heart of this technological revolution, forming the basis of smart city infrastructure made up of compact and specialized computers. Both these systems are pivotal in collecting, processing and transmission of data across urban environments for real time decision making and automation in multiple sectors. The direction for digital transformation, where cities worldwide are embracing, needs embedded systems integration to realize the vision of truly intelligent urban spaces. These miniature technological marvels are now revolutionizing urban life with their use from traffic management and energy conservation, to public safety and environmental monitoring. In this paper, we present a comprehensive review to capture on the interplay between the embedded systems and the development of the smart city from their applications, challenges, and the emerging future opportunity.
Smart Home Devices; Embedded System Prototyping; Robotics; Wearable Technologies; Automotive Embedded Systems
Advancing Autonomous Vehicle Technology: Embedded Systems Prototyping and Validation
The arrival of autonomous vehicles (AVs) is changing the automotive industry at the speed of light. Technological innovation in these self driving marvels will change the way we look at transportation. The pivotal role of embedded systems that embed the AVs in their environment and let them perceive, decide in split second, and navigate the arena with minimal human intervention, are at the heart of this revolution. Rapid advancement of artificial intelligence, sensor technology, and embedded software development has been the journey towards fully autonomous vehicles. And as these technologies converge, they’re building an amazingly rich ecosystem within each of those vehicles, able to process enormous amounts of data in real time, and to execute incredibly precise control commands. The drivers assistance features we are used to in our basic cars today have led us to cars that can operate completely in a very complex environment. The path to AV wide adoption isn’t without hurdles, however. Embedded systems for the design and validation of autonomous vehicles have their own challenges and hurdles for engineers and developers. There are many challenges such as ensuring real time performance as well as achieving robust safety measures and integrating externally developed subsystems seamlessly. The industry has started to become more rigorous about the prototyping and validation of ideas, realizing that there’s no point in having safe, reliable autonomous vehicles on the roads without doing as much.
Medical Embedded Systems; Embedded Network Protocols; Signal Processing in Embedded Systems; Hardware Acceleration; System Integration
Embedded System Architectures Optimization for High Performance Edge Computing
Only a few years ago, embedded computing was a very different landscape. While the traditional computing architectures are reimagined to satisfy the continuously increasing demands for more powerful and efficient computing solutions at the edge, demand for them also increases. With the move to high performance edge computing, we have revisited how we design and implement embedded systems. The heart of this evolution lies in systems that need to process vast unimaginable quantities of data, in real time, some times in resource constrained environments. As a result, this has spurred the creation of highly efficient architectures constrained by performance, power efficiency, and small size. To the extent that the artificial intelligence (AI) and machine learning (ML) applications at the edge have further accelerated this trend, the boundaries have seemingly been pushed as far as they can go in terms of what’s possible in edge computing. Integrated into smaller, more efficient packages, more powerful processing units have been one of the driv ing forces for this evolution. The rise of these sophisticated System on Chip (SoC) and System on Module (SoM) solutions has seen the fitting of impressive computing capabilities into very small form factors. The emergence of these advancements has provided new opportunities for embedded systems in a wide range of domains: from autonomous vehicles to industrial automation.
Embedded Computing; Cloud-Embedded Systems; Embedded System Simulation; Wireless Communication for Embedded Systems; IoT Protocols
Battery Powered Embedded System in IoT Applications low Power Design Techniques
Nowadays thanks to the Internet of Things (IoT) thing has become so easy and simple to use because everywhere technology has brought connectivity and intelligence to simple things. Battery powered, embedded systems are at the heart of this revolution: the spine of so many products that make up the IoT. By utilizing this type of power management, these systems including ubiquitous wearable fitness trackers and remote environmental sensors can provide decades of operation time while continuously delivering optimum performance. Mastering low power design techniques is essential in develop ers and engineers working in the IoT domain as demand for IoT applications are increasing. You can say that this comprehensive guide will cover low power design for battery powered embedded systems in the IoT applications. In this second article we will go into the fundamental principles, cutting edge techniques and best practices for creating energy efficient devices, which can work for long periods of time on minimal power sources. Knowing and applying these methods developers can use the technology available in the IoT to push the limits of what is possible in the design and manufacture of IoT devices to truly functional, sustainable, and environmentally friendly.
Embedded System Prototyping Tools; Embedded System Debugging Tools; Embedded Control Systems; Real-Time Embedded Systems; High-Speed Embedded Communication
Model-Driven Design Approaches for Embedded Systems Development: A Case Study
In this paper, we develop a variety of novel approaches to design and imple ment embedded systems given the nature of these systems and their appli cation market. In an environment of increasing complexity of these systems, traditional development methods tend to underestimate the extent to which traditional tools and methodologies do not address detailed requirements and constraints of embedded application software. In response to these chal lenges, the model driven design has emerged as a powerful paradigm for streamlining the process of development of embedded systems and for im proving quality and reliability of the embedded systems. In the context of embedded system development, thisarticle explores the use of model driven design, and draws an insight from as comprehensive case stud In this paper, we hope to offer helpful insights into model driven design by examining the key principles, methodologies and tools that typically support such a design approach applied to embedded systems development processes.
Embedded System Architecture; Event-Driven Embedded Systems; Embedded Vision Systems; Embedded Artificial Intelligence; Cloud-Connected Embedded Systems
IoT-Based Embedded Systems for Precision Agriculture: Design and Implementation
Internet of Things (IoT) technologies are turning the page for the agricultur al sector. Precision agriculture is an increasingly important consideration as global food demand climbs and environmental challenges become ever more pressing, focused on maximization of crop yields, conservation of resources, and promotion of sustainable farming practices. The essence of this agricul tural evolution relies on IoT based embedded systems that are redefining how farmers track, analyze and manage their operations. In this article, we consider the design and implementation of these novel systems and what it means for the possible future of modern agriculture. Precise means of technology harness in agriculture crop management, resource allocation and decision making. IoT devices, sensors, and data analytics allow farmers to achieve a level of visibility into their fields that was otherwise unimaginable, helping them choose better and fill their fields at highest productivity while minimizing impact on the environment. IoT based embedded systems inte gration in agriculture is paradigm shift from real time monitoring, automated control and data driven strategies, which were never possible in previous times. While preparing for this journey to IoT based embedded systems for precision agriculture, we will discover how all this works and how things were put together. We deconstruct the key building blocks that make these systems both powerful and practical for use in modern farming operations: sensor networks and cloud computing, remote management interfaces, and the machine learning algorithms that make it all possible.
Embedded System Debugging; Advanced Embedded Algorithms; Digital Signal Processing (DSP); High-Level Synthesis for Embedded Systems; Embedded Systems Testing
Integration of Embedded Systems in Healthcare Monitoring: Challenges and Opportunities
We are at the dawn of a new healthcare landscape, where old and tried tech nology is being revolutionized and new, cutting edge technology that promis es to change the way patient care, diagnostics and treatment methodologies are developed and delivered are being integrated. Embedded systems are the ones at the forefront of this technological renaissance, immensely spe cialized computing devices developed to perform dedicated functions within large medical equipment and devices. But these highly sophisticated systems – comprising both hardware and software components – are redefining the very fabric of healthcare delivery, delivering unprecedented opportunities for better patient monitoring, more accurate diagnostics and more personal ized treatment. In tandem with that, we start to uncover the extensive role embedded systems play in the healthcare domain, from transforming diag nostics and monitoring to enabling remote patient care to enabling precise surgical practice. Additionally, we will also explore the difficulties associated when these technologies are deployed in a healthcare setting and what the exciting emerging trends are that will continue to reshape the medical arena.
Smart Sensors in Embedded Systems; Embedded System Security Threats; Software-Defined Radio for Embedded Systems; IoT Devices Integration
Fault-Tolerant Embedded Systems: Reliable Operation in Harsh Environments Approaches
Embedded systems are playing an increasingly important role in delivering critical applications across industries ranging from aerospace and defense to telecommunications to healthcare. These systems operate in harsh environ ments where failure carry severe consequences. Consequently, fault toler ance has become a critical necessity for embedded systems to guarantee un interrupted and steady operation under both hardware and software faults. In this paper, we discuss different approaches and techniques to improve the reliability and availability of fault tolerant embedded systems working under hard operational conditions. In the following, we will study the basics of fault tolerance, look at different fault tolerant architectures and discuss the approaches of building robust embedded systems that can bear failures and work properly.
Embedded Systems; IoT (Internet of Things); Real-time Operating Systems; Microcontrollers; Hardware-Software Co-Design
Ferreira Martins Carvalho, Teusner Perscheid
Using FPGA-Based Embedded Systems for Accelerated Data Processing Analysis
Over the past years as the technological world changes the demand of high performance energy efficient computing is at its peak. Traditional computing architectures are usually inadequate for processing the massive amount of data required by industries in real time. FPGA-based embedded systems is a pioneering path which harnesses the power of field programmable gate arrays (FPGAs) and the power of embedded processing to realize accelerated data processing. This in-depth exploration of FPGA based embedded systems covers their advantages, challenges and change that serves as an essential entry point in the journey of FPGA based embedded systems. We will cover how these systems are revolutionizing these industries -- automotive appli cations, industrial automation, telecommunications, defense -- and more in rethinking a way to solve difficult computational problems. We will dissect the inner workings of FPGA embedded systems on this journey, analyze the role they play in modern computing paradigms and best practices in imple mentations. Whether you are an engineer with experience or a techophile at heart, this article seeks to equip you with a comprehensive knowledge of this fast moving technology so you’re not just aware of it, but also ready to combine it with some elementary engineering to realize some interesting, powerful methods of data processing.
Low-Power Embedded Systems; Embedded Systems Security; Wireless Sensor Networks; Autonomous Systems; Industrial Automation
Energy Harvesting Techniques for Sustainable Embedded Systems: Design and Applications
The realm of embedded systems is undergoing a radically different transfor mation with the common emerging technologies of energy harvesting. With the growing demand for self sustaining, lasting devices, engineers and de signers are looking to inventive ways to power these systems without relying on traditional energy sources. This paradigm shift is enabling applications in the remote sensing, wearable technology as well as Internet of Things (IoT) devices. Basically, you take energy from around you and convert it into elec trical energy. This is not only an environmentally friendly and economically viable approach to lengthen the operational lifespan of embedded system but also to reduce the cost of battery replacement and maintenance. Look forward to us delving further into this exciting and interesting space as we explore the many techniques, design considerations and real world applica tions playing a part in creating this sustainable embedded systems future.
System-on-Chip (SoC); FPGA (Field-Programmable Gate Array); ASIC (Application-Specific Integrated Circuit); Embedded Software Development; Energy-Efficient Design
Trends in Software Development for Embedded Systems in Cyber-Physical Systems
Current landscape for embedded systems is gradually shifting due to developments and trends in Cyber Physical Systems and Internet of Things (IoT). In the overall march into the year 2024 and beyond, software development for embedded systems is shifting in a direction that will suit the new world of interconnectivity and smart devices. This article covers the most exciting trends in today’s embedded systems and the directions where the future development of the sphere lies: from new types of security as the basis of modern systems to the progressive architecture and electronics. Currently, the overall embedded systems market in the world has a very high growth rate; according to the forecasts, it will increase from 91.95 billion USD in 2024 to 124.80 billion USD by 2029, CAGR 6.3 %. This expansion demonstrates how importance is the place of the embedded systems application in healthcare, automotive, industrial automation, and consumer electronics industries. With the advancement and advancement of those systems, there are new problems and prospects in building more resilient and dependable solutions. In the next few sections, we will be seeing how these and other trends are changing the embedded systems development, discussing methodology, hardware issues and software implications. These trends are quite existing in the future development of embedded systems in cyber-physical systems involving artificial intelligence, use of machine learning, connectivity technologies, and cybersecurity.
Machine Learning in Embedded Systems; Predictive Maintenance with Embedded Systems; Multi-Core Embedded Systems; Embedded System Applications
Embedded Systems for Real-Time Traffic Management: Design, implementation, and challenges
In the current world, blurred borders of structures and an ever-expanding tendency towards the creation of bigger and more megalopolis like city structures, traffic management has become one of the important considerations for city and transport planners. The enhancement of embedded systems and the connection with IoT presents immense potential to change the current paradigm of traffic monitoring, analyzing and controlling in real-time. In this article, the author discusses the specifics of the use of embedded systems for real-time traffic control, including the concepts of such systems, their application methods, and the difficulties arising from the integration of such powerful instruments into today’s complex urban planning systems. When cities expand, and more people own cars, conventional techniques to handle traffic fail to solve problems of congestion and increased risk. Enter embedded systems – small and dedicated computers designed to solve a particular set of problems most effectively and without such problems interfering with other problems. When used in traffic management these systems are the basis of intelligent transportation systems, their main components are responsible for data acquisition, processing and management in order to optimize traffic flow and improve road safety.
Autonomous Vehicles; Embedded System Design Tools; Mixed Signal Systems; Memory Management in Embedded Systems; Embedded Systems Optimization
R. Marwedel, U. Jacobson , K. Dobrigkeit
Ultra Low Latency Communication in Wireless Sensor Networks: Optimized Embedded System Design
With the fast development of wireless technologies and embedded systems, dramatic applications of such technologies in industrial automation, healthcare monitoring, and smart infrastructure have been achieved. The critical need of highly reliable and fast communication between these sensors, actuators and controllers underlies these applications. From real time data transmission with your wireless sensor network to the edge of the envelope, this article explores cutting edge techniques and protocols for designing embedded systems to communicate with ultra low latency in wireless networks. We shall explore the challenges of millisecond level responsiveness engineers and researchers meet while developing embedded system. We will study the multiphased method to build communication systems that can satisfy stringent control specifications from hardware as well as software optimization. Welcome to our journey in the wireless communication world with low latency where every micro second matters and innovation is driving the future of the connected devices.
Hardware Descriptions for Embedded Systems; Embedded System Modeling; Embedded System Reliability; Communication Protocols in Embedded Systems; Cyber-Physical Systems
The Evolution of Embedded Systems in Smart Wearable Devices: Design and Implementation
The rise of smart wearable devices has been dramatically changing the landscape of personal technology. These tiny, body worn gadgets have completely transformed how we deal with technology, how we monitor our health and how we lead our daily lives. Embedded systems are what lies at the heart of this wearable revolution, the hidden intelligence that enables these devices to do what they do. With the growing consumer demand for wearable technology, the embedded systems that power these devices are rapidly growing. Whether it’s fitness trackers that are watching every step we take or smartwatches that ensure we’re connected on the run, the sophistication of embedded software and hardware in today’s wearables is in no shortage of reach. The journeying to the next level of what is possible with miniaturized, energy efficient computing is under way. In this monograph, we inherit the fascinating world of smart wearable devices embedded systems. In this article, we’ll examine the latest design principles, implementation strategies, and technological innovations that define the future of wearable tech. We will see how intimate details figure out how modern wearables operate from power management challenges to sensor integration breakthroughs. In this talk, we take a deep dive into embedded systems in wearables, to understand the challenges and see how the latest advances are making wearables not just fancy accessories but embedded powerhouses that are enabling entirely new forms of interaction with the world around us. This is a journey through the world of the embedded systems that you might be either a tech enthusiast, a developer, or a person that wants to know how your favorite wearable gadget actually works.
Embedded System Power Management; Networked Embedded Systems; Real-Time Data Processing; Power-Aware Embedded Systems; Flexible Embedded Systems
Revolutionizing Industry: Real Time Industrial Automation using Embedded Systems
Embedded systems are the unsung heroes of innovation today across all of our industries, in a rapidly changing technological landscape. Small, versatile computing technologies are helping to revolutionize the way in which industrial automation is approached, leading to smarter, more efficient, more responsive manufacturing processes. And as we get into embedded systems and how embedded systems are changing the face of real time industrial automation, we’ll talk about how embedded systems are taking control and shaping the future of manufacturing and putting us on the path to Industry 4.0. With the embedding of systems in industrial automation comes the shift of a paradigm, from which has come unprecedented levels of precision and control, and efficiency of manufacturing processes. Thanks to the industrial revolution, embedded systems are also a beginning to streamline production lines, improve quality control or simplify supply chain management. Harnessing the power of these intelligent, purpose-built computing solutions allows industries to optimize operations, but also helps to discover innovative and competitive insights that will strengthen global market position. In our journey to fully understand embedded systems in industrial automation, we’ll discover the main motivations behind the ever growing uptake of embedded systems, see how they are implemented in multiple applications across various sectors, and gain a glimpse into the future of this pioneering technology. Over the next few chapters, strap yourselves in as we make our way through the complex territory of embedded systems, examining their role in the reconfiguration of industrial automation, chip by chip.
Advanced Robotics Embedded Systems; Smart Grid Systems; Embedded System Firmware; Embedded System Communication; Low-Level Embedded Systems Programming