Progress in Electronics and Communication Engineering
Aim: The aim of "Progress in Electronics and Communication Engineering"(ISSN -3048-7625) is to provide a prominent platform for researchers, academics, engineers, and industry professionals to disseminate their latest findings, innovations, and advancements in electronics and communication engineering. The journal aims to foster collaboration and knowledge exchange within the research community, accelerate scientific progress, and contribute to the development of cutting-edge technologies and solutions that address real-world challenges in the field. 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 readers seeking up-to-date information and insights in electronics and communication engineering. Subject Covered: "Progress in Electronics and Communication Engineering" is a peer-reviewed journal dedicated to publishing high-quality research articles, review papers, and technical notes in the field of electronics and communication engineering. The journal covers a broad range of topics, including but not limited to semiconductor devices and circuits, telecommunication systems, wireless communication, signal processing, embedded systems, Internet of Things (IoT), photonics and optoelectronics, nanoelectronics, electromagnetic theory and applications, and VLSI design and testing. Frequency of publication - Bi Annual - It has Two issue per year Language - English Subject - Engineering Year of Starting - 2024 format of publication - Online Only ISSN - 3048-7625
SOCIETY FOR COMMUNICATION AND COMPUTER TECHNOLOGIES
A Secure Boot and Firmware Update Framework for ARM Cortex-M Microcontrollers in Industrial IoT Environments
This paper aims at developing and implementing a secure boot and firmware upgrade framework that is uniquely tailored to ARM cortex-M microcontrollers, industrial internet of things (IIoT). Industrial use Industrial controllers using these microcontrollers are common and have low power requirements, and are real-time systems but are becoming exposed to firmware level attacks like code injection, code tampering and code rollback attack. To cope with this, we suggest the idea of a lightweight two-stage boot loader architecture with proper initialization security and authenticated program execution. The architecture uses elliptic curve cryptography (ECC) to verify the digital signature, SHA-256 to check integrity validity, and support confidentiality of the otherwise optional firmware used in over-the-air (OTA) updates using AES-GCM. There is also a secure check-in/check-out system that is built so that it can prevent a rollback attack. It is verified on STM32 Cortex-M4 and M33 microcontrollers and has a low memory cost (+14 KB Flash, +3 KB RAM) and no significant latency effects (<40 ms boot delay). The findings support the fact that the framework offers robust protection against unauthorized modification of the firmware down to the minimum of devices that are resource-constrained. The work scales up and standardizes a security solution that contributes to the safe embedded system design practices of the U.S. IIoT sphere. The framework is more appropriate in industrial control systems, intelligent factory, and critical infrastructure in which embedded trust and firmware integrity are necessary.
Secure Boot, Firmware Update, ARM Cortex-M, Industrial IoT (IIoT), Embedded Security, Over-the-Air (OTA) Update, Cryptographic Authentication, Lightweight Security Framework, Microcontroller Security, Trusted Execution, Firmware Integrity, Anti-Rollback Protection, Secure Embedded Systems.
Neuromorphic Computing Architectures for Low-Power Edge Inference in IoT Applications
The blistering advancements witnessed in the Internet of Things (IoT) ecosystem have led to the urgency of search outcomes that provide energy efficient, low latency, and smart processing support that manifests itself right at the edge. The traditional edge computing devices built on von Neumann architecture are inherently limited by being optimized to be adjacent in memory and processing components, which results in the high power required and latency, which are highly undesirable in devices with limited power resources; wearables, smart sensors, and remote-monitoring systems. A Neuromorphic computing approach based on the human brain blueprint presents a better alternative due to its implementation of spiking neural networks (SNNs) that allow asynchronous and event-driven computation, thus dramatically lowering power demand and yet being able to run real-time inference. This paper includes a holistic comparison of the most recent examples of the neuromorphic hardware platforms, such as Intel Loihi, IBM TrueNorth, and BrainScaleS system, with regard to their learning architectures, energy consumption, throughput, and suitability to the edge-based IoT applications. An empirical case study is carried out, which is a keyword spotting case study and event-based gesture recognition case study using Google Speech Commands and the DVS Gesture dataset sets, respectively. The neuromorphic systems presented a significant energy per inference improvement up to 15x and reduced latency at about 30 on traditional ARM Cortex-M7-based ANN implementations at very slight sacrifices in terms of classification accuracy. In addition, the Loihi platform showed on-chip learning utilizing spike-timing-dependent plasticity (STDP), which made the platform capable of dynamic and adaptive inference in fluctuating IoT applications. This paper also examines the integration issues of neuromorphic systems which include the compatibility with sensor modalities, immature software toolchains, and complexities to convert ANN to SNN. The results indicate that neuromorphic systems are highly suitable in ultra-low-power edge intelligence and provide a paradigm of scalable and biologically inspired, next-generation IoT systems. The lines of future research are described in such aspects as hybrid neuromorphic edge-cloud learning, unification of benchmark requirements, and designing secure, programmable, and reconfigurable neuromorphic co-processors to support adaptive edge AI.
Neuromorphic computing, edge inference, IoT, spiking neural networks, low-power AI, event-driven processing, Loihi, TrueNorth
Robust and Scalable LPWAN Architecture with Integrated Security for Industrial IoT Applications
Industrial Internet of Things (IIoT) is revolutionizing the means of critical infrastructure through real-time monitoring, automation and making smart decisions. The Low-Power Wide-Area Networks (LPWANs) like LoRaWAN, Sigfox, and NB-IoT are highly essential in enabling low energy, long-range two-way communication in IIoT-related use cases. Nevertheless, current implementations of LPWAN usually have serious gaps related to scalability, robustness, and security applied in an industrial world. In this study, a powerful and scalable framework of LPWAN is suggested combined with lightweight, multiple-layered security solutions depending on the specific requirements of IIoT communication. The framework provides hierarchical and intelligent adaptive network topography, dynamic clustering and intelligent gateway coordination. Its security is enforced by using symmetric encryption, key exchange on Elliptic curve and Device authentication using HMAC. Scalability and resilience are achieved by the architecture by incorporating adaptive channel allocation, fault-tolerant routing, and anomaly detection with edge intelligence in real-time. NS-3 simulation-based assessment with realistic industrial traffic patterns shows that a packet delivery ratio is more than 95 percent, latency reduces by up to 40 percent in crowded settings, and cryptographic overheads are less than 5 percent. The offered solution enhances the resistance to seemingly typical IIoT attacks (jamming, replay, node impersonation, etc.) considerably without affecting energy and latency consumption. The resultants substantiate the architecture in its ability to support secure and scalability of IIoTs and further research will integrate the architecture with real-life applicative industrial testbeds and blockchain efficient trust mechanisms.
Industrial IoT (IIoT), LPWAN, Security, Scalability, Robustness, LoRaWAN, Edge Intelligence, Lightweight Cryptography
High-Efficiency Solar-Piezo Hybrid Energy Harvester for Long-Term Autonomous Operation of Smart Agriculture Sensor Nodes
The paper presents the design and consideration of a highly efficient hybrid energy harvesting system, combined solar and piezo energy harvesting components to power autonomous wireless sensor nodes (WSNs) used in smart agriculture. The most important goal is to provide a maintenance free, long term operation of the sensor nodes in the envisioned variable conditions of environment. The suggested system consists of a monocrystalline solar panel and piezoelectric transducers that allow to focus both ambient light and mechanics vibrations. In-order to get maximum energy extraction, intelligent maximum power point tracking (MPPT) based power conditioning circuit is implemented, and the energy generated is stored using hybrid supercapacitor-Li-ion battery system. Up to the area of evaluation of the performance of the designed system in the real conditions, a particular WSN platform was created and deployed both in a greenhouse and an open field setting. The experimental output shows that the hybrid energy harvester records a 30-45 percent rise in energy capture than the solar-only systems. Moreover, efficiency of conversion of power is increased by about 22 % by using the MPPT implementation. The sensor nodes exhibited an outstanding reliability, surpassing 98 per cent of uptime in a 30-day non-stop deployment, and over 12 hours working autonomously without any illumination. The innovation in this study is the strength of the energy harvesting system, where supplementary sources are also utilized to increase the resilience and extend the operation weeks of smart agriculture WSNs, considering the shortcomings of using single sources in the uncontrollable outdoor situations.
Hybrid energy harvesting, solar energy, piezoelectric transducer, smart agriculture, wireless sensor nodes, MPPT, energy efficiency, autonomous operation.
Spiking Neural Network-Based Neuromorphic Signal Processing for Real-Time Audio Event Detection in Low-Power Embedded Smart Sensors
The following paper presents the design of a spiking neural network (SNN) based neuromorphic signal processing system that can be used in low power embedded smart sensors to perform real time detection of audio events. In this regard, the aim is to overcome the shortcomings of traditional deep learning frameworks that are both computationally heavy and demanding in terms of power consumption such that they are incompatible with battery-run or energy-limited edge devices. Citing the performance of biological neural systems, the presented framework should utilize event-driven SNNs executed on neuromorphic platforms to identify notable acoustic situations (like alarms, speech commands and ambient environmental sounds). The architecture envisages audio preprocessing part, audio spike encoding, and spike temporally contrast encoding or Poisson encoding, and a multi-layer core SNN trained by surrogate gradient descent. The model is tuned to run on an Intel Loihi and ARM Cortex-M7 microcontroller. The standard datasets were used to assess the performance, such as Google Speech Commands, ESC-10 and UrbanSound8K. Using the experimental results, it can be proven that the SNN-based model has up to 92.7% correct classification accuracy, using < 5mW power with a latency below 20 milliseconds. SNN approach is equivalent in accuracy to conventional CNN baselines in many cases with much lower energy consumption. Results confirm the prospect of SNNs at ultra-low-power, real-time signal processing in future generation edge-AI acoustic systems. The research directions will look at hardware-aware training and learning that can be used to improve on adaptability and scalability of embedded smart sensing systems.
Spiking Neural Network, Neuromorphic Computing, Audio Event Detection, Low-Power Sensing, Embedded AI, Edge Intelligence, Real-Time Signal Processing
AI-Driven Smart Irrigation System Using Edge-Based Embedded Controllers
This study brings out a new AI-based smart irrigation device aimed at solving the increasing problem of water shortage and poor agricultural procedures using emulated edge-based controllers. This combination of environmental sensing, lightweight machine learning algorithms and actuation on microcontrollers enables the system to do real time, adaptive irrigation scheduling. Their implementation of important parts like Raspberry Pi 4 as an AI inference module and an STM32 microcontroller as an actuator controller accelerates the responsiveness, increases security, and saves energy consumption since there is no need in cloud computing. Using the local climatic and soil moisture data the machine learning model identifies optimum irrigation time and duration looking at various parameters such as temperature, humidity, rain and crop-specific thresholds. The whole decision-making process is automated and carried out at the edge that makes it functional even without interruption in areas with poor or no internet connection. This was done by conducting extensive field tests within the semi-arid agricultural areas to prove the efficacy of the recommended solution. The findings show that there is a considerable gain in the efficiency of resources, with up to 38 percent of reduction of water use and 20 percent of increase in the yield of the crops contrasted to the conventional time-based irrigation schemes. Also, the system averaged a latency of only 34millisecs and consumed power at less than 2.5W which became very handy in deployment in rural areas. TinyML and its compatibility with cheap edge hardware are scalable and sustainable, which creates an efficient path to modernize the irrigation infrastructure in the developing world. This article does not only highlight the revolutionary nature of embedded AI in precision agriculture, but it precondition the further innovative development of the field of data-driven farm management and autonomous farm management. The one suggested is one of the options that could be implemented to achieve environmental sustainability, enhance the productivity of agriculture, and make smart and local decisions to use water intelligently.
Smart Irrigation System; Edge Computing; Embedded Controllers; Artificial Intelligence (AI); Precision Agriculture; TinyML; STM32 Microcontroller; Raspberry Pi; Environmental Sensing; Water Conservation; IoT in Agriculture; Real-Time Decision Making.
Real-Time Streaming Analytics for Predictive Fault Diagnosis in Cyber-Physical Energy Infrastructures
Cyber-Physical Energy Infrastructures (CPEIs) are becoming more complex and networked, and therefore there is an increased risk of failures occurring, especially the unexpected ones, thus predictive fault diagnosis becomes imperative to assure reliability and continuity of operations. Conventional methods of diagnosis are based on batch analysis and off-line-work and are exceedingly inadequate to deliver speedily and rapidly accessible understanding to make appropriate real-time decision-making in transient energy arrangements. The study offers a real-time streaming analytics solution that allows the early identification and forecast of faults in CPEIs that involve real-time processing of data streams received by various sources of heterogeneous data, including phasor measurement units (PMUs), smart meters, and SCADAs. It is a framework that incorporates edge computing, distributed stream processing (Apache Kafka and Apache Flink), light machine learning (Long Short-Term Memory (LSTM) networks and Random Forest classifiers). Edge preprocessing of real-time data is done on edge, and predictive models are updated on the fly to enable low latency anomaly detection and fault classification. It was verified through a simulated 33-bus IEEE test network with injected faults achieving sub-second fault prediction accuracy of up to 96.4 percent. The system is also scalable and fault tolerant when data loads and edge deployment scenarios change. Such findings reveal that the given architecture would work effectively and is scalable to the predictive fault diagnosis of CPEIs, contributing to the resilience of the grid and allowing proactive maintenance approaches in intelligent energy management systems.
Streaming analytics, predictive fault diagnosis, cyber-physical energy systems, edge computing, real-time anomaly detection, smart grid, machine learning.
Transformer-Based Deep Feature Learning for AI-Enhanced Fault Diagnosis in Deep Submicron VLSI Circuits
The paper introduces an artificial intelligence fault diagnostic system based on transformers to diagnose any fault(s) in deep submicron Very-Large-Scale Integration (VLSI) based circuits accurately and efficiently. Scaling in VLSI inevitably makes fault localization and classification harder because all models are highly sensitive to noise, variability, and smaller sizes in feature. The goal of the research is to scale a diagnostic model that could measure complex temporal and spatial correlations in circuit signal data. The suggested solution makes use of Deep feature learning, transformer, and multi-head self-attentions to make the model able to capture the long-range dependencies in the test waveforms and logic signatures. The architecture is trained on simulated datasets produced using industry-standard ISCAS, and ITC99 benchmark circuits, and extensive fault-types including stuck-at, bridging and delay faults. Examples of evaluation criteria are diagnosis accuracy, inference speed, and generalization in the face of variations over the processes. The experimental data demonstrate that transformer model works better compared to other traditional CNN and LSTM baselines, as the new model presents 96.3 percent of fault classification accuracy and 30 percent of inference time reduction. The model records a solid noise tolerance in the test data and generalization across circuits too. To summarize everything, this study concludes that transformer-based architectures are effective to improve VLSI fault diagnosis, and offers practical evidence to future AI-based post-silicon validation tools, capable of real-time diagnosis in the state-of-the-art semiconductor fabrication facilities.
Fault Diagnosis, VLSI, Deep Submicron, Transformers, Feature Learning, AI, IC Testing, Self-Attention, Fault Classification
Secure Boot and Firmware Update Framework for ARM Cortex-M Embedded IoT Devices
The paper presents a lightweight and well-impervious security framework suitable to ARM Cortex-M-based IOEs embedded devices, which will serve an important purpose of presenting secure boot and firmware update operations in resource-constrained systems. As IoT implementations and applications in sensitive parts of our life like healthcare, smart infrastructure, and industrial control systems increase exponentially, embedded devices become more exposed to firmware tampering, write malicious code and update securities. The framework that is proposed addresses such threats by employing a multi-layered cryptographic approach, suitable to the framework by symmetric encryption with the use of AES-GCM, offering confidentiality, elliptic curve digital signature algorithm (ECDSA) providing authentication, and the SHA-256 hash to provide integrity verification. A trusted bootloader (« a procedure incorporating cryptographic functions used to secure a bootloader implementation »,) in a trusted ROM who administers cryptographic verification of the firmware signature with the help of a hardware-based root of trust prior to handing off control to the application firmware. As an additional measure the rollback protection is implemented with the help of version control to avoid firmware downgrade until known vulnerabilities are eliminated. The firmware update process allows encrypted over-the-air ( OTA ) update process to execute safe remote updates, storing the data maliciously and risking transmission. An atomic and fail safe update process is achieved through double-buffering. The experimental system was used and tested on STM32F429 Cortex-M4 microcontroller, and it successfully detected unauthorized modification of the firmware and was able to withstand rollback attacks. Performance evaluation with the bootloader as the benchmark shows the latency was only 10 milliseconds and the number of Flash memory usage was less than 0.05 percent. These outcomes confirm the application of the framework in the low-power and memory-constrained embedded systems without undermining state-of-the-art security assurances. The approach is compared to the current solutions in security to show a very usable set of efficiency, scalability and ease of implementation, which makes the presented approach an optimal one to be used in production IoT systems. A discussion on future improvements is provided wherein they would want to integrate it with hardware security modules and supporting quantum-resilient cryptographic primitives that would make the system secure in the long term.
Secure Boot, Firmware Update, ARM Cortex-M, Embedded Security, IoT, OTA, Cryptography, STM32, Root of Trust
Terabit-per-Second Photonic–Nanoelectronic Devices for Data Center Interconnects
The cloud system as well as the workload and high-performance data analytics have been experiencing an exponential growth leading to unprecedented demand of ultrafast/energy-efficient data center interconnects (DCIs). Traditional electronic transceivers are more and more confined in band-density, power burning and signal uprightness at far reaches. We describe a terabit-per-second photonic nanoelectronic DCI architecture comprising silicon photonics, plasmonic modulators and novel CMOS-compatible nanoelectronics which integrates with the rest of the system. The combination of wavelength-division multiplexing (WDM), compact photonic integrated circuits (PICs) and low-power modulation allow the proposed system to provide an aggregate throughput of more than 1Tb/s per link and an energy efficiency of less than pJ/bit per link. Photonic components and nanoelectronic circuitry were modelled on a device-level basis in Lumerical FDTD and Cadence Spectre respectively, with system-level assessments made using VPItransmissionMaker on WDM link optimization. Simulation results show a 1.6 Tb/s link capacity, 0.28 pJ/bit and a reach of 2km with negligible BER penalty compared to their competitors which include conventional CMOS SerDes and stand-alone silicon photonics. This paper demonstrates the promises of photonicnanoelectronic co-design to next-generation hyperscale data center deployment providing a scalable low-latency and thermally resilient platform to future cloud and AI-enabled computing infrastructure.
Terabit-per-second interconnects, photonic–nanoelectronic integration, silicon photonics, wavelength-division multiplexing (WDM), plasmonic modulators, co-packaged optics (CPO), data center interconnects (DCIs), energy-efficient transceivers, high-speed optical communication, nanoelectronic driver circuits, photonic integrated circuits (PICs), low-power optical modulators, optical interconnect architecture, hyperscale computing, bandwidth–energy trade-off.
Secure and Scalable LPWAN Architectures for Industrial Internet of Things (IIoT)
With the blinding pace the process of the Industrial Internet of Things (IIoT) development acquires new and rather unobjectionable definitions, shaping up the traditional industrial systems with assistance through the interconnection of sensors, actuators, and control units into smart, distributed networks. In the middle of this change there is a necessity of energy efficient, long-range and scalable communication technologies that become reliable options in resource constrained and harsh industrial setting. Low-Power Wide-Area Networks (LPWANS) such as LoRaWAN, NB-IoT and Sigfox have become an important facilitator of IIoT because they consume less power, offer longer coverage and require lower deployment costs. Nevertheless, with the IIoT endpoints increase exponentially, the current LPWAN solutions suffer major setbacks in terms of security through device authentication, data confidentiality, scalability, and physical and cybersecurity attack resiliency. We present in this paper a new architecture of LPWAN customized to secure and wide-ranging IIoT integration, which exploits the manipulation of intelligence and security capabilities of a hierarchical edge-cloud paradigm representative of the distribution of those capabilities throughout the network. Lightweight cryptographic elements are used by the architecture such as elliptic curve-based mutual authentication, AES-CCM as payload encryption, and dynamic cryptographic keys exchanged using ECDH to provide data integrity and confidentiality without violating device energy budgets. Moreover, to resolve scalability, adaptive MAC scheduling, gateway clustering, edge-based data filtering is used to process millions of devices in terms of small latency and packet collision. A detailed simulation with NS-3 is made to compare the proposed architecture with the existing structures in terms of main performance indicators such as the packet delivery ratio, latency, energy consumption, and protection against eavesdropping and replay attacks, node spoofing, etc. Evidence suggests that the proposed framework delivers successful rates (>96%), has low latency (<1 second) and strong security postures, and thus is well suited to industrial use cases like predictive maintenance, smart grid and factory automation. Through a comprehensive approach to the issues of security and scalability, the piece of work presents an exemplary basis upon which to implement credible LPWAN infrastructures in the dynamically growing IIoT environment.
LPWAN, IIoT, LoRaWAN, NB-IoT, Secure Communication, Edge Computing, Key Management, Scalability, Industrial Automation, Lightweight Cryptography, Distributed Architecture, IoT Security, Cloud-Edge Integration
Design of Ultra-Low-Power Photonic Interconnects for Next-Generation System-on-Chip Communication Architectures
The paper is relevant to/concerned with the critical problem of interconnect energy efficiency enhancement in System-on-Chip (SoC) systems with the increasing load of artificial intelligence, edge, and high-performance applications. The current electrical interconnects are subject to physical and power scaling limits, making the key consideration in possible photonic interconnects one that can be considered in light of being low-energy and high-speed. New Designs For low power 3D pycnointerconnectthe design features, microring resonators, optical modulators, and waveguides made of silicon with low insertion loss and drive voltage. An electronic-photonic interface is developed, a hybrid electronic-photonic interface and could be used to bridge voltage domains with minimum dynamic energy overhead. The 7nm FinFET CMOS technology along with silicon photonics is used to implement the system. Simulation and testing show that energy-per-bit is less than 0.5 pJ/bit, data rates (>=20 Gbps) and an area overhead are low. Its interconnect has high thermal robustness, staying functional at +/- 20-degree C. The architecture is a response to the needs of SoC systems with a high-bandwidth and low-latency and power-constrained system architecture. It is more so appropriate to software scalable AI acceleration, chiplet designs, and heterogeneous multi-core systems. Directions are moving towards dynamic wavelength allocation, integration with machine learning workloads and transitioning to chip-scale prototyping to permit commercialization. It is the contribution of this work to make a scalable energy-efficient communication backbone of futuristic smart systems and continuous attempts towards energy-saving semiconductor technology innovations.
Photonic Interconnects, Ultra-Low Power, System-on-Chip (SoC), On-Chip Communication, Silicon Photonics, Microring Resonators, CMOS-Compatible Photonics, Energy-Efficient Interconnects, Optical Network-on-Chip (ONoC), High-Speed Data Transfer
Graph Signal Processing-Based Anomaly Detection Framework for Smart Grid Communication Networks
This research is aimed at developing a graphical framework of anomaly detection to improve cybersecurity of smart grid communication infrastructure. In the ever more digital-powered systems, smart grids have gained ground to use more elaborate communication systems, which facilitate real-time monitoring and control. Yet, this development brings with it systems susceptibilities that the more antique kinds of anomaly detection techniques which we are mostly non-topology-respecting really cannot do so much to help with. To address these shortcomings, instead, we suggest a Graph Signal Processing (GSP)-based framework, which casts the communication network in the form of a graph and views system metrics (e.g. the traffic volume, a latency measurement, or a voltage reading) as signals over this graph. We use graph Fourier transform, low-pass filtering and spectral residual analysis to identify localized and global anomalies using our approach. This circumvents exploitation of the spatial and structural data in detecting the non-normal behavior in real-time. The framework is benchmarked on both a mix of synthetic smart grid models and real-world data sets on communication, modeling different cyber and physical attack scenarios. The results indicate a detection accuracy rate of over 96 per cent and minimal false positive rates (<4 per cent) and detection latency of less than one second, which is better in comparison to conventional statistical and ML-based techniques. It offers the U.S. smart grid the scalable, explainable and real-time anomaly detection engine that fits the objectives of critical infrastructure security and resiliency agendas of the nation.
Graph Signal Processing (GSP), Smart Grid, Anomaly Detection, Communication Networks, Cybersecurity, Graph Spectral Analysis, Power Systems, Network Monitoring, Real-Time Detection, Graph Topology.
Self-Powered Solar-Piezoelectric Energy Modules for Resilient IoT Sensor Networks in Agri-Environmental Monitoring
The growing demand of stable, self-governing, green monitoring frameworks in smart farming has expedited advanced-energy insight into Internet of Things (IoT) sensor networks. This paper presents a fresh idea of a self-powered hybrid energy harvesting unit utilizing both solar and piezoelectric energy sources to allow continuous electricity supply to the wireless sensor nodes (WSNs) in agri-environmental set-ups. The system is also smart enough to adjust to the varying conditions of the environment taking advantage of solar energy when the rays are at work, and capturing vibration energy in the form of wind or mechanical activity when the light is dimmed. It has an embedded power management unit to guarantee effective switching and storage of energy facilitating battery-free usage. The actions of the hybrid system are tested both in simulation and their field-deployable prototypes. Findings show a better energy availability, resilience, and lifetime of the system compared to the traditional single-source harvesting methods. The architecture proposed shows that there is a high potential of a scalable and low-maintenance implementation of IoT-based monitoring precision agriculture and environmental sensing.
Hybrid Energy Harvesting, Solar-Piezoelectric, Smart Agriculture, IoT Sensor Networks, Resilient Monitoring, Battery-Free IoT, Energy-Aware Computing
Photonic Crystal Fiber Biosensor for Continuous, Non-Invasive Glucose Monitoring: Design and Performance Analysis
A newly designed and tested photonic crystal fiber (PCF)-based biosensor targeted at continuous, non-invasive glucose monitoring is presented in this study and the focus was on realizing high sensitivity, resolution and biomedical compatibility. A hexagonal-lattice PCF architecture was constructed wherein an analyte layer containing a glucose sensitive layer was incorporated in the core. The FEM was applied to predict the operation of the sensor with propagation of light and estimation of the important optical parameters, such as effective refractive index shifts, the sensitivity to wavelength, confinement loss and modal behaviors in the physiologically relevant glucose concentration range. The output of the simulation showed a linear spectral signature to glucose changes with high selectivity of 8100 nm/RIU and minimal low confinement losses of less than 0.05 dB/cm. The sensor was also validated to operate stably to both thermal and structural fluctuations, which make it suitable in real time applications. In general, the suggested PCF biosensor is a small, sensitive, and biocompatible biosensing platform that enables non-invasive admission of glucose by using biofluids, saliva, or tears, and has high commercial possibilities in incorporating the system into wearable and implantable medical gadgets.
Photonic Crystal Fiber, Glucose Biosensor, Non-Invasive Monitoring, Refractive Index Sensor, Finite Element Method, Biomedical Sensing
Low-Latency Communication Protocol Design for Ultra-Reliable IoT Applications in Smart Cities
Within the fast changing smart cities environment, the implementation of time-critical and ultra-reliable applications of IoT (e.g., intelligent transportation networks, emergency response systems and remote health care monitoring) require the creation of communication protocols that are able to send information to the user with a very low latency and a high probability. Current existing IoT communication protocols and standards such as IEEE 802.15.4 and 6LoWPAN and so on are not optimized enough to support the strict rationale of ultra-reliable low-latency communications (uRLLC) especially towards dense and dynamic urban markets. In this paper, a new communication protocol is developed which directly contends with these challenges by adapting the contention window scaling, lightweight forward error correction (FEC) and cooperative relaying techniques into one unified protocol stack. The cooperative relay cuts across the network and enhances transmission resilience in the non-line-of-sight (NLOS) prevailing environment often deemed in urban topology, and the adaptive contention control mechanism implements dynamic adjustment of parameters in MAC layer according to the network congestion and data priority in real-time. Lightweight FEC codes also incur minimal overhead Jain helping to avoid incurring considerable processing overhead whilst still reducing packet loss. The suggested protocol was rigorously tested on simulations in NS-3 under diverse aspects of smart city implementation with variable density of nodes and mobility follow various directions and speed. Performance indicators (end-to-end latency, packet delivery ratio (PDR) and energy efficiency have also been contrasted with baseline protocols. The findings show that the proposed system can offer a latency reduction of up to 40% and a better reliability of about 35% keeping a PDR higher than 99.999 percent in case of high network load. Moreover, the protocol is well-scaled to the congestion of nodes with a steady performance in the cases of up to 500 nodes per square kilometer. These results confirm the protocol capabilities in meeting the high communication requirements of smart city IoT infrastructures. The work triggers the implementation of mission-critical services in the city conditions, which help to achieve the goal of resilient, intelligent, and responsive smart cities.
Low-latency communication, ultra-reliable low-latency communication (uRLLC), smart cities, IoT protocols, cooperative relaying, adaptive MAC layer, real-time data transmission, protocol design, urban IoT, reliability optimization.
Design and Optimization of Energy-Efficient VLSI Architectures for Edge AI in Internet of Things (IoT) Applications
The demand of the edge of the network in processing Artificial Intelligence (AI) in real time and in low power has more increased due to the rapid growth in the number of Internet of Things (IoT) devices that are being connected to the network. Traditional cloud-based AI systems are also plagued with high latency, power and bandwidth, which makes them not ideal in large-scale time-bound IoT applications. The current work develops an overall exploration of energy-efficient Very Large-Scale Integration (VLSI) architectures and optimization of architecture to fit Edge AI applications. The targeted approach combines low-power design techniques, such as, approximate computing, clock gating, quantization and dataflow-driven hardware acceleration, to minimize the power use without compromising accuracy of inference. The architecture uses parallel processing units, light neural network models, and near-memory computing to reduce overhead of transferring data. They are implemented on FPGA prototypes and on post-layout VLSI simulations in a 28 nm CMOS process, up to 42% in power savings, 28% in latency and 15% in throughput over accelerators with the baseline design. Such findings highlight the promise of dedicated VLSI architectures to feasibly provide long-term, scalable and intelligent computing at the edge to support next-generation IoT ecosystems.
VLSI Architecture, Edge AI, Internet of Things (IoT), Energy Efficiency, Low-Power Design, Approximate Computing, Hardware Accelerators
AI-Driven Optimization Strategies for 6G Wireless Communication Systems: Advanced Architectures, Intelligent Algorithms, and Real-World Applications
The sixth generation (6G) wireless communications systems are proposed to achieve transformative functionality, such as terabit per second data rates, sub-millisecond latency and seamless interconnection of terrestrial and non-terrestrial networks. These performance goals need smart, adaptive as well as cross-layer optimization, which is perfectly well suited to Artificial Intelligence (AI). This paper provides an overall survey and discussion on the application of AI-based optimization strategies in 6G networks. We discuss modern architectures, including AI-native core, edge-intelligent infrastructures or integrated satellite-terrestrial networks, and more algorithmic techniques to use represent deep learning to channel estimation, reinforcement learning to dynamic resource management, federated learning to privacy-aware optimization, and semantic communication to bandwidth-efficient transmission. With an envisioned AI-native optimization system, it is found in simulation that overall gains are substantial: a maximum 25 percent increase in signal-to-interference-plus-noise ratio (SINR), a 35 percent decrease in latency to URLLC services, and a 27 percent saving of energy in heterogeneous networks. The use in self-driving cars, the extended reality, smart factory, and telemedicine is mentioned. The paper ends with open challenges of scalability, energy efficiency, security and explainability and offers future research directions to focus deployment of resilient, intelligent and sustainable 6G communication systems.
6G Wireless Communication, Artificial Intelligence, Optimization Strategies, Massive MIMO, Beamforming, Resource Allocation, Semantic Communication, Edge Intelligence
Energy-Efficient Beamforming in mmWave MIMO Systems Using Deep Reinforcement Learning: A Framework for Real-Time Adaptive Optimization
The issue presented in this paper is how to efficiently achieve energy-efficient beamforming in millimeter-wave (mmWave ) multiple-input multiple-output (MIMO) communication systems which are one of the essential components to overcome the high-speed data transmission in the 6G wireless network. Conventional beamforming algorithms tend to be computationally expensive and use sub-optimal energy utilization in dynamic environments and are not dynamically adaptable. In order to ensure that these limitations are overcome, we introduce a new framework, implementing Deep Reinforcement Learning (DRL), that allows real-time adaptive beamforming of mmWave MIMO networks. We turn the beamforming process into a Markov Decision Process (MDP), whose aims are different and dynamic, where DRL agent learns to know how to choose the best beam pairing and quantity of transmission power in order to achieve a high level of energy efficiency and guarantee the desired link quality. The framework integrates Deep Q-Network (DQN) to carry out discrete beam selection with the Deep Deterministic Policy Gradient (DDPG) to perform continuous power control. It has been shown, through comprehensive simulations, that our DRL-based approach is better than traditional schemes, such as exhaustive search and codebook-based schemes and performs better in terms of energy consumption when compared to those varieties (up to 35percent energy savings and quicker beam orienting). The extensibility of this framework to adapt to changes in channels and user mobility is also high, and thus it can be implemented in real-time in future mmWave networks. To sum up, the proposed DRL framework can constitute a universal and smart framework to energy-efficient beamforming, which holds a great potential of implementation into 5G/6G systems and other highly-frequent communication systems.
Deep Reinforcement Learning, mmWave MIMO, Energy Efficiency, Beamforming, 6G, Real-Time Optimization, DQN, DDPG, Smart Antennas
Quantum-Resilient Cryptographic Implementations for Embedded Communication Systems
The fast development of quantum computers poses a severe threat to classical cryptographic protocols, especially those installed in embedded communication technologies on which the Internet of Things (IoT), vehicular networks, and industrial automation (and a wide variety of other mission-critical applications) are based. Circumventing known quantum attacks like Shor algorithm on the traditional public-key systems i.e., RSA and ECC, there is an emerging need to move to quantum resistant cryptology. In this paper, it is focused on this novel menace and provides the design, implementation, as well as the full assessment of post-quantum cryptographic (PQC) algorithms that have been optimised and were deployed on the resource-constrained embedded systems. We pay attention to three of the NIST-recommended schemes Kyber512 (as a lattice-based key encapsulation), Dilithium2 (as a lattice-based digital signature) and SPHINCS+128s (as a hash-based stateless signature). The design of these algorithms was performed and benchmarked with two low-power examples of embedded processor-based encryption: The ARM Cortex-M4 and the RISC-V, of which the former is commonly used as an industrial standard in high-security embedded communication, and the latter is becoming a popular alternative. Performance metrics discussed in the study are realistic in nature where the execution time, memory footprint, power, and cryptographic strength are studied under the embedded constraint condition. Kyber512 was found to be the most efficient algorithm in terms of providing a good trade-off between speed and memory usage in a selected set of algorithms, whereas Dilithium2 was found to play a good role of designing an authentication on firmware and message integrity. SPHINCS+ was additionally deployed as a robust backup given its hash based structure, although with an additional computational cost. A number of optimizations were performed at implementation level; constant-time coding to fight side-channel attacks, memory segmentation to prevent fragmentation and loop unrolling to speed up execution. Moreover, the offered system was tested with respect to fault injection analysis, as well as side-channel resistance testing, thus, assuring its strength against physical-layer attacks. The message of the experimental findings is that a quantum-resistant form of cryptography can be amicably incorporated into embedded systems without sacrificing performance to a meaningful degree, and thus a viable means of future proofing embedded systems with exposure to a post-quantumized environment. The work establishes a baseline standard towards the implementation of standardized PQC in use cases of real-time secure communication.
Post-Quantum Cryptography (PQC), Embedded Systems Security, Kyber512, Dilithium2, SPHINCS+, Quantum-Resilient Algorithms, ARM Cortex-M4, RISC-V, Constant-Time Implementation, Secure Communication, IoT Security, Cryptographic Optimization, Fault Injection Resistance
Flexible and Wearable Electronics: Innovations, Challenges, and Future Prospects
In recent times, flexible and wearable electronics have emerged as transformative technologies with wide-ranging potential across various sectors, including healthcare and consumer electronics. This article offers an overview of the latest advancements, manufacturing methods, and design concepts in flexible and wearable electronics. It delves into their applications in healthcare monitoring, fitness tracking, and smart fabrics. Furthermore, it addresses the obstacles and constraints encountered in developing and commercializing flexible electronics, along with potential remedies. Finally, the article outlines upcoming trends and technologies expected to propel the field of flexible and wearable electronics forward.
Flexible electronics, Wearable technology, Innovation, Challenges.
Exploring Emerging Memory Technologies in Modern Electronics
Emerging memory technologies are poised to revolutionize modern electronics by overcoming the limitations of traditional memory solutions like DRAM and NAND flash. These innovative technologies offer significant advancements in speed, energy efficiency, scalability, and data retention. Resistive RAM (ReRAM) uses resistive switching to store data, providing faster read and write speeds, and is ideal for IoT and wearable devices due to its low power consumption and non-volatility. Magnetoresistive RAM (MRAM) leverages magnetic states to store information, offering high speed, endurance, and non-volatility, making it suitable for applications requiring data persistence such as in automotive and aerospace sectors. Phase-Change Memory (PCM) exploits the reversible switching between amorphous and crystalline states in chalcogenide glass, delivering high storage density and fast access times, beneficial for next-generation computing and data centers. Ferroelectric RAM (FeRAM) utilizes ferroelectric materials to achieve rapid write speeds and low power consumption, with applications in smart cards and security systems. As these technologies continue to evolve, they are expected to become integral components of future electronic devices, driving advancements in artificial intelligence, big data analytics, and ubiquitous computing, and thereby pushing the boundaries of modern electronics towards more efficient, faster, and reliable memory solutions.
Emerging Memory Technologies; ReRAM (Resistive RAM); MRAM (Magnetoresistive RAM); Phase-Change Memory (PCM); Ferroelectric RAM (FeRAM); Non-Volatile Memory; Modern Electronics
Metamaterials for Revolutionizing Modern Applications and Metasurfaces
Metamaterials, artificial structures engineered to possess properties not found in naturally occurring materials, are revolutionizing modern applications across various fields. These materials, composed of periodic or aperiodic arrangements of unit cells, manipulate electromagnetic waves in unprecedented ways, enabling unique control over wave propagation, refraction, and absorption. Metamaterials have paved the way for transformative advancements in optics, telecommunications, and medical imaging.One of the most promising areas of metamaterial research is the development of metasurfaces—two-dimensional analogs of metamaterials that consist of ultra-thin layers of nanostructures. Metasurfaces offer significant advantages in terms of compactness and ease of integration into existing technologies. They are capable of manipulating light at sub-wavelength scales, leading to innovative applications such as flat lenses, holograms, and beam steering devices. In telecommunications, metasurfaces enable the development of high-efficiency antennas and filters, enhancing signal quality and reducing interference. In medical imaging, metamaterials improve the resolution and depth of imaging techniques, facilitating more accurate diagnostics. Additionally, metamaterials are being explored for cloaking devices, which can render objects invisible by guiding light around them.As research progresses, the potential for metamaterials and metasurfaces to revolutionize various industries continues to expand, promising a future where the manipulation of electromagnetic waves is limited only by our imagination.
Metamaterials; Metasurfaces; Electromagnetic Wave Manipulation; Optical Devices; Flat Lenses; Beam Steering; Advanced Telecommunications
Graphene Innovations in Flexible and Wearable Nanoelectronics
Graphene, a two-dimensional form of carbon known for its exceptional electrical, mechanical, and thermal properties, has emerged as a revolutionary material in the field of flexible and wearable nanoelectronics. Its remarkable flexibility, high conductivity, and transparency make it ideal for applications requiring lightweight, stretchable, and durable electronic components. Innovations in graphene-based flexible electronics encompass a wide range of devices, including flexible transistors, sensors, supercapacitors, and transparent conductive electrodes. These advancements enable the development of next-generation wearable technologies for health monitoring, smart textiles, and portable energy storage.Graphene's unique attributes facilitate the creation of highly sensitive and accurate biosensors capable of real-time health tracking and diagnostics. Additionally, graphene-based flexible supercapacitors offer superior energy storage capabilities, essential for powering wearable devices. Transparent and flexible graphene electrodes enhance the functionality and aesthetics of smart textiles and electronic skin applications. Research is ongoing to optimize the synthesis, integration, and scalability of graphene in flexible electronics to overcome current challenges such as material defects and large-scale production.Overall, graphene's innovations are poised to transform the landscape of wearable nanoelectronics, driving advancements in health monitoring, personal electronics, and beyond, underscoring the material's potential to revolutionize future technologies.
Graphene-based Sensors; Flexible Electronics; Wearable Technology; Transparent Conductive Films; Energy Storage Devices
Photonic Integrated Circuits: Key Concepts and Applications
Photonic Integrated Circuits (PICs) represent a groundbreaking advancement in the field of photonics, akin to the role of electronic integrated circuits in electronics. By integrating multiple photonic functions into a single chip, PICs offer significant advantages in terms of size, power consumption, and performance for various applications. These circuits utilize light to perform operations traditionally handled by electronic circuits, enabling ultra-fast data processing and communication with minimal energy loss. The primary components of PICs include waveguides, modulators, detectors, and lasers, which are fabricated using materials like silicon, indium phosphide, and silicon nitride. The integration of these components on a single substrate facilitates complex optical signal processing, including multiplexing, demultiplexing, and switching. PICs are pivotal in advancing technologies such as high-speed optical communication, quantum computing, and biomedical sensing. Their ability to process large amounts of data at the speed of light is crucial for meeting the growing demands of data centers, telecommunications, and sensor networks. Ongoing research focuses on improving the scalability, efficiency, and integration density of PICs, as well as developing new materials and fabrication techniques. As a result, PICs are poised to revolutionize numerous industries by enabling faster, more efficient, and highly integrated photonic solutions.
Photonic Integrated Circuits (PICs); Optical Communication; Waveguides; Silicon Photonics; Modulators; Detectors; Quantum Computing; Biomedical Sensing
Transforming Smart Devices and Networks using Blockchain for IoT
Blockchain technology holds transformative potential for the Internet of Things (IoT), offering enhanced security, transparency, and efficiency for smart devices and networks. As IoT ecosystems proliferate, they encounter significant challenges, including data integrity, security vulnerabilities, and centralized control. Blockchain's decentralized ledger technology addresses these issues by ensuring tamper-proof data records, enabling secure peer-to-peer communication, and eliminating reliance on central authorities. Smart contracts, programmable and self-executing contracts on the blockchain, facilitate automated interactions between IoT devices, streamlining processes and reducing human intervention. Furthermore, blockchain enhances device authentication and authorization, mitigating risks of unauthorized access and cyber-attacks. The synergy between blockchain and IoT promotes more resilient, autonomous, and trustworthy networks, fostering innovations in various sectors, such as healthcare, supply chain, and smart cities. However, the integration of blockchain with IoT also presents challenges, including scalability concerns, energy consumption, and the need for new standards and protocols. Ongoing research and development aim to overcome these hurdles, paving the way for the widespread adoption of blockchain-enabled IoT systems. Ultimately, blockchain technology is poised to revolutionize IoT by creating more secure, efficient, and interoperable smart environments.
Blockchain; Internet of Things (IoT); Smart Devices; Decentralized Networks; Security; Smart Contracts; Data Integrity
Innovative Uses of Medical Embedded Systems in Healthcare
Medical embedded systems are revolutionizing healthcare by integrating advanced computing technologies into medical devices, enhancing patient care and clinical outcomes. These systems, which are specialized computer systems embedded within medical devices, enable real-time monitoring, diagnosis, and treatment of various medical conditions. They are pivotal in the development of wearable health monitors, implantable devices, and smart diagnostic tools. One of the most notable applications is in wearable health monitors, such as smartwatches and fitness trackers, which continuously measure vital signs like heart rate, blood pressure, and oxygen levels. These devices provide valuable data for early detection of health issues and ongoing patient management. Implantable devices, such as pacemakers and insulin pumps, leverage embedded systems to deliver precise therapeutic interventions, improving the quality of life for patients with chronic conditions. Moreover, smart diagnostic tools, including portable ultrasound machines and digital stethoscopes, use embedded systems to enhance diagnostic accuracy and accessibility, particularly in remote and underserved areas. These tools facilitate rapid diagnosis and decision-making, ultimately improving patient outcomes. The integration of medical embedded systems also supports telemedicine and remote patient monitoring, allowing healthcare providers to track patient health and deliver care from a distance. This is especially beneficial in managing chronic diseases and during pandemics, where minimizing physical contact is crucial. Overall, medical embedded systems are transforming healthcare by making it more proactive, personalized, and accessible.
Medical embedded systems; Wearable health monitors; Implantable devices; Smart diagnostic tools; Remote patient monitoring; Telemedicine
Detailed Guide to Machine Learning Techniques in Signal Processing
This article provides an in-depth exploration of the intersection between machine learning and signal processing, two pivotal areas in modern technology. Signal processing involves the analysis, interpretation, and manipulation of signals, which can be in the form of audio, video, or sensor data. Machine learning, on the other hand, involves the development of algorithms that allow systems to learn from and make decisions based on data. This guide covers a broad spectrum of techniques and applications where these fields converge.The guide begins with foundational concepts, detailing the basics of signal processing and machine learning, and progresses to advanced topics such as deep learning, convolutional neural networks, and reinforcement learning. It emphasizes the application of machine learning algorithms to enhance signal processing tasks, including noise reduction, feature extraction, and pattern recognition. Practical implementations are discussed, highlighting real-world scenarios in areas such as telecommunications, healthcare, and multimedia. The guide addresses the challenges and future directions in this dynamic field, providing insights into the integration of emerging technologies like quantum computing and edge AI. By bridging the gap between theory and practice, this comprehensive guide serves as an essential resource for researchers, practitioners, and students aiming to harness the power of machine learning to advance signal processing technologies.
Machine learning; Signal processing; Deep learning; Feature extraction; Pattern recognition; Noise reduction; Convolutional neural networks (CNNs)
Vehicular Ad-Hoc Networks (VANETs) for Enhancing Road Safety and Efficiency
Vehicular Ad-Hoc Networks (VANETs) represent an innovative technology poised to significantly enhance road safety and efficiency. VANETs, a specialized form of mobile ad-hoc networks (MANETs), enable direct communication between vehicles (Vehicle-to-Vehicle or V2V) and between vehicles and roadside infrastructure (Vehicle-to-Infrastructure or V2I). Utilizing wireless communication, VANETs facilitate the real-time exchange of critical information such as traffic conditions, road hazards, and emergency notifications.The primary objective of VANETs is to improve road safety. By enabling instant communication, vehicles can warn each other about potential hazards, sudden braking, or adverse weather conditions, thus preventing accidents. VANETs support cooperative systems for collision avoidance and emergency response, significantly enhancing drivers' situational awareness and reaction times. This communication framework also supports automated safety applications, crucial for the development and deployment of autonomous vehicles.In terms of efficiency, VANETs contribute to optimizing traffic flow by providing up-to-date information on traffic congestion, roadblocks, and optimal routing. This real-time data helps reduce travel times, fuel consumption, and emissions, promoting a more sustainable and efficient transportation system. Additionally, VANETs facilitate the development of Intelligent Transportation Systems (ITS), which integrate traffic management with advanced data analytics to improve overall transportation infrastructure.As technology continues to evolve, VANETs are expected to play a crucial role in the advancement of smart cities and autonomous driving, fostering a future of connected, intelligent, and safer roadways.
Vehicular Ad-Hoc Networks (VANETs), Road safety; Traffic efficiency; Vehicle-to-Vehicle communication (V2V); Vehicle-to-Infrastructure communication (V2I); Intelligent Transportation Systems (ITS); Real-time traffic data
The Evolution of Telecommunications: Analog to Digital
The evolution of telecommunications from analog to digital represents a transformative journey in the realm of communication technology. Initially dominated by analog systems, which relied on continuous signal transmission, early telecommunications were limited by signal degradation, interference, and bandwidth constraints. The introduction of digital technology in the late 20th century marked a pivotal shift, offering significant advancements in signal clarity, data compression, and transmission efficiency.Digital telecommunications convert information into binary code, allowing for enhanced data integrity and robust error correction. This transition facilitated the emergence of the Internet, mobile communications, and broadband networks, revolutionizing global connectivity. Innovations such as fiber optics, satellite communications, and wireless technologies further propelled the digital era, enabling faster, more reliable, and more secure communication across vast distances.The digital transformation has also spurred the development of advanced applications, including VoIP, streaming services, and IoT, integrating communication with various aspects of daily life and industry. As digital technologies continue to evolve, the telecommunications landscape is poised for further advancements, such as 5G and beyond, promising even greater speed, capacity, and interconnectivity. This evolution underscores the profound impact of digitalization on modern society, reshaping how we connect, share information, and interact with the world.
Analog telecommunications; Digital transformation; Signal transmission; Data compression; Internet revolution; Wireless technologies