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
Communication-Centric Security Models for Mobile Digital Learning Systems
The fast penetration of mobile digital learning systems has further enhanced high dependency on the heterogeneous wireless communication network, which poses strong security issues pertaining mobility, dynamic nature of traffic and continuity of real-time sessions. Traditional security infrastructures are largely application-oriented, and take on a static form, which cannot be suitable in both latency sensitive and mobility-based learning environment. The paper suggests a communication-based security model of mobile digital learning systems, where the security enforcement is closely coupled with dynamics of the communication layer. An adaptive communication algorithm that is security-conscious is created to dynamically change the decisions of authentication, encryption and secure handover decisions in response to network conditions, mobility patterns and perceived threat levels. The suggested model is tested with the use of extensive simulation with different learner densities, mobility speeds and attack cases. The evaluation of performance is based on a set of security metrics comprising of authentication success rate, attack mitigation rate, and secure session establishment time, and communication metrics, such as end-to-end latency, throughput, and packet delivery ratio, and handover delay. The comparison of the results with the results of a static and rule-based security schemes proves that the proposed approach is significantly better in improving the communication reliability and security robustness, with low latency and manageable overhead. These results emphasise the power of the communication-based security model as a comparable and viable approach to secure mobile digital learning in upcoming wireless networks of the next generation.
Security-aware communication algorithms, Wireless network security, Adaptive authentication, secure handover management, Quality of service (QoS), Mobility-aware security, End-to-end latency, and Packet delivery ratio
Kesufekad Metachew, Letahun Nemeon, Dinfe Egash, Kasil Teyene
Communication-Aware Design of Mobile Learning Systems for Seamless Content Delivery
The high rate of mobile learning has increased the pressure regarding the smooth and secure delivery of content in dynamic wireless networks. Nonetheless, variable bandwidth, latency, and disruptions caused by mobility have a serious negative effect on the quality of experience (QoE) experienced by learners when traditional adaptive streaming techniques are used. The paper introduces a design concept of mobile learning environment that is communication-aware and jointly addresses both network-level quality of service (QoS) parameters and human-centric quality of experience (QoE) demands to support continuous provision of content. The recommendation is an QoS/QoE conscious adaptive streaming system, whereby real-time network supply, such as available bandwidth, end-to-end delay, jitter, and buffer use are used to make judicious bitrate change decisions. It is also through the proposed approach that extensive simulation of the scheme is evaluated in different network load and mobility conditions as compared to representative throughput-based and buffer-based adaptive streaming schemes. Performance indicates that the proposed design greatly minimises start up delay and rebuffing occurrences and that the proposed design enhances the average delivered bitrate and the stability of the bitrate. Moreover, there are significant improvements in the network performance measured by the element of lower latency, higher percentage of packet delivery, and efficiency of throughputs. This result indicates that by integrating adaptation strategies that are communication-sensitive, mobile learning systems that run on heterogeneous wireless networks can be significantly improved in regard to reliability and scalability.
Quality of Service (QoS); Quality of Experience (QoE); Wireless networks; Network performance; Seamless content delivery; Bandwidth adaptation; Mobility management.
Mixed-Signal Front-End Design for Concurrent Acquisition of Electrophysiological and Hemodynamic Brain Signals
Based on experimental evidence, multimodal neuro engineering applications in the case of electrophysiological monitoring and hemodynamic monitoring of a brain region demands simultaneous functionality, but is difficult because of the difference in signal amplitude, bandwidth and noise specifications that is very high indeed. Electrophysiological signals are microvolt-scale amplitude and bandwidth (KHz) signals, and hemodynamic signals are millivolt-scale amplitude and sub-10-Hz signals, so attempt to record both simultaneously is likely to be saturation artefact, crosstalk, and or timing error. The paper will discuss a mix signal front-end system that allows simultaneous capture of both modalities with a single and time synchronized signal chain. The proposed design uses dual analogue conditions along conditions along with modality selective gain and bandwidth isolation, and then an identical mixed-signal conversion stage, to synchronise sampling. To suppress inter-path interference and at the same time maintain sensitivity to low-amplitude electrophysiological signals a noise-optimised low-noise amplifier and frequency-selective filtering strategy is adopted. Noise in Analytical Noise Analytical noise models are developed to design circuits and choice parameters. Sub-microvolt input-referred noise Experimental validation of synthesised brain-signal emulation containable levels in combination with reliable concurrent acquisition of the electrophysiological path, low inter-modal crosstalk, and stable tracking of hemodynamic signals under simultaneous operation have been demonstrated. The findings affirm that, the proposed mixed-signal front-end offers scalable and low-energy efficient model of deployed multimodal brain-monitoring systems and facilitates compact and synchronised acquisitions architectures of next-generation neuro interface systems.
Mixed-signal front-end, concurrent signal acquisition, electrophysiological signals, hemodynamic signals, low-noise analog design, multimodal brain monitoring
Distributed Communication Frameworks for Peer-Assisted Digital Learning Platforms
Digital learning environments that are peer assisted have been an important aspect of modern education since they enable mutual sharing of knowledge, peer tutoring and learners interacting with each other. Although they have gained adoption, the majority of existing platforms still use centralised communication infrastructures which often face critical issues such as limited scalability, higher latencies when there is heavy load, privacy risks due to centralised data storage, and failure due to there being a single point of vulnerability. In a bid to overcome these shortcomings, distributed communication frameworks provide a more compelling option through offering decentralised peer-to-peer communication, enhanced fault-tolerance and facilitating dynamically sharing resources amongst geographically-distributed learners. This paper suggests a scalable distributed communication architecture in peer assisted digital learning systems using distributed overlay network with efficient peer discovery, trust-based peer selection mechanisms to support reliable collaboration, adaptive routing policies to minimise the communication paths, and edge-based coordination as a way to minimise network congestion and maximise real time responsiveness of networks. The architecture proposed is capable of supporting a large number of learners and at the same time has a low communication overhead and also ensures that the quality of service is constant to the interactive educational sessions. When performance is measured in the environments of different network sizes, it has been found that the distributed approach is quite effective in terms of reducing communication latency, enhancing throughput and the reliability of the sessions as compared to traditional centralised learning management systems. Also, integration of reputation-based trust models enhances the security of the platform by ensuring bad or bad faith peer behaviour is lessened, thus enhancing the learning process. The findings prove that distributed and spatially-helping communication frameworks can be used as effective design foundations of peer-powered digital learning platforms of the next generation, which are well-scalable, resilient and privacy-compliant collaborations. The contribution of this work towards the creation of decentralised educational systems that can facilitate real-time peer interactions and scalable learning communication across the globe is possible.
Peer-assisted learning, distributed communication, decentralized platforms, P2P networking, edge computing, collaborative education.
Enhancing Industrial Automation Through Lightweight M2M Communication Frameworks in IIoT Systems
The blistering development of the fourth Industrial Revolution has enhanced the absorption of smart digital technology in the physical industrial practises that essentially change the level of efficiency in operational, scalably automate the processes, and make decisions based on information. The key to this development is The Industrial Internet of Things (IIoT), where machine-to-machine (M2M) communication allows real-time communication among heterogeneous devices and sensors, actuators, and cyber-physical systems. In spite of its importance, M2M communication systems still have serious challenges, such as, a large communication overhead, non-deterministic delay, low adaptability, and large protocol stacks which cannot be supported by energy limited industrial devices. The solution to these shortcomings is that lightweight communication solutions are needed that can support the dynamic workloads, provide deterministic response time, and have an industrial-grade reliability. In this study, the authors have conducted an in-depth study of lightweight M2M communication protocols, including MQTT, CoAP, LwM2M, AMQP, and DDS, and analysed them in real IIoT scenarios to determine their performance in latency, energy usage, scalability, and reliability. The paper also presents the most important architectural bottlenecks of the traditional M2M systems and presents a new Hybrid Adaptive Lightweight M2M (HAL-M2M) architecture which uses edge-assisted processing, semantic data compression, adaptive Quality of Service (QoS), and context-aware routing to streamline industrial communication flows. The proposed architecture is smart enough to reduce unnecessary transmission, increase real-time through local device inference and provide resilient connectivity despite occupying a densely deployed device layout. The suitability of the proposed approach is proven by the effectiveness of extensive simulations performed with the help of NS-3 and MATLAB that reveals the effect of 38 percentage points of communication efficiency improvement, end-to-end latency reduction by 40, and decreasing energy consumption by 32 percent relative to the traditional M2M systems. These findings confirm the presence of lightweight, adaptive, and intelligent communication models in the next-generation industrial automation to achieve next-generation industrial automation and improve the process of Industry 5.0 at any scale.
Industrial Internet of Things (IIoT); Machine-to-Machine (M2M) Communication; Lightweight Protocols; Industrial Automation; MQTT; CoAP; LwM2M; Edge Computing; Semantic Compression; Adaptive QoS; Context-Aware Routing; Cyber-Physical Systems; Real-Time Communication; Energy Efficiency; Smart Manufacturing.
Communication-Efficient AI Architecture for Real-Time Sentiment Analysis in Distributed Content Systems
This is due to the increasing presence of distributed content platforms which has led to the need to design real time sentiment analysis systems with the capacity to handle large volumes of privacy sensitive user data. Conventional centralized architecture has some problems such as bottlenecks in communication, latency and confidentiality of data which restricts its scalability in large network set ups. The study introduces an efficient federated learning architecture that is communication efficient and can be used to accomplish real time sentiment analysis over decentralized content networks. The suggested framework allows a local node to be trained to use sentiment models based on the data of user interaction without transferring raw data, hence guaranteeing privacy protection. To further reduce the efficiency of the network, the gradient compression and adaptive synchronization techniques are used which drastically minimise the communication load during model aggregation. Experimental tests performed on multi-node schemes indicate that the data transfer can be reduced by fifty percent and a speed of training can be increased by thirty percent as compared to traditional centralized learning methods and there is no noticeable decrease in model accuracy. The findings confirm the architecture as a possibility to implement scalable and low-latency and privacy-aware AI-based sentiment intelligence. The framework has promising application in the next-generation media analytics, smart content management and real-time digital communication eco-systems.
Federated learning, distributed AI, communication efficiency, real-time NLP, sentiment analysis, edge computing, privacy preservation
Communication-Efficient Machine Learning Architecture for Predicting User Information Needs in Distributed Systems
Overwhelming communication overhead, long synchronisation times, and scaling issues are the major issues that machine learning (ML) models deployed in distributed computing settings have to overcome. These are problems that hamper the effectiveness and responsiveness of the intelligent systems, especially in large-scale information networks whereby the user data are spatially distributed. To address these drawbacks, this research suggests a communication-efficient federated learning (FL) architecture optimised in predicting user information requirements to distributed data settings. The architecture will be based on the localised training of models in individual nodes for example institutional repository or digital library servers thus eliminating the necessity of transferring raw data to the central station and adhering to the provisions of privacy. An adaptive communication scheme hierarchically structured into aggregation mechanism is widely applicable in terms of bandwidth consumption minimization and does not undermine performance of the model convergence and predictive accuracy. The results of experimental validation in an experimental topology comprised of five interconnected nodes confirm a 42 percent saving of communication overhead and 15 percent enhancement of training efficiency over traditional centralised learning systems. In addition, the proposed architecture supports scalability of the system, power efficiency and compliance to privacy, which forms a formidable base on big-scale, smart data infrastructures. The study points out the promise of communication-optimized federated learning as one of the enabling factors of secure, adaptive, and resource-sensitive distributed machine learning ecosystems.
Federated learning, distributed systems, communication efficiency, machine learning, information retrieval, system optimization, privacy preservation.
Stability Analysis of Global BGP Routing Tables Using Time-Series and Correlation Models
The paper examines the temporal stability of the global routing tables of Border Gateway Protocol (BGP) by studying periodic changes, fluctuations in the routing table, and instability events through time-series and correlation based statistical models. BGP data at large Internet Exchange Points (IXPs) and repositories such as RouteViews and RIPE RIS were analysed using a ten-year period to determine the number of prefix oscillations, session resets, and changes in AS-paths. The analysis of autocorrelation and spectral density showed that there were cyclic patterns of instability, which were usually associated with local peering updates and a new route redistribution periodically. Besides, cross-correlation between Autonomous Systems (AS) was an indicator of localised disruption that spread around the world via interconnected backbones. The statistical anomaly detection model to predict the possible instability events was worked out with the adaptive thresholding and regression-based residual tracking. Findings support the supposition that routing instability has demonstrably temporal periodicity and that proactive forecasting of routing instability can be highly advantageous in improving network resilience and fault-tolerance in large-scale Internet routing systems. The results can be useful to network operators and researchers in the development of predictive stability models of the dynamic BGP ecosystem.
Routing stability, BGP dynamics, time-series modeling, route churn, prefix oscillation, Internet backbone, anomaly prediction.
Throughput Analysis and Performance Comparison Between Wireless Mesh and Cellular Architectures Under Dynamic Network Conditions
The present paper explores the comparative throughput feature of wireless mesh network (WMNs) and cellular network with LTE/5G NR in a unified analytical-simulation approach. We consider throughput performance, link occupancy, end-to-end delay in dynamic channel environment, dynamically changing user densities, interference pattern and mobility. The framework combines both a queueing-based throughput model and experiments using NS-3 to simulate topology adjustments, multi-hop contention in WMNs and scheduler policy in cellular systems. WMNs utilize adaptive path choice based on airtime conscious metrics and retransmission regulations to enhance localized peer-to-peer execution whilst cell systems apply proportional-fair and round-robin scheduling based on link changeovers to stabilize the capacity during intense mobility and handovers. Findings demonstrate that WMNs provide a better throughput of local flows in a static or low-mobility neighbourhood since spatial reuse and short paths are achieved but are not resilient to dense contention and route maintenance. Cellular networks have enhanced stability and aggregate throughput on greater mobilities and heterogeneous loads since they have access to centralised resource allocation, spectrum reuse and power control. The results trigger hybrid design that integrates WMN backhaul to access neighbourhood with 5G edge nodes to provide wide-area mobility which leads to better throughput
Throughput analysis, wireless mesh, cellular networks, 5G NR, performance modeling, dynamic traffic, QoS optimization
Adaptive Probing-Based Anomaly Detection Framework for Performance-Aware Network Management
Abnormal network behaviour is a fundamental problem to Quality of Service (QoS) and operational stability in the contemporary communication infrastructure, especially in the context of Software-Defined Networking (SDN) and 5G. This paper introduces an Adaptive Probing-Based Anomaly Detection Framework (APADF) which uses real-time network stability indicators and performance fluctuation as adaptive probe timing parameters. It is an active measurement framework that integrates such active measurement methods as latency, round-trip time (RTT) variance, jitter, and throughput analysis with hybrid machine learning algorithms to classify anomalies intelligently and context-sensitively. In comparison with the conventional fixed-interval surveillance systems, proposed adaptive mechanism uses feedback-related control to streamline the probe scheduling, such that when anomalies occur in a transient way, the proposed mechanism is responsive, and when the situation is stable, the mechanism consumes less bandwidth. Based on Gaussian Mixture Models (GMM) to perform unsupervised clustering and Random Forest (RF) to perform supervised classification, the Analytics Core facilitates correct differentiation between transient congestion, ongoing disruptions, and attack-related disruptions. Experimental analysis of both emulated SDN testbeds and experimental WAN link show that APADF can detect with 93 percent, false positives are reduced by 30 percent and has a measurement overhead of less than 1 percent of link capacity. The results provide a confirmation of the ability of the framework to operate with a high level of precision, low overhead network monitoring, yet also provides scalability with interoperability with the existing SDN controllers and edge-based network. On the whole, APADF offers proactive, performance-sensitive and self-scheduling monitoring paradigm, which is adapted to the dynamism of next-generation communication networks.
Adaptive probing, anomaly detection, software-defined networking (SDN), 5G networks, hybrid machine learning, network performance monitoring, QoS assurance
Design and Implementation of a Hybrid Framework for QoS Monitoring in 5G-Enabled Services
The fast development of 5G networks presents complicated Quality of the Service (QoS)-based management issues that cannot be managed using the conventional monitoring methods. In this paper, a new framework being proposed is a hybrid system between active probing and passive monitoring as a way of having a full evaluation of the QoS in 5G-enabled services. The proposed system will integrate real-time feedback with AI-driven analytics by using standardised KPIs throughput, latency, jitter, and packet loss. Open5GS and Wireshark (with a machine learning module to detect anomalies) were used as open-source platforms to implement the architecture. The experiments on an emulated 5G core and the comparison with single-mode methods showed an accuracy of measurement improvement by 27 %. The system has a scalable deployment, dynamic sampling, and visualization of network dynamics without interruptions to be made in the diversity of traffic. The hybrid model allows the assurance of constantly changing QoS of new applications, such as autonomous vehicles, industrial IoT, and smart city infrastructure.
5G QoS, hybrid monitoring, active-passive framework, AI analytics, latency measurement, real-time performance, KPI evaluation
Empirical Analysis of Cryptographic Handshake Delays in Secure Web Communication
The paper examines the performance overhead of the process of completing an SSL/TLS handshake in contemporary web-based contexts by what quantifiable delay different cryptography-related settings impose. The study involves a custom-made passive monitoring framework to study the latency of handshake with different cypher suites, certificate chain, elliptic curve algorithms, and varying network conditions. It is analysed on 120 distributed test endpoints on AWS, Azure, and Google Cloud platforms, and both HTTP/2 and QUIC protocols are tested. Findings reveal that the latency of handshakes is nearly proportional to the depth of the certificate chain and the cryptographic complexity, so that handshakes based on RSA-4096 were 2.3 times later in comparison with handshakes based on ECDHE-RSA-P256. The mechanisms of optimization like TLS 1.3 session resumption, 0-RTT early data, and caching of certificates decreased the overall connexion set up times by up to 40 per cent on average. The empirical evidence of this research informs developers and security architects to modify the cryptographic strength and low-latency in web-scale secure communication systems.
SSL handshake, latency analysis, secure web communication, cipher suites, certificate optimization, TLS optimization.
Data-Driven Internet Weather Maps: Real-Time Analysis of Global Network Performance Fluctuations
In this paper, a data-driven model of creating Internet weather maps is introduced to visualise current network performance around the world. The system analyses the latency, jitter and packet-loss data using passive telemetry on more than 500 distributed monitoring nodes and compiles them into a scalable multi-layered visualisation model. The raw telemetry is converted to colour mapped maps of network climate zones around the globe by advanced interpolation and clustering algorithms. The suggested system is based on the distributed edge-cloud processing and GPU-accelerated rendering to provide responsiveness and scalability. A twelve-month analysis indicated the presence of apparent spatio-temporal variations in Internet performance in form of congestion propagation, cross-continental asymmetry and routing instability. The findings indicate that Internet weather maps can be used as a visual decision-support tool by the operators and researchers who follow the trends in Internet resilience and performance across the world.
Internet weather map, real-time visualization, global network performance, passive telemetry, latency analysis, Internet resilience.
AI-Driven Anomaly Detection and Behavior Analytics in Heterogeneous IoT Ecosystems
With the growth of the IoT ecosystems, scalable, automated behaviour analytics becomes a major necessity. The paper provides an AI-based scheme in passive anomaly detection and behavioural profiling of heterogeneous IoT systems. The methodology is a blend of feature extraction on network flows and autoencoder-based unsupervised learning in order to identify subtle behavioural changes in devices. The model was tested on multi-protocol datasets (Wi-Fi, Zigbee and LoRaWAN) and recorded 94.6% detection and very few false positives. The findings show that smart passive surveillance can improve situational awareness and strengthen security in massive and multi-vendors infrastructures using IoT.
IoT security, anomaly detection, AI analytics, autoencoder, passive observation, heterogeneous networks.
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