Progress in Electronics and Communication Engineering
type: channel
description: 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
publisher: SOCIETY FOR COMMUNICATION AND COMPUTER TECHNOLOGIES
language: en
publication name: Progress in Electronics and Communication Engineering
issn: 3048-7625
items: Enhancing Industrial Automation Through Lightweight M2M Communication Frameworks in IIoT Systems
items: Communication-Efficient AI Architecture for Real-Time Sentiment Analysis in Distributed Content Systems
items: Communication-Efficient Machine Learning Architecture for Predicting User Information Needs in Distributed Systems
items: Stability Analysis of Global BGP Routing Tables Using Time-Series and Correlation Models
items: Throughput Analysis and Performance Comparison Between Wireless Mesh and Cellular Architectures Under Dynamic Network Conditions
items: Adaptive Probing-Based Anomaly Detection Framework for Performance-Aware Network Management
items: Design and Implementation of a Hybrid Framework for QoS Monitoring in 5G-Enabled Services
items: Data-Driven Assessment of IP Geolocation Accuracy Using Hybrid Active–Passive Measurement Techniques
items: Empirical Analysis of Cryptographic Handshake Delays in Secure Web Communication
items: Data-Driven Internet Weather Maps: Real-Time Analysis of Global Network Performance Fluctuations
items: AI-Driven Anomaly Detection and Behavior Analytics in Heterogeneous IoT Ecosystems
items: A Secure Boot and Firmware Update Framework for ARM Cortex-M Microcontrollers in Industrial IoT Environments
items: Neuromorphic Computing Architectures for Low-Power Edge Inference in IoT Applications
items: Robust and Scalable LPWAN Architecture with Integrated Security for Industrial IoT Applications
items: High-Efficiency Solar-Piezo Hybrid Energy Harvester for Long-Term Autonomous Operation of Smart Agriculture Sensor Nodes
items: Spiking Neural Network-Based Neuromorphic Signal Processing for Real-Time Audio Event Detection in Low-Power Embedded Smart Sensors
items: AI-Driven Smart Irrigation System Using Edge-Based Embedded Controllers
items: Real-Time Streaming Analytics for Predictive Fault Diagnosis in Cyber-Physical Energy Infrastructures
items: Transformer-Based Deep Feature Learning for AI-Enhanced Fault Diagnosis in Deep Submicron VLSI Circuits
items: Secure Boot and Firmware Update Framework for ARM Cortex-M Embedded IoT Devices
items: Terabit-per-Second Photonic–Nanoelectronic Devices for Data Center Interconnects
items: Secure and Scalable LPWAN Architectures for Industrial Internet of Things (IIoT)
items: Design of Ultra-Low-Power Photonic Interconnects for Next-Generation System-on-Chip Communication Architectures
items: Graph Signal Processing-Based Anomaly Detection Framework for Smart Grid Communication Networks
items: Self-Powered Solar-Piezoelectric Energy Modules for Resilient IoT Sensor Networks in Agri-Environmental Monitoring
items: Photonic Crystal Fiber Biosensor for Continuous, Non-Invasive Glucose Monitoring: Design and Performance Analysis
items: Low-Latency Communication Protocol Design for Ultra-Reliable IoT Applications in Smart Cities
items: Design and Optimization of Energy-Efficient VLSI Architectures for Edge AI in Internet of Things (IoT) Applications
items: AI-Driven Optimization Strategies for 6G Wireless Communication Systems: Advanced Architectures, Intelligent Algorithms, and Real-World Applications
items: Energy-Efficient Beamforming in mmWave MIMO Systems Using Deep Reinforcement Learning: A Framework for Real-Time Adaptive Optimization

Enhancing Industrial Automation Through Lightweight M2M Communication Frameworks in IIoT Systems
type: item
description: 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 per…
subject: Articles
  topic: section
subject:
  topic: keywords
  value: 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.
creator: Jinfe Regash
creator: Tasil Leyene
rights: Copyright (c) 2024
date: 2025-09-18
publication date: 2025-09-18
starting page: 112
ending page: 119
doi: 10.31838/q9j3a764

Communication-Efficient AI Architecture for Real-Time Sentiment Analysis in Distributed Content Systems
type: item
description: 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.
subject: Articles
  topic: section
subject: Federated learning, distributed AI, communication efficiency, real-time NLP, sentiment analysis, edge computing, privacy preservation
  topic: keywords
creator: M. Mejail
creator: B.K. Nestares
creator: L. Gravano
creator: E. Tacconi
creator: G.R. Meira
creator: A. Desages
rights: Copyright (c) 2024
date: 2025-09-09
publication date: 2025-09-09
starting page: 104
ending page: 111
doi: 10.31838/4mmx5g78

Communication-Efficient Machine Learning Architecture for Predicting User Information Needs in Distributed Systems
type: item
description: 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.
subject: Articles
  topic: section
subject: Federated learning, distributed systems, communication efficiency, machine learning, information retrieval, system optimization, privacy preservation.
  topic: keywords
creator: K. Geetha
creator: P.Dineshkumar
rights: Copyright (c) 2024
date: 2025-09-23
publication date: 2025-09-23
starting page: 97
ending page: 103
doi: 10.31838/43zr8g45

Stability Analysis of Global BGP Routing Tables Using Time-Series and Correlation Models
type: item
description: 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.
subject: Articles
  topic: section
subject: Routing stability, BGP dynamics, time-series modeling, route churn, prefix oscillation, Internet backbone, anomaly prediction.
  topic: keywords
creator: O.L.M. Smith
creator: K.N. Kantor
rights: Copyright (c) 2024
date: 2025-09-19
publication date: 2025-09-19
starting page: 91
ending page: 96
doi: 10.31838/1mxxp805

Throughput Analysis and Performance Comparison Between Wireless Mesh and Cellular Architectures Under Dynamic Network Conditions
type: item
description: 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
subject: Articles
  topic: section
subject: Throughput analysis, wireless mesh, cellular networks, 5G NR, performance modeling, dynamic traffic, QoS optimization
  topic: keywords
creator: Metahun Lemeon
creator: Jinfe Regash
rights: Copyright (c) 2024
date: 2025-09-15
publication date: 2025-09-15
starting page: 85
ending page: 90
doi: 10.31838/f3awyq76

Adaptive Probing-Based Anomaly Detection Framework for Performance-Aware Network Management
type: item
description: 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.
subject: Articles
  topic: section
subject: Adaptive probing, anomaly detection, software-defined networking (SDN), 5G networks, hybrid machine learning, network performance monitoring, QoS assurance
  topic: keywords
creator: Y. Rimada
creator: K.L Mrinh
rights: Copyright (c) 2024
date: 2025-09-27
publication date: 2025-09-27
starting page: 78
ending page: 84
doi: 10.31838/sq3a0h71

Design and Implementation of a Hybrid Framework for QoS Monitoring in 5G-Enabled Services
type: item
description: 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.
subject: Articles
  topic: section
subject: 5G QoS, hybrid monitoring, active-passive framework, AI analytics, latency measurement, real-time performance, KPI evaluation
  topic: keywords
creator: M. Mejail
creator: B.K. Nestares
creator: L. Gravano
creator: E. Tacconi
creator: G.R. Meira
creator: A. Desages
rights: Copyright (c) 2024
date: 2025-07-30
publication date: 2025-07-30
starting page: 72
ending page: 77
doi: 10.31838/180gr108

Data-Driven Assessment of IP Geolocation Accuracy Using Hybrid Active–Passive Measurement Techniques
type: item
description: Proper IP geolocation is essential to the areas of cybersecurity, content delivery, and enforcing compliance regionally. Nevertheless, traditional geolocation databases are usually affected by irregularities including dynamism of IP allocation, asymmetry of the route, and noise on measurements. This paper suggests a hybrid approach to be used in measuring and enhancing the accuracy of IP geolocation through a combination of active delay-based probing and passive data correlation. The study measures the distribution of spatial error and patterns of cross-continent deviation by using a dataset of more than 1.2 million traceroute and latency observations made in the vantage points throughout the world, paired with the data of Border Gateway Protocol (BGP) and Autonomous System (AS) metadata. The experimental findings reveal that the hybrid model proposed will generate an accuracy rate 28 higher than the conventional database searches. The analysis also shows regional biases where routing aggregation and lack of infrastructure transparency in the developing regions make the error rates higher. An artificial intelligent correction model was created, which uses location deviation prediction based on spatial and time scales and with a mean error of less than 25km. The results underscore the opportunity of hybrid geolocation systems to facilitate Internet measurement systems and offer a platform of real-time optimization of accuracy in both business and academic setting.
subject: Articles
  topic: section
subject: IP address mapping, hybrid measurement, network analytics, BGP correlation, machine learning, Internet performance, spatial modeling
  topic: keywords
creator: Q. Hugh
creator: Freddy Soria
creator: C.C. Kingdon
creator: Robert G. Luedke
rights: Copyright (c) 2024
date: 2025-08-22
publication date: 2025-08-22
starting page: 67
ending page: 71
doi: 10.31838/6arstm76

Empirical Analysis of Cryptographic Handshake Delays in Secure Web Communication
type: item
description: 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.
subject: Articles
  topic: section
subject: SSL handshake, latency analysis, secure web communication, cipher suites, certificate optimization, TLS optimization.
  topic: keywords
creator: Dahlan Abdullah
rights: Copyright (c) 2024
date: 2025-09-30
publication date: 2025-09-30
starting page: 60
ending page: 66
doi: 10.31838/4e2drj73

Data-Driven Internet Weather Maps: Real-Time Analysis of Global Network Performance Fluctuations
type: item
description: 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.
subject: Articles
  topic: section
subject: Internet weather map, real-time visualization, global network performance, passive telemetry, latency analysis, Internet resilience.
  topic: keywords
creator: M. Kavitha
rights: Copyright (c) 2024
date: 2025-09-25
publication date: 2025-09-25
starting page: 54
ending page: 59
doi: 10.31838/zh9eqk05

AI-Driven Anomaly Detection and Behavior Analytics in Heterogeneous IoT Ecosystems
type: item
description: 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.
subject: Articles
  topic: section
subject: IoT security, anomaly detection, AI analytics, autoencoder, passive observation, heterogeneous networks.
  topic: keywords
creator: Srikanth Reddy Keshireddy
rights: Copyright (c) 2024
date: 2025-09-20
publication date: 2025-09-20
starting page: 47
ending page: 53
doi: 10.31838/c37qct97

A Secure Boot and Firmware Update Framework for ARM Cortex-M Microcontrollers in Industrial IoT Environments
type: item
description: 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.
subject: Articles
  topic: section
subject:
  topic: keywords
  value: 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.
creator: Barek F. Fatem
creator: José Urebe
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 62
ending page: 68
doi: 10.31838/ECE/03.02.09

Neuromorphic Computing Architectures for Low-Power Edge Inference in IoT Applications
type: item
description: 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 co…
subject: Articles
  topic: section
subject: Neuromorphic computing, edge inference, IoT, spiking neural networks, low-power AI, event-driven processing, Loihi, TrueNorth
  topic: keywords
creator: Letahun Nemeon
creator: P. Kharabi
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 53
ending page: 61
doi: 10.31838/ECE/03.02.08

Robust and Scalable LPWAN Architecture with Integrated Security for Industrial IoT Applications
type: item
description: 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.
subject: Articles
  topic: section
subject: Industrial IoT (IIoT), LPWAN, Security, Scalability, Robustness, LoRaWAN, Edge Intelligence, Lightweight Cryptography
  topic: keywords
creator: Cristine Re-Ann
creator: Ashu Nayak
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 44
ending page: 52
doi: 10.31838/ECE/03.02.07

High-Efficiency Solar-Piezo Hybrid Energy Harvester for Long-Term Autonomous Operation of Smart Agriculture Sensor Nodes
type: item
description: 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.
subject: Articles
  topic: section
subject: Hybrid energy harvesting, solar energy, piezoelectric transducer, smart agriculture, wireless sensor nodes, MPPT, energy efficiency, autonomous operation.
  topic: keywords
creator: N. K. Havalam
creator: Ragaba Mean Bosco
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 36
ending page: 43
doi: 10.31838/ECE/03.02.06

Spiking Neural Network-Based Neuromorphic Signal Processing for Real-Time Audio Event Detection in Low-Power Embedded Smart Sensors
type: item
description: 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.
subject: Articles
  topic: section
subject: Spiking Neural Network, Neuromorphic Computing, Audio Event Detection, Low-Power Sensing, Embedded AI, Edge Intelligence, Real-Time Signal Processing
  topic: keywords
creator: Fateh M. Aleem
creator: Ahmed Ulkilan
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 31
ending page: 35
doi: 10.31838/ECE/03.02.05

AI-Driven Smart Irrigation System Using Edge-Based Embedded Controllers
type: item
description: 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 suggest…
subject: Articles
  topic: section
subject:
  topic: keywords
  value: 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.
creator: Fahad Al-Jame
creator: Perera Manthila
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 23
ending page: 30
doi: 10.31838/ECE/03.02.04

Real-Time Streaming Analytics for Predictive Fault Diagnosis in Cyber-Physical Energy Infrastructures
type: item
description: 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.
subject: Articles
  topic: section
subject: Streaming analytics, predictive fault diagnosis, cyber-physical energy systems, edge computing, real-time anomaly detection, smart grid, machine learning.
  topic: keywords
creator: Ishrat Z. Mukti
creator: Ebadur R. Khan
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 15
ending page: 22
doi: 10.31838/ECE/03.02.03

Transformer-Based Deep Feature Learning for AI-Enhanced Fault Diagnosis in Deep Submicron VLSI Circuits
type: item
description: 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.
subject: Articles
  topic: section
subject: Fault Diagnosis, VLSI, Deep Submicron, Transformers, Feature Learning, AI, IC Testing, Self-Attention, Fault Classification
  topic: keywords
creator: El Manaa Barhoumia
creator: T Shimada
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 10
ending page: 14
doi: 10.31838/ECE/03.02.02

Secure Boot and Firmware Update Framework for ARM Cortex-M Embedded IoT Devices
type: item
description: 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 …
subject: Articles
  topic: section
subject: Secure Boot, Firmware Update, ARM Cortex-M, Embedded Security, IoT, OTA, Cryptography, STM32, Root of Trust
  topic: keywords
creator: Lau W. Cheng
creator: Beh L. Wei
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 1
ending page: 9
doi: 10.31838/ECE/03.02.01

Terabit-per-Second Photonic–Nanoelectronic Devices for Data Center Interconnects
type: item
description: 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.
subject: Articles
  topic: section
subject:
  topic: keywords
  value: 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.
creator: Nisha Milind Shrirao
creator: Nidhi Mishra
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 73
ending page: 79
doi: 10.31838/ECE/03.01.11

Secure and Scalable LPWAN Architectures for Industrial Internet of Things (IIoT)
type: item
description: 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 a…
subject: Articles
  topic: section
subject:
  topic: keywords
  value: LPWAN, IIoT, LoRaWAN, NB-IoT, Secure Communication, Edge Computing, Key Management, Scalability, Industrial Automation, Lightweight Cryptography, Distributed Architecture, IoT Security, Cloud-Edge Integration
creator: Robert G. Luedke
creator: Moti Ranjan Tandi
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 65
ending page: 72
doi: 10.31838/ECE/03.01.10

Design of Ultra-Low-Power Photonic Interconnects for Next-Generation System-on-Chip Communication Architectures
type: item
description: 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.
subject: Articles
  topic: section
subject:
  topic: keywords
  value: 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
creator: Charpe Prasanjeet Prabhakar
creator: Gaurav Tamrakar
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 59
ending page: 64
doi: 10.31838/ECE/03.01.09

Graph Signal Processing-Based Anomaly Detection Framework for Smart Grid Communication Networks
type: item
description: 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.
subject: Articles
  topic: section
subject: Graph Signal Processing (GSP), Smart Grid, Anomaly Detection, Communication Networks, Cybersecurity, Graph Spectral Analysis, Power Systems, Network Monitoring, Real-Time Detection, Graph Topology.
  topic: keywords
creator: Prerna Dusi
creator: F Rahman
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 54
ending page: 58
doi: 10.31838/ECE/03.01.08

Self-Powered Solar-Piezoelectric Energy Modules for Resilient IoT Sensor Networks in Agri-Environmental Monitoring
type: item
description: 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.
subject: Articles
  topic: section
subject: Hybrid Energy Harvesting, Solar-Piezoelectric, Smart Agriculture, IoT Sensor Networks, Resilient Monitoring, Battery-Free IoT, Energy-Aware Computing
  topic: keywords
creator: Sumit Ramswami Punam
creator: Pushplata Patel
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 45
ending page: 53
doi: 10.31838/ECE/03.01.07

Photonic Crystal Fiber Biosensor for Continuous, Non-Invasive Glucose Monitoring: Design and Performance Analysis
type: item
description: 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.
subject: Articles
  topic: section
subject: Photonic Crystal Fiber, Glucose Biosensor, Non-Invasive Monitoring, Refractive Index Sensor, Finite Element Method, Biomedical Sensing
  topic: keywords
creator: H. Ashour
creator: Fahad Al-Jame
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 38
ending page: 44
doi: 10.31838/ECE/03.01.06

Low-Latency Communication Protocol Design for Ultra-Reliable IoT Applications in Smart Cities
type: item
description: 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 …
subject: Articles
  topic: section
subject:
  topic: keywords
  value: 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.
creator: Ashu Nayak
creator: L. Salabi
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 29
ending page: 37
doi: 10.31838/ECE/03.01.05

Design and Optimization of Energy-Efficient VLSI Architectures for Edge AI in Internet of Things (IoT) Applications
type: item
description: 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.
subject: Articles
  topic: section
subject: VLSI Architecture, Edge AI, Internet of Things (IoT), Energy Efficiency, Low-Power Design, Approximate Computing, Hardware Accelerators
  topic: keywords
creator: Vaduganathan D
creator: B.M.Brinda
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 24
ending page: 28
doi: 10.31838/ECE/03.01.04

AI-Driven Optimization Strategies for 6G Wireless Communication Systems: Advanced Architectures, Intelligent Algorithms, and Real-World Applications
type: item
description: 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.
subject: Articles
  topic: section
subject: 6G Wireless Communication, Artificial Intelligence, Optimization Strategies, Massive MIMO, Beamforming, Resource Allocation, Semantic Communication, Edge Intelligence
  topic: keywords
creator: K. Geetha
creator: P.Dinesh kumar
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 18
ending page: 23
doi: 10.31838/ECE/03.01.03

Energy-Efficient Beamforming in mmWave MIMO Systems Using Deep Reinforcement Learning: A Framework for Real-Time Adaptive Optimization
type: item
description: 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.
subject: Articles
  topic: section
subject: Deep Reinforcement Learning, mmWave MIMO, Energy Efficiency, Beamforming, 6G, Real-Time Optimization, DQN, DDPG, Smart Antennas
  topic: keywords
creator: Namrata Mishra
creator: Aakansha Soy
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 12
ending page: 17
doi: 10.31838/ECE/03.01.02