Journal of Wireless Sensor Networks and IoT
type: channel
description: Aim: The aim of the "Journal of Wireless Sensor Networks and IoT", ISSN : 3048-8729 published by the SCCTS society, is to provide a prominent platform for researchers, practitioners, and academics to publish their latest research findings, innovations, and advancements in the fields of wireless sensor networks (WSNs) and the Internet of Things (IoT). The journal strives to foster collaboration, promote technological innovation, and address challenges in the design, implementation, and deployment of wireless sensor networks and IoT systems for various applications. Subject Covered: The journal focuses on research related to wireless sensor networks (WSNs) and the Internet of Things (IoT), covering topics such as design, protocols, energy efficiency, localization, security, and applications in various domains. It encompasses a wide range of wireless communication technologies, including RFID, Bluetooth, Zigbee, and cellular networks, as well as emerging technologies like 5G and cognitive radio. The journal also addresses challenges in energy harvesting, management, and optimization. Overall, it serves as a platform for publishing original research, review articles, case studies, and technical notes that advance the understanding and application of WSNs and IoT technologies. The "Journal of Wireless Sensor Networks and IoT," published by the SCCTS society, welcomes original research articles, review papers, case studies, and technical notes that contribute to the advancement of knowledge and understanding in the field. Through rigorous peer review and editorial excellence, the journal aims to maintain high standards of scholarly integrity and serve as a valuable resource for researchers, academics, engineers, and industry professionals worldwide. Frequency of Publication - Bi Annual - It has Two issue per year Language - English Subject - Engineering Year of Starting - 2024 Format of Publication - Online Only ISSN : 3048-8729
publisher: SOCIETY FOR COMMUNICATION AND COMPUTER TECHNOLOGIES
language: en
publication name: Journal of Wireless Sensor Networks and IoT
issn: 3048-8729
items: Cognitive Radio-Enabled Dynamic Spectrum Access Framework for High-Density IoT Sensor Deployments
items: Hybrid Solar–RF Energy Harvesting Architecture for Self-Sustaining Wireless Sensor Networks
items: Edge-Intelligent Adaptive MAC Protocol for Ultra-Low-Power Wireless Sensor Networks
items: Secure Lightweight Authentication Framework for Resource-Constrained IoT Nodes Using Blockchain-Assisted Key Management
items: AI-Driven Energy-Aware Routing Protocol for Scalable Wireless Sensor Networks in 5G-Enabled IoT Environments
items: Edge-Intelligent Wireless Sensor Networks: A Federated Learning Framework for Energy-Aware Distributed Inference
items: Machine Learning-Based Optimization of High-Frequency Injection Amplitude in Sensorless PMSM Drives
items: Smart Antimicrobial Carbon Interfaces Integrated with Distributed Environmental Monitoring Nodes
items: Distributed Multimodal Brain Monitoring Using Body-Area Sensor Arrays
items: IoT-Integrated Mobile Learning Platforms Using Cloud Infrastructure: A Scalable Architecture for Smart Education
items: IoT Based Glucose Monitoring and Alert System Using Contact Less Sensor
items: Low-Power Wireless Sensor Framework for Continuous Environmental Monitoring in Cultural Heritage Repositories
items: Edge-Enabled Internet Weather Mapping for IoT Traffic Visualization
items: Deep Learning–Assisted Passive Traffic Profiling in Large-Scale IoT Networks
items: Adaptive Cognitive Radio-Enabled Spectrum Access for Power-Constrained IoT Communication Systems
items: Real-Time Environmental Monitoring Using LoRa-Enabled Wireless Sensor Networks: Challenges, System Design, and Mitigation Strategies
items: Deployment of WSN-Based Structural Health Monitoring for Smart Bridges: A Real-World Case Study
items: Comparative Evaluation of Machine Learning-Based Localization Algorithms in Dense IoT Sensor Networks
items: A Solar-Piezo Hybrid Energy Harvesting Architecture for Autonomous Wireless Sensor Nodes in Smart Agriculture
items: Hybrid Zigbee-LPWAN Communication Model for Smart Urban Infrastructure
items: Blockchain-Enhanced Security Framework for Federated Wireless Sensor Networks in Smart City Infrastructures
items: Smart City Waste Management Using Sensor-Driven IoT Architecture and Predictive Analytics
items: Energy-Aware MAC Protocol for Prolonging Network Lifetime in Solar-Powered Wireless Sensor Networks
items: AI-Driven Anomaly Detection Framework for Industrial IoT Using Edge-Enabled Wireless Sensor Networks
items: 5G-Enabled Wireless Sensor Networks for Time-Critical IoT Applications: Design Challenges and Opportunities
items: Design and Implementation of Edge-Enabled IoT Framework for Real-Time Environmental Monitoring
items: Design and Performance Evaluation of Energy-Efficient Routing Protocols for Scalable IoT-Enabled Wireless Sensor Networks in Smart Environments
items: Fog-Assisted Anomaly Detection in IoT-WSN Ecosystems Using Hybrid Deep Learning Models
items: Blockchain Security for IoT Applications using Role of Wireless Sensor Networks
items: Smart Traffic Management Using IoT and Wireless Sensor Networks: A Case Study Approach

Cognitive Radio-Enabled Dynamic Spectrum Access Framework for High-Density IoT Sensor Deployments
type: item
description: The exploratory growth in the number of the Internet of Things (IoT) devices has created more congestion than ever in the unlicensed Industrial, Scientific and Medical (ISM) bands, causing compromised Quality of Service (QoS) and the inability to scale the network. In this paper, I suggest a Cognitive Radio (CR)-based Dynamic Spectrum Access (DSA) architecture that could be applicable to high-density sensor networks. The framework adopts a simple Energy Detection (ED) sensing principle, so that the IoT sensors can be able to automatically discover and use the spectrum holes of licenced primary bands without inducing destructive interference. We come up with an adaptive thresholding algorithm so as to maximize the innate sensing-throughput tradeoff, which takes into consideration the changing noise floors in dense urban deployments. Simulation of the evaluation of performance is done over a high level of simulations based on Spectral Efficiency and Aggregate Network Throughput. Findings show that the resulting spectrum utilisation is substantially higher than that of the usual techniques of performing the assignments in a static manner. In particular, the model ensures a high throughput with a dense node area and is somewhat effective in eliminating the hidden node issue and the chances of collision witnessed in massive IoT networks. The study has offered an architectural basis that can be extended to the 6G-powered IoT ecosystems in future where efficient, non-intrusive coexistence between the spectrums is the key factor to sustainable connectivity.
subject: Articles
  topic: section
subject: Cognitive Radio, Dynamic Spectrum Access, Energy Detection, IoT, Spectral Efficiency, High-Density Sensors.
  topic: keywords
creator: K. Geetha
rights: Copyright (c) 2026
date: 2026-02-12
publication date: 2026-02-12
starting page: 42
ending page: 47

Hybrid Solar–RF Energy Harvesting Architecture for Self-Sustaining Wireless Sensor Networks
type: item
description: This study offers a solar-RF energy harvesting architecture merger that is expected to address the energy constraints in Wireless Sensor Networks (WSNs) that relies on batteries as well as support unlimited, self-sustaining functions. The system combines high-power density photovoltaic cells with ubiquitous low-power radio frequency (RF) rectennas to allow the system to overcome intermittent behaviour of single sources in the ambient environment and therefore to provide a continuous power stream even at night-time or indoor environments. The key idea behind such architecture is a dynamic duty cycling algorithm controlled under the principals of Energy-Neutral Operation (ENO) that adjusts the active and sleep mode of sensor node depending on the real-time state-of-charge of a supercapacitor buffer. Test data confirms that the energy harvesting efficiency (η) has been increased significantly and there is a constant voltage profile that is not below operational levels, at 24-hour cycles. This two-source declaration decelerates the stochastic power inflow combined with optimised power usage to produce a scalable remedy to long-term IoT systems in remote or inaccessible areas that is maintenance-free.
subject: Articles
  topic: section
subject: Hybrid Power Systems, IoT, Duty Cycling, Energy Neutral Operation (ENO), RF Energy Scavenging, Solar Photovoltaic, Self-Sustaining Systems.
  topic: keywords
creator: Megat Sufian
rights: Copyright (c) 2026
date: 2026-02-10
publication date: 2026-02-10
starting page: 36
ending page: 41

Edge-Intelligent Adaptive MAC Protocol for Ultra-Low-Power Wireless Sensor Networks
type: item
description: This paper presents an innovative Edge-Intelligent Adaptive MAC Protocol that will be proposed to achieve the optimal energy efficiency in low power Wireless Sensor Networks (WSNs). Conventional MACs lack the ability to diversify to frequency-varying network characteristics, leading to inefficient energy usage, especially in the WSN that has limited resources. The edge computing solution will be proposed in our solution, making it possible to perform intelligent changes in MAC protocol parameters in real-time real with adaptive duty cycling, transmission power, and schedule changes under local network conditions. The protocol can dynamically adjust to varying traffic conditions, node failures, battery levels by actively using edge devices (e.g., gateways or base stations) without affecting network performance, and using this to achieve better energy efficiency. We show by heavy simulations that the proposed protocol can save up to 70 percent of energy usage, increase the delivery ratio of packets as well as keep the latency low when compared to conventional protocols like TDMA, CSMA/CA, and EE-MAC. This paper will help to develop smarter and more efficient communication protocols to use in energy-constrained WSNs, and the potential areas of application include IoT, environmental monitoring, and industrial automation.
subject: Articles
  topic: section
subject: Reinforcement Learning, Network Performance, Internet of Things (IoT), Energy-Efficient Communication, Dynamic Protocol Adjustment, Real-Time Decision-Making.
  topic: keywords
creator: P.Dineshkumar
rights: Copyright (c) 2026
date: 2026-02-08
publication date: 2026-02-08
starting page: 29
ending page: 35

Secure Lightweight Authentication Framework for Resource-Constrained IoT Nodes Using Blockchain-Assisted Key Management
type: item
description: Smart environments have increased the difficulties in authentication and key management by the rapid proliferation of resource constrained or Internet of Things (IoT) nodes. Traditional on-demand centralised public key infrastructures have single points of failure and can scale only to a very small size, whereas fully on-chain blockchain-based proposals present high computational overhead to cost-effective devices and high communication overhead to allow both. This article describes an authenticated lightweight protection structure based on elliptic curve cryptography (ECC) and key anchoring with the aid of block chain to implement the decentralised trust with a minimum overhead. The hybrid architecture proposed has the public key hash stored on a distributed ledger, and mutual authentication on off-chain and determination of session keys up towards efficient ECC-based challenge-response mechanisms. Formal system and threat model are created and an analytic energy consumption model is obtained to compute the cost of computations and transmission within realistic IoT limitations. Analysis of performance indicates that the framework uses less authentication energy and communication overhead than to corresponding blockchain-integrated methods that represent systems and guarantees forward secrecy and replay resistance, impersonation, and man-in-the-middle attacks. The scheme provides scalable, constant-time authentication, and can therefore be used in large-scale applications of resource-constrained IoT networks.
subject: Articles
  topic: section
subject: Lightweight authentication, Blockchain-assisted key management, Internet of Things security, Elliptic curve cryptography, Energy-efficient protocol design, Resource-constrained embedded devices
  topic: keywords
creator: M. Mejail, B.K. Nestares, L. Gravano, E. Tacconi, G.R. Meira, A. Desages
rights: Copyright (c) 2026
date: 2026-02-06
publication date: 2026-02-06
starting page: 19
ending page: 28

AI-Driven Energy-Aware Routing Protocol for Scalable Wireless Sensor Networks in 5G-Enabled IoT Environments
type: item
description: Large-scale wireless sensor networks (WSN) deployment using Internet of Things (IoT) infrastructures enabled by 5G has been accelerated by its rapid growth. Nonetheless, the incorporation of dense WSNs into the 5G backhaul creates highly problematic issues in energy capacity, scalability, and latency controls. The traditional routing algorithms like LEACH and AODV are based on established systems and do not have adaptive intelligence, and they cannot fit dynamic 5G-IoT systems. The current energy-aware routing schemes do not collectively optimise the energy usage, delay, and path reliability. In this paper, we suggest an AI-assisted multi-objective routing architecture that employs reinforcement learning to provide scalable WSN operation in 5G-downstream IoT systems. The formulation of routing decisions is sequentially optimised whereby every node chooses the best next-hop based on the residual energy, path distance, and latency. With a weighted cost function and a Q-learning, adaptive state-aware routing is allowed to exist in the different network conditions. The presented simulation outcomes show that the suggested protocol is more likely to prolong the lifetime of a network by 30 percent, decrease the average energy usage by 22 percent, and minimise end-to-end latency than traditional protocols. This paper authorises that learning-based routing is a significant energy efficiency and scalability contributor in next-generation 5G-integrated IoT sensor networks.
subject: Articles
  topic: section
subject: Wireless Sensor Networks, 5G-Enabled IoT, Energy-Aware Routing, Reinforcement Learning, Network Scalability, Intelligent Routing Protocols
  topic: keywords
creator: Hassan Yacin
rights: Copyright (c) 2026
date: 2026-02-04
publication date: 2026-02-04
starting page: 11
ending page: 18

Edge-Intelligent Wireless Sensor Networks: A Federated Learning Framework for Energy-Aware Distributed Inference
type: item
description: The internet of things (IoT) is also using sensor networks that are increasingly becoming wireless as well as incorporating edge computing to achieve distributed inference instead of processing the data through a centralised cloud computing service. While, the traditional federated learning (FL) models are not specifically optimised to operate within energy constraints characteristic of battery-operated sensor nodes, which results in untimely depletion of nodes and network saturation. This paper: this paper suggests a federated learning model that is energy- aware on edge-intelligent WSNs that optimises computation and communication overhead on joint distribution training. A mathematical model of energy consumption is formulated that models node-based computation and transmission costs and re-formulates the global learning optimization with hard constraints on the available energy. The adaptive mechanism of participation is proposed, where only those nodes that have enough residual energy and good channel conditions are eligible in contributing to every training round. Moreover, update schemes that are communication efficient are incorporated to minimise further the transmission overhead. Large-scale simulations show that the suggested framework might save as much as 28 % of the total energy usage as compared to typical FL with equivalent inference competence. The methodology also increases the network life by more than 35 % and convergence speed in non-homogeneous node environments. The above findings suggest that sustainable edge intelligence in costly wireless sensor networks needs an energy-conscious federated optimization.
subject: Articles
  topic: section
subject: Wireless Sensor Networks, Federated Learning, Edge Intelligence, Energy-Aware Optimization, Distributed Inference, Network Lifetime Enhancement
  topic: keywords
creator: Dahlan Abdullah
rights: Copyright (c) 2026
date: 2026-02-02
publication date: 2026-02-02
starting page: 1
ending page: 10

Machine Learning-Based Optimization of High-Frequency Injection Amplitude in Sensorless PMSM Drives
type: item
description: The methods known as high-frequency injection (HFI) are widely used in sensorless permanent magnet synchronous motor (PMSM) drives to provide adequate rotor position measurements in the low-speed and zero-speed ranges where back-EMF methods fail to work. Although effective, the choice of amplitude of injection is also a critical issue. A too low amplitude compromises the accuracy of position estimation and noise immunity and a too high amplitude increases the ripple on the torque, acoustic noise, and copper losses. Further, ideal amplitude changes dynamically with operating condition like load torque, rotational speed, variation of DC-link voltage, and machine parameter change and hence the approach of fixed or look-up table is not the best. This work gives a new system of optimization based on machine learning, which optimally identifies the amplitude of the HFI based on real-time operating state values. The suggested learning agent reduces structured multi-objective cost criterion which is the combined evaluation of rotor position estimation error, torque ripple magnitude, and incremental copper losses. The framework provides close-to-optimal operating space amplitude settings based on different conditions through data-driven exploration of the nonlinear operating space. The simulation findings identify stronger resistance to voltage drop across inverter fed and measurement noise, and lower steady-state torque oscillations relative to traditional techniques, confirming that simulation has been shown to be suitable in low-voltage electric mobility applications.
subject: Articles
  topic: section
subject: Sensorless PMSM, High-Frequency Injection, Machine Learning Optimization, Adaptive Control, Low-Speed Drives
  topic: keywords
creator: Deepika J
rights: Copyright (c) 2025
date: 2025-11-19
publication date: 2025-11-19
starting page: 85
ending page: 91

Smart Antimicrobial Carbon Interfaces Integrated with Distributed Environmental Monitoring Nodes
type: item
description: The increasing demands of a powerful and sustainable environmental monitoring have enhanced the evolution of the distributed sensor networks that can work in polluted, biologically active and aggressive ecological conditions. Regardless of the great advancement in environmental Internet of Things (IoT)-based architectures, traditional monitoring nodes are very susceptible to the colonisation of microorganisms, biofouling, and degradation of surfaces caused by pathogens that critically affect the sensing accuracy, operational integrity, and efficiency in long-term deployments. This paper suggests a new model of incorporating intelligent antimicrobial carbon-based interfaces into a distributed system of environmental monitoring nodes to improve their resistance to wear and tear coupled with biofouling and at the same time sustain high sensing capabilities. Novel carbon nanostructures such as graphene, carbon nanotubes, and activated carbon composites are designed and antimicrobial functionalized by adding metal nanoparticles and modifying surfaces to effectively overcome bacterial adhesion and formation of biofilms. The suggested architecture can facilitate 24 hours monitoring of water, soil, and air conditions through the use of a combination of antimicrobial protection with an enhanced level of electrochemical responsiveness and increased signal stability. The test research evidences an excellent level of microbial sample inhibition ability over representative Gramme-positive and Gramme-negative strains, as well as supported sensor drift and extended working life on extended exposure circumstances. Moreover, integration of antimicrobial interfaces in the distributed IoT nodes exert decreasing effect on maintenance and enhances real-time data stability in the problematic field implementation. The results identify antimicrobial carbon interfaces as an initiating technology to transformative high-resilience environmental surveillance platforms, and the result is also a…
subject: Articles
  topic: section
subject: Antimicrobial carbon materials, graphene coatings, environmental IoT, distributed sensor networks, biofouling mitigation, smart monitoring nodes.
  topic: keywords
creator: Madhanraj
rights: Copyright (c) 2026
date: 2025-11-30
publication date: 2025-11-30
starting page: 133
ending page: 139

Distributed Multimodal Brain Monitoring Using Body-Area Sensor Arrays
type: item
description: The wearable sensing technologies of continuous brain monitoring have become a central facilitator of healthcare, assistive systems, and humancomputer interaction. But the majority of solutions available to date use centralised data acquisition and processing designs, which have been plagued by scalability, cost issues, go directly through the communication line and cannot be trusted in long-term deployments. It is necessary to overcome these shortcomings; to this end, the present paper introduces a distributed Internet of Things (IoT)-based body area sensor network (BASN) architecture in multimodal brain monitoring. The presented system incorporates the electroencephalography (EEG), inertial, and auxiliary physiological sensors in a wireless body-area network to supply it with the possibility of real-time operation, energy efficiency, and scalability. Its architecture takes the form of hierarchical and distributed architecture that includes the low powered body worn sensor nodes, edge-enabled body coordinator, and cloud / fog based services. Preprocessing and feature extraction are done locally at sensor and coordinator levels to minimise communication overhead as well as enhance responsiveness. The energy-conscious communication plan is used to optimise the data transfer in order to ensure the consistent synchronisation of multiple sensor streams of various modalities. The system is tested by way of simulation in different networking conditions and sensor densities. Performance data prove that the distributed architecture proposed allows to reduce end-to-end latency by a factor, end node energy used by a factor, and the percentage of packet delivery by a factor than the same architecture would provide in a traditional centralised scheme based on monitoring. These results prove the usefulness of distributed sensing and edge-assistance data management in body area networks. The suggested framework offers a scalable and implementable platform of next-generation IoT-b…
subject: Articles
  topic: section
subject: Body Area Sensor Networks, Internet of Things, Brain Monitoring, Distributed Systems, Multimodal Sensing, Wireless Sensor Networks
  topic: keywords
creator: Metahun Lemeon, Jinfe Regash, Tasil Leyene
rights: Copyright (c) 2026
date: 2025-11-29
publication date: 2025-11-29
starting page: 125
ending page: 132

IoT-Integrated Mobile Learning Platforms Using Cloud Infrastructure: A Scalable Architecture for Smart Education
type: item
description: The increasing rate of mobile learning (m-learning) has dramatically changed the way education is delivered in modern society to provide an opportunity of access to digital learning materials flexibly and ubiquitously. Although it has been widely used, traditional m-learning solutions are usually characterised by the lack of real-time flexibility, the absence of contextual intelligence and insufficient personalization of learning materials. One such solution is the combination of the Internet of Things (IoT) with a cloud computing infrastructure that can provide intelligent classrooms which can constantly gather data on the interactions of learners, the conditions of the classroom and the environment, and dynamically improve the delivery of education. The proposed study presents an opportunity to introduce scaled IoT-based and mobile education into the context of cloud-based infrastructure that will facilitate the distribution of the content in real-time, adaptive learning analysis and personalised feedback schemes. The proposed architecture also features IoT sensing devices to obtain contextual data, lightweight communication protocols to transmit the media safely, cloud-based storage and processing modules to maintain scalable resources and monitor the learners performance, and AI-based analytics engines to develop learners performance and recommendation models. The testing results of the framework indicate that there were significant progress in scaling the system, a lower delay in delivering the content, and an augmented interest among learners due to personalization to adaptability. The findings show that cloud automatic scaling and distributed analytics are useful in enhancing the response of the platform to growing user demand, whereas IoT-powered contextual intelligence intensifies the performance of learning interventions. Incorporating IoT, cloud services, and intelligent learning analytics into one architecture will enable the proposed approach to offer a…
subject: Articles
  topic: section
subject: IoT, Mobile Learning, Cloud Infrastructure, Smart Education, Learning Analytics, Adaptive Systems
  topic: keywords
creator: N .Saranya
rights: Copyright (c) 2026
date: 2025-11-29
publication date: 2025-11-29
starting page: 118
ending page: 124

IoT Based Glucose Monitoring and Alert System Using Contact Less Sensor
type: item
description: Today Technologyhas grown to that level where rapid support could be provided to patient’s health and provide them with early recovery. In hospitals, almost all patients, mainly ICU Patients, have to regulate the volume of fluids which is done using drip. These Drips needs regular monitoring or changing to maintain a constant flow of Fluids in our system we will be using Contactless Water Level Sensorto measure the weight, though this system looks less complex, overcrowded medical facilities and lesser medical team support can put patients at risk which may affect the health of patient severely .Manual observing of drips level sometimes get missed or they are not able to monitor intensely because of hectic work schedule and overcrowded patients by using our system the risk of manual observation comes down and IoT based autonomous monitoring is done effectively. Since we are using an IoT Cloud Platform to store the data, health parameters of the patient are vital for us the know their rate of recovery which helps to solve the existing challenges which is in the current usage along with this we also integrate solution for dehydration for Pregnant women and women’s working in field. Dehydration is more normal during pregnancy than at different occasions. Although most casesof dehydration in pregnancy are mild, severe dehydration can be dangerous for both the mother and the baby. The baby puts a lot of demands on the body, therefore pregnant women need to eat alot of extra nutrients and drink a lot of water. Morning sickness, like conditions that induce excessive vomiting, can contributetodehydration.When dehydration becomes severe, thirst feelings may disappear. Dizziness and disorientation, a beating heart, and changes in the baby’s rhythmic movement are all signs of more significant dehydration during pregnancy. A serious shortage of hydration can result in poor amniotic fluid production and organ malfunction,whichcanleadtotheinfant’s growth being stunted. We created…
subject: Articles
  topic: section
subject: Drips
  topic: keywords
subject: Contactless sensor,
  topic: keywords
subject: IOT cloud platform,
  topic: keywords
subject: Dehydration,
  topic: keywords
subject: Fall detection
  topic: keywords
creator: A. Suresh, N.Saravanakkumar
rights: Copyright (c) 2026
date: 2026-01-29
publication date: 2026-01-29
starting page: 110
ending page: 117

Low-Power Wireless Sensor Framework for Continuous Environmental Monitoring in Cultural Heritage Repositories
type: item
description: Conservation of cultural collections should be accompanied by constant monitoring of the environmental parameters in order to regulate the temperature, humidity, and concentration of particulates. This paper constructs a low power wireless sensor network (WSN) architecture to be used in long-term library, museum, and archive depository monitoring. The suggested system is a hybrid of the Zigbee and LoRa communication technologies to provide a reliable two-range connectivity between the rooms- Zigbee will be used to transmit data intra-room, and LoRa will be used to transmit data to the gateway. All sensor nodes will incorporate efficient energy consuming DHT22 and SDS011 sensors with an ATmega328P microcontroller and controlled by an adaptive duty-cycling algorithm that dynamically modulates the time between samples in response to changes in the environment. A pilot system was tested in one of the archives of a university and during the period of 18 months it worked independently and demonstrated a 98 percentage of data transmission rate and zero packet loss. The comparative analysis of the traditional Zigbee-only and LoRa-only systems had shown a 32 percent lower average power consumption and 24 percent reduced maintenance rate, with the level of latency to be negligible. The hybrid architecture is highly efficient in terms of energy, communication resilience, and scalability of the architecture, which makes it appropriate to large and geographically spread heritage environments. The study provides a scalable and sustainable IoT-based monitoring architecture that can operate autonomously on the long-term basis. The suggested framework promotes preventive conservation, making it easy to control the conditions with regular environmental monitoring and reducing energy and maintenance expenses, which can be used as a reasonable example of intelligent, low-power heritage management systems.
subject: Articles
  topic: section
subject: Wireless sensor networks, IoT, low-power design, LoRa, Zigbee, heritage monitoring, environmental data analytics.
  topic: keywords
creator: M. Kavitha
rights: Copyright (c) 2025
date: 2025-09-19
publication date: 2025-09-19
starting page: 78
ending page: 84

Edge-Enabled Internet Weather Mapping for IoT Traffic Visualization
type: item
description: The Internet of Things (IoT) has grown at a very fast rate resulting in a very high heterogeneous network traffic, which causes the dynamism in the edge networks. These swings have a huge impact to the end-to-end quality service and the health of the entire Internet. This paper suggests an edge based Internet weather mapping architecture that can be used to visualise and interpret IoT traffic patterns in real time. The suggested system combines edge-based passive flow tracking and the cloud-based visualisation pipeline that links the key performance indicators, including latency, throughput, and packet loss. Through temporal and spatial changes in IoT data streams, the system produces visualised weather maps which can represent congestion hotspots, transmission issues, and degradation of connectivity in a given region. The framework makes use of the adaptive edge analytics to minimise the latency involved in gathering the data and also utilises self-organising map (SOM)-based clustering to improve cognitive readability of the performance dynamics. Experimental deployment results in the various IoT zones reveal enhanced visualisation granularity and predictive awareness which help operators of the network to optimise the network proactively. The architecture fills the divide between the localized IoT data and the visualization of the global network, and provides a scalable and understandable model of future internet telemetry.
subject: Articles
  topic: section
subject: IoT performance, Internet weather maps, edge analytics, passive flow monitoring, network visualization, traffic optimization.
  topic: keywords
creator: P.Joshua Reginald
rights: Copyright (c) 2025
date: 2025-09-12
publication date: 2025-09-12
starting page: 72
ending page: 77

Deep Learning–Assisted Passive Traffic Profiling in Large-Scale IoT Networks
type: item
description: The fast trend in the Internet of Things (IoT) device proliferation has greatly complicated the modern network ecosystems and required the development of new strategies to track device behavior and identify anomalies without violating user privacy. The current work introduces a passive traffic profiling framework with the help of deep learning, characterized by a classification of IoT devices relying on the data presented in the network in terms of packet timing, header entropy, and the flow properties on the basis of network metadata only. CNN-LSTM model is a hybrid convolutional neural network that is trained using a dataset of more than 25 million packets measured in various IoT applications, and it has an average classification accuracy of 95 percent on sixteen device types. The comparative analysis indicates that the proposed framework is better than the traditional statistical and shallow learning models in terms of scalability, latency, and the ability to resist the effect of encryption. These findings confirm that passive profiling based on deep learning is a convenient and non-invasive method of monitoring a large network of IoT devices, allowing to strengthen network security, behavioral analytics, and provide early warning of threats.
subject: Articles
  topic: section
subject: IoT device profiling, passive traffic analysis, deep learning, CNN–LSTM, network behavior, security analytics.
  topic: keywords
creator: Srikanth Reddy Keshireddy
rights: Copyright (c) 2025
date: 2025-09-05
publication date: 2025-09-05
starting page: 66
ending page: 71

Adaptive Cognitive Radio-Enabled Spectrum Access for Power-Constrained IoT Communication Systems
type: item
description: The exploding number of Internet of Things (IoT) devices has created new, extreme demands on both wireless spectrum and energy source especially in power-limited communication settings. The needs that IoT systems have in terms of dynamic spectrum access and energy budget constraints cannot match well with conventional fixed-spectrum allocation schemes. In order to address them, in the research, a new adaptive spectrum access protocol with the use of the cognitive radio (CR) technology, which is designed specifically to support power-limited IoT networks is presented. The suggested protocol integrates real-time spectrum sensing, context-based decision and lightweight energy-conservation control to dynamically find and acquire unused spectral channels whilst keeping power consumption to a minimum. An end-to-end simulation-driven summation shows that the proposed scheme substantially outperforms the spectrum use efficiency, energy per transmission and provides high packet delivery performance than the conventional static and greedy access schemes. The findings confirm the promising of the suggested framework to enable energy-saving, large-scale, and smart communication in IoT ecosystems of the next generation.
subject: Articles
  topic: section
subject:
  topic: keywords
  value: Cognitive Radio, Internet of Things (IoT), Spectrum Access Protocol, Energy Efficiency, Power-Constrained Devices, Dynamic Spectrum Allocation, Lightweight Communication, Adaptive Protocol Design, Green IoT, Low-Power Wireless Networks.
creator: José Uribe
creator: Andrés Rivera
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 102
ending page: 109
doi: 10.31838/WSNIOT/03.01.14

Real-Time Environmental Monitoring Using LoRa-Enabled Wireless Sensor Networks: Challenges, System Design, and Mitigation Strategies
type: item
description: The issue of urban health management, climate resilience, and disaster mitigation requires real-time environmental monitoring when reaching informed decisions. Amongst Low Power Wide Area Network (LPWAN) protocols, LoRa (Long Range) has emerged as a dominant framework that makes it possible to send long-range, low power consuming messages within Wireless Sensor Networks (WSNs). The current paper includes an in-depth investigation of the implementation, deployment and testing of a LoRa-based WSN system, optimized to be used in real-time environmental monitoring. The designed system structure combines environmental sensor, LoRa transceivers, and edge/cloud connectivity and have a scalable star-shaped topology. We explore some major performance limitations such as latency, energy usage, interference, and a small payload capacity and provide a set of corrective measures: adaptive duty cycle, priority scheduling and interference sensitive channel selection. Performance gains are achieved to a considerable extent as shown on simulations and real world tests at a 1.5 km 2 field trial with 20 sensor nodes. Findings indicate that adaptive duty cycling cut down energy consumption by 40 percent, priority-aware transmission and dynamic channel selection enhanced data latency and packet delivery ratio by 27 percent and 14 percent respectively. These results verify that low-power, LoRa-based WSNs have the potential to be used in a variety of terrain and application configurations because they provide reliable and timely delivery of environmental data given proper architectural and protocol-level optimizations are applied to them. This project helps in researching the future of monitoring systems in infrastructures of smart environments, building resilience and energy-efficient systems.
subject: Articles
  topic: section
subject:
  topic: keywords
  value: LoRa, Wireless Sensor Networks (WSNs), Environmental Monitoring, Real-Time Data, Low-Power Wide Area Network (LPWAN), Duty Cycling, Latency, Interference Mitigation, Channel Adaptation, IoT Communication.
creator: Salma Ait Fares
creator: Nadine M. Hassana
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 95
ending page: 101
doi: 10.31838/WSNIOT/03.01.13

Deployment of WSN-Based Structural Health Monitoring for Smart Bridges: A Real-World Case Study
type: item
description: The most significant research on SHM systems to date has been done on bridges, and these systems have become necessary in ensuring the safety, performance, and durability of the modern bridge infrastructure systems located in dynamic and stressful environments. It is a real-life implementation of Wireless Sensor Network (WSN) based SHM system on the Rajiv Gandhi Cable Bridge in India. The system consists of heterogeneous sensors that measure vibration strain, as well as tilt and, a mesh Zigbee-based communication protocol whose purpose is to guarantee energy-efficient and scalable data transfer. Sensors run on solar energy and are assisted by an edge gateway that facilitates preprocessing of real-time data and transfers the information to a distant cloud. The deployment uses the adaptive sampling and the energy-aware duty cycling functionality to distribute power in a way that does not lead to sacrificing data quality. Periodally monitored during a period of six months, the system reached a 98.7 percent network uptime, had a proven 36 percent decrease of energy usage, and had an accuracy of 93.5 percent in detecting anomalies verified by a manual review. Latency of the whole network of sensor to cloud was maintained less than 250 milliseconds that enabled near real-time decision-making. The practical applicability of using WSN technology to introduce SHM systems into large-scale infrastructure at a reduced maintenance rate and at a high-reliability of detecting faults is evidenced in the findings of this study. The planned future extensions are the utilization of AI-based predictive maintenance algorithms and the growth to the multi-bridge monitoring system.
subject: Articles
  topic: section
subject: Structural Health Monitoring, Wireless Sensor Networks, Smart Bridge, Zigbee, Real-Time Monitoring, Edge Computing, Energy Efficiency, Anomaly Detection
  topic: keywords
creator: Martin Kigarura
creator: Moris Mlein
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 86
ending page: 94
doi: 10.31838/WSNIOT/03.01.12

Comparative Evaluation of Machine Learning-Based Localization Algorithms in Dense IoT Sensor Networks
type: item
description: Precise positioning of sensor nodes is an important facilitator of context-aware services, effective routing, and data interpretation in dense Internet of Things (IoT) sensor networks. Nonetheless, the traditional localization algorithms are limiting in dense deployment because of signal interference, non-line-of-sight situations and lack of scalability. The work includes a detailed comparative analysis of machine learning (ML)-specific localization algorithms designed to be used in densely populated settings of IoT. In particular we test the ability of k-Nearest Neighbors (k-NN), Support Vector Machines (SVM), Random Forests (RF) and Deep Neural Networks (DNN) on important measures like localization accuracy, computational efficiency, resistance to environmental noise and scalability. Both synthetic and real-world datasets based on dense network scenarios are explored to mean experimental analyses. Examination of the results has shown that DNN models are more accurate because of sensitivity to complicated signal-space interactions, and the RF provides an attractive tradeoff of precision versus overhead. Instead, SVM presents scalability issues and k-NN is not performing well in highly dynamic or noisy environments despite being fast. The findings can be used in practice to choose which algorithm to use in order to deploy the algorithm into real-world IoT systems where the node distribution is dense. Our work adds another piece of research to the area of smart positioning solutions and can be used to develop energy efficient, scalable, and precise positioning systems to be used in the future IoT devices.
subject: Articles
  topic: section
subject:
  topic: keywords
  value: Machine Learning, Localization Algorithms, Dense IoT Sensor Networks, Wireless Sensor Networks (WSN), Node Localization, Received Signal Strength Indicator (RSSI), Supervised Learning, Deep Learning, Random Forest, k-Nearest Neighbors (k-NN), Support Vector Machines (SVM), Performance Evaluation, IoT Positioning, Smart Environment Localization, Signal-Based Localization.
creator: Wesam Ali
creator: A. A. Zaky
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 79
ending page: 85
doi: 10.31838/WSNIOT/03.01.11

A Solar-Piezo Hybrid Energy Harvesting Architecture for Autonomous Wireless Sensor Nodes in Smart Agriculture
type: item
description: Wireless sensor nodes (WSNs) in remote and resource-constrained assets in smart agriculture is a major issue of concern since there is little supply of regular sources of energy. In this paper, an intense energy and efficient hybrid energy harvesting scheme is proposed which combines both solar and piezoelectric energy sources so as to make the WSNs work independently in precision agriculture. The hybrid design aims at using ambient solar radiance in the daytime and mechanical vibrations produced by the movement of the wind, farm vehicles, or animal knees to generate electricity. The heart of the system is a dual-input power management unit to manage intelligently, all harvested energy through regulation, rectification, and storage in high capacity supercapacitors. Such a circuit guarantees optimal energy consumption because its relative priorities among the sources available are made flexible depending on the situation on the environment, hence preserving energy continuity under periodical or low-input scenarios. The system has been tested in a semi-arid agricultural testbed and is a real-world testbed in which sensor nodes have been deployed to keep an eye on the soil moisture, light intensity, and ambient temperature. The findings have shown a high level of energy sustainability, whereby the hybrid scheme compares to 45 percent energy harvesting proficiency as opposed to solar-only settings. In addition, the sensor nodes uptime was increased by 62%, which provides regular data transmission, and minimizes the periods of a node maintenance or battery switch. The amount of power collected was enough to maintain multi-modal-sensing and periodic wireless connection without energy storage loss. The small size footprint, low cost and environmental friendliness of the suggested system are features that contribute to the high application potential of the proposed system at large scale to smart agriculture infrastructure. On the whole, the study is a scalable and robust me…
subject: Articles
  topic: section
subject: Hybrid Energy Harvesting, Solar-Piezo, Wireless Sensor Networks, Smart Agriculture, Energy Management, LoRa, Precision Farming
  topic: keywords
creator: Y. Charabi
creator: Haitham M. Snousi
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 71
ending page: 78
doi: 10.31838/WSNIOT/03.01.10

Hybrid Zigbee-LPWAN Communication Model for Smart Urban Infrastructure
type: item
description: The transformational of urban areas in smart cities depends on the successful implementation of Internet of Things (IoT) devices, and, therefore, wireless connectivity standards should be scalable, energy-wise, and demonstrate a wide coverage of the areas. Although the current solutions, such as Zigbee and Low Power Wide Area Networks (LPWAN), on their own have their respective advantages, e.g., Zigbee has low power mesh networking and LPWAN has long-range connectivity, none of them can fully support the compound needs of dynamic urban infrastructures in isolation. The new hybrid communication model presented in the paper combines the Zigbee and LPWAN to take advantage of their potential synergetic and exclusive capabilities to offer a unified, solid architecture with respect to smart city applications. The suggested two-tier system makes use of Zigbee mesh networks because of their capability to efficiently convey short-distance communications between IoT devices densely clustered in a defined regional area within the physical environment of a localized urban sector, like buildings, intersections, and neighbourhoods, but gives a small number of Zigbee coordinators LPWAN connectivity so that these clusters may be attached to an urban-wide convergence of the latter in an LPWAN-network. In this hierarchical design, the main technical issues are solved such as easy gateway selection, safe data transmission, effective data aggregation, and protocol networking between Zigbee and LPWAN layers. The hybrid architecture shows better coverage that covers the both small urban and large areas in the city having better network energy efficiency and lifetime due to hierarchical data aggregation of the network and network scalability and availability ever when there is radio interference. The general purpose and adaptability of the model are demonstrated by illustrating the inference to the cases of smart cities, e.g. real-time traffic control, air sensing, and lighting control. F…
subject: Articles
  topic: section
subject: Smart Urban Infrastructure, Hybrid Communication Architecture, Zigbee-LPWAN Integration, IoT Scalability, Energy-Efficient Networking, Smart City Connectivity
  topic: keywords
creator: Namrata Mishra
creator: Yeonjin Kim
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 62
ending page: 70
doi: 10.31838/WSNIOT/03.01.09

Blockchain-Enhanced Security Framework for Federated Wireless Sensor Networks in Smart City Infrastructures
type: item
description: The growth in smart urban facilities necessitates safe and expandable structures of wireless sensor networks that uphold the disseminate cognition and instantaneous receptiveness. Federated Wireless Sensor Networks (FWSNs) permit the decentralised data collection and distributed learning but open up the complex issue of management and trust, of data integrity, and authentication of the nodes involved because they are both heterogenous and dynamic. We propose in this study a security framework based on blockchain technology that concerns a lightweight blockchain infrastructure combined with federated learning protocols to cope with these issues. The proposed system enables smart contracts to handle their secure access control, node trust management, and trust negotiations between sensor clusters, and invariant of auditing. With the deployment of Contiki-NG to simulate WSN and Hyperledger Fabric to orchestrate blockchain development, the architecture has been subjected to numerous smart city use cases, namely, control of the traffic and air quality supervision. Experimental findings indicate 92.% accuracy of Sybil attack and replay attack and the latency does not exceed 150 ms within a blockchain transaction despite up to 300 nodes. The solution design is also proposed to be low communications and computational overhead compatible with the real-time anomaly detection and data exchange in bandwidth and energy-limited mediums. This study shows that a combination of blockchain and federated learning can be used to ensure resilience, energy efficiency, and scalability of security architecture for existing critical parts of smart cities.
subject: Articles
  topic: section
subject:
  topic: keywords
  value: Blockchain Security, Federated Wireless Sensor Networks (FWSNs), Smart City Infrastructure, Lightweight Consensus Mechanisms, Trust Management in IoT, Secure Federated Learning, Edge Computing, IoT Data Integrity, Smart Contracts, Attack Detection in WSNs.
creator: Sumit Ramswami Punam
creator: Chandrakumar Rasanjani
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 56
ending page: 61
doi: 10.31838/WSNIOT/03.01.08

Smart City Waste Management Using Sensor-Driven IoT Architecture and Predictive Analytics
type: item
description: The problem of efficient and sustainable waste management has been one of the urgent issues when considering the sensibly increasing populations of cities and the current appearance of smart city programs. Conventional systems of municipal waste collection have been rather inefficient due to fixed-route timetabled flow, resources waste, and the failure to respond to overflowing bins in time which poses a problem to public health and environment. This paper presents a novel, detailed sensor-based architecture of the Internet of Things (IoT) with predictive analytics on the management of solid waste, on the one hand, in order to overcome these difficulties. The regime includes a series of smart bins with ultrasonic sensors recording current fill-level and gas sensors to detect dangerous emission. Powered with energy-saving microcontrollers and data delivery over Low Power Wide Area Networks (LPWAN) like LoRa and Nb-IoT these sensor nodes can be relied upon to deliver data effectively whilst maintaining minimum power levels. The obtained information is sent to an edge-cloud system that can handle the ingestion and processing of data on a scalable basis. Filtering and detection of anomalies done in real-time is done at the edge layer to provide data quality and send alerts. In the cloud layer, high-order machine learning algorithms such as Long Short-Term Memory (LSTM) networks and gradient boosting algorithms are used in predicting growth trends of waste materials over time following past trends and environmental factors. This is based on the predictions and bins are prioritized in a dynamic way and optimized collection routes are created using path planning algorithms that minimize usage of fuel, and operational cost. A mock urban implementation of the system that was implemented in three months indicated that the proposed system could save 37 percent fuel use, increase the efficiency of waste pick-up operations by 45 percent, and greatly reduce instances of missed pi…
subject: Articles
  topic: section
subject: Smart City, Waste Management, IoT, Predictive Analytics, Sensor Networks, LSTM, LPWAN, Route Optimization
  topic: keywords
creator: Mrunal Salwadkar
creator: P.Dinesh kumar
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 48
ending page: 55
doi: 10.31838/WSNIOT/03.01.07

Energy-Aware MAC Protocol for Prolonging Network Lifetime in Solar-Powered Wireless Sensor Networks
type: item
description: Wireless Sensor Networks (WSNs) that have been used in remote areas are highly constrained in terms of energy and this requires energy-efficient communication protocols. The harvesting of solar energy has become an alternative power supply to such networks but with elastic solar irradiance, there is a problem in reliability of communication and life time of the networks. The proposed study introduces an energy-competent Medium Access Control (MAC) protocol; which is a medium that specially addresses the energy consumption of solar-powered WSNs with the goal to maximize network lifetime. The added value of the proposed protocol is the available real-time solar energy forecasting, awareness of the remaining energy and duty cycling adaptive mechanisms to smartly schedule the communication between the nodes depending on the energy, available and the buffer level. Massive simulations with real solar irradiance data were done and the performance of the protocol was compared with the traditional methods of SMAC and EH-MAC. The findings show a significant result whereby the network lifetime has improved by up to 30 per cent, delivery ratio by packets improved, and there was a reduced amount of energy consumption. The above results illustrate the effectiveness of the suggested MAC protocol in the provision of sustainable and energy-efficient operations of WSN. In addition, the protocol can provide a growing platform upon which future work can be made such as cross-layer optimization and real-time performance monitoring in environmental monitoring as well as smart infrastructure systems.
subject: Articles
  topic: section
subject: Wireless Sensor Networks, Solar Energy Harvesting, Energy-Aware MAC Protocol, Network Lifetime, Duty Cycling, Energy Forecasting
  topic: keywords
creator: Aakansha Soy
creator: Kasil Teyene
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 40
ending page: 47
doi: 10.31838/WSNIOT/03.01.06

AI-Driven Anomaly Detection Framework for Industrial IoT Using Edge-Enabled Wireless Sensor Networks
type: item
description: Industrial Internet of Things (IIoT) is becoming more dependent on Wireless Sensor Networks (WSNs) to monitor real-time asset and process. Nonetheless, identifying the anomalies in streaming data with high throughput is difficult as there is latency, a low bandwidth available, and low computing capabilities of the edge devices. This paper suggests an AI-based anomaly detection model capable of operating in the specific context of latency-sensitive industrial application with an edge-enabled WSN-based implementation of lower latency, distributed analysis. The architecture combines the use of lightweight Convolutional Autoencoders (CAEs) and Long Short-Term Memory (LSTM) models on edge devices in finding spatial and temporal anomalies on the parameters including temperature, pressure, and vibration. It also achieved a detection accuracy of more than 95 percent and an inference latency of less than 200 ms by relying on simulation results gathered using NS-3 and TensorFlow Lite. The system has a slight data transmission overhead reduction of 68 percent as compared to traditional cloud-based schemes and allows responding to faults in real-time. A solution that is scalable, energy efficient and reliable in terms of locating anomalies in mission-critical IIoT environments has been proposed based on the framework. The next steps will involve incorporating federated learning to support live industrial applications and the ability to upgrade the model dynamically and the capability of supporting multimodal sensor fusion.
subject: Articles
  topic: section
subject: Industrial IoT (IIoT), Wireless Sensor Networks (WSNs), Edge Computing, Anomaly Detection, Autoencoders, LSTM, Edge AI, Real-Time Monitoring, Fault Detection, AI at the Edge
  topic: keywords
creator: M. Ahmad
creator: Felip Cide
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 33
ending page: 39
doi: 10.31838/WSNIOT/03.01.05

5G-Enabled Wireless Sensor Networks for Time-Critical IoT Applications: Design Challenges and Opportunities
type: item
description: This has created a new paradigm of implementing time-constrained Internet of Things (IoT), in sectors like, industrial automation, smart transportation, healthcare monitoring, and disaster response systems by integrating fifth-generation (5G) wireless communication with the Wireless Sensor Networks (WSNs). Conventional WSNs, based on historic communication protocols such as IEEE 802.15.4 or LoRa, are bound to suffer fundamental impediments to ultra-reliable low-latency communication (URLLC), especially during massive deployment of nodes or when nodes come to be mobile or harsh. This paper provides in-depth study of the design principles, system architecture and performance trade-offs when constructing 5G-enabled WSNs able to satisfy the strict services of latency and reliability. We suggest the combination of edge and cloud networks according to the so-called hybrid edge-cloud where the advantage of the 5G peculiarities such as the network slicing, massive MIMO, and millimeter-wave (mmWave) communication are used to prioritize essential traffic, decrease transmission latency, and guarantee high compliance of packet delivery. The architecture also includes NR-Light nodes, SDN-based slicing management as well as edge intelligence offering real-time analytics and distributed decision-making. Both simulation (with NS-3 and 5G NR modules) and hardware prototyping (the usage of 5G modem-equipped sensor nodes and edge servers) confirm the described approach. The outcomes show the up to 68 percent cut of end-to-end latency and a +45 percent increase in QoS compliance in comparison to LTE-based WSN systems. In addition, power overheads are minimised by energy-sensitive scheduling and node sleep cycles despite the addition of higher-throughput 5G radios. According to our results, 5G integration greatly increases WSNs scalability, responsiveness, and efficiencies in a mission-critical environment. Lastly, we describe future directions with AI-based resource optimization, the 6…
subject: Articles
  topic: section
subject: 5G, Wireless Sensor Networks (WSNs), Time-Critical IoT, URLLC, Edge Computing, Network Slicing, Latency, mmWave, Real-Time Communication, Smart Sensors
  topic: keywords
creator: C. Jakhir
creator: Ahmed Alkilany
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 25
ending page: 32
doi: 10.31838/WSNIOT/03.01.04

Design and Implementation of Edge-Enabled IoT Framework for Real-Time Environmental Monitoring
type: item
description: The growing need of valid and efficient information on environmental readings has been the leading force behind the large-scale propagation of the Internet of Things (IoT) technologies. Conventional cloud-centric IoT solutions are however characterised by high latency, high bandwidth usage, lack of scalability and high energy consumption hence may not be appropriate in real time environmental monitoring solutions. Here the proposed design and implementation of a new IoT framework with edge-enabled capabilities is unique in the sense that it combines environmentally deployed low-power sensor nodes with a lightweight edge computing level and flexible communication protocols will allow effective and real-time acquisition and processing of environmental data. The architecture is based on the utilization of microcontroller-based nodes in the sensor framework that are used to track such parameters as temperature, humidity, and air quality and process the received data on the edge apprehending anomalies and synchronizing cloud in accordance with generated events. The actual prototype taking Raspberry Pi as the edge gateway and the LoRa / Wi-Fi as the communication channel has been deployed in a semi-urban test set-up. In the course of experiments, the suggested system shows 45 percent of reduction in response latency, 38 percent in network bandwidth consumption, and 35.6 percent in energy efficiency in comparison to the conventional cloud-dependant systems. The findings demonstrate that the architecture offers efficient, scalable, and responsive platform capable of supporting various real-time environmental monitoring solutions, such as smart cities, precision farming, and areas that are prone to disaster. The proposed study helps add a practical and scalable model of the deployment of edge intelligence in IoT-based environmental systems, which are extendable in the future to make use of federated learning and fog-to-cloud orchestration.
subject: Articles
  topic: section
subject: Edge Computing, Internet of Things (IoT), Environmental Monitoring, Real-Time Systems, Low-Latency Processing, Energy Efficiency, Smart Cities, LoRaWAN, Embedded Sensors.
  topic: keywords
creator: Rebert H. Luedke
creator: R. Prashanth
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 18
ending page: 24
doi: 10.31838/WSNIOT/03.01.03

Design and Performance Evaluation of Energy-Efficient Routing Protocols for Scalable IoT-Enabled Wireless Sensor Networks in Smart Environments
type: item
description: The high growth rate of the Internet of Things (IoT) system augmented the demand of energy-efficient and scalable routing protocol in Wireless Sensor Networks (WSNs), most notably in smart infrastructures, urban systems, precision agriculture, and industrial automation. In this paper, the authors are to design and test the performance of three commonly known energy-optimized routing protocols commonly cited in the literature namely LEACH, PEGASIS, and RPL modified to be used in IoT-based WSNs. An extensive simulation model based on NS-3 and MATLAB was designed to rate the performances of each of the protocols in our various simulation parameters such as the energy consumption, packet delivery ratio (PDR), network lifetime, and scalability with dynamic node density. The findings reveal that RPL has better network scalability and network stability with more than 95 percent PDR in densely deployed networks and supports increased node uptime. LEACH is not scalable, but applies a cluster-based architecture to achieve competitive energy efficiency in terms of small networks. The PEGASIS has satisfactory performance in moderate-density situation with low transmission overhead. These results give desirable design recommendations in choosing optimum routing protocols that match requirements of the IoT applications. This study has been able to help in the development of sustainable and high-performance WSN architectures to facilitate the further usage of intelligent and energy-aware IoT solutions on real-life smart environments.
subject: Articles
  topic: section
subject:
  topic: keywords
  value: Energy-Efficient Routing, Wireless Sensor Networks (WSNs), Internet of Things (IoT), Smart Environments, Routing Protocols (LEACH, PEGASIS, RPL), Network Scalability, Packet Delivery Ratio (PDR), Network Lifetime, Low-Power and Lossy Networks (LLNs), NS-3 Simulation, IoT Communication Protocols, Smart City Infrastructure, Cluster-Based Routing, IPv6-Based Routing, IoT Optimization.
creator: G.F. Frire
creator: K. L. Mleh
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 10
ending page: 17
doi: 10.31838/WSNIOT/03.01.02

Fog-Assisted Anomaly Detection in IoT-WSN Ecosystems Using Hybrid Deep Learning Models
type: item
description: The massive implementation of Internet of Things (IoT) devices and Wireless Sensor Networks (WSNs) has allowed data sensing and monitoring to be ubiquitous in various domains of human operation that are of critical nature including smart cities, industrial automation, healthcare, and environmental surveillance. Nonetheless, due to the high degree of dispersion of these ecosystems, and their scarce presence of both computational and energy capacities, it is very problematic to guarantee security and the real-time capability of detecting anomalies. Conventional cloud-based analytic schemes are usually characterized by a large latency, bandwidth bottleneck, and a threat to the privacy of data, which makes them inappropriate in occasions when the IoT-WSN application has time constraints and is resource-limited. To circumvent such limitations, this paper captures a fog-assisted anomaly detection system that can exploit hybrid deep learning systems by using both Convolutional Neural Networks (CNNs) which capture spatial feature wiring and Long Short-Term Memory (LSTM) networks that capture temporal sequence learning. The proposed system, due to the ability to leverage the computing power at fog nodes, which are located near the sensor layers, enables local entities to process the data on a localized basis, thereby greatly decreasing the need to communicate continuously with one of the clouds. Such architecture does not only increase the speed of detection but also decreases the amount of energy consumed by the IoT end devices. The performance of the model is compared to benchmark datasets, like SWaT, and UNSW-NB15, and in synthetic WSN settings with anomalies that have been injected. Demonstrated experimental data shows high accuracy of detection through the detection error of 98.3%, zero error false positive of 1.1%, and a decrease in the data latency by 63 percent in relation to traditional cloud-only mechanisms. The framework also keeps a minor memory and energy impact…
subject: Articles
  topic: section
subject: IoT, Wireless Sensor Networks, Anomaly Detection, Fog Computing, CNN-LSTM, Edge Intelligence, Deep Learning
  topic: keywords
creator: Lee Wei
creator: K. Maidanov
rights: Copyright (c) 2025
date: 2025-10-16
publication date: 2025-10-16
starting page: 1
ending page: 9
doi: 10.31838/WSNIOT/03.01.01

Blockchain Security for IoT Applications using Role of Wireless Sensor Networks
type: item
description: The rise of the Internet of Things (IoT) has brought untold numbers of devic es into our lives, using them all to build a commonplace of smart and data driven spaces. Wireless Sensor Networks (WSNs), at the central core of this technological revolution, are the foundation of IoT applications by collecting and transmitting vital data. But as the networks get more complex and more important, so do the security challenges it faces. Here comes blockchain technology, a novel method to make WSNs in the IoT more secure, trust worthy and accountable. With WSNs becoming more and more a part of our critical appointments such as healthcare, smart cities and industrial auto mation now more than ever we cannot risk their security and availability. It is well known, however, that traditional security mechanisms are typically insufficient when applied to these distributed networks as they are highly re source constrained sensor nodes. Blockchain technology enters the arena as a watershed solution functionally decentralizing and tamper-resistant data management and network security. In this that explores from an aspect of WSNs and IoT applications, blockchain technology and its intricate relation ship with WSNs in the age of IoT applications is explored comprehensively. In this paper, we explore how blockchain’s built in features could be used to overcome the security vulnerabilities of WSNs, improve data integrity and establish robust accountability mechanism. This article strives to be a one stop read to understand from the fundamentals of blockchain integration to advanced implementation strategies how this ground breaking technology is changing the world of IoT security.
subject: Articles
  topic: section
subject: Low Power Consumption;
  topic: keywords
subject: Wireless Communication;
  topic: keywords
subject: Zigbee Protocol;
  topic: keywords
subject: Bluetooth Low Energy;
  topic: keywords
subject: LoRaWAN
  topic: keywords
creator: Christopher James
creator: Alexander Michael
creator: William Harrison
rights: Copyright (c) 2025
date: 2025-05-28
publication date: 2025-05-28
starting page: 58
ending page: 65
doi: 10.31838/WSNIOT/02.02.07

Smart Traffic Management Using IoT and Wireless Sensor Networks: A Case Study Approach
type: item
description: Today’s world is being rapidly urbanized, and managing traffic congestion has become a highly complex task. Urban mobility is evolving as cities grow and vehicle ownership increases and traditional traffic management systems cannot keep up with the new dynamics of contemporary city life. This arti cle presents an idea to address this important problem by means of IoT and smart traffic management systems with wireless sensor networks. With the convergence of IoT and wireless sensor networks now open for possibilities, the real time traffic monitoring, analysis, and control has found new ways. These cutting edge technologies provide opportunities for cities to design more adaptive and forward looking traffic management solutions that even tually enable an optimized traffic flow, reduced congestion and better urban mobility. A smart traffic management system implemented in a populous me tropolis combining IoT, and wireless sensor network has been the subject of this comprehensive case study. Through this innovative approach to urban traffic control, we examine the system architecture, key components, data collection and analysis methods and tangible benefit resulting from this ap proach to urban traffic control.
subject: Articles
  topic: section
subject: Edge Computing;
  topic: keywords
subject: Cloud Computing;
  topic: keywords
subject: Data Aggregation;
  topic: keywords
subject: Real-Time Monitoring;
  topic: keywords
subject: Smart Sensors;
  topic: keywords
subject: The Traffic Congestion Challenge
  topic: keywords
creator: Johann Christian
creator: Maximilian Paul
creator: Felix Alexander
rights: Copyright (c) 2025
date: 2025-05-25
publication date: 2025-05-25
starting page: 45
ending page: 57
doi: 10.31838/WSNIOT/02.02.06