Journal of Wireless Sensor Networks and IoT
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
SOCIETY FOR COMMUNICATION AND COMPUTER TECHNOLOGIES
IoT Based Glucose Monitoring and Alert System Using Contact Less Sensor
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 a Modern Dehydration Detecting and Auto Feedback Recommendation System for Pregnant Women to address this dehydration problem in pregnant women and we have also in addition integratedfall detection sensor to analyse fall and send Emergency Notification for Immediate Rescue.
Low-Power Wireless Sensor Framework for Continuous Environmental Monitoring in Cultural Heritage Repositories
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.
Wireless sensor networks, IoT, low-power design, LoRa, Zigbee, heritage monitoring, environmental data analytics.
Edge-Enabled Internet Weather Mapping for IoT Traffic Visualization
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.
IoT performance, Internet weather maps, edge analytics, passive flow monitoring, network visualization, traffic optimization.
Deep Learning–Assisted Passive Traffic Profiling in Large-Scale IoT Networks
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.
IoT device profiling, passive traffic analysis, deep learning, CNN–LSTM, network behavior, security analytics.
Adaptive Cognitive Radio-Enabled Spectrum Access for Power-Constrained IoT Communication Systems
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.
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.
Real-Time Environmental Monitoring Using LoRa-Enabled Wireless Sensor Networks: Challenges, System Design, and Mitigation Strategies
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.
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.
Deployment of WSN-Based Structural Health Monitoring for Smart Bridges: A Real-World Case Study
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.
Structural Health Monitoring, Wireless Sensor Networks, Smart Bridge, Zigbee, Real-Time Monitoring, Edge Computing, Energy Efficiency, Anomaly Detection
Comparative Evaluation of Machine Learning-Based Localization Algorithms in Dense IoT Sensor Networks
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.
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.
A Solar-Piezo Hybrid Energy Harvesting Architecture for Autonomous Wireless Sensor Nodes in Smart Agriculture
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 means to overcome the energy independence issues that may arise in precision farming that reinforces long term, data-driven agricultural decision making and sustainability of the environment in general. The results validate the possibility of hybrid ambient energy harvester systems in becoming energy-resilient IoT networks, specifically, in remote and off-grid farms.
Hybrid Energy Harvesting, Solar-Piezo, Wireless Sensor Networks, Smart Agriculture, Energy Management, LoRa, Precision Farming
Hybrid Zigbee-LPWAN Communication Model for Smart Urban Infrastructure
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. Finally, the model proposed addresses the shortcomings of isolated wireless technologies by providing a scalable, adaptive and resilient communications infrastructure in smart cities with its implementation considerations, security precautions and open research issues outlined to facilitate future design choices and rollups.
Smart Urban Infrastructure, Hybrid Communication Architecture, Zigbee-LPWAN Integration, IoT Scalability, Energy-Efficient Networking, Smart City Connectivity
Blockchain-Enhanced Security Framework for Federated Wireless Sensor Networks in Smart City Infrastructures
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.
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.
Smart City Waste Management Using Sensor-Driven IoT Architecture and Predictive Analytics
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 pick ups. Such a combination of predictive intelligence and energy-efficient communication makes the system highly applicable even on smart cities. This study demonstrates how the perceptions of IoT sensing and machine learning could transform the waste management system in cities and shift towards data-driven and sustainable services in cities.
Smart City, Waste Management, IoT, Predictive Analytics, Sensor Networks, LSTM, LPWAN, Route Optimization
Energy-Aware MAC Protocol for Prolonging Network Lifetime in Solar-Powered Wireless Sensor Networks
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.
Wireless Sensor Networks, Solar Energy Harvesting, Energy-Aware MAC Protocol, Network Lifetime, Duty Cycling, Energy Forecasting
AI-Driven Anomaly Detection Framework for Industrial IoT Using Edge-Enabled Wireless Sensor Networks
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.
Industrial IoT (IIoT), Wireless Sensor Networks (WSNs), Edge Computing, Anomaly Detection, Autoencoders, LSTM, Edge AI, Real-Time Monitoring, Fault Detection, AI at the Edge
5G-Enabled Wireless Sensor Networks for Time-Critical IoT Applications: Design Challenges and Opportunities
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 6G extensions and lightweight security mechanisms that are suitable with URLLC sensors. The framework that is suggested provides an energy-efficient, scalable framework of the next-generation WSN in line with the trending needs of smart environments and the Industry 5.0 agenda.
5G, Wireless Sensor Networks (WSNs), Time-Critical IoT, URLLC, Edge Computing, Network Slicing, Latency, mmWave, Real-Time Communication, Smart Sensors
Design and Implementation of Edge-Enabled IoT Framework for Real-Time Environmental Monitoring
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.
Edge Computing, Internet of Things (IoT), Environmental Monitoring, Real-Time Systems, Low-Latency Processing, Energy Efficiency, Smart Cities, LoRaWAN, Embedded Sensors.
Design and Performance Evaluation of Energy-Efficient Routing Protocols for Scalable IoT-Enabled Wireless Sensor Networks in Smart Environments
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.
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.
Fog-Assisted Anomaly Detection in IoT-WSN Ecosystems Using Hybrid Deep Learning Models
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 that can fit to be implemented in real world fog environments. On the whole, the present research highlights the capabilities of fog-aided edge intelligence to support the reliability, scalability, and responsiveness levels of the anomaly detection mechanisms of IoT-WSN structures in ways that will help it to develop secure, context-aware, and efficient smart systems.
IoT, Wireless Sensor Networks, Anomaly Detection, Fog Computing, CNN-LSTM, Edge Intelligence, Deep Learning
Blockchain Security for IoT Applications using Role of Wireless Sensor Networks
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.
Smart Traffic Management Using IoT and Wireless Sensor Networks: A Case Study Approach
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.
Improvements in Environmental Monitoring in IoT Networks through Sensor Fusion Techniques
With the Internet of Things (IoT), environmental monitoring has been revolutionized by the ability to deploy large sensor networks to collect real-time environmental data on numerous parameters. But managing data from these networks, consisting of an extraordinary amount and breadth of information, is both voluminous and unmanageable for anything from data integration to understanding and taking action in an efficient and timely manner. As these technologies become commonplace, sophisticated algorithms have advanced enough to be able to process and analyze huge amounts of heterogeneous data in real time, yielding useful knowledge for environmental management and decision making. The application of various sensor fusion techniques in environment monitoring IoT networks, their advantages, challenges, and future prospects are the basis of this article. In this work, we explore the various types of sensor fusion algorithms, apply them to certain environmental monitoring scenarios, and how machine learning can aid in augmenting fusion processes. In addition, we will expose implementation challenges and best practices of deploying sensor fusion systems in the real world environmental monitoring applications.
Real-Time Data Analytics for Industrial IoT Systems: Edge and Cloud Computing integration
Following the rapid evolution of Industrial Internet of Things (IIoT) technologies, we enter a new era where industrial data is used to make data driven decisions and enhance operational efficiency across multiple industrial sectors. Now that the volume and velocity of data driven by interconnect devices keep increasing, it becomes necessary to have robust real time analytics. In this article, I describe what happens if we blend in edge and cloud computing paradigms into our industrial IoT systems with the purpose of performing sophisticated real time data analytics. It finds that the convergence of edge and cloud computing offers a powerful solution for those challenges inherent in traditional cloud-centric architectures: latency, bandwidth constraints, and data privacy. The alignment of the strengths of the two paradigms enables organizations to leverage IIoT deployments to create new innovative solutions, optimizing processes, and to gain unmatched insights in order to increase their overall operational performance. In this comprehensive exploration we will explore fundamental concepts, architectural considerations, the benefits, challenges and emerging trends influencing the running of real time data analytics in Industrial IoT environments. This article attempts to give a complete take on the subject by going from the study of the role of the edging computing in the reducing of the latency and the data processing capacity as well as the study of the advanced analytic techniques and the security consideration.
Low-Power Wide Area Networks for IoT: Challenges, Performance and Future Trends
The Internet of Things (IoT) has changed connectivity forever, opening the door for countless devices to talk and exchange information across great distances. At the heart of this technological transformation lies a crucial innovation: Low Power Wide Area Networks (LPWAN). They have taken shape as game changers, solving the requirements of the IoT by providing long range communication with small power consumption to its applications. IoT deployments continue to explode across many sectors, such as smart cities, agriculture, and industrial monitoring, all of which rely on efficient and scalable connectivity solutions more than ever. Enter LPWANs, which f ill the gap and offer a reliable basis for massively connecting many low power devices spread out over large geographical space. In this article, we look at the world of LPWANs, their performance characteristics, discuss the challenges they face and gape at the crystal ball to see the future. We’ll take a look at how these networks are changing the IoT, and how they’re preparing the landscape for exciting new applications that were, until recently, thought to be impossible.
IoT-Enabled Smart Healthcare Systems: Improvements to Remote Patient Monitoring and Diagnostics
Recently, Internet of Things (IoT) technologies are wreaking havoc with the healthcare industry, and it’s not going back. This digital evolution is changing the way medical professionals treat, monitor, and take care of decisions. The work at the heart of this change centres on IoT enabled smart healthcare systems that greatly increase remote patient monitoring and diagnostic capabilities. With this in mind we’ll unravel how connected devices and data based approaches are shifting the face of patient care, enhancing clinical results, and enhancing efficiency in overall medical conditions. IoT is enabling new frontiers in personalized medicine and proactive health management — from wearable health trackers to complicated implantable sensors. In this comprehensive guide, we’ll cover the different dimensions of IoT in healthcare, how it’s used in remote patient monitoring, what it brings to patients and providers, and what the challenges are around implementing these very advanced technological tools. In addition, we will also take a look at some real world case studies and emerging trends of smart healthcare system.
IoT-Based Smart Grid Systems: New advancement on Wireless Sensor Network integration
With the development of the power distribution systems in an intelligent energy management era, IoT technologies and WSNs are emerging as new revolutions of power distribution systems. Smart grid systems, which prom ise increased electricity delivery efficiency, reliability and sustainability, have emerged as the result of this convergence. These innovations are in creasingly embraced by utilities and energy providers alike as a landscape of power infrastructure is transformed with real-time monitoring, adaptive control, and better decision-making in place. Smart grid applications repre sent a stark new paradigm in which we will conceive and manage electrical networks by fusing IoT and WSNs. These systems exploit the capabilities of coupled devices and data analytics to provide its unparalleled views of the energy consumption patterns, the behavior in the grid, and vulnerabilities of current distribution grids. This technological synergy extends beyond mere resource optimization and facilitates future more resilient, more responsive power distribution mechanisms. We dig into the underpinnings of IoT based smart grid systems, from the fundamental components and communication protocols to architectural frameworks, as we explore this next wave in ener gy management. Each of these aspects—advanced metering infrastructures, cyber security—matter here in the degree to which they are integrated fully into hyperconnected devices.
Reliable Data Delivery in large scale IoT networks using Hybrid Routing Protocols
The Internet of Things has turned the world upside down and brought bil lions of devices on line to share data effortlessly. The need for reliable and efficient data delivery in IoT networks has grown along with network size and complexity. In an effort to optimize communication in large scale IoT ecosystems, this article explores the emergence of hybrid routing protocols as a potential solution. The continuously growing IoT deployments have made heterogeneous and dynamic network environments more and more a reality. Classical routing schemes are insufficient in serving the special characteris tics of the IoT – limited energy resources, diverse traffic grades, and frequent topology modification. In order to get over these limitations, Hybrid routing protocols are designed to combine the strengths of multiple routing para digms. Hybrid protocols combine proactive and reactive routing elements to accommodate changes in IoT applications’ demands, while still utilizing the best performing routing solution. Through this adaptive approach, more efficient use of network resources, better scalability and reliability in data delivery is achieved over extensive IoT infrastructures. In the subsequent sections we provide an overview of some of the basic concepts of hybrid routing for IoT and consider protocol design and how they work in practical implementations. In addition, we will also talk about the current emerging trends and future directions of this fast advancing field. By understanding this comprehensive analysis, readers will understand the role hybrid routing plays in allowing next generation large scale IoT networks.
Energy Efficient Communication Protocols for Long Range IoT Sensor Networks
The rise of Internet of Things (IoT) devices in smart cities and massive net works has made energy efficiency an important genic research study and development. But as these networks grow, the demand for sustainable com munication protocols goes up. In this article we take a closer look at the cur rent state of the art and strategies for efficient energy consumption in long range IoT sensor networks, and specific to these are emerging technologies like NB-IoT and LTE-M. As the complexity of IoT deployments increases, novel methods to extend the life span of battery powered nodes and reduce energy consumption across systems are needed. If we examine futuristic protocols, adaptive power control mechanisms and new topologies of IoT network, we can take the potential of IoT infrastructure up to a new level and make her alive for longer period of time. In the following sections we explore the stakes in IoT communication, review groundbreaking work, and what prom ises to be the likely impact of these advances on smart cities and large scale sensor networks. This comprehensive exploration will offer key learnings to researchers, engineers, and decision makers crafting an IoT ecosystem of greater efficiency and sustainability, from zone based architectures to event driven data transmission.
Wireless Sensor Network Energy Harvesting for IoT Applications: Emerging Trends
Thanks to the Internet of Things (IoT), we now live in an era of connectivity and interaction between a growing set of devices to the internet, other de vices and ourselves. Wireless sensor networks (WSNs) are the core of most IoT applications and they constitute the very heart of the IoT technology revolution. Yet as these networks intensify use and become more sophisticat ed, they leave the one challenge behind: the question of how to power them efficiently and sustainably. Energy harvesting techniques for WSNs were born out as a game-changer of sorts, a solution that can increase the lifespan and scope of IoT devices, while leaving a light footprint on the environment. This extensive exploration covers the cutting edge of energy harvesting for the wireless sensor network, taking a look at how these innovations are set to reshape IoT applications. We will find out about a huge variety of technique, including solar and thermal energy capture, and kinetic and electromagnetic harvesting. In addition, we discuss the challenges this field presents as well as the promise of some energy storage solution and the interesting future trends for WSN energy harvesting. As the conversation unfolds, you’ll hear that energy harvesting is not only a technological requirement, but a fuel that feeds innovation in the IoT landscape. These techniques are enabling longer lasting, more autonomous devices, while enabling new applications in environmental monitoring, smart cities, industrial automation, and beyond. Let’s take a peek into the worlds of wireless sensor network energy harvest ing, and what it could mean for IoT in the future.
Designing Scalable IoT Architectures for Smart Cities: Challenges and Solutions
Weather the Internet of Things has come to watch. Urban landscapes are advancing towards smarter, more efficient cities. In light of this, it is paramount for Robust Refurbishment, Scalable IoT architectures as more and more municipalities lean into such digital transformation all across the world. This guide gives a complete overview of what it takes to create an IoT framework that can respond to the on the go requirements of a smart city – the challenges, that need to be addressed, presented with possible innovative solutions. In recent years, the promise of enhanced quality of life and optimized resource management, plus better urban services, have resulted in the concept of smart cities gaining much momentum. The heart of this transformation is the Internet of Things, a wide network of connected devices and sensors that bring data together in real time. But as cities increase in size and technology becomes more complex, IoT systems exponentially more complex, creating architectures that are deployable at scale. In this article we examine IoT architectures for smart cities, and the layers of this architecture that underpin smart city systems. Each component makes their purpose in an efficient urban ecosystem starting from the application layer, where insights are transformed into actionable intelligence, and then the device layer where sensors and actuators collect critical data. What challenges arise when you scale these architectures to handle millions of devices and petabytes of data, and what cutting edge solutions have been developed to overcome these challenges will be discussed.
Revolutionizing Healthcare: Wearable IoT Sensors for Health Monitoring Applications: Design and Optimization
As the intersection between wearable technology and the Internet of Things (IoT) becomes increasingly entrenched, this is ushering in the era of person alized healthcare. At the same time there is an urgent need for new solutions that enable continuous monitoring of health parameters outside of clinical settings, as populations are getting older and more and more often suffer from chronic diseases. To address this challenge, wearable IoT sensors are emerging as powerful tools to conduct real time health tracking and early detection of potential disease. In this paper, we explore how the cutting edge developments in wearable IoT device design and optimization for health monitoring applications could revolutionize patient care and medical deci sion making. It was due to the rapid development of the microscopically sized sensors, wireless communication devices, and data analytical techniques that have led to the emergence of a whole new generation of wearable health monitoring devices. They’re smart, connected gadgets, which means they can even track your vital signs, physical activity, sleep patterns, and other kinds of physiological parameters around the clock. With IoT connectivity, collected data can be smoothly transmitted to solve providers to carry out proactive and personalized interventions. There are however hurdles to the widespread adoption of wearable health monitors. There are a number of key challenges to ensuring user comfort for long term wear, maximizing battery life and developing robust algorithms for accurate health predictions. In this article we look into how these obstacles have been overcome with flexible electronics, energy harvesting techniques and advanced machine learning algorithms specific to wearable applications. In this work, we analyze how wearable IoT sensor researchers and engineers are pressing the limits of wearable IoT sensor design, from novel materials and form factors to opti mize comfort to edge computing architectures enabling on device analytics. And we’ll also discuss how artificial intelligence and IoT technologies can be integrated to extract useful knowledge from the deluge of data produced by these devices. We also explore enabling applications in such emerging areas of medical domain as well as potential of wearable IoT sensors in enabling disease management, rehabilitation as well as preventive health care.