Sensor Hubs Research Articles

Intelligent Efficient Routing and Localization in the Underwater Wireless Sensor Network to Improve Network Lifetime

The inefficiency of the localization process mostly causes delays and operational difficulties in wireless sensor network (WSN) topologies. When localization is suboptimal, the time required for path creation significantly increases, leading to reduced network lifetime and diminished routing efficiency. To address these critical issues, our research focuses on developing a novel procedure that optimizes routing and localization. In this context, localization is the initial step for reaching sensor terminals, followed by activating the routing mechanism. To tackle these issues in Underwater Wireless Sensor Networks (UWSN), the proposed work presents the Elman Bat-based Hello Routing (EBbHR). The primary objective of our research is to locate wireless sensor nodes within the designed UWSN effectively. The process is initiated to achieve this by creating the necessary wireless sensor hubs within the MATLAB Simulink environment. Subsequently, the novel EBbHR function is activated to facilitate the precise localization of all sensor hubs, thereby enhancing the overall data transmission. The success of the designed EBbHR is assessed by considering UWSN factors, and a comparison with current approaches is carried out to show the advancements made by the model.

Characterizing teacher support of debugging with physical computing: Debugging pedagogies in practice

Objectives . Physical computing systems are increasingly being integrated into secondary school science and STEM instruction, yet little is known about how teachers, especially those with little background and experience in computing, help students during the inevitable debugging moments that arise. In this paper, we describe a framework, comprising two dimensions, for characterizing how teachers support students as they debug a physical computing system called the Data Sensor Hub (DaSH). The DaSH enables students to program sensors to measure, analyze, and visualize data as they engage in science inquiry activities. Participants . Five secondary school teachers implemented an inquiry-oriented instructional unit designed to introduce students to working with the DaSH as a tool for scientific inquiry. Study Method . Findings drew on video analysis of the teachers’ classroom implementations of the unit. A review of the data corpus led to the selection of 23 moments where the teachers supported an individual or small groups of students engaged in debugging. These moments were analyzed using a grounded perspective based on Interaction Analysis to characterize the teachers' varied interactional approaches. Findings . Our analysis revealed how teachers’ moves during debugging moments fell along two dimensions. The first dimension characterizes teachers’ positioning during the debugging interactions, ranging from a positioning for teacher understanding to a positioning for student understanding of the bug. The second dimension characterizes the inquiry orientation of the teachers’ questions and guidance, ranging from focusing on the debugging process to focusing on the product – or fixing the bug. Further, teachers’ moves often fell along different points on these dimensions in any given interaction given nuances in the instructional context. Conclusions . The framework offers a first step towards characterizing teachers’ debugging pedagogy as they support students during debugging moments. It also calls attention to how teachers do not necessarily need to be programming experts to effectively help students learn independent and generalizable debugging strategies. Further, it illustrates the variety of expertise that teachers can bring to debugging moments to support students learning to debug. Finally, the framework provides implications for the design of professional learning and supports for teachers as they increasingly are asked to support students in computing – and debugging – activities across a range of disciplines.

Low cost artificial intelligence Internet of Things based water quality monitoring for rural areas

Safe drinking water is quite possibly one of the most difficult-to-find issues on our earth intently influencing the well-being and cleanliness of humanity, animals, and plants. The smart speed of industrialization and the conspicuous significance of horticulture with cultivating pesticides have impelled water contamination to a colossal degree. Safe water is likewise worried about its verdure. In this article, a low-cost and low-power is proposed to screen the nature of water that can be utilized in all networks wherever consumption of water is typically provided by means of the water dispersion framework. Planned Sensor hubs are viable with existing circulation organizations. Sensor nodes can collect the data for the electrochemical properties of water to the server. A developed algorithm using the Decision Tree and Naive Bayes methods identifies cloud predictions for safe drinkable water and alerts on divergence from a World Health Organization-specified safer range. For efficacy, the suggested method is scientifically evaluated in two villages, Mandalgarh and Bassi, Rajasthan, India, where drinking water is in short supply. Naive Bayes, Gradient Boosted Classifier, support vector machine, and artificial neural network models are applied to collected data of water quality and analyzed by the Naive Bayes. The obtained results efficiently with 0.56 F1-Score. The nodes of the distributed network can work in harsh conditions as well and low power consumption and information transmission viability are precisely estimated. The empirical results are verified by laboratory studies, and it is demonstrated that the method has a significant impact on the prevention of water-borne infections, especially in rural regions.

Energy Efficiency Examination and Analysation in Wireless Sensor Networks

Wireless sensor networks (WSNs) have risen as a vital innovation empowering a horde of applications extending from natural checking to mechanization. In any case, the constrained vitality assets of sensor hubs pose critical challenges to the life span and execution of these systems. This theory presents a comprehensive audit of procedures and techniques to move vitality proficiency forward in WSNs. The survey starts by analyzing power efficient equipment plan approaches, counting low power microcontrollers, energy harvesting frameworks, and ultra low power communication conventions. Besides, it investigates energy aware steering conventions that optimize information transmission ways to minimize vitality utilization, such as Filter (Moo Vitality Versatile Clustering Progression) and its variations. Moreover, rest planning instruments are examined, which permit sensor hubs to interchange between dynamic and rest states to preserve vitality while keeping up an organized network. Additionally, the theoretical dives into the part of information conglomeration and compression procedures in decreasing excess transmissions and maintaining vitality at the arranged level. It highlights the importance of versatile obligation cycling instruments, where sensor hubs powerfully alter their obligation cycles based on natural conditions and application prerequisites. Moreover, progressions in energy efficient MAC (Medium Get to Control) conventions are analyzed, centring on conventions like IEEE 802.15.4 and their improvements for WSNs. Furthermore, the integration of machine learning and optimization calculations for energy efficient operation and asset assignment in WSNs is examined. At last, the theory concludes with a viewpoint on rising patterns and future headings in the field, counting the integration of energy harvesting advances, the improvement of self sustainable sensor hubs, and the investigation of novel communication standards such as unmistakable light communication.

Identification of Faulty Sensor Nodes in WBAN Using Genetically Linked Artificial Neural Network

Wireless Body Area Networks (WBANs) have risen as a promising innovation for checking humanphysiological parameters in real time. Be that as it may, the unwavering quality and precision of WBANs dependon the right working of sensor hubs. The distinguishing proof of defective sensor hubs is vital for guaranteeing thequality of information collected by WBANs. In this paper, we propose a novel approach to recognizing faultysensor hubs in WBAN employing a hereditarily linked artificial neural organize (GLANN). The GLANN isprepared to employ a crossbreed fuzzy-genetic calculation to optimize its execution in distinguishing defectivesensor hubs. The proposed approach is assessed employing a dataset collected from a real-world WBAN. Thecomes about appears that the GLANN-based approach beats existing strategies in terms of exactness andproficiency. The proposed approach has potential applications within the field of healthcare, where exact and solidobserving of human physiological parameters is basic for conclusion and treatment. By and large, this ponderpresents a promising approach to progressing the unwavering quality and exactness of WBANs by identifyingflawed sensor hubs utilizing GLANN .

Multimodal Daily-Life Logging in Free-living Environment Using Non-Visual Egocentric Sensors on a Smartphone

Egocentric non-intrusive sensing of human activities of daily living (ADL) in free-living environments represents a holy grail in ubiquitous computing. Existing approaches, such as egocentric vision and wearable motion sensors, either can be intrusive or have limitations in capturing non-ambulatory actions. To address these challenges, we propose EgoADL, the first egocentric ADL sensing system that uses an in-pocket smartphone as a multi-modal sensor hub to capture body motion, interactions with the physical environment and daily objects using non-visual sensors (audio, wireless sensing, and motion sensors). We collected a 120-hour multimodal dataset and annotated 20-hour data into 221 ADL, 70 object interactions, and 91 actions. EgoADL proposes multi-modal frame-wise slow-fast encoders to learn the feature representation of multi-sensory data that characterizes the complementary advantages of different modalities and adapt a transformer-based sequence-to-sequence model to decode the time-series sensor signals into a sequence of words that represent ADL. In addition, we introduce a self-supervised learning framework that extracts intrinsic supervisory signals from the multi-modal sensing data to overcome the lack of labeling data and achieve better generalization and extensibility. Our experiments in free-living environments demonstrate that EgoADL can achieve comparable performance with video-based approaches, bringing the vision of ambient intelligence closer to reality.

An N-Type Pseudo-Static eDRAM Macro with Reduced Access Time for High-Speed Processing-in-Memory in Intelligent Sensor Hub Applications

This paper introduces an n-type pseudo-static gain cell (PS-nGC) embedded within dynamic random-access memory (eDRAM) for high-speed processing-in-memory (PIM) applications. The PS-nGC leverages a two-transistor (2T) gain cell and employs an n-type pseudo-static leakage compensation (n-type PSLC) circuit to significantly extend the eDRAM’s retention time. The implementation of a homogeneous NMOS-based 2T gain cell not only reduces write access times but also benefits from a boosted write wordline technique. In a comparison with the previous pseudo-static gain cell design, the proposed PS-nGC exhibits improvements in write and read access times, achieving 3.27 times and 1.81 times reductions in write access time and read access time, respectively. Furthermore, the PS-nGC demonstrates versatility by accommodating a wide supply voltage range, spanning from 0.7 to 1.2 V, while maintaining an operating frequency of 667 MHz. Fabricated using a 28 nm complementary metal oxide semiconductor (CMOS) process, the prototype features an efficient active area, occupying a mere 0.284 µm2 per bitcell for the 4 kb eDRAM macro. Under various operational conditions, including different processes, voltages, and temperatures, the proposed PS-nGC of eDRAM consistently provides speedy and reliable read and write operations.

The measurement and monitoring of Quality of service based on security analysis in wireless sensor network using deep learning architecture

Wireless sensor networks comprise of unattended little sensor hubs having low energy and low scope of correspondence. Machine learning has demonstrated its effectiveness in creating proficient cycles to deal with complex issues in different organization perspectives. This exploration propose novel procedure in wireless sensor network routing and security improvement by network observing utilizing profound learning model. The organization routing is done utilizing cluster based scalable hierarchical energy routing protocol (CSHERP) and the organization checking is completed utilizing fuzzy Boltzmann adversarial learning (FBAL). This routing protocol adopts an information driven strategy, with moderate hubs collecting information and sending it to a sink hub. The arrangement gave is valuable to additional investigation of expanded WSN networks. The trial examination is completed as far as energy efficiency, throughput, end-end delay, latency, QoS. The proposed technique attained energy efficiency of 96%, throughput of 91%, end-end delay of 86%, latency of 85%, QoS of 96%.

English

Virtualization of the Sensor network empowers the chance of distribution of actual normal assets in various partner uses. The current thesis centers around tending to the unique transformation of currently doled out virtual sensor network assets in answering timespan changing uses requests. We have suggested n advancement structure which powerfully designates implementations in Sensor's hubs while representing qualities with the remote Sensor's climate restrictions. This likewise considers the extra energy utilization connected with initiating new hubs and moving currently dynamic applications. Different goal capacities connected with the accessible energies with hubs were broken down. The suggested structure was assessed with recreation assuming reasonable boundaries out of genuine Sensor's hubs with sending uses in surveying proficiency of proposition.

IoT-Enabled Chlorine Level Assessment and Prediction in Water Monitoring System Using Machine Learning

The significance of user participation in sustaining drinking water quality and assessing other factors, such as cleanliness, sanitary conditions, preservation, and waste treatment, is essential for preserving groundwater quality. Inadequate water quality spreads disease, causes mortality, and hinders socioeconomic growth. In addition, disinfectants such as chlorine and fluoride are used to remove pathogens, or disease-causing compounds, from water. After a substantial amount of chlorine has been added to water, its residue causes an issue. Since the proposed methodology is intended to offer a steady supply of drinkable water, its chlorine concentration must be checked in real-time. The suggested model continually updates the sensor hub regarding chlorine concentration measurements. In addition, these data are transmitted over a communication system for data analysis to analyze chlorine levels within the drinking water and residual chlorine percentage over time using a fuzzy set specifically using a decision tree algorithm. Additionally, a performance investigation of the proposed framework is undertaken to determine the efficiency of the existing model for predicting the quantity of chlorine substance employing metrics such as recall, accuracy, F-score, and ROC. Henceforth, the proposed model has substantially better precision than the existing techniques.

Accelerometer intensity vector sensor network for environmental noise monitoring with source direction and location

AIVS (Accelerometer-based Intensity Vector Sensors, /āvs/) represent a new way to measure 3-d sound and are designed to integrate into existing noise monitoring solutions. Standard microphones measure sound pressure, which cannot alone deduce the direction of sound propagation. AIVS is based on the measurement of the velocity of a small parcel of air surrounding a triaxial accelerometer, from which a vector-based representation of sound intensity is calculated. AIVS integrates a MEMS triaxial accelerometer with one MEMS microphone and synchronously measures particle velocity and pressure, resulting in a 3-d intensity vector at each AIVS node. An AIVS network is synchronized to GNSS time and sensors are deployed in groups surrounding and/or within a local measurement site. Low power AIVS nodes are location-aware and estimate azimuth and elevation angles to detected noise sources as a function of frequency. Range to source is computed when noise events are observed from multiple nodes. AIVS nodes are managed by a Raspberry-PI (RPI) sensor hub in a wired CAN-bus supporting distances up to 100 m, or via the Bluetooth Low Energy (BLE) protocol. More widely separated nodes are joined through WWLAN technologies via the local RPI hub.

Review of Localization and Clustering in USV and AUV for Underwater Wireless Sensor Networks

Oceanographic data collection, disaster prevention, aided navigation, critical observation sub-missions, contaminant screening, and seaward scanning are just a few of the submissions that use underwater sensor hubs. Unmanned submerged vehicles (USVs) or autonomous acoustic underwater vehicles (AUVs) through sensors would similarly be able to explore unique underwater resources and gather data when utilized in conjunction with integrated screen operations. The most advanced technological method of oceanic observation is wireless information routing beneath the ocean or generally underwater. Water bottoms are typically observed using oceanographic sensors that collect data at certain ocean zones. Most research on UWSNs focuses on physical levels, even though the localization level, such as guiding processes, is a more recent zone. Analyzing the presenting metrics of the current direction conventions for UWSNs is crucial for considering additional enhancements in a procedure employing underwater wireless sensor networks for locating sensors (UWSNs). Due to their severely constrained propagation, radio frequency (RF) transmissions are inappropriate for underwater environments. This makes it difficult to maintain network connectivity and localization. This provided a plan for employing adequate reliability and improved communication and is used to locate the node exactly using a variety of methods. In order to minimize inaccuracies, specific techniques are utilized to calculate the distance to the destination. It has a variety of qualities, such as limited bandwidth, high latency, low energy, and a high error probability. Both nodes enable technical professionals stationed on land to communicate data from the chosen oceanic zones rapidly. This study investigates the significance, uses, network architecture, requirements, and difficulties of undersea sensors.

An Energy-Efficient Protocol for Internet of Things Based Wireless Sensor Networks

The performance of Wireless Sensor Networks (WSNs) is an important fragment of the Internet of Things (IoT), where the current WSNbuilt IoT network’s sensor hubs are enticing due to their critical resources. By grouping hubs, a clustering convention offers a useful solution for ensuring energy-saving of hubs and Hybrid Media Access Control (HMAC) during the course of the organization. Nevertheless, current grouping standards suffer from issues with the grouping structure that impacts the exhibition of these conventions negatively. In this investigation, we recommend an Improved Energy-Proficient Algorithm (IEPA) for HMAC throughout the lifetime of the WSN-based IoT. Three consecutive segments are suggested. For the covering of adjusted clusters, an ideal number of clusters is determined first. Then, fair static clusters are shaped, based on an updated calculation for fluffy cluster heads, to reduce and adapt the energy use of the sensor hubs. Cluster heads (CHs) are, ultimately, selected in optimal locations, with the pivot of the cluster heads working among cluster members. Specifically, the proposed convention diminishes and balances the energy utilization of hubs by improving the grouping structure, where the IEPA is reasonable for systems that need a long time. The assessment results demonstrate that the IEPA performs better than existing conventions.

WOGRU-IDS — An intelligent intrusion detection system for IoT assisted Wireless Sensor Networks

One of the key mechanisms of the current electronic and wireless frameworks is the assistance of Wireless Sensor Networks (WSN) in Internet of Things (IoT) networks. A WSN typically consists of multipurpose sensor hubs for data sensing, processing, and communication. These networks are more suited to conveying medical data from various geographical regions and sending private medical data to the network owner. However, the worry about various attacks on health care data normally grows daily. These assaults could quickly have adverse impacts on the WSN-IoT (Internet of Things) nodes. Additionally, the low detection rate, significant processing overhead, and resource limitations of current intrusion detection systems all contribute to an increase in false alarm rates when trying to identify various attacks. The unique Whale Optimized Gate Recurrent Unit (WOGRU) Intrusion Detection System (IDS) for WSN-IoT networks is proposed in this research in light of the aforementioned issues in order to effectively identify various attacks. The whale algorithm was used in the proposed framework to tune the hyperparameters of the deep long short-term memory in order to achieve low computational overhead and great performance. Last but not least, validations are carried out using the WSN-DS dataset, and the performance of the suggested work is evaluated using the parameters accuracy, recall, precision, specificity, and F1-score. Additionally, the comparison study was conducted using the current frameworks. The data demonstrates that the suggested framework had an average performance of 99.85 percent for the detection of flooding, scheduling, black hole, and gray hole attacks.

Detection of Intrusion behavior in cloud applications using Pearson's chi-squared distribution and decision tree classifiers

• The main objective is to identify unauthorised fog nodes in cloud applications. • The optimal feature selection for security is perfomed by distribution algorithm. • The Decsion tree classifiers are used for intruded fog nodes isolation The information transmission in machine learning is a challengable task which must be performed productively in cloud based fog applications. There is a few methodologies has been pronounced for the issue of information transmission in cloudbased machines, which utilizes a few techniques and components in the choice of portable sensor hubs which has information put away. However, not just the decision could work on the presentation of information transmission, and there are such countless elements which influence information transmission in a roundabout way. The proposed technique keeps up with the rundown of versatile fog nodes and the follow about their information accessibility, unwavering quality, and the last time window information assortment performed number of supporting hub accessible in wakeup mode. In light of all the above factors the strategy registers the proficient information transmission for every one of the area considered, and at every locale, the technique computes the information accessibility measure for various information hubs to choose them for information assortment. At first utilized Pearson's Chi-Squared Distribution for choosing the ideal highlights from dataset. Then, at that point, human-in the loop experiment is proposed for arranging the hubs in an organization as typical or strange which brings about further developed intrusion discovery rate and time. The recreation of human-in-the loop experiment is directed on parameters, such as, intrusion detection rate, Data transmission proportion, intrusion identification time and prediction rate. The evaluated result shows that the proposed work performs better compared to different strategies.

Fuzzy Logic Based WSN with High Packet Success Rate and Security

Considering the evidence that conditions accept a considerable place in each structure, owing to limited assets available at each sensor center, it is a difficult problem. Vitality safety is the primary concern in many remote sensor hub implementations. This is critical as the improvement in the device's lifetime depends primarily on restricting the usage of vitality in sensor hubs. The rationing and modification of vitality usage are of the most serious value in this context. In a remote sensor arrangement, the fundamental test is to schedule measurements for the least use of vitality. These classification frameworks are used to frame the classes in the structure and help efficiently use the strength that burdens out the network's lifespan. Besides, the degree of the center was taken into account in this work considering the measurement of cluster span as an improvement to the existing methods. The crucial piece of leeway of this suggested approach on affair clustering using fuzzy logic can increase the system's lifespan by reducing the problem area problem word.

On the Efficient Delivery and Storage of IoT Data in Edge-Fog-Cloud Environments.

Cloud storage has become a keystone for organizations to manage large volumes of data produced by sensors at the edge as well as information produced by deep and machine learning applications. Nevertheless, the latency produced by geographic distributed systems deployed on any of the edge, the fog, or the cloud, leads to delays that are observed by end-users in the form of high response times. In this paper, we present an efficient scheme for the management and storage of Internet of Thing (IoT) data in edge–fog–cloud environments. In our proposal, entities called data containers are coupled, in a logical manner, with nano/microservices deployed on any of the edge, the fog, or the cloud. The data containers implement a hierarchical cache file system including storage levels such as in-memory, file system, and cloud services for transparently managing the input/output data operations produced by nano/microservices (e.g., a sensor hub collecting data from sensors at the edge or machine learning applications processing data at the edge). Data containers are interconnected through a secure and efficient content delivery network, which transparently and automatically performs the continuous delivery of data through the edge–fog–cloud. A prototype of our proposed scheme was implemented and evaluated in a case study based on the management of electrocardiogram sensor data. The obtained results reveal the suitability and efficiency of the proposed scheme.

A LoRa-Cloud Based Water pH and Air Temperature Sensor Hub

Water pollution has become one of the important issues in Malaysia which occurs frequently especially in the rivers of Selangor. One of the indicators of pollution is the pH of the water. Thus, the main purpose of this study was to design a LoRa-Cloud-based water pollution sensor hub to monitor the real-time water quality. Sensor devices for pH (SEN0161-V2), and temperature and humidity (DHT11) were used to accurately detect water pH, and air temperature and humidity, respectively. REYAX RYLR896 Long Range Low Power (LoRa) transceiver module enabled the transmission of sensor data from the sensor node to the gateway node, while NodeMCU ESP8266 enabled transmission of sensor data to the Cloud. Ubidots IoT platform was used to monitor the real-time sensor data. A 3 W power supply was continuously supplied to the prototype and autonomy was guaranteed with the addition of monocrystalline solar cells. The performance of the prototype device was tested based on the monitoring function, and the overall pH level of Taman Metropolitan Batu Park Lake was slightly alkaline as the pH of the lake water ranged from 7.32 to 9.08 with data updating every 5 minutes on the Ubidots dashboards. The LoRa-Cloud-based water pollution sensor hub was successfully developed to monitor the real-time water quality through the Ubidots IoT platform.

COPD Patient Monitoring by Virtue of Internet of Things: A Review

The disease that often affects the lung by blocking the airways is known as Chronic Obstructive Pulmonary Disease (COPD). There are several cause of COPD disease and especially smoking behaviour is the major cause of chronic illness. Howeover, offering treatment for COPD patients is a challengeable task, because the symptoms and actions are not similar for all patients. To diminish this problem, the technology called Internet of Things (IoT) is been introduced to monitor the patients and submit the report with a wide range of exactness rates. Furthermore, IoT is designed with different sensor hubs to sense the patient’s body condition with high accuracy. Thus, identification of the disease severity rate is possible by monitoring several biological parameters using different processes and sensors. Analysis of parameters are performed using an efficient Machine Learning (ML) or Deep Learning (DL) approach in a suitable platform. By, using IoT technology, anybody from anywhere can able to access the medical specialist suggestion based on their body conditions. The current review article has aimed to present the uses of IoT in COPD patient monitoring in different ways as well as the function of different models are compared in the form of graphs. Keywords: Biological Parameters, Chronic Diseases, Internet of Things, Sensors, Severity Prediction

A Temperature Repaid Shrewd Sensor for Agrarian Industry

A temperature remunerated brilliant sensor for the agrarian business. This undertaking presents the plan and advancement of a shrewd nitrate sensor for checking nitrate focus in surface and groundwater. A temperature repaid interdigital capacitive sensor has been created in the ongoing review to quantify nitrate at low fixations. A convenient, novel detecting framework has been fostered that could be utilized nearby as an independent gadget, as well as an IoT-based remote observing brilliant sensor hub, to quantify nitrate focus in surface and groundwater. Electrochemical Impedance Spectroscopy was utilized to recognize and show nitrate focuses, by assessing the impedance change read by the interdigital transducer submerged in the surface water tests. The test tests were assessed by business gear (LCR meter) and the planned framework. These outcomes were additionally approved involving standard research center strategies to evaluate nitrate fixations in water tests. The planned framework showed a decent direct connection between the deliberate nitrate fixations (which went from 0.01 to 0.5 mg/L) to those deliberate by the business gear in the gathered water tests. Be that as it may, the flow framework can possibly be utilized to gauge nitrate fixations in water tests, continuously. The framework can transfer the deliberate nitrate information on a site in view of IoT. This framework could be utilized to incorporate water quality by observing locales inside ranches, or between streams, waterways, and lakes. For the in-situ establishment, a powerful box containing the entire framework would be introduced at the checking site.