Wireless Body Sensor Networks Research Articles

Determination of the time location points and investigation of the body sensor network using RATDMA and PATDMA

Wireless body sensor networks have gained significant importance across diverse fields, including environmental monitoring, healthcare, and sports. This research is concentrated on sports applications, specifically exploring the viability of a wireless body area network tailored for high-performing athletes. The paper is divided into three sections. First, the design of the node location that is used for real-time monitoring of a sportsperson in which the node position, such as the human thigh, foot, arm, wrist, and chest, was estimated and the best position was selected. Second, the accuracy of an application when related to the other schemes such as TDMA with ZigBee and RA-TDMA & PA-TDMA was done. The reliability using RA-TDMA performed well and showed approximately 98% reliability. Finally, the features of wireless communiqués that affect the presentation of the network for RA-TDMA were estimated, such as delay and jitter. These findings collectively contribute to advancing the understanding of optimizing wireless body sensor networks for sports applications, with notable achievements including the identification of the arm as the optimal sensor placement, achieving a 98% success rate, and surpassing alternative techniques in network performance parameters like packet delivery rate.

Energy Efficiency in Wireless Body Area Network Using Various Radios

Abstract
 Wireless Body Sensor Networks (WBSNs) promote continuous and low-cost health monitoring for a better healthcare system. Sensors connected to a human body are normally battery powered devices with brief life times. IEEE 802.15.6 standard is the typical choice for low power ingestion and reliable short-range communication for WBSNs. In this paper, we have evaluated the performance of IEEE 802.15.6 standard for WBSNs. Our tests examined three radios while varying their transmission power and data rates. The results show that altering these parameters affected the number of packets delivered and overall system energy consumption. The effects are demonstrated using Omnet++ simulator and its Castalia extension for WBANs.

Research on Motion Capture and Phase Segmentation Based on Wireless Body Sensor Networks in Competitive Equestrian

Research on Motion Capture and Phase Segmentation Based on Wireless Body Sensor Networks in Competitive Equestrian

Atrial Fibrillation Detection from Compressed ECG Measurements for Wireless Body Sensor Network

Recent years have witnessed an increasing prevalence of wearable devices in the public, where atrial fibrillation (AF) detection is a popular application in these devices. Generally, AF detection is performed on cloud whereas this paper describes an on-device AF detection method. Technically, compressed sensing (CS) is first used for electrocardiograph (ECG) acquisition. Then QRS detection is proposed to be performed directly on the compressed CS measurements, rather than on the reconstructed signals on the powerful cloud server. Based on the extracted QRS information, AF is determined by quantitatively analyzing the ( RR , dRR ) plot. Databases with ECG samples collected from both medical-level (MIT-BIH afdb) and wearable ECG devices (Physionet Challenge 2017) are introduced for performance validation. The experiment results well demonstrate that our on-device AF detection algorithm can approach the performance of those implemented on the raw signals. Our proposal is suitable for AF screening directly on the wearable devices, without the support of the data center for signal reconstruction and intelligent analysis.

Enhanced Routing Protocol for Health Monitoring in Urban Traffic Networks via VANET: Analyzing IW-GeoR Against WGeoR

The primary aim of this initiative is to establish an effective routing system for managing both human-induced and natural calamities using a Wireless Body Sensor Network (WBSN) integrated with VANET within urban traffic networks. This is accomplished through a comparison between the Weighed Geographic Routing (WGeoR) protocol and the Improved Weighed Geographical Routing (IWGeoR) protocol, targeting minimized packet loss and reduced delay. The IWGeoR protocol stands out by efficiently selecting cluster heads to mitigate delay and packet loss, considering various factors such as differences in vehicle speeds, inter-vehicle distances, and traffic mobility. The protocol’s effectiveness relies on innovative node degrees, channel quality, proximity factors, and communication connection expiration times. For this study, twenty samples (n=20) were gathered from each of the two groups using diverse vehicles. Clinical.com established a pre-test power of 80% (G-power) for each group, with alpha and beta coefficients set at 0.05 and 0.2 respectively, in order to assess the protocol’s performance in terms of packet loss and average latency. The simulation outcomes indicate that the IWGeoR protocol surpasses the WGeoR protocol in crucial metrics such as packet loss and mean delay. Specifically, the IWGeoR protocol reduces packet loss by 10.77% and delay by 31.03%. The Independent Sample T-test calculates a significant p-value of 0.001 for the IWGeoR and WGeoR protocols, demonstrating statistical significance (p<0.005). Based on experimental findings and the Independent Sample T-test, the proposed IWGeoR protocol has demonstrated superior performance compared to the WGeoR protocol.

Multi-sensor Data Fusion based Medical Data Classification Model using Gorilla Troops Optimization with Deep Learning

Wireless Body Sensor Network (BSN) comprises wearables with different sensing, processing, storing, and broadcast abilities. Once several devices acquire the data, multi-sensor fusion was needed for transforming erroneous sensor information into maximum quality fused data. Deep learning (DL) approaches are utilized in different application domains comprising e-health for applications like activity detection, and disease forecast. In recent times, it can be demonstrated that the accuracy of classification techniques is enhanced by the combination of feature selection (FS) approaches. This article develops a Multi-sensor Data Fusion based Medical Data Classification Model using Gorilla Troops Optimization with Deep Learning (MDFMDC-GTODL) algorithm. The proposed MDFMDC-GTODL method enables collection of various daily activity data using different sensors, which are then fused to produce high-quality activity data. In addition, the MDFMDC-GTODL technique applies optimal attention based bidirectional long short term memory (ABLSTM) for heart disease prediction. In this study, Gorilla Troops Optimization Algorithm based FS (GTOA-FS) technique is involved to improve the classification performance. The simulation outcome of the MDFMDC-GTODL technique are validated and the results are investigated in different prospects. A wide-ranging simulation analysis stated the better performance of the MDFMDC-GTODL method over other compared approaches.

Enhanced Random Forest Classifier with K-Means Clustering (ERF-KMC) for Detecting and Preventing Distributed-Denial-of-Service and Man-in-the-Middle Attacks in Internet-of-Medical-Things Networks

In recent years, the combination of wireless body sensor networks (WBSNs) and the Internet ofc Medical Things (IoMT) marked a transformative era in healthcare technology. This combination allowed for the smooth communication between medical devices that enabled the real-time monitoring of patient’s vital signs and health parameters. However, the increased connectivity also introduced security challenges, particularly as they related to the presence of attack nodes. This paper proposed a unique solution, an enhanced random forest classifier with a K-means clustering (ERF-KMC) algorithm, in response to these challenges. The proposed ERF-KMC algorithm combined the accuracy of the enhanced random forest classifier for achieving the best execution time (ERF-ABE) with the clustering capabilities of K-means. This model played a dual role. Initially, the security in IoMT networks was enhanced through the detection of attack messages using ERF-ABE, followed by the classification of attack types, specifically distinguishing between man-in-the-middle (MITM) and distributed denial of service (DDoS) using K-means. This approach facilitated the precise categorization of attacks, enabling the ERF-KMC algorithm to employ appropriate methods for blocking these attack messages effectively. Subsequently, this approach contributed to the improvement of network performance metrics that significantly deteriorated during the attack, including the packet loss rate (PLR), end-to-end delay (E2ED), and throughput. This was achieved through the detection of attack nodes and the subsequent prevention of their entry into the IoMT networks, thereby mitigating potential disruptions and enhancing the overall network efficiency. This study conducted simulations using the Python programming language to assess the performance of the ERF-KMC algorithm in the realm of IoMT, specifically focusing on network performance metrics. In comparison with other algorithms, the ERF-KMC algorithm demonstrated superior efficacy, showcasing its heightened capability in terms of optimizing IoMT network performance as compared to other common algorithms in network security, such as AdaBoost, CatBoost, and random forest. The importance of the ERF-KMC algorithm lies in its security for IoMT networks, as it provides a high-security approach for identifying and preventing MITM and DDoS attacks. Furthermore, improving the network performance metrics to ensure transmitted medical data are accurate and efficient is vital for real-time patient monitoring. This study takes the next step towards enhancing the reliability and security of IoMT systems and advancing the future of connected healthcare technologies.

Classification of pathological ECG beats based on wireless body sensor networks and fractional Fourier transform and convolutional neural network

Classification of pathological ECG beats based on wireless body sensor networks and fractional Fourier transform and convolutional neural network

OTP-Q encryption and Diffie-Hellman mutual authentication for e-healthcare data based on lightweight S-WBSN framework.

Protection of wireless technology-enabled e-healthcare data transfer over constrained devices of body sensor networks using lightweight security mechanisms is the demand of health sector nowadays. A new secure wireless body sensor network architecture (S-WBSN) with reduced CPU cycles and computational cost is proposed. S-WBSN uses OTP-Q (One-Time Pad-Quasi) and Diffie-Hellman key exchange algorithms for encryption and mutual authentication, respectively. To ensure mutual authentication among <WBSN, Local Processing Center (LPC)> and <WBSN, Data Server> components, the Diffie-Hellman key exchange algorithm is used. Using the S-WBSN architecture, security requirements such as mutual authentication, and privacy preservation thwarting security attacks are perfectly met comparing other security-based research works on healthcare data monitoring. The one-time pad-based quasi-group algorithm is a stream block cipher that operates on the data observed from the sensors of the WBSN. Before transmitting encrypted data, authentication is to be established. The proposed system methodology proves to be efficient and consumes fewer CPU cycles. The encryption and decryption processing times are comparatively less than the state-of-the-art approaches.

Design and evaluation of novel hybrid quantum resistant cryptographic system for enhancing security in wireless body sensor networks

Design and evaluation of novel hybrid quantum resistant cryptographic system for enhancing security in wireless body sensor networks

Design and implementation of low-power, high-speed, reliable and secured Hardware Accelerator using 28 nm technology for biomedical devices

This paper implements a Hardware Accelerator (HA) for Wireless Body Sensor Networks (WBSNs) using 28 nm technology on Zynq system-on-a-chip (SOC) using Vivado and VITIS high-level synthesis tool. The Vitis integrated development environment (IDE) is a software-only development environment that Integrates with other Xilinx tools. The Zynq® SoC combines a state-of-the-art processor system (PS) and programmable logic (PL) into a single cohesive device. WBSNs contain different processing techniques like predictor, compressor, encryption encoder, and Error Control Coding (ECC) before transmission. These techniques are implemented and validated using the Xilinx® Software Development Kit (SDK) tool along with the PYNQ Z2 board having Zynq SOC respectively. Similarly, the processing techniques are implemented on PS block as HA. The results of the work show the data collection and processing through the dual-core ARM Cortex A9 processor of the board. The purpose of the work is to calculate the time required to process the bio-signals through the controller solely and using HA. The proposed work, mainly WBSN using HA shows an improvement of 81% in the speed compared with existing methods. Further, results of the synthesis and implementation strategy show improvement in power and area of HA with WBSN.

Ensemble Learning-Based Atrial Fibrillation Detection From Single Lead ECG Wave for Wireless Body Sensor Network

With the popularity of wireless techniques and portable devices, continuous and real-time monitoring patient&#x2019;s health status by collecting physiological signals is becoming more popular. Data mining of electrocardiograph (ECG) attracts widespread attention, where automatic atrial fibrillation (AF) detection has become a hot research field. In this paper, an ensemble learning algorithm is proposed for AF detection from single lead ECG recordings collected by wearable devices. This algorithm includes two modules. First, denoised 1-D time series (ECG), time-frequency spectrum and Poincare plot are used to train three component learners through a parallel style, respectively, and each component learner produces four probability values. Then, all the outputs are combined using a weighted matrix constructed by a Bayesian optimization algorithm, which is capable of producing the final classification result. Quantities of experiments have been implemented, and the results well prove the algorithm&#x2019;s effectiveness and its advantage over some state-of-the-art counterparts.

Cooperative Hyper-Scheduling based improving Energy Aware Life Time Maximization in Wireless Body Sensor Network Using Topology Driven Clustering Approach

The Wireless Body Sensor Network (WBSN) is an incredible developing data transmission network for modern day communication especially in Biosensor device networks. Due to energy consumption in biomedical data transfer have impacts of sink nodes get loss information on each duty cycle because of Traffic interruptions. The reason behind the popularity of WBSN characteristics contains number of sensor nodes to transmit data in various dense regions. Due to increasing more traffic, delay, bandwidth consumption, the energy losses be occurred to reduce the lifetime of the WBSN transmission. So, the sensor nodes are having limited energy or power, by listening to the incoming signals, it loses certain amount of energy to make data losses because of improper route selection. To improve the energy aware lifetime maximization through Traffic Aware Routing (TAR) based on scheduling. Because the performance of scheduling is greatly depending on the energy of nodes and lifetime of the network. To resolve this problem, we propose a Cooperative Hyper-scheduling (CHS) based improving energy aware life time maximization (EALTM) in Wireless Body sensor network using Topology Driven Clustering Approach (TDCA).Initially the method maintains the traces of transmission performed by different Bio-sensor nodes in different duty cycle. The method considers the energy of different nodes and history of earlier transmission from the Route Table (RT) whether the transmission behind the Sink node. Based on the RT information route discovery was performed using Traffic Aware Neighbors Discovery (TAND) to estimate Data Transmission Support Measure (DTSM) on each Bio-sensor node which its covers sink node. These nodes are grouped into topology driven clustering approach for route optimization. Then the priority is allocated based on The Max-Min DTSM, the Cooperative Hyper-scheduling was implemented to schedule the transmission with support of DTSM to reduce the energy losses in WBSN. This improves the energy level to maximization the life time of data transmission in WBSN than other methods to produce best performance in throughput energy level.

A Study on the Development of Interactive Technology for the Convenience of the Visually Impaired

In this paper, as human-computer interface (HCI) technology advances, opportunities are analyzed to provide a better service environment for the visually impaired. The system and user interaction for the visually impaired will become possible through the convergence of technologies that include location-based services, wireless body sensor networks, and smart devices, which can have direct and positive impacts. The system connects to the user's mobility assistance device through a smartphone app and helps the user walk to identify and guide through obstacles. This interactive technology is expected to provide a convenient environment for visually impaired users.

RLTD: A Reliable, Link Quality, Temperature and Delay Aware Routing Protocol for Wireless Body Sensor Networks

RLTD: A Reliable, Link Quality, Temperature and Delay Aware Routing Protocol for Wireless Body Sensor Networks

Fork-Hook encryption policy based secured Data Centric Routing Gateway for proactive trust ware data transmission in WBSN

A wireless body sensor network is a collection of sensor nodes interconnected to monitor the patient to collect data and make cooperative on-route communication to medical healthcare. Due to increasing security issue on the transmission path, the data get leaked or false injected. So the security on routing is essential to make centralized routing with secure data transmission and protection against malicious activities. To resolve this problem of the security issue, to propose a Fork-Hook Encryption Policy based on Secured Data Centric Routing Gateway Protocol (FHEP-SDCRGP) to ensure the security depends on crypto blockchain policy for proactive trust ware routing creates encrypted transmission in WBSN. This initially verifies the lookup transmission from neighbor nodes and creates a cluster head path that demonstrates transition containing hop count. Then routing approach gets validated through Trust aware controller node key aggregation. Begin on the transmission medium, the packets get hashed through blockchain principle to encrypt through Fork-Hook Encryption Policy (FHEP) to create Successive Hop Index (SHI), and the private Key to each transmission node gets verified. This removes the sleeper cell nodes on the path that get non-authenticated to secure the data packets through the Sleeper node threading protocol. Each node receives verified through master node aggregation by Hop Count Rollback Node Aggregator (HCRNA) to transmit the encrypted packet from the cooperative routing node. After ascertaining the hop count matched broadcasting cluster head on controller node aggregation, the Key gets received from the authenticator to decrypt the data. The proposed system performs better in secure routing and authentication based on secured proactive cluster head aggregation. This attains higher verification on each flow, and the aggregator rollback acknowledgment improves the security and other existing approaches.

Compact, flexible triple band Sierpinski fractal antenna on a Hilbert patterned ground for W‐WBSN

AbstractWearable Wireless Body Sensor Networks (W‐WBSNs) are the best substitute for traditional healthcare systems by effectively monitoring patients within or remotely from the hospitals. Quite a few tiny sensors linked with one another on the body can efficiently observe the functional status of the human body. The paper introduces a new triple‐band step‐shaped Sierpinski fractal wearable antenna with a Hilbert fractal slot on the partial ground for W‐WBSN applications. It comprises the step‐shaped Sierpinski fractal radiator, situated on the upper side, and a partial ground placed on the lower side of the substrate. A partial ground with a Hilbert fractal slot is used to achieve a compact wearable antenna with improved bandwidth and electromagnetic exposure rate characteristics. The antenna is constructed using a flexible Jeans substrate (permittivity 1.7, loss tangent 0.025) of size 60 × 60 mm2 with a thickness of 2.5 mm as it is a potential candidate. It demonstrates an impedance bandwidth of 6%, 7%, and 10.1%, a gain of 2.57 dBi, 3.608 dBi, and 4.19 dBi, the radiation efficiency of 54.19%, 52.5%, and 54.8% at 3.3 GHz, 5.2 GHz, and 5.8 GHz correspondingly in free space. Also, the peak SAR value of 0.64 W/kg, 0.46 W/kg, and 1.17 W/kg at 3.3 GHz, 5.2 GHz, and 5.8 GHz correspondingly, for 10 g of an average human tissue. The compact, flexible low SAR makes the anticipated antenna a superior contender for wireless wearable network applications.

An Energy Efficiency Based Secure Data Transmission in WBSN Using Novel Id-Based Group Signature Model and SECC Technique

&lt;p&gt;A wireless network composed of wearable sensing along with computing systems connected via a wireless communication channel is termed Wireless Body Sensor Network (WBSN). It enables continuous monitoring through sensors for medical and nonmedical applications. WBSN faces several security problems such as loss of information, access control, and authentication. As WBSN collects vital information and operates in an unfriendly environment, severe security mechanisms are needed in order to prevent the network from anonymous interactions. The different security threats are evaluated with the support of the data transmitted via the sensor networks amongst smart wearable devices. The whole network lifetime together with the Data Transmission (DT) quality is mitigated whilst performing DT utilizing sensor networks, which consume more energy. Hence, in this paper, an energy-efficient secure data transmission mechanism is proposed in WBSN using a novel authentication id-based group signature model and SECC technique. At first, the Group Manager (GM) is selected from the sensors in the remote body sensor system using Normalized Opposition Based Learning BAT Optimization Algorithm (NOBL-BOA). Afterward, clustering with Information Entropy induced K-Means Algorithm (IEKMA) takes place to improve energy efficiency. Next, to provide security to the WBSN, message authentication is carried out based on novel authentication ID-based group signature protocol. Finally, Secret key induced Elliptic Curve Cryptography (SECC) is used to encrypt the message for secure transmission. The simulation results reveal that in comparison with existing works, the proposed work achieves improved security and energy efficiency.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

A Hybrid Cryptography and LogiXGBoost Model for Intelligent and Privacy Protection in Wireless Body Sensor Networks (WBSNS)

A Hybrid Cryptography and LogiXGBoost Model for Intelligent and Privacy Protection in Wireless Body Sensor Networks (WBSNS)

Correction to: Green and friendly media transmission algorithms for wireless body sensor networks

Correction to: Green and friendly media transmission algorithms for wireless body sensor networks