Abstract:Considering the characteristics of lower power resources in multi-point wireless body area networks (WBANs), the wireless energy and information transmission in full duplex (FD) mode is considered. We propose a transmission protocol based on time division multiple access, which includes two phases. In the inactive phase, the signal access point (AP) broadcasts Radio Frequency (RF) energy to the sensors. When the energy collected by all sensors reaches their respective energy thresholds, the active phase will s… Show more
“…WBAN has the ability to monitor bodily health. Early diagnosis is essential for disease diagnosis and treatment [2]. Users are exhibiting a remarkable interest in actually wearing these devices, which is something that wearable's can do better than a Smartphone alone [3].…”
Real-time monitoring of patients' health conditions is now possible because to wireless body area networks (WBANs). In WBANs, nodes are placed inside, on, or around the human body to collect a range of physiological data, such as body temperature, heart rate, and blood pressure. The routing of data packets in WBANs, however, confronts a number of difficulties because of the dynamic nature of the body, including limited power, interference, and movement. Data packet routing can be made more efficient by using a multiobjective routing protocol architecture to handle these problems. In this research work, WBAN's multiobjective routing protocol architecture is made possible by two phases as clustering and optimal path selection.To group nodes based on several objectives, such as energy consumption, transmission delay, and network longevity, the proposed multiobjective routing protocol design in WBAN uses an optimised Fuzzy C-means clustering method. The membership function in Fuzzy C-means clustering is determined using a novel hybrid optimization model HBWBF that combines the Black Widow Optimization (BWO) and the Bacterial Foraging Optimization Algorithm (BFOA). The hybrid deep learning approach for determining the optimal path between clusters combines Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).The proposed model is implemented in MATLAB. To validate the efficiency of the proposed model, a comparative evaluation is performed.
“…WBAN has the ability to monitor bodily health. Early diagnosis is essential for disease diagnosis and treatment [2]. Users are exhibiting a remarkable interest in actually wearing these devices, which is something that wearable's can do better than a Smartphone alone [3].…”
Real-time monitoring of patients' health conditions is now possible because to wireless body area networks (WBANs). In WBANs, nodes are placed inside, on, or around the human body to collect a range of physiological data, such as body temperature, heart rate, and blood pressure. The routing of data packets in WBANs, however, confronts a number of difficulties because of the dynamic nature of the body, including limited power, interference, and movement. Data packet routing can be made more efficient by using a multiobjective routing protocol architecture to handle these problems. In this research work, WBAN's multiobjective routing protocol architecture is made possible by two phases as clustering and optimal path selection.To group nodes based on several objectives, such as energy consumption, transmission delay, and network longevity, the proposed multiobjective routing protocol design in WBAN uses an optimised Fuzzy C-means clustering method. The membership function in Fuzzy C-means clustering is determined using a novel hybrid optimization model HBWBF that combines the Black Widow Optimization (BWO) and the Bacterial Foraging Optimization Algorithm (BFOA). The hybrid deep learning approach for determining the optimal path between clusters combines Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).The proposed model is implemented in MATLAB. To validate the efficiency of the proposed model, a comparative evaluation is performed.
“…Cross-layer routing protocols coordinate information from layer to layer by improving synchronization between each other without interfering with the main functionality [12]. Cross-Layer Design Optimal (CLDO) is proposed by [13] to improve transmission reliability, energy efficiency, and network lifetime by working across physical, MAC, and network layers.…”
Section: A Cross-layer Routingmentioning
confidence: 99%
“…(11) Equation ( 6) computes the thermal variations of a node. To declare a threshold of node temperature, the MBCF (𝛄 𝒊 𝟐) is the difference between the total thermal effect of a node (𝚫𝐓 𝑵 ) and the initial thermal value of a node (𝚫𝐓 𝑵.𝑰𝑵𝑰𝑻𝑰𝑨𝑳 ) levels with assigned minimum thresholds (𝚫𝐓 𝑴𝑰𝑵𝑰𝑴𝑼𝑴 ) is given in (12).…”
Section: Multi-parameter Maximum Benefit Cost Functionmentioning
The Internet of Healthcare Things has significantly altered traditional patient-doctor relationships by allowing essential healthcare treatment from the comfort of one's home. The wireless body area network is an IEEE 802.15.6 standard that focuses on healthcare data, necessitating various cross-layer and thermal-aware protocols. However, most cross-layer protocols have long convergence delays and a single failure point. Moreover, these protocols exploit excessive broadcasts and handshake acknowledgments, causing communication and processing overheads. Furthermore, thermal-aware protocols focus on thermal variations, disperse data collection, and do not support cross-layer techniques. To address these limitations, this study proposes an optimal distributive cross-layer and thermal-aware convergecast protocol. The proposed protocol enforces a novel hybrid convergecast using probability and both minimum attenuation strategies to collect data from leaf nodes to the root to improve data flow and adaptability in the network. In addition, it accelerates the convergence process by reducing recurrent broadcasts and unnecessary acknowledgments, resulting in improved energy efficiency and thermal control. The proposed protocol supports a distributive hierarchy by establishing multiple parent-child relationships to avoid a single root point failure. A multi-parameter maximum benefit-cost function calculates the next hop according to the extracted weights. Packet loss probability validates the number and sequence of packets received at the sink node. The simulation results demonstrate that cross-layer and thermal-aware protocols can coexist effectively. The proposed protocol reduces delays to 19.4%, improves throughput from 8% to 13.75%, and retains a packet loss probability of 0.3% by keeping the thermal rise within bounds.
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