Analytical Study on Data Transmission in WBAN With User Mobility Support

Saha, Ramesh; Biswas, Suparna

doi:10.1109/wispnet.2018.8538573

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…Delays are most likely being reduced as a result of distributive parent-child relationships and low hop counts. The use of an alternate route adds a small amount of delay, which is negligible in the context of WBAN because delay usually improves throughput [45]. Using an alternate route, which is provided by convergecast, is less expensive in terms of delay than repolling for the parent node.…”

Section: E2e Delaymentioning

confidence: 99%

Optimized Distributive Cross-Layer and Thermal-Aware Convergecast Protocol for Wireless Body Area Network

et al. 2022

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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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Section: E2e Delaymentioning

confidence: 99%

Optimized Distributive Cross-Layer and Thermal-Aware Convergecast Protocol for Wireless Body Area Network

et al. 2022

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“…Ying Zhang and Hannan Xiao [7] proposed a Bluetooth based sensor network system which remotely monitors patient physiological signal. Authors focus on implementation issues and describe these system architecture using heart activity sensors.…”

Section: Related Workmentioning

confidence: 99%

“…It has many potential applications to give rise to healthcare applications like remote health monitoring, a fitness program for home-based health-monitoring, chronic disease detection, and older people care, etc. IoT based healthcare service utilizes wireless body area network (WBAN) [6][7][8] which is a subset of wireless sensor network (WSN) [9,10]. Usage of WBAN in healthcare increases the QoS and reduces the costs with enhanced usability.…”

Section: Introductionmentioning

confidence: 99%

“…Usage of WBAN in healthcare increases the QoS and reduces the costs with enhanced usability. Initially, IoT based WBANs [6,7,11] are one of the most desirable technology for building smart health monitoring for the patient. The application of healthcare monitoring using WBAN consists of a specified number of heterogeneous sensor nodes.…”

Section: Introductionmentioning

confidence: 99%

“…With the help of WBAN, a user can be monitored at anytime, anywhere, and for any duration. In the WBAN-Based health-monitoring architecture is three-layer architecture [6,7,14,15] shown in Fig. 1 [3,5,6].…”

Section: Introductionmentioning

confidence: 99%

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