Leveraging machine learning for optimize predictive classification and scheduling E-Health traffic

Kathuria, Madhumita; Gambhir, Sapna

doi:10.1109/icraie.2014.6909132

Cited by 9 publications

(6 citation statements)

References 11 publications

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“…However, the challenges that need to be addressed while using machine learning in WBAN are non-perfect predictions (cannot be 100% accurate), shortage of available datasets, and attacks by adversaries (adding noise in the input model to mislead the classifier). [192][193][194][195][196] Cognitive radio in WBAN: Cognitive radio (CR) is a form of radio or wireless communication system that can be configured to detect unoccupied and occupied channels in a smart manner for an unlicensed user while avoiding user interference. The system assists in the increased utilization of unused or less used channels in an intelligent manner through its unique features namely "learn," "sense," and "adapt."…”

Section: Challenges In Routing Protocolsmentioning

confidence: 99%

Dissecting wireless body area networks routing protocols: Classification, comparative analysis, and research challenges

Narwal,

Malik,

Mohapatra

et al. 2023

Int J Communication

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SummaryIn the present scenario, wireless body area network (WBAN) use case has gone beyond medical monitoring (e.g., E‐Healthcare) and has become a vital part of our daily lives. WBANs, applied in E‐Healthcare systems, extract the physiological parameters through the implanted (in‐body) and/or wearable (on‐body) sensors applied to the human body and route them to the remote server in a systematized and well‐grounded manner. Thus, routing is the most primitive and non‐trivial task in WBANs for prompt and proper delivery of sensed data. The design of routing protocol is crucial for efficacious communication and high network performance. Therefore, the target of this paper is to present an all‐encompassing taxonomy of the state‐of‐the‐art routing protocols in WBANs. Firstly, this paper provides an insight into WBANs and their related projects. Next, WBAN routing challenges are explored, and after clustering these challenges based on their operating mechanism, the routing protocols are categorized into eight categories, which correspond to cluster‐ and tree‐based, cross‐layer‐based, fuzzy‐based, interference‐based, posture‐based, QoS‐based, SDN‐based, and temperature‐based routing protocols. Furthermore, comparison tables highlighting the goals, strengths, limitations, and novelty of each protocol, along with a qualitative comparison table of the routing protocols for each category based on their features are furnished. This exhaustive survey can serve as a starting point for WBAN researchers, as open research issues have been highlighted in the discussion section.

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Section: Challenges In Routing Protocolsmentioning

confidence: 99%

Dissecting wireless body area networks routing protocols: Classification, comparative analysis, and research challenges

Narwal,

Malik,

Mohapatra

et al. 2023

Int J Communication

View full text Add to dashboard Cite

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“…For system optimization, ML can improve the power consumption, the routing of the data, and predicting sensor failure. Kathuria and Gambhir 19 used a combination of decision trees and genetic algorithm for classification of heterogeneous traffic flow according to rules. The traffic flow management module is responsible for managing the traffic at different levels to insure the flow of both real time and nonreal health data is optimized.…”

Section: Applicationsmentioning

confidence: 99%

Machine learning for wearable IoT‐based applications: A survey

Al‐Turjman

Baali

2019

Trans Emerging Tel Tech

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This paper gives an overview about applying machine learning (ML) in wearable Wireless Body Area Network (WBAN). It highlights the main challenges and open issues for deploying ML models in such sensitive networks. The WBAN is an emerging technology in the last few years, which attracts lots of interest from the academic and industrial communities. It enables a wide range of IoT‐based applications in medical, lifestyle, sport, and entertainment. WBAN are constrained in many aspects such as those related to power resources, communication capabilities, and computation power. Moreover, these networks generate ample amount of sensory data. To overcome the limitations of these networks and make use of the big data generated, artificial intelligence is the best way to deal with gigantic data and help in automating several aspects of the network and its applications. This survey paper aims at reporting numerous ways ML is used to benefit these networks, the design factors that are considered when implementing the ML algorithms, and the communication technologies used in connecting wearable WBAN in the IoT era. The reported studies are based on real overviewed experiment and extensive simulation results.

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“…In healthcare WBAN system reliable data transmission [10][11] with low delay [15] is very important, to improve the quality of life and to reduce treatment cost. The main motive of the proposed protocol is to offer reliable transmission of heterogeneous packets [3][4][5][6] within the time bound. It is also deals with duplicate packets along with performing congestion control by adjusting data sending rate dynamically.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Quality of Service Parameters in Dynamic and Heterogeneous WBAN

Kathuria

Gambhir

2016

IJEEI

Self Cite

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With growth in population and diseases, there is a need for monitoring and curing of patients with low cost for various health issues. Due to life threatening conditions, loss-free and timely sending of data is an essential factor for healthcare WBAN. Health data needs to transmit through reliable connection and with minimum delay, but designing a reliable, and congestion and delay free transport protocol is a challenging area in Wireless Body Area Networks (WBANs). Generally, transport layer is responsible for congestion control and reliable packet delivery. Congestion is a critical issue in the healthcare system. It not only increases loss and delay ratio but also raise a number of retransmissions and packet drop rates, which hampers Quality of Service (QoS). Thus, to meet the QoS requirements of healthcare WBANs, a reliable and fair transport protocol is mandatory. This motivates us to design a new protocol, which provides loss, delay and congestion free transmission of heterogeneous data. In this paper, we present a Dynamic priority based Quality of Service management protocol which not only controls the congestion in the network but also provides a reliable transmission with timely delivery of the packet.

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Leveraging machine learning for optimize predictive classification and scheduling E-Health traffic

Cited by 9 publications

References 11 publications

Dissecting wireless body area networks routing protocols: Classification, comparative analysis, and research challenges

Dissecting wireless body area networks routing protocols: Classification, comparative analysis, and research challenges

Machine learning for wearable IoT‐based applications: A survey

Improvement of Quality of Service Parameters in Dynamic and Heterogeneous WBAN

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