Error Reduction Scheme in WBSNs Based on Channel Assessment

Anwar, Arifa; Sridharan, D.

doi:10.1109/icwise.2018.8633286

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…They used the Baum–Welch algorithm to estimate parameters of the HMM; then, a Viterbi-algorithm is used to check if a new sensor reading is faulty. The work in [ 16 ] illustrated that, before sending data to the PDA, a high connectivity link is chosen using probabilistic routing. In [ 17 ], the authors have used the cloud-computing hierarchy to detect sensor failures and abnormality in detected data, using historical data to calculate the average, which is then used as a decision criteria.…”

Section: Related Workmentioning

confidence: 99%

Towards an Accurate Faults Detection Approach in Internet of Medical Things Using Advanced Machine Learning Techniques

Bahache

Tahari

Herrera-Tapia

et al. 2022

Sensors

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Remotely monitoring people’s healthcare is still among the most important research topics for researchers from both industry and academia. In addition, with the Wireless Body Networks (WBANs) emergence, it becomes possible to supervise patients through an implanted set of body sensors that can communicate through wireless interfaces. These body sensors are characterized by their tiny sizes, and limited resources (power, computing, and communication capabilities), which makes these devices prone to have faults and sensible to be damaged. Thus, it is necessary to establish an efficient system to detect any fault or anomalies when receiving sensed data. In this paper, we propose a novel, optimized, and hybrid solution between machine learning and statistical techniques, for detecting faults in WBANs that do not affect the devices’ resources and functionality. Experimental results illustrate that our approach can detect unwanted measurement faults with a high detection accuracy ratio that exceeds the 99.62%, and a low mean absolute error of 0.61%, clearly outperforming the existing state-of-art solutions.

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Section: Related Workmentioning

confidence: 99%

Towards an Accurate Faults Detection Approach in Internet of Medical Things Using Advanced Machine Learning Techniques

Bahache

Tahari

Herrera-Tapia

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

Predictive estimation of low density parity check decoded data based on error pattern modelling in wireless body area networks

Anwar

Duraisamy

2021

Int J Communication

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Summary Wireless body area networks (WBANs) dynamically track a person's health data wirelessly. The implant‐to‐implant communication (CM1) inside the human body requires low power, highly reliable channels amidst the high losses and attenuation due to the various portions of the body. This attenuation and noise results in complete variation of the monitored health data, resulting in faulty medical diagnosis and alarms. Low density parity check (LDPC) algorithms are popular for error correction in this scenario. However, they still need to be improved for a more reasonable analysis of the health status. We suggest processing of the sensed data to increase the sparsity, thereby improving the bit error probability performance of the LDPC coding technique. Further, we propose the use of Kalman filter to estimate the actually transmitted bit from the LDPC decoded message output. The simulation results show that the proposed technique improve the error correction capability of the LDPC code. The increased energy, time and computations required for the same is found to be within a satisfactory level for the proper functioning of the WBANs.

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Error Reduction Scheme in WBSNs Based on Channel Assessment

Cited by 2 publications

References 5 publications

Towards an Accurate Faults Detection Approach in Internet of Medical Things Using Advanced Machine Learning Techniques

Towards an Accurate Faults Detection Approach in Internet of Medical Things Using Advanced Machine Learning Techniques

Predictive estimation of low density parity check decoded data based on error pattern modelling in wireless body area networks

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