Channel Deviation-Based Power Control in Body Area Networks

Dinh-Van, Son; Cotton, Simon L.; Smith, David B.

doi:10.1109/jbhi.2017.2741720

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…The maximum scaled progressive similarity metric between the active frame, and the trained data frame is called the RRS metric. Similarly, calculate the scaled progressive similarity metric (SPS m ) between the active frame and trained data frames of activity found in the active frame as in Equation (24).…”

Section: Rrs Techniquementioning

confidence: 99%

“…Communication loss is avoided by adding a margin value to the predicted channel gain. Van et al 24 proposed a dynamic power control algorithm and updated the high threshold power level depending upon the channel deviation. In the sample and hold algorithm, Van et al 24 reported that the back‐off period is increased when the channel is static.…”

Section: Related Workmentioning

confidence: 99%

“…Van et al 24 proposed a dynamic power control algorithm and updated the high threshold power level depending upon the channel deviation. In the sample and hold algorithm, Van et al 24 reported that the back‐off period is increased when the channel is static. The transmission power is constant for up to two or more time slots.…”

Section: Related Workmentioning

confidence: 99%

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Long short‐term memory‐based power‐aware algorithm for prompt heterogenous activity

Nasreen¹,

Ravindran

2022

Int J Communication

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Summary Human–computer interaction plays a vital role in wireless body area networks, internet of things, and big data. Wearables are low‐power devices with minimal battery capacity. In general, wearables suffer from energy losses due to changes in the user's body posture, diffraction, reflection, and shadowing of the human body. As a result, many control packets are needed to ensure proper communication in wireless body area network. Hence, this research proposes a long short‐term memory‐based power‐aware (LSTM‐PA) algorithm to ensure burst data transmission during prompt heterogeneous activities in the presence and absence of inter‐WBAN interference. The proposed algorithm predicts the best quality time (BQT) for data transmission by activity classification and robust R2 similarity (RRS) metric. The minimum transmission power is estimated by the critical point classification technique. The activity classification accuracy is 92% in the LSTM‐PA algorithm. The energy consumption in the node is reduced by up to 46.34% compared to benchmark algorithms.

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Section: Rrs Techniquementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%