Breath to Pair (B2P): Respiration-Based Pairing Protocol for Wearable Devices

Abstract

We propose Breath to Pair (B2P), a protocol for pairing and shared-key generation for wearable devices that leverages the wearer's respiration activity to ensure that the devices are part of the same body-area network. We assume that the devices exploit different types of sensors to extract and process the respiration signal. We illustrate B2P for the case of two devices that use respiratory inductance plethysmography (RIP) and accelerometer sensors, respectively. Allowing for different types of sensors in pairing allows us to include wearable devices that use a variety of different sensors. In practice, this form of sensor variety creates a number of challenges that limit the ability of the shared-key establishment algorithm to generate matching keys. The two main obstacles are the lack of synchronization across the devices and the need for correct noise-induced mismatches between the generated key bit-strings.B2P addresses the synchronization challenge by utilizing Change Point Detection (CPD) to detect abrupt changes in the respiration signal and consider their occurrences as synchronizing points. Any potential mismatches are handled by optimal quantization and encoding of the respiration signal in order to maximize the error correction rate and minimize the message overheads. Extensive evaluation on a dataset collected from 30 volunteers demonstrates that our protocol can generate a secure 256-bit key every 2.85 seconds (around one breathing cycle). Particular attention is given to secure B2P against device impersonation attacks.

Extraction of Time-Domain Characteristics and Selection of Effective Features Using Correlation Analysis to Increase the Accuracy of Petroleum Fluid Monitoring Systems

“…The purpose of training a perceptron is to find the values of its weights, so that the perceptron generates the correct values for the training examples. Different kinds of numerical calculations [68][69][70][71][72][73][74][75][76][77][78] and soft computing [79][80][81][82][83][84][85][86] have been used in various fields such as electrical engineering problems [87][88][89][90][91][92][93][94][95][96][97][98][99][100][101][102], computer sciences problems [103,104], and basic sciences [105][106][107][108][109], etc. In this paper, the perceptron learning algorithm is as follows-this algorithm is shown in Figure 4 as a flowchart:…”

Introducing a Precise System for Determining Volume Percentages Independent of Scale Thickness and Type of Flow Regime

“…A self-control model was developed with the power to monitor the synthesis and related system problems and to solve predictions and classifications. It is worth mentioning that, in the recent years, various mathematical approaches have been used in different research fields such as electrical and computer engineering [26][27][28][29][30], mechanical engineering [31][32][33][34], civil and urban engineering [35][36][37], biomedical engineering [38,39], industrial engineering [40][41][42][43], and physics [44,45], but among them, ANN is the most well-known and powerful numerical tool for prediction and classification [46][47][48]. Neuron values, invisible layer, effective input profile, and efficient network fabrication were determined As can be seen in Figure 4, performing the wavelet transform operation from step 5 onwards no longer provides acceptable high-frequency information, so to reduce the computational volume, the wavelet operation is performed up to the fourth step.…”

S3D: Squeeze and Excitation 3D Convolutional Neural Networks for a Fall Detection System

“…Although not about the research of FDS, Pourbemany et al proposed Breath to Pair (B2P), a protocol for pairing and shared-key generation for wearable devices that uses the wearer's respiration activity to ensure that the devices become part of the same body-area network [12]. In addition, authors surveyed context-based pairing in wearable devices by focusing on the signals and sensors exploited, and they reviewed the steps needed for generating a common key and provided a survey of existing techniques used in each step [13].…”

“…F1 score = 2RecallPrecision Recall + Precision (12) where TP, TN, FP, and FN represent the true positive, true negative, false positive, and false negative, respectively. In particular, TP and TN are the correctly predicted positive (fall class) and negative (no-fall class) cases by our proposed network, whereas FP and FN are the incorrectly predicted positive and negative cases, respectively.…”