An approach to detect Travel Patterns using Smartphone Sensing

Kar, Devroop; Middya, Asif Iqbal; Roy, Sarbani

doi:10.1109/ants47819.2019.9118073

Cited by 10 publications

(2 citation statements)

References 13 publications

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“…The main focus of this paper is to come up with an incentive mechanism for the sensing tasks that are related to different dynamics of a city (e.g. road condition monitoring [3,4], mobility pattern [5], air pollution monitoring [6,7], noise pollution monitoring [8,9], etc.). From this perspective, a city can be viewed as different functional regions such as educational areas, residential areas, business areas, areas with major road segments, etc.…”

Section: Introductionmentioning

confidence: 99%

Double Auction Based Incentive Mechanism for Participatory Sensing Systems

Middya

Roy

2023

Preprint

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Section: Introductionmentioning

confidence: 99%

Double Auction Based Incentive Mechanism for Participatory Sensing Systems

Middya

Roy

2023

Preprint

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“…More specifically, in these paradigms of sensing, participants use their smartphones to collect high granular data that can be used to explore different dynamics of a city (e.g. road condition monitoring [3], pollution monitoring [4][5][6], travel pattern monitoring [7], etc.). Now, as sharing smartphones with guest users (e.g.…”

Section: Introductionmentioning

confidence: 99%

Privacy protected user identification using deep learning for smartphone-based participatory sensing applications

Middya

Roy

Mandal

et al. 2021

Neural Comput & Applic

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In smartphone-based crowd/participatory sensing systems, it is necessary to identify the actual sensor data provider. In this context, this paper attempts to recognize the users' identity based on their gait patterns (i.e. unique walking patterns). More specifically, a deep convolution neural network (CNN) model is proposed for the user identification with accelerometer data generated from users smartphone sensors. The proposed model is evaluated based on the real-world benchmark dataset (accelerometer biometric competition data) having a total of 387 users accelerometer sensor readings (60 million data samples). The performance of the proposed CNN-based approach is also compared with five baseline methods namely Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), and K-Nearest Neighbours (KNN). It is observed that the proposed model achieves better results (accuracy = 98.8%, precision = 0.94, recall = 0.97, and F1-score = 0.95) as compared to the baseline methods.

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