Research Article
Smartphone-Based Lifelogging: An Investigation of Data Volume Generation Strength of Smartphone Sensors
@INPROCEEDINGS{10.1007/978-3-030-32216-8_6, author={Inayat Khan and Shaukat Ali and Shah Khusro}, title={Smartphone-Based Lifelogging: An Investigation of Data Volume Generation Strength of Smartphone Sensors}, proceedings={Simulation Tools and Techniques. 11th International Conference, SIMUtools 2019, Chengdu, China, July 8--10, 2019, Proceedings}, proceedings_a={SIMUTOOLS}, year={2019}, month={10}, keywords={Smartphone Sensors Lifelogging Personal big data Information overload Memory augmentation}, doi={10.1007/978-3-030-32216-8_6} }- Inayat Khan
Shaukat Ali
Shah Khusro
Year: 2019
Smartphone-Based Lifelogging: An Investigation of Data Volume Generation Strength of Smartphone Sensors
SIMUTOOLS
Springer
DOI: 10.1007/978-3-030-32216-8_6
Abstract
The lifelogging enable people to digitally record information about their daily life events for a variety of purposes including human memory augmentation. However, the lifelogging systems have several challenges regarding capturing, managing, semantic analyses, indexing, and retrieval of error-prone and noisy data produced by the sensors. The ubiquitous nature and technological developments makes smartphone as de-facto lifelogging device. The smartphone integrates a rich set of sensors, which provide unique opportunities for capturing contents and contextual information into a comprehensive lifelog archive. However, the continuous use of sensors can generate large amount of data that could raise problems for smartphone-based lifelogging systems. In addition, insight understanding of smartphone sensors data generation strength is needed for effective smartphone-based lifelogging systems development. These estimations will also help in understanding of smartphone sensors capability of fulfilling lifelogging systems objectives. To fulfill objective of this paper, an Android based application namely Sensors dAta Volume Estimator (SAVE) is developed using a proposed architecture. The SAVE utilizes smartphone sensors to capture and estimate sensors data from different real world scenarios. The results indicated that smartphone sensors can generate significant amount of data that can create storage, retrieval, and battery power issues for smartphone-based lifelogging systems.