Title: Time series representation for information gathering via low resolution wireless sensor networks
Authors: Jorge Navarro; Isaac Martín de Diego; Ana R. Redondo
Addresses: Data Science Laboratory, Rey Juan Carlos University, C/ Tulipán, s/n, Móstoles, Madrid, Spain; Sensowave, Av. de Castilla, 1, San Fernando de Henares, Madrid, Spain ' Data Science Laboratory, Rey Juan Carlos University, C/ Tulipán, s/n, Móstoles, Madrid, Spain ' Data Science Laboratory, Rey Juan Carlos University, C/ Tulipán, s/n, Móstoles, Madrid, Spain
Abstract: New applications of the internet of things are emerging in sectors as diverse as military, environmental, health, or food due to the improvements achieved in the development of wireless sensor networks (WSNs). In many of these applications, as the connected devices are hardly accessible and energy harvesting is not possible, it is essential to provide long-term autonomies to the end user. Hence, there is a need for developing energy-efficient strategies for data gathering that send few messages accumulating as much information as possible. This paper proposes a new strategy for sending summarised information from commercial accelerometers deployed through WSNs. An exhaustive evaluation has been performed using data from experimental devices that gathered 106 different daily time series related to animal behaviour. The obtained results show that the proposed strategy highly improves the current operation mode of the commercial devices.
Keywords: internet of things; IoT; wireless sensor networks; WSN; data science; time series; animal behaviour; information gathering; accelerometers.
International Journal of Sensor Networks, 2022 Vol.40 No.1, pp.57 - 66
Received: 14 Jan 2022
Accepted: 20 Jan 2022
Published online: 05 Sep 2022 *