Vibration Detector based on GMR Sensors

2007

Up to now, vibrations have been mostly sensed by measuring displacement, velocity and acceleration. The most common types of vibration sensors are piezoelectric, capacitive, null-balance, strain gage, optoelectronic, resonance beam or piezoresistive. We present a low cost and low power vibration detector based on the measurement of magnetic field variations induced in a recent SS501 GMR magnetic sensor, which has never been applied for that. Vibrations on small ferromagnetic pieces disturb the Earth's magnetic field. These weak perturbations can be detected and measured over the assumed constant Earth's magnetic field, which is uniform over a wide area. A novel array configuration of 3 half…

Solar Inexhaustible Power Source for Wireless Sensor Node

2008

Currently the appearance of really low power wireless transceivers is motivating the use of renewable energies to power embedded wireless sensor nodes in many applications. Nevertheless, energy storage and its degradation still keep on being the main issues in the design of any battery powered device. We present an autonomous power source based on a new system architecture, which uses the energy scavenging to replenish two different rechargeable energy buffers instead of the conventional single one. Combining appropriately a degradable large backup battery (Lithium-Ion) and a shorter but non degradable storage element (Supercapacitor), the lifetime of the group can be widely extended to wha…

A method for modeling the battery state of charge in wireless sensor networks

2015

In this paper we propose a method for obtaining an analytic model of the battery State-of-Charge (SoC) in wireless sensor nodes. The objective is to find simple models that can be used to estimate accurately the real battery state and consequently the node lifetime. Running the model in the network nodes, we can provide the motes with the required information to implement applications that can be considered as battery-aware. The proposed methodology reduces the computational complexity of the model avoiding complicated electrochemical simulations and treating the battery as an unknown system with an output that can be predicted using simple mathematical models. At a first stage, during a se…

Power Consumption Analysis of Operating Systems for Wireless Sensor Networks

2010

In this paper four wireless sensor network operating systems are compared in terms of power consumption. The analysis takes into account the most common operating systems-TinyOS v1.0, TinyOS v2.0, Mantis and Contiki-running on Tmote Sky and MICAz devices. With the objective of ensuring a fair evaluation, a benchmark composed of four applications has been developed, covering the most typical tasks that a Wireless Sensor Network performs. The results show the instant and average current consumption of the devices during the execution of these applications. The experimental measurements provide a good insight into the power mode in which the device components are running at every moment, and t…

Method for measuring internal resistance of batteries in WSN

2014

Predicting the Batteries' State of Health in Wireless Sensor Networks Applications

2018

[EN] The lifetime of wireless sensor networks deployments depends strongly on the nodes battery state of health (SoH). It is important to detect promptly those motes whose batteries are affected and degraded by ageing, environmental conditions, failures, etc. There are several parameters that can provide significant information of the battery SoH, such as the number of charge/discharge cycles, the internal resistance, voltage, drained current, temperature, etc. The combination of these parameters can be used to generate analytical models capable of predicting the battery SoH. The generation of these models needs a previous process to collect dense data traces with sampled values of the batt…