Wireless Power Transfer and Data Collection in Wireless Sensor Networks

In a rechargeable wireless sensor network, the data packets are generated by sensor nodes at a specific data rate, and transmitted to a base station. Moreover, the base station transfers power to the nodes by using Wireless Power Transfer (WPT) to extend their battery life. However, inadequately scheduling WPT and data collection causes some of the nodes to drain their battery and have their data buffer overflow, while the other nodes waste their harvested energy, which is more than they need to transmit their packets. In this paper, we investigate a novel optimal scheduling strategy, called EHMDP, aiming to minimize data packet loss from a network of sensor nodes in terms of the nodes' energy consumption and data queue state information. The scheduling problem is first formulated by a centralized MDP model, assuming that the complete states of each node are well known by the base station. This presents the upper bound of the data that can be collected in a rechargeable wireless sensor network. Next, we relax the assumption of the availability of full state information so that the data transmission and WPT can be semi-decentralized. The simulation results show that, in terms of network throughput and packet loss rate, the proposed algorithm significantly improves the network performance.Comment: 30 pages, 8 figures, accepted to IEEE Transactions on Vehicular Technolog

Outage minimization of energy-harvesting wireless sensor network supported by UAV

Due to their adaptability, mobility, and capacity to offer an ideal channel, unmanned aerial vehicles (UAVs) have become a potential option for wireless power transfer and data collection in wireless sensor networks (WSNs). This paper examines energy-constrained WSNs, where data transfer to the data center is facilitated by UAV and sensors rely on radio frequency (RF) energy obtained by a Power Beacon (PB). However, due to energy limitations, sensors can only send data using the harvested energy. We consider a WSN in which the nodes are randomly distributed within a circular area, with the PB placed at the center of the WSN. To evaluate the system performance, we consider the dynamic nature of the wireless channel, which includes factors such as signal reflection, scattering, and diffraction. Through numerical analysis and simulations, the main aim is to identify the optimal system parameters that minimize the outage probability. This analysis provides valuable insights for designing more effective and reliable energy-harvesting WSNs with UAV as data collector. By leveraging UAV in WSNs, system performance can be improved, ensuring data transmission to destination nodes placed at a large distance from the WSN

Micro air vehicles energy transportation for a wireless power transfer system

The aim of this work is to demonstrate the feasibility use of an Micro air vehicles (MAV) in order to power wirelessly an electric system, for example, a sensor network, using low-cost and open-source elements. To achieve this objective, an inductive system has been modelled and validated to power wirelessly a sensor node using a Crazyflie 2.0 as MAV. The design of the inductive system must be small and light enough to fulfil the requirements of the Crazyflie. An inductive model based on two resonant coils is presented. Several coils are defined to be tested using the most suitable resonant configuration. Measurements are performed to validate the model and to select the most suitable coil. While attempting to minimize the weight at transmitter’s side, on the receiver side it is intended to efficiently acquire and manage the power obtained from the transmitter. In order to prove its feasibility, a temperature sensor node is used as demonstrator. The experiment results show successfully energy transportation by MAV, and wireless power transfer for the resonant configuration, being able to completely charge the node battery and to power the temperature sensor.Peer ReviewedPostprint (published version

Wireless body sensor networks for health-monitoring applications

This is an author-created, un-copyedited version of an article accepted for publication in Physiological Measurement. The publisher is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at http://dx.doi.org/10.1088/0967-3334/29/11/R01