scholarly journals AA-Battery Sized Energy Harvesting Power Management Module for Indoor Light Wireless Sensor Applications

2015 ◽  
pp. 91-97 ◽  
Author(s):  
Alessandro Vinco ◽  
Rashid Siddique ◽  
Davide Brunelli ◽  
Wensi Wang
Keyword(s):  
Energy Harvesting ◽  
Power Management ◽  
Wireless Sensor ◽  
Sensor Applications

Power management in SMAC-based energy-harvesting wireless sensor networks using queuing analysis

2013 ◽  
Vol 36 (3) ◽  
pp. 1008-1017 ◽  
Author(s):  
Navid Tadayon ◽  
Sasan Khoshroo ◽  
Elaheh Askari ◽  
Honggang Wang ◽  
Howard Michel
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Harvesting ◽  
Power Management ◽  
Wireless Sensor ◽  
Queuing Analysis

A HIERARCHICAL MODEL FOR DISTRIBUTED COLLABORATIVE COMPUTATION IN WIRELESS SENSOR NETWORKS

2004 ◽  
Vol 15 (03) ◽  
pp. 485-506 ◽  
Author(s):  
Mitali Singh ◽  
Viktor K. Prasanna
Keyword(s):  
Sensor Networks ◽  
Power Management ◽  
Network Architecture ◽  
Low Cost ◽  
Algorithm Design ◽  
Wireless Sensor ◽  
Hierarchical Network ◽  
Sensor Applications ◽  
Multiple Power ◽  
Key Features

In-network collaborative computation is essential for implementation of a large number of sensor applications. We approach the problem of computation in sensor networks from a parallel and distributed system's perspective. We define COSMOS, the Cluster-based, heterOgeneouSMOdel for Sensor networks. The model abstracts the key features of the class of cluster-based sensor applications. It assumes a hierarchical network architecture comprising of a large number of low cost sensors with limited computation capability, and fewer number of powerful clusterheads, uniformly distributed in a two dimensional terrain. The sensors are organized into single hop clusters, each managed by a distinct clusterhead. The clusterheads are organized in a mesh-like topology. All sensors in a cluster are time synchronized, whereas the clusterheads communicate asynchronously. The sensors are assumed to have multiple power states and a wakeup mechanism to facilitate power management. To illustrate algorithm design using our model, we discuss implementation of algorithms for sorting and summing in sensor networks.

Scavenging Energy From Piezoelectric Materials for Wireless Sensor Applications

2005 ◽  
Author(s):  
Christopher Green ◽  
Karla M. Mossi ◽  
Robert G. Bryant
Keyword(s):  
Energy Harvesting ◽  
Wireless Sensors ◽  
Piezoelectric Materials ◽  
Cost Effective ◽  
Electrical Energy ◽  
Wireless Sensor ◽  
Battery Life ◽  
Sinusoidal Vibration ◽  
Sensor Applications ◽  
Energy Harvesting Devices

Wireless sensors are an emerging technology that has the potential to revolutionize the monitoring of simple and complex physical systems. Prior research has shown that one of the biggest issues with wireless sensors is power management. A wireless sensor is simply not cost effective unless it can maintain long battery life or harvest energy from another source. Piezoelectric materials are viable conversion mechanisms because of their inherent ability to covert vibrations to electrical energy. Currently a wide variety of piezoelectric materials are available and the appropriate choice for sensing, actuating, or harvesting energy depends on their characteristics and properties. This study focuses on evaluating and comparing three different types of piezoelectric materials as energy harvesting devices. The materials utilized consisted on PZT 5A, a single crystal PMN 32%PT, and a PZT 5A composite called Thunder. These materials were subjected to a steady sinusoidal vibration provided by a shaker at different power levels. Gain of the devices was measured at all levels as well as impedance in a range of frequencies was characterized. Results showed that the piezoelectric generator coefficient, g33, predicts the overall power output of the materials as verified by the experiments. These results constitute a baseline for an energy harvesting system that will become the front end of a wireless sensor network.

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