Investigating Wireless Sensor Network lifetime under static routing with unequal energy distribution
Data
2012Soggetto
Abstract
In a Wireless Sensor Network (WSN) the sensed data must be gathered and transmitted to a base station where it is further processed by end users. Since that kind of network consists of low-power nodes with limited battery power, power efficient methods must be applied for node communication and data gathering in order to achieve long network lifetimes. In such networks where in a round of communication many sensor nodes have data to send to a base station, it is very important to minimize the total energy consumed by the system so that the total network lifetime is maximized. The lifetime of such sensor network is the time until base station can receive data from all sensors in the network. In this work1, besides the conventional protocol of direct transmission or the use of dynamic routing protocols proposed in literature that potentially aggregates data, we propose an algorithm based on static routing among sensor nodes with unequal energy distribution in order to extend network lifetime and find a near-optimal node energy charge scheme that leads to both node and network lifetime prolongation. Our simulation results show that our algorithm achieves longer network lifetimes mainly because the final energy charge of each node is not uniform, while each node is free from maintaining complex route information and thus less infrastructure communication is needed. © 2012 APSIPA.
Collections
Related items
Showing items related by title, author, creator and subject.
-
An internet of things architecture for preserving privacy of energy consumption
Beligianni F., Alamaniotis M., Fevgas A., Tsompanopoulou P., Bozanis P., Tsoukalas L.H. (2016)Energy consumption by residential customers represents today around 30 to 40% of the total consumed energy, with the residential loads often to be charged for significant contribution to the peak demands both seasonal and ... -
Enhanced short-term load forecasting using artificial neural networks
Arvanitidis A.I., Bargiotas D., Daskalopulu A., Laitsos V.M., Tsoukalas L.H. (2021)The modernization and optimization of current power systems are the objectives of research and development in the energy sector, which is motivated by the ever-increasing electricity demands. The goal of such research and ... -
A learning approach for strategic consumers in smart electricity markets
Foti M., Vavalis M. (2016)In this paper we consider the design and the implementation of a machine learning approach and its integration with a widely used energy simulation platform. We focus on auction based energy markets which require their ...