Research on Self-prediction Algorithm of Residual Energy for WSN

DU Yong-wen; FENG Ke; LIAN Yun-xiang

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Research on Self-prediction Algorithm of Residual Energy for WSN

更新时间：2026-09-04

- Research on Self-prediction Algorithm of Residual Energy for WSN
- Vol. 33, Issue 12, Pages: 113-116(2016)
- 作者机构：
  
  兰州交通大学机构电子与信息工程学院,甘肃,兰州,730070
- 作者简介：
- 基金信息：
- DOI：
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- Published：2016
- 稿件说明：
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杜永文, 冯珂, 练云翔. 无线传感器节点能量自主预测算法研究[J]. 微电子学与计算机, 2016, 33(12): 113-116, 120.

DU Yong-wen, FENG Ke, LIAN Yun-xiang. Research on Self-prediction Algorithm of Residual Energy for WSN[J]. 2016, 33(12): 113-116.
杜永文, 冯珂, 练云翔. 无线传感器节点能量自主预测算法研究[J]. 微电子学与计算机, 2016, 33(12): 113-116, 120. DOI：

DU Yong-wen, FENG Ke, LIAN Yun-xiang. Research on Self-prediction Algorithm of Residual Energy for WSN[J]. 2016, 33(12): 113-116. DOI：

摘要

结合节点能耗模型

提出了传感器节点能量自主预测算法.算法考虑了不同工作状态对能耗的影响以及电池放电对传感器节点的影响.在TinyOS平台上实现了算法和仿真

同时在硬件上进行了实测

并对得到的仿真数据和实测数据进行分析.实验数据表明算法能比较准确地预测节点的工作时间和剩余能量.为能量感知算法提供准确的能量依据

提高节点能量使用效率和网络生命期.

Abstract

A self-estimation algorithm of residual energy is proposed

according to discharging process of battery and energy consumption model. The algorithm is improved to take into consideration the influence of different working conditions and the discharging time of battery. It is implemented and simulated on TinyOS

and also tested on real node device. Comparing the simulation data and measured data

the result indicates that the algorithm can more precisely predict work time and residual energy. For energy aware algorithm provide accurate energy basis

and improve the energy efficiency and life.

关键词

Keywords

references

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成小良, 邓志超, 董志然. 基于无线通信和计算特征分析的能量模型[J]. 计算机研究和发展, 2009, 46(12): 1985-1993. DOI: 1985

李文泽. 基于云模型的WSN能量消耗平衡研究[J]. 微电子学与计算机, 2014, 31(2): 90-93. DOI: 90

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