Abstract
In this paper, a flow of ZigBee end-device energy characterization is introduced. The variation of the energy and power consumption of wireless communication through ZigBee end-device is studied. The remainder of this article details the methods used to determine energy and power overheads of the end- device in four parts: start-up stage, basic energy expenditure, data-sending consumption and polling consumption. The impacts of software parameters like polling rate and data size on the energy consumption are analysed. Also, models of the power and energy are extracted. Our methods allow estimating the energy consumption of the ZigBee end-device when running a Z-Stack application on a specific hardware platform.
Highlights
ZigBee Wireless Technology is emerging as the leading method for implementing low-cost, low-data rate, shortrange wireless networks with extended battery life for use in creating the “Internet of Things” [1]
Accurate energy characterization can make it easy to estimate battery life according to its capacity, or to choose optimum battery capacity according to designed life cycle
We proposed a method and flow of characterizing energy and power consumption of end-device in ZigBee network, taking both hardware factor and software factor into consideration
Summary
ZigBee Wireless Technology is emerging as the leading method for implementing low-cost, low-data rate, shortrange wireless networks with extended battery life for use in creating the “Internet of Things” [1]. ZigBee wireless sensor networks are becoming widely applied in a large number of fields including health, agriculture, industrial and geology, military, home and emergency management. We proposed a method and flow of characterizing energy and power consumption of end-device in ZigBee network, taking both hardware factor and software factor into consideration. Xia et al [4] propose a quantitative approach, in which EOS and applications are executed on two cooperated simulators, to estimate and analyse the energy consumption of embedded operating system (EOS). They used a cycle-accurate micro-architecture power model to estimate instruction execution energy. Their models are not deduced from measurements on actual hardware platform
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