Low Power Design Strategy in Internet of Things Engineering
Abstract
As an important part of the new generation of information technology, the Internet of Things is profoundly changing the way we
live and work. Low-power design involves multiple levels of hardware and software, which requires comprehensive consideration and innovation in system architecture, chip selection, power management, communication protocol, algorithm optimization and other aspects. With
the continuous emergence of new technologies, such as efficient energy collection technology, new low-power materials, intelligent power
management algorithm, etc., providing new possibilities and development directions for the low-power design of the Internet of Things. Based
on this, this paper analyzes the energy consumption characteristics of Internet of Things devices, and discusses the low power design methods
of hardware level and software level, in order to effectively reduce the energy consumption of Internet of Things devices, prolong battery life,
improve system reliability and sustainability.
live and work. Low-power design involves multiple levels of hardware and software, which requires comprehensive consideration and innovation in system architecture, chip selection, power management, communication protocol, algorithm optimization and other aspects. With
the continuous emergence of new technologies, such as efficient energy collection technology, new low-power materials, intelligent power
management algorithm, etc., providing new possibilities and development directions for the low-power design of the Internet of Things. Based
on this, this paper analyzes the energy consumption characteristics of Internet of Things devices, and discusses the low power design methods
of hardware level and software level, in order to effectively reduce the energy consumption of Internet of Things devices, prolong battery life,
improve system reliability and sustainability.
Keywords
Internet of Things engineering; Low power consumption design; Energy consumption optimization
Full Text:
PDFReferences
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DOI: http://dx.doi.org/10.70711/aitr.v2i6.5753
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