Energy is essential for human existence,and its high consumption is a growing concern in today’s technologydriven society.Global initiatives aim to reduce energy consumption and pollution by developing and deploying ...Energy is essential for human existence,and its high consumption is a growing concern in today’s technologydriven society.Global initiatives aim to reduce energy consumption and pollution by developing and deploying energy-efficient sensing technologies for long-term monitoring,control,automation,security,and interactions.Wireless Body Area Networks(WBANs)benfit a lot from the continuous monitoring capabilities of these sensing devices,which include medical sensors worn on or implanted in the human body for healthcare monitoring.Despite significant advancements,achieving energy efficiency in WBANs remains a significant challenge.A deep understanding of the WBAN architecture is essential to identify the causes of its energy inefficiency and develop novel energy-efficient solutions.We investigate energy efficiency issues specific to WBANs.We discuss the transformative impact that artficial intelligence and Machine Learning(ML)can have on achieving the energy efficiency of WBANs.Additionally,we explore the potential of emerging technologies such as quantum computing,nano-technology,biocompatible energy harvesting,and Simultaneous Wireless Information and Power Transfer(SWIPT)in enabling energy efficiency in WBANs.We focus on WBANs’architecture,hardware,and software components to identify key factors responsible for energy consumption in the WBAN environment.Based on our comprehensive review,we introduce an innovative,energy-efficient three-tier architecture for WBANs that employs ML and edge computing to overcome the limitations inherent in existing energy-efficient solutions.Finally,we summarize the lessons learned and highlight future research directions that will enable the development of energy-efficient solutions for WBANs.展开更多
文摘Energy is essential for human existence,and its high consumption is a growing concern in today’s technologydriven society.Global initiatives aim to reduce energy consumption and pollution by developing and deploying energy-efficient sensing technologies for long-term monitoring,control,automation,security,and interactions.Wireless Body Area Networks(WBANs)benfit a lot from the continuous monitoring capabilities of these sensing devices,which include medical sensors worn on or implanted in the human body for healthcare monitoring.Despite significant advancements,achieving energy efficiency in WBANs remains a significant challenge.A deep understanding of the WBAN architecture is essential to identify the causes of its energy inefficiency and develop novel energy-efficient solutions.We investigate energy efficiency issues specific to WBANs.We discuss the transformative impact that artficial intelligence and Machine Learning(ML)can have on achieving the energy efficiency of WBANs.Additionally,we explore the potential of emerging technologies such as quantum computing,nano-technology,biocompatible energy harvesting,and Simultaneous Wireless Information and Power Transfer(SWIPT)in enabling energy efficiency in WBANs.We focus on WBANs’architecture,hardware,and software components to identify key factors responsible for energy consumption in the WBAN environment.Based on our comprehensive review,we introduce an innovative,energy-efficient three-tier architecture for WBANs that employs ML and edge computing to overcome the limitations inherent in existing energy-efficient solutions.Finally,we summarize the lessons learned and highlight future research directions that will enable the development of energy-efficient solutions for WBANs.
