Deep Reinforcement Learning Optimal Transmission Algorithm for Cognitive Internet of Things with RF Energy Harvesting

Author(s):  
Shaoai Guo ◽  
Xiaohui Zhao
Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3078 ◽  
Author(s):  
Hilal Bello ◽  
Zeng Xiaoping ◽  
Rosdiadee Nordin ◽  
Jian Xin

Wake-up radio is a promising approach to mitigate the problem of idle listening, which incurs additional power consumption for the Internet of Things (IoT) wireless transmission. Radio frequency (RF) energy harvesting technique allows the wake-up radio to remain in a deep sleep and only become active after receiving an external RF signal to ‘wake-up’ the radio, thus eliminating necessary hardware and signal processing to perform idle listening, resulting in higher energy efficiency. This review paper focuses on cross-layer; physical and media access control (PHY and MAC) approaches on passive wake-up radio based on the previous works from the literature. First, an explanation of the circuit design and system architecture of the passive wake-up radios is presented. Afterward, the previous works on RF energy harvesting techniques and the existing passive wake-up radio hardware architectures available in the literature are surveyed and classified. An evaluation of the various MAC protocols utilized for the novel passive wake-up radio technologies is presented. Finally, the paper highlights the potential research opportunities and practical challenges related to the practical implementation of wake-up technology for future IoT applications.


Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4465 ◽  
Author(s):  
Nermeen A. Eltresy ◽  
Osama M. Dardeer ◽  
Awab Al-Habal ◽  
Esraa Elhariri ◽  
Ali H. Hassan ◽  
...  

Museum contents are vulnerable to bad ambience conditions and human vandalization. Preserving the contents of museums is a duty towards humanity. In this paper, we develop an Internet of Things (IoT)-based system for museum monitoring and control. The developed system does not only autonomously set the museum ambience to levels that preserve the health of the artifacts and provide alarms upon intended or unintended vandalization attempts, but also allows for remote ambience control through authorized Internet-enabled devices. A key differentiating aspect of the proposed system is the use of always-on and power-hungry sensors for comprehensive and precise museum monitoring, while being powered by harvesting the Radio Frequency (RF) energy freely available within the museum. This contrasts with technologies proposed in the literature, which use RF energy harvesting to power simple IoT sensing devices. We use rectenna arrays that collect RF energy and convert it to electric power to prolong the lifetime of the sensor nodes. Another important feature of the proposed system is the use of deep learning to find daily trends in the collected environment data. Accordingly, the museum ambience is further optimized, and the system becomes more resilient to faults in the sensed data.


IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Nermeen A. Eltresy ◽  
Abd Elhamid M. Abd Elhamid ◽  
Dalia N. Elsheakh ◽  
Hadia M. Elhennawy ◽  
Esmat A. Abdallah

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