Abstract
The Internet of Things (IoT) systems create a large amount of sensing information. The consistency of this information is an essential problem for ensuring the quality of IoT services. The IoT data, however, generally suffers due to a variety of factors such as collisions, unstable network communication, noise, manual system closure, incomplete values and equipment failure. Due to excessive latency, bandwidth limitations, and high communication costs, transferring all IoT data to the cloud to solve the missing data problem may have a detrimental impact on network performance and service quality. As a result, the issue of missing information should be addressed as soon as feasible by offloading duties like data prediction or estimations closer to the source. As a result, the issue of incomplete information must be addressed as soon as feasible by offloading duties such as predictions or assessment to the network’s edge devices. In this work, we show how deep learning may be used to offload tasks in IoT applications.
Highlights
For each Internet of Things (IoT) network, some terminals carry out their activities locally, while other terminals simultaneously outsource their activities in the gateway
The Internet of Things (IoT) is a new paradigm that integrates a large number of sensor-based elements such as devices, sensors and actuators on the Internet
By constantly monitoring certain measurements and automatically alerting them of their vital signs, the Internet of Things helps improve patient care and prevent fatal events in high-risk patients. Another area of application is the integration of IoT technology in hospital beds, which are being replaced by smart beds equipped with special sensors to monitor vital signs, blood pressure, oximeters and body temperature, among others
Summary
For each IoT network, some terminals carry out their activities locally, while other terminals simultaneously outsource their activities in the gateway. The Internet of Things (IoT) is a new paradigm that integrates a large number of sensor-based elements such as devices, sensors and actuators on the Internet. These elements constantly generate huge amounts of data with many characteristics such as large, heterogeneous, missing, corrupt and noisy. Unstable network communication, timing issues, device errors, and manual shutdown. This missing and corrupted data impacts the reliability, scalability, and interoperability of applications causing incorrect conclusions and decisions. This problem can lead to catastrophic results [3]
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