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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Device-to-Device (D2D) Multi-Criteria Learning Algorithm Using Secured Sensors.

Khalid Haseeb1, Amjad Rehman2, Tanzila Saba2

  • 1Department of Computer Science, Islamia College Peshawar, Peshawar 25000, Pakistan.

Sensors (Basel, Switzerland)
|March 26, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a D2D multi-criteria learning algorithm for Internet of Things (IoT) networks, enhancing data exchange and security for mobile sensors. The new method improves efficiency and trustworthiness in smart environments.

Keywords:
D2DInternet of thingsmobile sensorstechnological developmentwireless systems

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Security

Background:

  • Wireless networks and IoT are crucial for smart environments, enabling data collection via sensors.
  • Battery-powered sensors in IoT networks face energy efficiency and data delay challenges.
  • Node mobility in IoT networks causes data discontinuity, compromising data trust and security.

Purpose of the Study:

  • To develop a D2D multi-criteria learning algorithm for IoT networks using secured sensors.
  • To enhance data exchange efficiency and reduce costs for mobile sensors.
  • To improve the trustworthiness and security of IoT communication systems against anonymous devices.

Main Methods:

  • Implementation of a D2D (Device-to-Device) multi-criteria learning algorithm.
  • Integration of secured sensors for reliable data collection.
  • Utilization of machine learning to enhance communication security and trustworthiness.

Main Results:

  • Significant improvement in packet delivery ratio by 17%.
  • Reduction in packet disturbances by 31% and data delay by 22%.
  • Decreased energy consumption by 24% and computational complexity by 37%.

Conclusions:

  • The proposed D2D multi-criteria learning algorithm effectively addresses energy efficiency and data delay in IoT networks.
  • The algorithm enhances data security and trustworthiness, mitigating risks from anonymous devices.
  • Simulation results validate the algorithm's superior performance in realistic network configurations.