5,209 research outputs found
Prediction-Based Energy Saving Mechanism in 3GPP NB-IoT Networks
The current expansion of the Internet of things (IoT) demands improved communication platforms that support a wide area with low energy consumption. The 3rd Generation Partnership Project introduced narrowband IoT (NB-IoT) as IoT communication solutions. NB-IoT devices should be available for over 10 years without requiring a battery replacement. Thus, a low energy consumption is essential for the successful deployment of this technology. Given that a high amount of energy is consumed for radio transmission by the power amplifier, reducing the uplink transmission time is key to ensure a long lifespan of an IoT device. In this paper, we propose a prediction-based energy saving mechanism (PBESM) that is focused on enhanced uplink transmission. The mechanism consists of two parts: first, the network architecture that predicts the uplink packet occurrence through a deep packet inspection; second, an algorithm that predicts the processing delay and pre-assigns radio resources to enhance the scheduling request procedure. In this way, our mechanism reduces the number of random accesses and the energy consumed by radio transmission. Simulation results showed that the energy consumption using the proposed PBESM is reduced by up to 34% in comparison with that in the conventional NB-IoT method
Implementation and Evaluation of Networked Model Predictive Control System on Universal Robot
Networked control systems are closed-loop feedback control systems containing
system components that may be distributed geographically in different locations
and interconnected via a communication network such as the Internet. The
quality of network communication is a crucial factor that significantly affects
the performance of remote control. This is due to the fact that network
uncertainties can occur in the transmission of packets in the forward and
backward channels of the system. The two most significant among these
uncertainties are network time delay and packet loss. To overcome these
challenges, the networked predictive control system has been proposed to
provide improved performance and robustness using predictive controllers and
compensation strategies. In particular, the model predictive control method is
well-suited as an advanced approach compared to conventional methods. In this
paper, a networked model predictive control system consisting of a model
predictive control method and compensation strategies is implemented to control
and stabilize a robot arm as a physical system. In particular, this work aims
to analyze the performance of the system under the influence of network time
delay and packet loss. Using appropriate performance and robustness metrics, an
in-depth investigation of the impacts of these network uncertainties is
performed. Furthermore, the forward and backward channels of the network are
examined in detail in this study
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