Energy-Efficient Joint Resource Allocation Algorithms for MEC-Enabled Emotional Computing in Urban Communities

This paper considers a mobile edge computing (MEC) system, where the MEC server first collects data from emotion sensors and then computes the emotion of each user. We give the formula of the emotional prediction accuracy. In order to improve the energy efficiency of the system, we propose resources...

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Bibliographic Details
Published in:IEEE access Vol. 7; pp. 137410 - 137419
Main Authors: Yang, Ziyan, Du, Yao, Che, Chang, Wang, Wenyong, Mei, Haibo, Zhou, Dongdai, Yang, Kun
Format: Journal Article
Language:English
Published: Piscataway IEEE 2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Computational geometry
Computational modeling
Convexity
Data collection
Edge computing
Emotion recognition
emotional computing
Emotions
Energy consumption
Energy efficiency
Feature extraction
Internet of Things
Mobile computing
mobile edge computing (MEC)
Optimization
Optimization techniques
Resource allocation
Resource management
resources allocation
Sensors
Servers
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:This paper considers a mobile edge computing (MEC) system, where the MEC server first collects data from emotion sensors and then computes the emotion of each user. We give the formula of the emotional prediction accuracy. In order to improve the energy efficiency of the system, we propose resources allocation algorithms. We aim to minimize the total energy consumption of the MEC server and sensors by jointly optimizing the computing resources allocation and the data transmitting time. The formulated problem is a non-convex problem, which is very difficult to solve in general. However, we transform it into convex problems and apply convex optimization techniques to address it. The optimal solution is given in closed form. Simulation results show that the total energy consumption of our system can be effectively reduced by the proposed scheme compared with the benchmark.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2942391