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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Vydáno v:IEEE access Ročník 7; s. 137410 - 137419
Hlavní autoři: Yang, Ziyan, Du, Yao, Che, Chang, Wang, Wenyong, Mei, Haibo, Zhou, Dongdai, Yang, Kun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
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Abstract 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.
AbstractList 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.
Author Wang, Wenyong
Yang, Kun
Du, Yao
Yang, Ziyan
Che, Chang
Mei, Haibo
Zhou, Dongdai
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crossref_primary_10_1109_ACCESS_2020_2991108
crossref_primary_10_1016_j_seta_2021_101201
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SubjectTerms 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
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Title Energy-Efficient Joint Resource Allocation Algorithms for MEC-Enabled Emotional Computing in Urban Communities
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