Sa Math¹, Prohim Tam¹, Seokhoon Kim^2,*

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3433-3450, 2021, DOI:10.32604/cmc.2021.015490

Abstract In the Next Generation Radio Networks (NGRN), there will be extreme massive connectivity with the Heterogeneous Internet of Things (HetIoT) devices. The millimeter-Wave (mmWave) communications will become a potential core technology to increase the capacity of Radio Networks (RN) and enable Multiple-Input and Multiple-Output (MIMO) of Radio Remote Head (RRH) technology. However, the challenging key issues in unfair radio resource handling remain unsolved when massive requests are occurring concurrently. The imbalance of resource utilization is one of the main issues occurs when there is overloaded connectivity to the closest RRH receiving exceeding requests. To handle this issue effectively, Machine Learning… More >

Zaiwar Ali¹, Sadia Khaf², Ziaul Haq Abbas², Ghulam Abbas³, Lei Jiao⁴, Amna Irshad², Kyung Sup Kwak⁵, Muhammad Bilal^6,*

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1461-1477, 2021, DOI:10.32604/cmc.2020.013743

Abstract In mobile edge computing (MEC), one of the important challenges is how much resources of which mobile edge server (MES) should be allocated to which user equipment (UE). The existing resource allocation schemes only consider CPU as the requested resource and assume utility for MESs as either a random variable or dependent on the requested CPU only. This paper presents a novel comprehensive utility function for resource allocation in MEC. The utility function considers the heterogeneous nature of applications that a UE offloads to MES. The proposed utility function considers all important parameters, including CPU, RAM, hard disk space, required… More >

Tengfei Yang^1,2, Xiaojun Shi³, Yangyang Li^1,*, Binbin Huang⁴, Haiyong Xie^1,5, Yanting Shen⁴

Journal on Big Data, Vol.2, No.3, pp. 105-115, 2020, DOI:10.32604/jbd.2020.010958

Abstract Mobile Edge Computing (MEC) has become the most possible network architecture to realize the vision of interconnection of all things. By offloading compute-intensive or latency-sensitive applications to nearby small cell base stations (sBSs), the execution latency and device power consumption can be reduced on resource-constrained mobile devices. However, computation delay of Mobile Edge Network (MEN) tasks are neglected while the unloading decision-making is studied in depth. In this paper, we propose a workload allocation scheme which combines the task allocation optimization of mobile edge network with the actual user behavior activities to predict the task allocation of single user. We… More >

Sang-Min Park¹, Young-Gab Kim^{2, *}

CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 569-590, 2020, DOI:10.32604/cmc.2020.08666

Abstract Emotions of users do not converge in a single application but are scattered across diverse applications. Mobile devices are the closest media for handling user data and these devices have the advantage of integrating private user information and emotions spread over different applications. In this paper, we first analyze user profile on a mobile device by describing the problem of the user sentiment profile system in terms of data granularity, media diversity, and server-side solution. Fine-grained data requires additional data and structural analysis in mobile devices. Media diversity requires standard parameters to integrate user data from various applications. A server-side… More >

Yifei Wei^1,*, Zhaoying Wang¹, Da Guo¹, F. Richard Yu²

CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 89-104, 2019, DOI:10.32604/cmc.2019.04836

Abstract To reduce the transmission latency and mitigate the backhaul burden of the centralized cloud-based network services, the mobile edge computing (MEC) has been drawing increased attention from both industry and academia recently. This paper focuses on mobile users’ computation offloading problem in wireless cellular networks with mobile edge computing for the purpose of optimizing the computation offloading decision making policy. Since wireless network states and computing requests have stochastic properties and the environment’s dynamics are unknown, we use the model-free reinforcement learning (RL) framework to formulate and tackle the computation offloading problem. Each mobile user learns through interactions with the… More >