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Article citations


Khodashenas, P.S., Ruiz, C., Siddiqui, M.S., Betzler, A. and Riera, J.F. (2017) The Role of Edge Computing in Future 5G Mobile Networks: Concept and Challenges. Cloud and Fog Computing in 5G Mobile Networks: Emerging Advances and Applications, 70, 349.

has been cited by the following article:

  • TITLE: Energy-Efficient MTC Data Offloading in Wireless Networks Based on K-Means Grouping Technique

    AUTHORS: Juma Saidi Ally, Muhammad Asif, Qingli Ma

    KEYWORDS: Machine-Type Communication, Correlation, Data Offloading, Grouping Technique, Differential Entropy, Power Exponential Function

    JOURNAL NAME: Journal of Computer and Communications, Vol.7 No.2, February 20, 2019

    ABSTRACT: Machine-type communication (MTC) devices provide a broad range of data collection especially on the massive data generated environments such as urban, industrials and event-enabled areas. In dense deployments, the data collected at the closest locations between the MTC devices are spatially correlated. In this paper, we propose a k-means grouping technique to combine all MTC devices based on spatially correlated. The MTC devices collect the data on the event-based area and then transmit to the centralized aggregator for processing and computing. With the limitation of computational resources at the centralized aggregator, some grouped MTC devices data offloaded to the nearby base station collocated with the mobile edge-computing server. As a sensing capability adopted on MTC devices, we use a power exponential function model to compute a correlation coefficient existing between the MTC devices. Based on this framework, we compare the energy consumption when all data processed locally at centralized aggregator or offloaded at mobile edge computing server with optimal solution obtained by the brute force method. Then, the simulation results revealed that the proposed k-means grouping technique reduce the energy consumption at centralized aggregator while satisfying the required completion time.