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.

Highlights

  • The correlation model verified by using the covariance function that decreases with Euclidean distance for 0 at l = ∞ and 1 at l = 0, where l represents the Euclidean distance between the locations of Machine-type communication (MTC) devices

  • We investigate the problem of data correlated in MTC devices based on the resource-constrained allocated at Centralized Aggregator for computing and processing

  • We propose k-means grouping technique to group MTC devices corresponding to a spatial correlation on the event-based area

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Summary

Introduction

Several approaches are proposed to deal with that challenge such as edge computing, data offloading and data caching [4] [5] [6], where the data computation and terminal requests move very close to the data source It attempts to reduce the pressure on the mobile operators regarding with the limits of data generated from the terminal devices such as MTC devices, but still encounters the problem of data overloading when the amount of data generated are increases. The MTC devices are deployed to perform specific tasks collectively; the data collected from each device are not completely independent rather correlated In such case to avoid the resource wasted for the individual device processing at the aggregator, the CA combines the correlated devices together to form a group [9]. With the limited computational resources at CA, some data will be offloaded to the nearby server allocated to the base station called

Related Work
Network Model
Description of Correlation Model
Device Grouping Technique
Computation Model for CA and MEC
MEC Execution Analysis
Problem Formulation
Performance Analysis and Evaluation
Comparison Based on Computation Decision
Findings
Conclusion and Future Work
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