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Journal of Computer and Communications, 2019, 7, 47-61

http://www.scirp.org/journal/jcc

ISSN Online: 2327-5227

ISSN Print: 2327-5219

DOI:

10.4236/jcc.2019.72004 Feb. 20, 2019 47 Journal of Computer and Communications

Energy-Efficient MTC Data Offloading in

Wireless Networks Based on K-Means Grouping

Technique

Juma Saidi Ally, Muhammad Asif, Qingli Ma

Department of Information and Communication Engineering, University of Science and Technology of China,

Key Laboratory of Wireless-Optical Communications, Chinese Academy of Sciences, Hefei, China

Abstract

Machine-type communication (MTC) devices provide a broad range of data

collection especially on the massive data generated environments such as ur-

ban, industrials and event-enabled areas. In dense deployments, the data col-

lected at the closest locations between the MTC devices are spatially corre-

lated. 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 devic

es 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 mo-

bile 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.

Keywords

Machine-Type Communication, Correlation, Data Offloading, Grouping

Technique, Differential Entropy, Power Exponential Function

1. Introduction

Market trends of Machine-Type Communication (MTC) devices deployments

How to cite this paper:

Ally, J.S., Asif, M

and

Ma, Q.L. (2019) Energy-

Efficient MTC

Data Offloading in Wireless Networks

Based on K

-Means Grouping Technique

Journal of Computer and Communications

, 47-61.

https://doi.org/10.4236/jcc.2019.72004

Received:

October 17, 2018

Accepted:

February 17, 2019

Published:

February 20, 2019

9 by author(s) and

Scientific

Research Publishing Inc.

This work is licensed under the Creative

Commons Attribution International

License (CC BY

4.0).

http://creativecommons.org/licenses/by/4.0/

Open Access

J. S. Ally et al.

DOI:

10.4236/jcc.2019.72004 48 Journal of Computer and Communications

are exponential increases. This is determined by many applications and services

discovered such automatic industries monitoring, smart metering, surveillance

cameras, environment monitoring and trace devices [1]. Additionally, according

to a broadly used report by Cisco VNI in 2016 up to 2020 the traffic generated

from MTC devices expected to reach 49 exabytes [2]. MTC usually includes a

large number of devices deployed randomly in a hostile and highly dynamic en-

vironment. Since the data collected coverage and network coverage (communi-

cation range) are normally constant at randomly allocated, a high density of the

redundancy devices is used to conserve the preferred level of network coverage

in order to accomplish enough data collection. Addition, MTC devices are nor-

mally the event-driven systems where various devices try to send data at any

event of interest occurred [3]. Since the MTC devices are densely deployed, then

the data transmitted from devices to the aggregator (sink) or base station for

computational are spatially correlated.

Therefore, the wasted of the computational resources occurred at CA when

the data collected from different MTC devices are processing independently. The

significant amount of computational resources can be saved by considering the

advantages of existence of a spatial correlation.

However, the growth of the data traffic collected by MTC devices increase the

pressure on the mobile operators specifically for the delay-sensitive applications,

which requires a very short time to be processed. Several approaches are pro-

posed 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. Besides, 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 overload-

ing when the amount of data generated are increases. That amount of data re-

quired to be analyzed. Recently, the big data analysis implemented in cloud or

enterprises centralized data centers to analyze the data generated from terminal

devices. Moreover, data aggregation technique proposed in [7] where the net-

work congestion originated from the massive data generated by MTC devices

reduced. In addition, the data aggregator act as intermediate nodes on the cellu-

lar networks can further help minimize the power consumed and transmission

delay by MTC devices while transmitting data to the networks [8].

Generally, the MTC devices are deployed to perform specific tasks collectively;

the data collected from each device are not completely independent rather cor-

related. Thus, 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]. In this paper, we use the centralized aggregator (CA) as a sink

or nearby devices computing the data collected from MTC devices in the

event-based area. By examining the existence of correlated between the MTC

device we propose k-means grouping technique to combine all the spatial corre-

lated devices together. With the limited computational resources at CA, some

data will be offloaded to the nearby server allocated to the base station called

J. S. Ally et al.

DOI:

10.4236/jcc.2019.72004 49 Journal of Computer and Communications

mobile edge computing server. We investigate the energy consumption at the

CA by computing the optimal solution using the brute force method. The main

contributions of this paper described on the following:

 We define the correlation model to compute the correlation coefficient be-

tween the MTC devices by considering the device coordinate points on the

hyperplane.

 We propose device k-means grouping technique to groups MTC devices

based on spatial correlation around the data collected coverage.

 We use the differential entropy framework to compute the size of the data in

each group.

 Based on the size of the data we introduce the concepts of the data offloading

and we define the optimization problem for minimizing the energy consump-

tion on CA for processing the data collected from MTC devices.

 We use the brute force method to find the optimal solution for total energy

consumption on the centralized aggregator.

The remaining part of this structured as follows. Related work discussed in

Section 2. In Section 3, we discuss the network model that includes the network

model together with details of the correlation model and the proposed k-means

grouping technique. The Section 4 present the theoretical concepts of computa-

tion together with the optimization problem used to minimize the energy con-

sumption and Section 5 discuss the results used to verify our context and lastly

we conclude by summarizing the idea presented on whole paper.

2. Related Work

There are many emerging works exist in MTC communication and wireless

networks. In [10] authors proposed the medium access control (MAC) protocol

with low latency and energy for hierarchical structured of M2M networks. It

permits the effective data originated from the terminal nodes transmitted to a

sink node via a cluster head. The cloud based lightweight for mobile core net-

works investigated by [11] that improve the challenges MTC occurred from sys-

tem overload and network congestion .Similarly extended access barring (EAB)

in the LTE-A studied in [12] to deals with the problem of overloaded that origi-

nated from a massive number of MTC devices that compete to access networks

in a limited burst. The efficiency of data aggregation for M2M network discussed

in [13] with the goal of reducing the total energy expenditure together with cov-

erage probability.

Conventionally, to describe the concept of computational capability, a mobile

cloud computing (MCC) have been widely introduced [14]. However, a cloud

allocated far away from terminal devices cannot offer an extensive solution for

the low latency services (emergence applications), real-time applications and

regular data transmission such as location updates. Therefore, to allow terminal

devices communicate directly with cloud will not be feasible and cost-effective

because it needs more resources allocation and complex routing protocol design.

To deal with these challenges a mobile edge computing technique proposed in

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