A lightweight heterogeneous network clustering algorithm based
on edge computing for 5G
Ruizhong Du
1,2
•
Yan Liu
1,2
•
Liqun Liu
3
•
Wenpeng Du
3
Ó Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract
Edge computing is a promising paradigm to provide computing capabilities in close proximity to mobile devices in fifth-
generation (5G) networks. However, most wireless sensor devices connected to the 5G network have limited battery life,
and how to effectively reduce energy consumption and extend the network life cycle has become one of the hot problems
in current research. Due to this motiv ation, an improved Stable Election Protocol (SEP), named Lightweight in Edge
Computing-SEP (LEC-SEP) is proposed. LEC-SEP algorithm considers the heterogeneity of the initial energy of the nodes
and the cluster head election is determined by the probability that the relative level of the initial energy and the residual
energy. According to the influence of the number of cluster heads, the optimal clustering number is calculated to balance
the network traffic. At the same time, the location of the base station is redefined to facilitate adding the edge server, which
can store the data aggregated and fused by base station, providing powerful and real-time storage and com puting power to
effectively offload the pressure of the central cloud. The simulation results show that the energy consumption is well
distributed in the proposed algorithm, and LEC-SEP algorithm achieves a longer stabilization period in the network than
other typical clustering algorithms. The network life of LEC-SEP improved by 8.17% and 20.34% in comparison with the
P-SEP algorithm and the IDEEC algorithm respectively.
Keywords Clustering algorithms Edge computing Energy efficiency Internet of Things 5G
1 Introduction
With the rapid development of Internet of Things, the
emergence and maturity of the 5G enable the era of
Internet of Everything [1–4]. As the advanced technology
and design of smart mobile devices, a wide-range of new
applications and services for 5G are emerging, such as real-
time online games, virtual reality (VR) and ultra-high-
definition (UHD) video streaming, etc. In particular, in the
2015–2020 Global Cloud Index, Cisco pointed out that
about 50 billion IoT devices (e.g., cell phones, base sta-
tions, IoT boxes, and wearable devices) will be added to
the Internet by 2020 and the continuous generated data
volume will reach 600ZB [2], which will pose a severe
challenge for the development demand of local manage-
ment with high energy consumption and low delay. Cloud
computing usually provides an efficient computing plat-
form for data processing. However, its linear growth of
centralized computing power can no longer match the
explosive growth of massive edge data [5, 6], and the cost
of cloud computing has fallen slowly, while complex net-
work environment makes network delays difficult to
achieve breakthroughs. Large-scale sensor data is uploaded
to the cloud computing center for processing, which is
time-consuming and laborious, leading to the result that the
data processing is not timely and loses the meaning and
value of utilization. Therefore, the real-time and flexible
requirements of data processing bring unsolvable chal-
lenges to the traditional cloud computing model.
In this application context, edge computing emerges as
the current situation requires and has received extensive
attention [6–8]. The edge in edge computing refers to the
& Yan Liu
1119250989@qq.com
1
College of Cyberspace Security and Computer, Hebei
University, East Qiyi Road No. 2666, Baoding 071002, China
2
Key Lab on High Trusted Information System in Hebei
Province, Baoding 071002, China
3
China Telecom (Tianjin), Xiqing, Tianjin, China
123
Wireless Networks
https://doi.org/10.1007/s11276-019-02144-x
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