Instrumentation & Measurement Magazine 25-7 - 32

Feedback Learning in SoftwareDefined
Mobile Network for
Resource Aware Load Balancing
Under Fault Tolerance Conditions
Usman Ahmed, Jerry Chun-Wei Lin, and Gautam Srivastava
F
ault-tolerant-based load balance resource allocation
can help in the explosive data flow in the mobile
network. System parameter function, system load factors,
network-level configuration, network characteristics,
and routing parameters are all affected by volatile data. In today's
era of Big Data, one of the most important areas of study
in mobile communications is how to adapt to traffic flow. The
accessibility of load balancing sensors helps eliminate delays,
which in turn helps reduce energy consumption and shorten
execution time. In this research, we present a load balancing
method for software-defined mobile networks (SDMNs) that
is known to maximize the utility of the sensors by considering
both the processing power of the sensors and the requirements
of their sources. A proactive action technique that takes advantage
of the wireless facility is proposed, and a wireless load
balancing design solution using the learning method is then
utilized in the designed framework. To achieve high resource
utilization, we use a method based on convergence. Intelligent
resource utilization by multiple sensor devices can help
to cope with high bandwidth applications such as multimedia
in mobile networks. Compared to conventional methods, the
model has the potential to achieve better results.
Introduction
The growth of heterogeneous applications has been enabled
by the Internet of Things (IoT) [1] and distributed computing
[2]. Connecting heterogeneous devices with the ability
to communicate directly with the network is what the IoT
will be in the future. The next generation of networks will
include billions of objects (i.e., sensor networks) connected
to the Internet. As a result, huge amounts of data will be
generated, and data transmission issues will arise. For
instance, home applications, traffic flow analysis, and irrigation
systems contain multiple sensors or nodes; sensors/
nodes evaluate the environment in real-time; cloud-based
applications can store, process, and update real-time data
for better efficiency.
Geographically centralized data centers host dispersed applications.
Distributed applications can be efficient and fast.
32
Cloud, Fog, and IoT can increase the efficiency, uptime, and
price of distributed applications. These factors benefit compute
resources, storage latency, and networking. With minimal
latency, data can interact quickly. IoT gateways consist of
smaller gateways connected via wireless sensor networks. Future
cloud computing will connect billions of intelligent items
and IoT gateways.
SDMN can leverage 5G networks and artificial intelligence
(AI) to support high data transferability. Efficient
resource allocation can help improve efficiency, reduce costs,
and mitigate security and privacy threats. This is due to the
trusted sharing of data and computing environments. Load
balancing resource allocation with the learning environment
can help address the challenges posed by resilient mobile
networks [3].
In real-world applications, the Internet of Things (IoT) is
integrated with human-based services using next-generation
SDMN (5th generation wireless networks) and software as services
(SAS). To structure heterogeneous networks, an SDMN
uses a task-based approach as follows:
◗ Transport: terminals, sensors, and nodes create a large
volume of data.
◗ Allocation: centralized and decentralized data centers for
computation and storage.
◗ Processing: effectively gathering information.
SDMN can be reconfigurable wireless networks that efficiently
collect necessary data [4]. By using AI in SDMN, it
can provide significant security improvements. SDMN has
the potential to address both the networking and security
challenges facing IoT networks. IP networks are difficult to
maintain because of reconfiguration. SDMN is a network architecture
designed to solve these difficulties. Reconfigurable
wireless networks (RWNs) consist of nodes that are capable of
performing the function of the software. The nodes must be
reconfigured, the hardware must be controlled, and the protocol
associated with each node must be changed. To perform
the reconfiguration, the node task and node reconfiguration
are included and excluded. This also includes updating routing
protocols.
IEEE Instrumentation & Measurement Magazine
1094-6969/22/$25.00©2022IEEE
October 2022

Instrumentation & Measurement Magazine 25-7

Table of Contents for the Digital Edition of Instrumentation & Measurement Magazine 25-7

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