Quality of Service in Wireless Sensor Networks using Machine Learning

QUALITY OF SERVICE IN
WIRELESS SENSOR
NETWORKS USING MACHINE
LEARNING: RECENT AND
FUTURE TRENDS
An Academic presentation by
Dr. Nancy Agnes, Head, Technical Operations, Phdassistance
Group www.phdassistance.com
Email: info@phdassistance.com

Today's Discussion
Introduction
Wireless Sensor Network
Recent trends in Quality of Service
Conclusion
Future Scope

INTRODUCTION
WSNs, or wireless sensor networks, are extremely creative networks
used for extensive deployments in challenging environments.
Sensing and gathering environmental data, sensor nodes send this
information to the sink node for further processing. The
development of varied WSN applications is a difficult and
demanding task.
When designing a WSN, the designer must take into account a
number of different factors, including localization, routing, Quality
of Service (QoS), security, fault detection, anomaly detection, energy
harvesting, event scheduling, data dependability, node clustering,
and data aggregation (Pundir & Sandhu, 2021).

The most important problem in WSN is QoS, which has
generated a lot of interest in it.
The performance, privacy, and security of the network in
a real-world setting all depend heavily on quality
assurance. According to the classification shown in Fig. 1,
this performance is dependent on the QoS parameter's
priority.
According to network- or application-oriented criteria,
the priority can be determined. A significant amount of
energy is consumed by the network when trying to
improve all QoS factors at once, such as reducing latency
(Rawat & Chauhan, 2021).

WIRELESS SENSOR
NETWORK
Wireless Sensor Networks (WSNs) are self-organizing
systems that allow for multi-hop communication
throughout the network. It is described as "a collection of
scattered mobile sensor nodes utilized for monitoring and
recording the external elements present in the
environment and centrally arranging the obtained data."
Small hardware components called "motes" or "wireless
sensor nodes" are used in these networks' development.
The sensor node perceives the dynamic environment in
which it is placed and collects data for a variety of uses,
including industrial monitoring, tracking fires started by
wildlife, monitoring agricultural practices, and defense
systems (Shafique et al., 2020).

This data, which is in raw format, was sensed by a sensor
node located in a particular cluster.
The cluster head (local aggregator) receives this
information, which is then sent to the base station in
order to conserve network energy.
The gathered data is processed by the base station,
which then derives accurate and valuable information.
Finally, utilising an internet gateway, the base station
transmits this data to the remote locations.

RECENT
TRENDS IN
QUALITY OF
SERVICE
Known as a group of services required by a network for the transfer
of data in the form of packets from source to destination, QoS is a
key parameter of WSN.
It can be assessed using metrics including packet loss, throughput,
latency, jitter, delay, scalability, availability, maintainability, priority,
packet error ratio, reliability, bandwidth, deadline, energy usage, and
periodicity (Mekonnen et al., 2020). Two tiers can be used to classify
a network's quality of service:

Performance level: The deployment phase, layered
architecture, measurability, network, and application
specific QoS metrics are divided into four categories
that are taken into account at the performance level.
Privacy and security level. This level's parameters
address network safety, security, confidentiality, and
integrity concerns.
To meet the QoS requirements for various application
areas, there is a crucial problem. ML offers promise and
is applied at the base station in order to address the
dynamic nature of WSN.=

CONCLUSION
A group of dispersed, autonomous tiny devices known as a wireless
sensor network (WSN) can sense and monitor the physical
conditions of their surroundings.
As per the statistical analysis, among the many uses for WSN are
natural catastrophe prediction, habitat monitoring, medical
monitoring, environmental monitoring, and border surveillance.
WSN performance can be measured in a number of ways, including
localization, Quality of Service (QoS), data aggregation, energy use,
event detection, and anomaly detection (Alsheikh et al., 2014).

The most well-known and important network parameter today that improves the
performance of the network is QoS. Depending on how demand is applied, machine
learning (ML) improves the QoS goal parameter.
There has been very little study done to improve the deadline parameter of QoS, with the
majority of researchers concentrating on the energy efficiency parameter.
The reinforcement learning method is most frequently used in publications to improve
energy efficiency. Finally, the unexplored potential for each QoS parameter has mostly
been studied from a machine learning standpoint since the performance is better in ML
when compared with other methods..

FUTURE SCOPE
In the future, an ensemble ML-based
integrated approach based on artificial
intelligence can be used to enhance a variety
of QoS parameters, including bandwidth,
energy consumption, throughput, delay,
jitter, residual energy, packet loss ratio,
packet error ratio, and packet delivery ratio,
availability, reliability, priority, and deadline.
To improve the overall performance of the
WSN, these parameters can be calculated
utilising cross-layered design.

In order to improve a specific parameter at a given layer, multiple mechanisms can be offered
at different layers of the WSN. Additionally, the heterogeneous traffic must be examined for a
number of network metrics, including dependability, jitter, energy usage, bandwidth, packet
loss, and energy consumption. These have a significant impact on the MAC layer metrics
including channel access delay, congestion factor, and queuing delay.
The network layer can integrate fault tolerance and a trust-based multichannel routing system.
To increase dependability and lessen network congestion, a distortion-based rate adaptation
technique can be implemented at the MAC layer.
By finishing the task by the deadline, the application layer's responsiveness parameter can be
increased. At the application layer, new priority-based algorithms can be added to distinguish
between sensitive and non-sensitive data, maintaining the integrity and reliability of the data.

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