The document presents a novel framework for improving quality of service (QoS) in internet of things (IoT) applications. It proposes a three-layer hourglass architecture with functional modules at each layer to enable adaptability and distribution. A neural network model is used to learn patterns from edge node data to optimize bandwidth and energy usage based on QoS predictions. Evaluation results found the computational model with decision making had better outcomes through increased efficiency. The framework aims to address the complex challenge of providing QoS in heterogeneous IoT environments.

1. PAPER ID: 725
A Novel Framework on QoS In IoT Applications
Applications for Improvising Adaptability and
and Distributiveness
2023 International Conference on Computer Communication
and Informatics (ICCCI ), Jan. 23 – 25, 2023, Coimbatore,
INDIA

2. OBJECTIVE
■ An Internet of Things (IoT) system can also be thought of as an extension to
preexisting heterogeneous network systems with additional and extended nodes
such as Radio Frequency Identification (RFID) devices, sensors, actuators,
machines, Global Positioning System (GPS) and smart mobiles, as well as other
identifiable objects and applications that are accessible via the internet.
■ The use of AI/ML approaches for pattern and speech recognition, image
processing, and other tasks necessitates an expansion to include massive volumes
of data generated by IoT sensors, with a focus on quality of service (QoS) as a
critical consideration in both research and implementation.

3. ABSTRACT
■ The Internet of Things, or IoT, is a broad term referring to a pattern of technology
that can detect, compute, transmit, and store the data that must be exchanged
and stored in end user applications and systems.
■ IoT also performs the functions of gathering the information, processing the
information, analyzing the data.
■ Due to wider range of scope and increase in number of IoT devices, it has become
a critical challenge in meeting the demands of Quality of Service (QoS) in IoT
devices.
■ So there is a great need to access various parameters of QoS based on many
perspectives, likely user specific, operator and network specific, depending on
communication resources, edges nodes and all other enabling technologies.

4. EXISTING SYSTEM
■ In the study, around 5% of the energy generated today is utilized for the Internet
alone, which suggests the crucial need for increasing energy efficiency, bandwidth,
storage optimization, coverage in IoT, and accuracy as some of the important
Quality of Service factors.
■ At a high level, many layers may be created for ease of installation, maintenance,
and support of the IoT architecture.
■ Regardless of the application area, enabling technologies used, and quality
aspects needed, the fundamental functions of every IoT system will stay constant.
■ An IoT system's Quality of Service (QoS) should be built into every aspect of the
system, including software, hardware, user interfaces, and other methods of
interaction.

5. PROPOSED SYSTEM
■ The use of AI/ML approaches for pattern and speech recognition, image
processing, and other tasks necessitates an expansion to include massive volumes
of data generated by IoT sensors, with a focus on quality of service (QoS) as a
critical consideration in both research and implementation.
■ The use of ML approaches in IoT to improve QoS is surprisingly underutilized. So
adoption of a wide variety of ML can be considered as one of the key techniques to
be applied for large data for prediction based research and solutions in the area of
QoS in IoT .
■ An overall structure of the research to propose the research approach, design,
solution to the problem, related functions and functional modules and their
representation and an overall interconnectivity among all the structures and
components is considered as framework.

6. PROPOSED SYSTEM
■ An approach to represent and explain a specific operation(s)methodology adopted
within the defined framework to validate and verify the applicability of the
framework is considered as a model.
■ A framework can have multiple models for different operations, actions and
situations.
■ Adopting to the situation or component or layer or framework to achieve the
defined or intended functionality defined or intended functionality as adoptability.
■ Self-adaptivity is defined as the capacity to adjust the behavior based on the
conditions in the environment.
■ For a multi-model system adaptability is defined as switching between models
based on the context.

7. ADAPTABILITY
■ Adapting may include using an existing algorithm or protocol or standard or
method to achieve the required task or functionality.
■ This research also adapts some of the existing methods for proposing and
defining models for prediction, decision making and security.
■ The IoT systems where the data volume is on a large scale and increasing . There
are five categories of machine learning algorithms based on their similarity of
working styles: regression regression classification classification clustering
association and control.
■ Regression type of learning model is built on the relation between variables which
are iteratively refined based on the prediction errors.

8. SYSTEM MODELS
■ Regression type of learning model is built on the relation between variables which are iteratively
refined based on the prediction errors .
■ Regression as a process and model for learning, it is generally used for predicting the quantity or
size in the range of values based on the input parameters.
■ In the classification model the predefined labels are classified to instances by their properties.
■ Generally the classification is used for predicting a label and classify, for example classify email
as spam or not spam and regression is to predict a label category.
■ Though clustering sounds and looks similar to classification, this model uses the similarity and
relationship among input samples to group or predict the group they belong to .
■ This model is an unsupervised learning style with a working style to predict and group the output
based on their data values and their relationships.

9. SYSTEM MODELS
■ Dimensionality reduction model or algorithm is used to reduce the complexity of
dimension by using a representative set of data so that the result or prediction can
be achieved using minimum data which is a representative of a larger data set.
■ Further, this unsupervised learning method can be used to provide inputs to a
supervised learning and can also be adapted in regression and classification.
■ Generally the machine learning algorithm can be a combination of learning and
working style or a combination of multiple learning and working style algorithms
for solving a problem.
■ For example you can use a combination of classification (which is supervised
learning) and clustering (which is unsupervised learning) to solve a prediction
problem in a practical situation.

10. THE ML TECHNIQUES
■ The ML technique to be chosen would depend on the type of problem and the type of data associated
with the problem.
■ Deep Neural Networks (DNN) from the concept of Artificial Neural Networks (ANN) are learning systems
inspired by learning based biological systems which are basically complex interconnected neurons.
■ In these systems neurons take data from the real world and produce an output [9]. The multi-layer
neural network (MNN) models which are basically the DNN with multiple hidden layers between input
and output layers.
■ The neurons are structured in multiple layers and the layers in the previous layer feed data or signals to
the neurons in the next layers.
■ Feedback neural networks are described as those that send data back to the previous layer, and feed-
forward networks are defined as those that send data forward.
■ Feed-forward networks are commonly used for solving static classification problems which may be
classified under semi-supervised learning style and are limited to providing static output with a static
mapping of corresponding inputs.

11. PROPOSED CROSS LAYER QoS FRAME
WORK
■ It is necessary to do research and develop solutions for Quality of Service (QoS) in the Internet of
Things (IoT).
■ For both technological and commercial reasons, quality of service (QoS) is essential. One of the
main problems identified for the IoT environments is to provide quality of service for the complex
and heterogeneous IoT environments there by making the IoT systems more adoptable, stable
and dependable for many more domain areas.
■ The problem is complex due to the heterogeneity of devices, volume of devices and scale of
transactions .
■ Defining and proposing a new cross-layer framework to enable QoS in heterogeneous IoT settings
is the primary goal of this study.
■ From the reviews it is found that many of the QoS implementations in IoT are achieved by
different functional modules or algorithms or by tweaking new protocols or standards.
■ This study shows that QoS in IoT is an important research field in need of more study and
solutions as an entire architecture and framework for dynamic and heterogeneous IoT settings.

12. PROPOSED ARCHITECTURE

13. PROPOSED ARCHITECTURE
■ The proposed architecture and cross-layer QoS framework and architecture of the
hourglass model considers defined functional modules for adaptability and distribution.
■ The suggested study uses a three-layer hourglass-shaped design (with a thin neck) to
demonstrate the limited control over network layer functions and the significant
functional components at the end levels.
■ It is possible to manage and regulate functional modules of the internet of things at
application and perception levels.
■ These functions are typically implemented at two end layers in the majority of internet
of things systems.
■ The functions of the three-layer architecture are discussed briefly in order to locate the
cross-layer functions that are flexible and distributable and to have a better
understanding of the functions that are involved in the execution of the quality of
service framework.

14. LAYERS OF ARCHITECTURE

15. IMPLEMENTATION OF DATA CAPTURE
MODEL

16. NEURAL NETWORK MODEL
■ Edge node data in an IoT environment with users, systems, and QoS needs is chaotic and changes
depending on the time, place, and context. A learning and prediction model must be built for edge
node data in the setting of a wide range of QoS situations that have never been observed before.
■ A choice may be made to optimize bandwidth and energy needs in the IoT system based on the
prediction findings at the resource-constrained perception layer.
■ An IoT edge node's EP is a collection of event patterns that have been gathered over a period of
time that spans days, hours, minutes and milliseconds. When a sequence of events occurs at
regular intervals, it is called an event pattern (EP) (t1, t2).
■ The edge nodes might be anything from a CCTV camera to a mobile device to a sensor. An IoT
environment's event patterns may be represented as a collection of event patterns known as EP =
'E1, E2 En' The pattern at interval ti is represented by the event pattern Ei, which is represented
above as a pair of numbers like (Oi, Li).
■ In this case, the sample values for each event instance Ei are as follows: Ei = Object Type, Geo
Location,

17. RESULTS

18. CONCLUSION
■ The study will investigate different IoT QoS implementations, as well as ongoing
and future QoS-related IoT research concerns and difficulties.
■ A lesser-addressed aspect of QoS may be found in the cross-layer QoS model's
decision-making and flexibility, as shown by the evaluations and investigations.
■ It has been proven that the computational model with decision-making has better
outcomes due to better transceiving and storage efficiency, which has led to
increased energy efficiency and bandwidth optimization.
■ Selected QoS characteristics such as bandwidth energy, latency, and security are
measured using a suggested framework by various computer models, resulting in
an overall implementation of operational efficiency.

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20. THANK YOU