Evaluation of Different Machine Learning

1. M. Tech
Introductory seminar
ON
Akansha Dhoke
Guided by-
Prof. Rahul Nawkhare
Presentation by-
M.TECH (EN)
“Evaluation of Different Machine Learning
Algorithms in Wireless Sensor Networks ”

2. CONTENT
 Problem Statement
 Project Objectives
 Introduction
 Literature Review
 Block Diagram
 Instruments details, software details
 Expected Output
 Future Scope and Application
 Plan of work
 References

3. PROBLEM STATEMENT
Wireless sensor networks contain the following issues
Self-organization
Inadequate Battery Power
Harshly guarded memory
Lossy wireless communication

4. PROJECT OBJECTIVE
1. To evaluate different Machine Learning Algorithms in Wireless
Sensor Networks.
2. To optimize the quantity of information transmitted over the network
using machine learning algorithms.
3. To detect the redundancies, and to represent them by means of
mathematical models.
4. To increase sensor node life time. To use mathematical models
instead of the raw data can allow to substantially reduce the amount of
data transmitted in the network, and thus to extend application lifetime.

5. INTRODUCTION
• A wireless sensor network (WSN) is composed typically of multiple
autonomous, tiny, low cost and low power sensor nodes.
• The sensor nodes could be equipped with various types of sensors, such as
thermal, acoustic, chemical, pressure, weather, and optical sensors.
• Developing efficient algorithms that are suitable for many different
application scenarios is a challenging task.
• In particular, WSN designers have to address common issues related to data
aggregation, data reliability, localization, node clustering, energy aware
routing, events scheduling, fault detection and security.
• New uses and integrations of WSNs, such as in cyber physical systems
(CPS), machine-to-machine (M2M) communications, and Internet of things
(IoT) technologies, have been introduced with a motivation of supporting more
intelligent decision-making and autonomous control.
• Here, machine learning is important to extract the different levels of
abstractions needed to perform the AI tasks with limited human intervention.

6. LITERATURE REVIEW
1] Amjad Mehmood, Zhihan Lv, Jaime Lloret& Muhammad Muneer
Umar 2017, ‘ELDC: An Artificial Neural Network based Energy-
Efficient and Robust Routing Scheme for Pollution Monitoring in
WSNs’
Amjad Mehmood et al. (2017) presented an Artificial Neural Network
(ANN) according to the energy-efficient and robust routing system for
wireless sensor networks. The network was trained in this method over 39
massive data set including every scenario to create the network with high
reliability and flexibility to the environment. Besides, the group-based
method has been utilized to enhance an entire network’s e life-span,
anywhere groups may be varied in size. Effective threshold values were
given by an artificial neural network for the head node selection of each
group and a cluster head has been chosen according to the backpropagation
method that was provided an efficient and robust group organization.

7. LITERATURE REVIEW
2] Mohammad Abu Alsheikh, Shaowei Lin, Dusit Niyato, Member,
IEEE, and Hwee-Pink Tan, Senior Member, IEEE, Machine Learning
in Wireless Sensor Networks: Algorithms, Strategies, and
Applications
In this paper, an extensive literature review over the period 2002–2013 on
such studies was presented. In summary, adopting machine learning
algorithms in wireless sensor networks has to consider the limited
resources of the network, as well as the diversity of learning themes and
patterns that will suit the problem at hand. Moreover, numerous issues are
still open and need further research efforts such as developing lightweight
and distributed message passing techniques, online learning algorithms,
hierarchical clustering patterns and adopting machine learning in resource
management problem of wireless sensor networks.

8. LITERATURE REVIEW
3] Chagas, S, Martins, J & de Oliveira, L 2012, ‘An approach to
localization scheme of wireless sensor networks based on artificial
neural networks and genetic algorithms’,
Chagas et al. (2012) have proposed ANNs method to localization process in
WSN throughout the ANNs structures regulation applying Genetic
Algorithms (GAs). In a genetic code, a feedforward ANNs residents contain
their structure that will be enhanced in twenty generations. Using the
artificial neural network training process, every individual was estimated and
additional computation of its root mean square error for every the testing set
has also been performed. Here, the nodes were localized by the RSSI
measurements that have been applied as the inputs of ANN.
Shareef et al. (2008) compared the three various families of NN such as
Multi-Layer Perceptron (MLP), Recurrent Neural Networks (RNN), and
Radial Basis Function (RBF). These network performance has also been
contrasted with Kalman Filter’s two variants that can be conventionally
exploited for the localization process. The computation and memory resource
necessities were also compared.

9. BLOCK DIAGRAM
Fig.1.Block diagram

10. INSTRUMENTS DETAILS & SOFTWARE DETAILS
1. SVM Vs Random forest technique
2. PYTHON

11. EXPECTED OUTPUT
1. Machine learning methods can then be used to discover important correlations in
the sensor data and propose improved sensor deployment for maximum data
coverage.
2. The performance of sensor networks on given tasks without the need for re-
programming is improved.
3. This will minimize the communication overhead for detecting the structure of the
sensor data. Collectively, these results serve as an important step in developing a
distributed learning framework for wireless networks using linear regression
methods.

12. FUTURE SCOPE & APPLICATION
As the research is going in field of WSN & ML, the group-based method has been
utilized to enhance an entire network’s e life-span, anywhere groups may be varied
in size. Effective threshold values were given by an artificial neural network for the
head node selection of each group and a cluster head has been chosen according to
the backpropagation method that was provided an efficient and robust group
organization.
The main advantages of utilizing this algorithm are the good fitting results, and the
small overhead of the learning phase.
We also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.

13. PLAN OF WORK
1) Literature survey.
2) Study of present mechanisms used in Prosthetic hand.
3) Design of linkages.
4) Design of new mechanism.
5) Modeling of an mechanism
6) Analyze the mechanism.

14. REFERENCES
[1] Amjad Mehmood, Zhihan Lv, Jaime Lloret& Muhammad Muneer Umar 2017, ‘ELDC: ‘An
Artificial Neural Network based Energy-Efficient and Robust Routing Scheme for Pollution
Monitoring in WSNs’, IEEE Transactions on Emerging Topics in Computing, DOI:
10.1109/TETC.2017.2671847, pp. 1-6
[2] Mohammad Abu Alsheikh, Shaowei Lin, Dusit Niyato, Member, IEEE, and Hwee-Pink Tan,
Senior Member, IEEE, Machine Learning in Wireless Sensor Networks: Algorithms, Strategies,
and Applications, IEEE COMMUNICATION SURVEYS & TUTORIALS, VOL. 16, NO. 4,
FOURTH QUARTER 2014.
[3] Chagas, S, Martins, J & de Oliveira, L 2012, ‘An approach to localizationscheme of wireless
sensor networks based on artificial neural networks and genetic algorithms’, in 10th
International Conference on New Circuits and Systems, pp. 137–140
[4] Madiha Razzaq, Devarani Devi Ningombam & Seokjoo Shin 2018, ‘Energy efficient K-
means clustering-based routing protocol for WSN using optimal packet size’, International
Conference on Information Networking (ICOIN), pp. 632-635. 36.
(cont’d)

15. [5] Mary Livinsa, Z & Jayashri, S 2015, ‘Localization with beacon based support
vector machine in Wireless Sensor Networks’, 2015 International Conference on
Robotics, Automation, Control and Embedded Systems (RACE), pp. 1-4.
[6] SamareshBera, SudipMisra, Sanku Kumar Roy & Mohammad S. Obaidat
2018, ‘Soft-WSN: Software-Defined WSN Management System for IoT
Applications’, IEEE Systems Journal, vol. 12, no. 3, pp. 2074 – 2081.
[7] Farzad Tashtarian, Yaghmaee Moghaddam, MH, Khosrow Sohraby & Sohrab
Effati 2015, ‘ODT: Optimal deadline-based trajectory for mobile sinks in WSN: A
decision tree and dynamic programming approach’, Computer Networks, vol. 77,
pp. 128-143

16. Thank you