1. Wireless Sensor Network Localization with Neural Networks
Student name: Amit Geron
Supervisors: Dr. Zvi Lotker
Eng. Yaniv Weizman

2. Outline
Introduction
Project Goals
Implementation
Experiments

3. Outline
Introduction
Project Goals
Implementation
Experiments

4. Introduction What is a WSN?
What is Localization and why is it an important issue?
What are Neural Networks?

5. Introduction - WSN Unique characteristics of a WSN include:
Limited power they can harvest or store
Ability to withstand harsh environmental conditions
Ability to cope with node failures
Mobility of nodes
Dynamic network topology
Communication failures
Heterogeneity of nodes
Large scale of deployment
Unattended operation

6. Introduction - WSN
Topology

7. Introduction - Localization
Localization- Location Estimation.
Many methods, mainly distinguished according to either one-hop / multi-hop and centralized / distributed estimation algorithm.
Main disadvantages- high complexity, long headers, many messages between units.

8. Introduction – Neural Networks
Neural Network- A graph with weighted edges and linear and non-linear functions at nodes.
3 main usages:
Function Approximation.
Classification.
Data Processing.
Training set has a major effect over performance.

9. Outline
Introduction
Project Goals
Implementation
Experiments

10. Project Goals
Neural networks computation & learning algorithms concept.
High and Low level design for algorithmic implementation under sensors networks characteristics.
Algorithm Implementation within real sensor network framework.

11. Outline
Introduction
Project Goals
Implementation
Experiments

12. Implementation
The tools and methods that have been used to implement the project:
Motes and Sensors – Tmote Sky.
Communication method – Local.
Measurement method – RSSI.
IDE – Sentilla.
Neural Network:
encog-java package by Jeff Heaton.
Function Approximation.
FeedForward.
BackPropagation.
Multiple hidden layers.

13. Implementation – Messages structure
Tmote
Tmote
Tmote
Tmote
Tmote
Sampled Tmote
Gateway
Tmote
Tmote
Neural Network

14. Implementation – Planned Vs. Implemented
S R G

15. Outline
Introduction
Project Goals
Implementation
Experiments

16. Experiments Performance and error rate will be
examined under the variation of:
Training set Characteristics:
Number of samples.
Samples’ Locations.
WSN Characteristics:
Number of motes.
Area size.
NN Characteristics:
Number and size of hidden layers.
Classification Vs. Approximation.

17. Experiments - Example

18. Experiments - Example

19. Experiments - Example