1. Fuzzy Logic Based Range Free Localization Using Bacterial Foraging Optimization in Wireless Sensor Networks
Paper ID – VC000133
Gaurav Sharma*, Ashok Kumar, Prabhleen Singh
Department of Electronics and Communication Engineering,
National Institute of Technology
Hamirpur, Himachal Pradesh , India

2. Outline Introduction of WSN Motivation Related Works Proposed Method
Results
Conclusion
Future Work
References            

3. Introduction of WSN
Wireless sensor network is the system of small, low powered networked sensing devices deployed over an area of interest to monitor interesting events and perform application specific task in response to them.
WSN is composed of distributed independent sensors called nodes to monitor the physical conditions i.e. Pressure, Heat ,Air pollution , Velocity, Temperature etc.

4. Typical Sensor Network

5. (Application specific)
Node Hardware
1Kbps - 1Mbps,
3-100 Meters
128KB-1MB
Limited Storage
Transceiver
8-bit, 10 MHz
Slow Computations
Memory
Embedded
Processor
Sensors
Battery
Used for Supervision
(Application specific)
Limited Lifetime
Energy Harvesting
System

6. Wireless Sensor Node .
22 mm
16 mm

7. Applications of WSNs

8. Challenges in WSN Energy Constrain Scalability Fault Tolerance
Network Topology
Efficient Data Routing
Time Synchronization
Self Configuration
Responsiveness
Heterogeneity
Security
LOCALIZATION

9. Motivation
Where ???? .
Event Occur

10. Localization in WSN What? Why?
To determine the physical coordinates of a group of sensor nodes in a wireless sensor network (WSN).
Due to application context, use of GPS is unrealistic, therefore sensors need to self-organize a coordinate system.
Why?
To report data that is geographically meaningful.
Services such as routing rely on location information; geographic routing protocols; context-based routing protocols, location-aware services.
Low cost of nodes allows massive scale & highly parallel computation
Each node has limited power, limited reliability, and only local communication with neighbors

11. Classifications of Localization.
Node Connectivity Topology Based
Single Hop and Multihop
Range information Based
Range Based and Range Free
WSN Localization Algorithms
Anchor Information Based
Anchor Based and Anchor Free
Computational Model Based
Centralized and Distributed
Mobility Based
Static and Dynamic

12. Range Based and Range Free Loc. Algorithms
Range Based Techniques
Range Free Techniques
Highly Accurate
Less Accurate Comparatively
Need Additional ranging device
Don’t need additional hardware device
Easily affected by multi-path fading and noise
More robust .
Extra Hardware requirement ,Cost ,Complexity ,Noise sensitivity and Additional energy consumption are the important drawbacks of Range based methods.
Range Free Techniques

13. Problems in Existing Range Free Algorithms
We investigated a number of range free localization algorithms to formulate the problems in existing techniques. Few of them are :-
Centroid Localization
CPE ( Convex Position Estimation)
WCL (Weighted Centroid Localization)
Mid Perpendicular Algorithm

14. Low accuracy in both of the cases.
Contd... .
Centroid Technique
CPE Technique
Low accuracy in both of the cases.

15. Mid-Perpendicular Algorithm
Contd... .
Mid-Perpendicular Algorithm
WCL
More complex, highly unstable and some times shows large errors.

16. FLS and IWO based node localization
Proposed Scheme
FLS and IWO based node localization
Fuzzy Logic System (FLS):
Bacterial foraging Optimization (BFO):-
The process of BFO is divided in four steps:-
(i) Chemotaxis
(ii) Reproduction
(iii) Swarming
(iv) Elimination and dispersal operation
Crisp Data
Crisp Input
Inference Engine
Defuzzifier Unit
Output Scaling Factor(s)
Fuzzy Rule Base
Fuzzifier
Unit
Input Scaling Factor(s)
Fuzzy Data
Crisp Output

17. Anisotropic Property
Some existing localization techniques used perfect circular radiation pattern of the radio range. But in practical it is not like a perfect circle. So, the radio irregularity is also the main aspect to be studied for the analysis of an algorithm for practical scenarios.
DOI =0
DOI = 0.05
DOI = 0.2
RSS=Sending Power - DOI Adjusted Path Loss+ Fading
where DOI Adjusted Path Loss = Path loss×

18. Assumptions and Steps of Algorithm
Radio propagation is considered as circular and transmission ranges of all nodes are considered same.
Anchor nodes’ positions are known either through GPS or by manual deployment.
Steps of Algorithm:
Each target node maintained a list of RSS values from their adjacent anchor nodes after receiving the beacon signal.
Check whether the number of adjacent anchor nodes to a particular target node ≥3 or not, if ≥ 3, then the target node is considered as localizable node.
Calculate the edge weights between anchor nodes and each target node according to their RSS value. These edge weights are modeled using FLS.
After calculating the edge weights ,calculate the positions of the target nodes according to the formula given as follows :

19. Fuzzy Modeling with edge weight optimization using BFO
When anchor nodes transmit beacon signal throughout the network, each target node collects the beacon signal containing RSS value and location of the anchor.
Input variable in rule base of Mamdani fuzzy model is RSS value of the anchors and it is taken in the interval of [0, RSSmax], where RSSmax is100 dB (i.e. maximum RSS value).
Five membership functions are used to map input variable RSS i.e. VLOW, LOW, MEDIUM, HIGH, VHIGH.
The output variable is edge weight of the anchor node to target node and it is taken in the interval of [0, wmax], where wmax is 1 (i.e. maximum edge weight).
Bases of the output variable (edge weight) are optimized using IWO.
Rule base of edge weights

20. Contd…

21. Simulation Results and Discussions

22. Simulation Parameters
Sr.No.
Parameters
Optimal Value 1.
Number of bacterial population 40 2.
Number of maximum iterations 50 3.
Chemotactic steps 10 4.
Number of reproduction steps 5 5.
Swimming length 3 6.
Number of elimination-dispersal events 4 7.
Probability of elimination-dispersal events
0.35 8.
Depth of attractant (datt)
0.1 9.
Width of attractant (watt)
0.2
10.
Height of repellent (hrep)
11.
Width of repellent (wrep)
12.
Steps taken in random direction
0.01
13.
DOI
0.02

23. Nodes Distribution

24. Formulation and Results Comparisons
Localization error (LE) and average location error (ALE) is calculated respectively, according to the distance between actual coordinates of target nodes and estimated coordinates of target nodes.
Range Free Methods
Max. Loc. Error
Min. Loc. Error
Avg. Loc. Error
Centroid
3.924
0.945
2.434
Weighted Centroid
3.247
0.593
1.920
RFBBO+Fuzzy
1.731
0.0247
0.878
RFIWO+Fuzzy
1.084
0.0102
0.547
RFBFO+Fuzzy
0.947
0.0024
0.474

26. Effect of Anchor Node Density

27. Effect of Network Connectivity

28. Frequency of Error Occurrence

29. Conclusion and Future Scope
After analysis ,we found that RFBFO+fuzzy scheme performed well in almost every aspect .
It improved the localization accuracy about 45 % on average with existing range free schemes.
It may give better performance in real scenario compared to other schemes.
It is a little bit more time consuming and has more complexity than other algorithms.
Future Scope:
The proposed method can be extended for 3D space with consideration of anisotropic property of the propagation.

30. References             
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31. Thank You..... 