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-177005, India *ergaurav209@yahoo.co.in

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

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.

Typical Sensor Network

(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

Wireless Sensor Node . 22 mm 16 mm

Applications of WSNs

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

Motivation Where ???? . Event Occur

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

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

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

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

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

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

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

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×

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 :

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

Contd…

Simulation Results and Discussions

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

Nodes Distribution

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

Effect of Anchor Node Density

Effect of Network Connectivity

Frequency of Error Occurrence

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.

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