# A Tuned Fuzzy Logic Relocation Model in WSNs Using Particle Swarm Optimization

## Summary (3 min read)

### Introduction

- Personal use of this material is permitted.

### III. METHODS AND ASSUMPTIONS

- With the given sensing range Rs and transmission range Rc, sensor nodes are modeled as unite disk graphs (UDG) and are bi-directionally connected when they reside within their one another’s ranges.
- Nodes are randomly deployed in 2D rectangular field of [xmin xmax] × [ymin ymax] with the uniform distribution.
- Nodes’ locations are known by either centralized or distributed localization algorithms [19], [20].
- Circular zone around the node is defined as a circle with radius of Rzone (Rzone = k · Rc) with the node in the center of circle and are used to obtain the fuzzy parameters from nodes’ neighbours residing in the given zone via PSO.

### A. Fuzzy Logic Parameters

- Fuzzy rule-based systems are applied in a variety of research areas [21], [22].
- Similar to [10] two different fuzzy inference systems are used: fuzzy radial pair force and fuzzy angular force.
- Figure 1 is brought as the example of respectively tuned radial and angular membership functions for angular strategy A1, boundary condition B2 and movement strategy FRAM .
- Thus, parameters can be tuned based on one or linear combination of the metrics.
- PSO is applied for each selected node with a zone-range of Rzone (Rzone = k · Rc) around selected node by taking account node’s neighbours residing within its Rzone range.

### B. PSO structures

- The constriction coefficient PSO used similar to the [23].
- The parameter k in the equation 6 controls the exploration and exploitation.
- Each particle consists of two arrays, which one is related to the memberships of the pair force fuzzy systems and another one is related to the memberships of the angular force fuzzy systems.
- Each fuzzy system has 5 memberships and each membership is specified by its mean and variance, therefore each array has 10 cells.

### C. Boundary Strategies

- In relocation algorithm, behaviour of moving nodes while approaching to the given area’s boundaries (i.e. [xmin, xmax]× [ymin, ymax]) with respect to different boundary conditions should be taken into account.
- Boundary strategies applied in [10] are adopted here which are non-stop at boundary, stop at boundary, wrap around.
- (B1)-In non-stop at boundary, regardless of boundaries of given area, nodes relocate towards their new locations without limit.
- (B2)-In stop at boundary, nodes stop at boundaries of given area and their movements are limited if their new computed locations are beyond the area boundaries.
- (B3)-In wrap around, according to toroidal surface, nodes are wrapped around to other sides if new computed locations go beyond the area boundaries.

### D. Angular Force Strategies

- Angular force strategies in [10] based on exerted forces from node’s neighbours can be considered as:(A1)-Smallest Angular Movement Strategy, among exerted angular forces from node’s neighbours, the one is selected that causes smallest node angular movement.
- (A2)-Closest Neighbour Movement Strategy, among exerted angular forces from nodes’ neighbours, the closest neighbour is selected as the exerting angular nodes.

### E. Fuzzy Node Movement Algorithms

- In their model, similar to [10], fuzzy node movement algorithms are as: Fuzzy radial movement (FRM)-.
- Nodes are mutually affected by radial force from their neighbours.
- The amount of node movement is related to overall push/pull virtual forces from their in-range neighbours.
- Nodes exert a force to their in-range neighbours depending on aforementioned angular force strategies.
- FRM then FAM (FRAM)- FAM is applied to result of FRM in consecutive iterations.

### IV. PERFORMANCE METRICS

- The performance metrics presented are: Percentage of Coverage(C)-Suppose that a 2-D rectangular area of [xmin, xmax] × [ymin, ymax] is divided into grid cells.
- The coverage of the given grid cells is defined as the number of nodes covering the cells’ corner coordinates zi=(xi, yi).
- Thus, percentage of 1-coverage is defined as the ratio of grid cells within range of at least one sensor node to the total number of area’s grid cells.
- This metric illustrates how an efficient relocation algorithms are able to cover the given area.

### V. RESULTS

- The proposed node relocation algorithm was simulated by Matlab and N=100 nodes with the transmission and sensing range of Rc=Rs=15 are distributed uniformly in the rectangular 2-D space of [−100 100] × [−100 100]m2.
- For the sake of brevity and page limit, only the result based on tuned fuzzy parameters with (ω1, ω2, ω3) = (0, 0, 1) (Equation 4), with zone range Rzone=1 ·Rc and A1 and B2 and movement algorithm of FRM are presented in Figure 3.
- The rest of the results more or less follow the same trends.
- Figure 3 also shows that proposed model either outperform or is comparable to DSSA for different movement strategies, even DSSA benefits from expected global node density.
- It should be noted that depending on different linear combinations of weights (ω1,ω2, ω3) (Equation 4), performance of relocation algorithms with different movement strategies FRM, FAM, FRAM and FARM can vary.

### VI. CONCLUSION AND FUTURE WORK

- A tuned fuzzy logic relocation model is proposed in which its fuzzy parameters are tuned either globally or locally via particle swarm optimization technique so proper amount of virtual forces can be exerted on nodes.
- The results show that their proposed model either outperform or closely matches the performance DSSA in terms of percentage of coverage, uniformity and average movement even the tuned parameters are obtained locally within nodes’ transmission range Rc.
- As a possible extension, by using light PSO computations, instead of only the first relocation iteration, fuzzy parameters can continuously be tuned and modified in consecutive iterations in each node according to the neighbours behaviour in the given iteration.

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### Cites background from "A Tuned Fuzzy Logic Relocation Mode..."

...By providing a degree of control over the coverage and connectivity of networks, topology control schemes using distributed node relocation algorithms are able to maintain or recover network integrity in networks subject to dynamic topological perturbation [6], [7], [16], [9], [4], [10], [17]....

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##### References

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...As promising surveillance, detection and tracking solutions for many applications, wireless sensor networks (WSNs) are becoming more available and ubiquitous in recent years [1], [2]....

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### "A Tuned Fuzzy Logic Relocation Mode..." refers background in this paper

...As promising surveillance, detection and tracking solutions for many applications, wireless sensor networks (WSNs) are becoming more available and ubiquitous in recent years [1], [2]....

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1,792 citations

### "A Tuned Fuzzy Logic Relocation Mode..." refers background in this paper

...Nodes’ locations are known by either centralized or distributed localization algorithms [19], [20]....

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### "A Tuned Fuzzy Logic Relocation Mode..." refers background or methods in this paper

...For k ∼ 0, fast convergence is expected and for k ∼ 1 we can expect slow convergence with high degree of exploration [23]....

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...In this paper, the constriction coefficient PSO used similar to the [23]....

[...]