A Tuned Fuzzy Logic Relocation Model in WSNs Using Particle Swarm Optimization
Summary (2 min read)
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.
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.
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.
- 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.
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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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...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]....
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