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

Rafiei, Ali; Maali, Yashar; Franklin, Daniel; Safaei, Farzad; Smith, Stephen

doi:10.1109/vtcfall.2013.6692070

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…To recover from or reduce the effects of dynamicallyformed coverage holes of different scales [2], [3], [4], a wide spectrum of topology control schemes has been devised [15], [11]. 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]. Node relocation algorithms can be broadly classified into a number of major categories [4], including Force-based algorithms in which nodes mutually exert virtual repulsive and attractive forces in the radial [10] or angular [16] directions, voronoibased approaches in which movement is based on Voronoi cells formed by the nodes [7], and flip-based and cell-based relocation algorithms, in which nodes are flipped to adjacent cells in a manner depending on cell resolution, node density and decisions of cells' elected head nodes [9].…”

Section: Related Work and Motivationmentioning

confidence: 99%

A geometrical sink-based cooperative coverage hole recovery strategy for WSNs

Rafiei

Maali

Franklin

2015

2015 9th International Conference on Signal Processing and Communication Systems (ICSPCS)

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Unlike sporadic node failures, coverage holes emerging from multiple temporally-correlated node failures can severely affect quality of service in a network and put the integrity of entire wireless sensor networks at risk. Conventional topology control schemes addressing such undesirable topological changes have usually overlooked the status of participating nodes in the recovery process with respect to the deployed sink node(s) in the network. In this paper, a cooperative coverage hole recovery model is proposed which utilises the simple geometrical procedure of circle inversion. In this model, autonomous nodes consider their distances to the deployed sink node(s) in addition to their local status, while relocating towards the coverage holes. By defining suitable metrics, the performance of our proposed model performance is compared with a force-based approach.

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Section: Related Work and Motivationmentioning

confidence: 99%

A geometrical sink-based cooperative coverage hole recovery strategy for WSNs

Rafiei

Maali

Franklin

2015

2015 9th International Conference on Signal Processing and Communication Systems (ICSPCS)

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“…The fuzzification module of the T-S fuzzy models employs the LTs assigned to the input variables defined as follows using (18). The first input variable, λ , uses five LTs, with the notation 5 ...…”

Section: Experimental Validationmentioning

confidence: 99%

“…Particle Swarm Optimization (PSO) algorithms are efficient solvers in these optimization problems. Some recent approaches to the optimal tuning of fuzzy models by PSO algorithms include the heterogeneous multi-swarm PSO applied to the parameters suitable for the sub-spaces of T-S fuzzy models [14], the adaptive neuro-fuzzy inference systems for time series prediction [15], the fuzzy unit commitment problems solving in power systems [16], the optimal tuning of fuzzy relational models [17] and of fuzzy logic relocation models [18], and the optimal tuning of initial states of fuzzy cregression models by Euclidian PSO [19].…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization of fuzzy models for Anti-Lock Braking Systems

Precup

Sabau

Dragoş

et al. 2014

2014 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)

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This paper suggests a Particle Swarm Optimization (PSO) approach to the optimal tuning of fuzzy models for Antilock Braking Systems (ABSs). A set of ten local state-space models of the ABS is first obtained by the linearization of the nonlinear state-space model of the ABS process at ten operating points. The initial Takagi-Sugeno (T-S) fuzzy models are next obtained by the modal equivalence principle, namely by placing the local state-space models of the process in the rule consequents. The optimization problem targets the minimization of the objective function (OF) expressed as the mean squared modeling error, and the vector variable of the OF consists of the feet of the triangular input membership functions. A PSO algorithm solves the optimization problem and gives the optimal T-S fuzzy models. A set of real-time experimental results is included to validate the PSO approach and the optimal T-S fuzzy models for real-world ABS laboratory equipment.

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