A probabilistic fuzzy approach for sensor location estimation in wireless sensor networks

Kadkhoda, Mahnaz; Mohammad-R, Akbarzadeh Totounchi; Yaghmaee, Mohammad Hossein; Davarzani, Zohreh

doi:10.1109/fuzzy.2010.5584410

Cited by 8 publications

(3 citation statements)

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“…In deterministic methods, the location information is driven by a solution to some analytical or approximate problems through some deterministic mappings without a precise account of any uncertainty as opposed to probabilistic, fuzzy, or statistics-based models, which encounters the first class, k-means like-matching in fingerprint association, or deterministic range intersection method [55]. Moreover, non-deterministic methods include Bayesian-like reasoning for fingerprint matching, Kalman filtering, belief propagation approaches [57][58][59][60][61][62], joint probability distribution using factorization on a graphical model [36,63,64], and various soft-computing related techniques [65][66][67]. In general, if knowledge regarding the distribution is available, then the probabilistic techniques outperform the deterministic ones and are preferred.…”

Section: Constraints On Positioning Algorithmsmentioning

confidence: 99%

A survey of fuzzy logic in wireless localization

Alakhras

Oussalah

Hussein

2020

J Wireless Com Network

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Positioning and location estimation of targets either for mobile or stationary nodes is very important, especially after the astonishing developments of the wired and/or wireless communications, as well as the popularity of handheld devices. To assist in accessing services or managing resources, numerous users, application developers, and service providers require accurate position information. Owing to the boom of multisensory systems, wireless technology, and autonomous systems, many positioning systems have been proposed. These positioning systems differ based on the adopted technology, type of sensory information, communication protocol, algorithm, and accuracy level. These different approaches impose a non-negligible amount of uncertainty and challenges; in such case, fuzzy-logic-based localization systems offer an attractive and feasible approach to overcome some of the reported challenges and handle uncertainties, depending on its proven mathematical framework for handling vagueness and uncertainty. Intuitively, fuzzy sets and fuzzy inference systems are initially intended to incorporate granularity and flexibility of human knowledge, which characterize complex system behaviors without explicitly requiring precise mathematical models or when a complete and exhaustive list of comprehending factors are ill known. This study demonstrates the benefits of fuzzy sets, fuzzy logic, and fuzzy inference systems in wireless positioning problems. Notably, as a way to benefit both the academic community and practitioners of system positioning, we review and investigate various fuzzy-related techniques and methodologies. To the best of our knowledge, this is the first comprehensive review that focuses solely on fuzzy-based positioning systems.

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Section: Constraints On Positioning Algorithmsmentioning

confidence: 99%

A survey of fuzzy logic in wireless localization

Alakhras

Oussalah

Hussein

2020

J Wireless Com Network

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“…Kadkhoda et al, [17]modeled the problem of location estimation in WSN for the first time by a Probabilistic Fuzzy Logic System (PFLS). The concept of Probabilistic Fuzzy Logic was first introduced in 2001 by Meghdadi and Akbarzadeh [18] as a way of representing and/or modeling existing uncertainty in many real world systems.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Handling for Sensor Location Estimation in Wireless Sensor Networks using Probabilistic Fuzzy System

Kadkhoda¹,

Akbarzadeh-T²

2013

IJAIASD

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With the increasing use of location-sensitive applications, a variety of localization techniques in wireless sensor networks (WSN) have been introduced to date; each aiming to improve accuracy of location estimation. However, most of these studies do not consider the factor of uncertainty which is naturally present in the localization problem. Focusing on range-free localization, this research examines RSS-based location estimation models. Due to environmental conditions, RSS can be influenced by several stochastic factors at the receiving node. In this research, we propose an algorithm using Probabilistic Fuzzy Logic Systems (PFLS) for the first time to address randomness and uncertainty in the location estimation problem. After optimizing PFLS by Genetic Algorithm (GA), the performance of both PFLS and the GA-optimized PFLS are compared with several other competing strategies under varying noise levels. Experimental results clearly indicate the improved efficiency of the proposed algorithm, particularly at higher noise levels.

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“…A few more existing works on fuzzy system based location estimation have been done in [6] [7]. All of these works model the range component such as RSSI or ToA and weight as set of rules.…”

Section: Related Workmentioning

confidence: 99%