Quantized lower bounds on grid-based localization algorithm for wireless sensor networks

Al-Qaisi, Aws; Alhasanat, Abdullah; Mesleh, Abdelwadood; Sharif, B.S.; Tsimenidis, Charalampos C.; Neasham, Jeffrey

doi:10.1007/s12243-016-0494-y

Cited by 14 publications

(5 citation statements)

References 21 publications

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“…CRB is widely used in wireless sensor network (WSN) node localization performance evaluation [51] [52]. In an application of a RSSI based ranging measurement localization system [36], the authors proposed a novel iterative tree search algorithm (I-TSA) in comparison with the maximum likelihood estimator (MLE) and multidimensional scaling (MDS) and proposed CRB as a performance reference to show the advantages and limitations of proposed new algorithms and systems.…”

Section: System Performance Evaluationmentioning

confidence: 99%

“…In an application of a RSSI based ranging measurement localization system [36], the authors proposed a novel iterative tree search algorithm (I-TSA) in comparison with the maximum likelihood estimator (MLE) and multidimensional scaling (MDS) and proposed CRB as a performance reference to show the advantages and limitations of proposed new algorithms and systems. Similarly, [52] introduced a Quantized Cramer Rao Bound (Q-CRB) method to adapt the CRB, to characterize the behavior of location errors of the LS position estimation for various system parameters, e.g. granularity levels, measurement accuracies, and localization boundaries.…”

Section: System Performance Evaluationmentioning

confidence: 99%

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Wi-Fi RTT Ranging Performance Characterization and Positioning System Design

Poslad

et al. 2022

IEEE Trans. on Mobile Comput.

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The aim of this research is to implement a precise Wi-Fi indoor positioning system (IPS) or localization system based upon the IEEE 802.11mc Fine-Timing Measurement (FTM) scheme also known as the Wi-Fi Round Trip Time (RTT) ranging technique, where ranging refers to a sub-process of positioning that determines the distance between a transmitter and receiver. Our system and its algorithms were implemented using a COTS (Commercial-Off-The-Shelf) smartphone and Wi-Fi access points. Experiments were conducted in several real-life indoor environments. This paper presents the detailed Wi-Fi RTT ranging performance of these devices in different system configurations and characterizes the systematic biases and noise model to improve the ranging accuracy. A novel three-step-positioning method is proposed to overcome the issues of no or multiple intersect points in trilateration due to ranging errors to improve positioning accuracy. This consists of first, systematic bias determination and removal; second, Clustering-based Trilateration (CbT) supported by Weighted Concentric Circle Generation (WCCG), namely CbT & WCCG; third, positioning result and trajectory optimization using a Kalman filter. As a result, the evaluation experiments gave a position accuracy of ±1.2 m in 2D static positioning and ±1.3 m for dynamic motion tracking. Also, our CbT & WCCG method demonstrate good tolerance against ranging errors. Moreover, the computational cost and positioning accuracy of CbT & WCCG methods are compared with Least Square (LS) and Recursive Least Square (RLS) methods and the accuracy standard deviation of our algorithm is the closest to the Cramer-Rao bound (CRB).

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Section: System Performance Evaluationmentioning

confidence: 99%

Section: System Performance Evaluationmentioning

confidence: 99%

Wi-Fi RTT Ranging Performance Characterization and Positioning System Design

Poslad

et al. 2022

IEEE Trans. on Mobile Comput.

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“…for achieving a particular set of n sensors

within the possible combinations of sensors (S) which maximizes the system properties that are enhanced during the optimization process. The definition of the Cartesian coordinates during the optimization must follow a discrete characterization of the LPS coverage space (i.e., Target Location Environment (TLE)) and the space in which the sensor nodes can be located (i.e., Node Location Environment (NLE)) due to the impossibility for dealing with a direct optimization of the NLP without a discretization [ 35 , 58 ].…”

Section: Node Location Problem In Wireless Sensor Networkmentioning

confidence: 99%

Memetic Chains for Improving the Local Wireless Sensor Networks Localization in Urban Scenarios

Verde

Díez-González

Ferrero-Guillén

et al. 2021

Sensors

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Local Positioning Systems (LPS) have become an active field of research in the last few years. Their application in harsh environments for high-demanded accuracy applications is allowing the development of technological activities such as autonomous navigation, indoor localization, or low-level flights in restricted environments. LPS consists of ad-hoc deployments of sensors which meets the design requirements of each activity. Among LPS, those based on temporal measurements are attracting higher interest due to their trade-off among accuracy, robustness, availability, and costs. The Time Difference of Arrival (TDOA) is extended in the literature for LPS applications and consequently we perform, in this paper, an analysis of the optimal sensor deployment of this architecture for achieving practical results. This is known as the Node Location Problem (NLP) and has been categorized as NP-Hard. Therefore, heuristic solutions such as Genetic Algorithms (GA) or Memetic Algorithms (MA) have been applied in the literature for the NLP. In this paper, we introduce an adaptation of the so-called MA-Solis Wets-Chains (MA-SW-Chains) for its application in the large-scale discrete discontinuous optimization of the NLP in urban scenarios. Our proposed algorithm MA-Variable Neighborhood Descent-Chains (MA-VND-Chains) outperforms the GA and the MA of previous proposals for the NLP, improving the accuracy achieved by 17% and by 10% respectively for the TDOA architecture in the urban scenario introduced.

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“…The TLP aims to find the optimal combination of tables in space maintaining the social distancing. This combinatorial problem must be discretized in order to explore a contained number of possible solutions configuring an exploratory attainable problem [43]. The number of possible combinations can be calculated as follows:…”

Section: Mathematical Characterisationmentioning

confidence: 99%

Optimal COVID-19 Adapted Table Disposition in Hostelry for Guaranteeing the Social Distance through Memetic Algorithms

et al. 2021

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The COVID-19 pandemic has challenged all physical interactions. Social distancing, face masks and other rules have reshaped our way of living during the last year. The impact of these measures for indoor establishments, such as education or hostelry businesses, resulted in a considerable organisation problem. Achieving a table distribution inside these indoor spaces that fulfilled the distancing requirements while trying to allocate the maximum number of tables for enduring the pandemic has proved to be a considerable task for multiple establishments. This problem, defined as the Table Location Problem (TLP), is categorised as NP-Hard, thus a metaheuristic resolution is recommended. In our previous works, a Genetic Algorithm (GA) optimisation was proposed for optimising the table distribution in real classrooms. However, the proposed algorithm performed poorly for high obstacle density scenarios, especially when allocating a considerable number of tables due to the existing dependency between adjacent tables in the distance distribution. Therefore, in this paper, we introduce for the first time, to the authors’ best knowledge, a Memetic Algorithm (MA) optimisation that improves the previously designed GA through the introduction of a Gradient Based Local Search. Multiple configurations have been analysed for a real hostelry-related scenario and a comparison between methodologies has been performed. Results show that the proposed MA optimisation obtained adequate solutions that the GA was unable to reach, demonstrating a superior convergence performance and an overall greater flexibility. The MA performance denoted its value not only from a COVID-19 distancing perspective but also as a flexible managing algorithm for daily table arrangement, thus fulfilling the main objectives of this paper.

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Quantized lower bounds on grid-based localization algorithm for wireless sensor networks

Cited by 14 publications

References 21 publications

Wi-Fi RTT Ranging Performance Characterization and Positioning System Design

Wi-Fi RTT Ranging Performance Characterization and Positioning System Design

Memetic Chains for Improving the Local Wireless Sensor Networks Localization in Urban Scenarios

Optimal COVID-19 Adapted Table Disposition in Hostelry for Guaranteeing the Social Distance through Memetic Algorithms

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