IoT based localization and tracking

Ramnath, Swathi; Javali, Abhishek; Narang, Bhumika; Mishra, Pallavi; Routray, Sudhir K.

doi:10.1109/iciota.2017.8073629

Cited by 74 publications

(21 citation statements)

References 8 publications

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“…Localization of users or IoT devices is indispensable for Localization-Based Services (LBSs) such as tracking in smart buildings, advertising in shopping centers, or routing and navigation in large public buildings [ 40 ]. Indoor Localization Systems (ILSs) are used to locate IoT or mobile devices inside environments where the Global Positioning System (GPS) cannot be deployed.…”

Section: Library Architecture and Descriptionmentioning

confidence: 99%

RcdMathLib: An Open Source Software Library for Computing on Resource-Limited Devices

Kasmi

Norrdine

Schiller

et al. 2021

Sensors

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We developped an open source library called RcdMathLib for solving multivariate linear and nonlinear systems. RcdMathLib supports on-the-fly computing on low-cost and resource-constrained devices, e.g., microcontrollers. The decentralized processing is a step towards ubiquitous computing enabling the implementation of Internet of Things (IoT) applications. RcdMathLib is modular- and layer-based, whereby different modules allow for algebraic operations such as vector and matrix operations or decompositions. RcdMathLib also comprises a utilities-module providing sorting and filtering algorithms as well as methods generating random variables. It enables solving linear and nonlinear equations based on efficient decomposition approaches such as the Singular Value Decomposition (SVD) algorithm. The open source library also provides optimization methods such as Gauss–Newton and Levenberg–Marquardt algorithms for solving problems of regression smoothing and curve fitting. Furthermore, a positioning module permits computing positions of IoT devices using algorithms for instance trilateration. This module also enables the optimization of the position by performing a method to reduce multipath errors on the mobile device. The library is implemented and tested on resource-limited IoT as well as on full-fledged operating systems. The open source software library is hosted on a GitLab repository.

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Section: Library Architecture and Descriptionmentioning

confidence: 99%

RcdMathLib: An Open Source Software Library for Computing on Resource-Limited Devices

Kasmi

Norrdine

Schiller

et al. 2021

Sensors

View full text Add to dashboard Cite

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“…, t r k }. In short, KNN classifies the pattern y to one of the possible M classes of the problem at hand [31] as shown in (1).…”

Section: A Knnmentioning

confidence: 99%

“…The implementation of the Internet of Things (IoT) is in a wide diversity of fields ranging from smart infrastructure, healthcare applications, industrial automation to real-time monitoring and tracking, etc. The IoT-based object localization and tracking are considered as one of the recent active and immense developments of IoT applications [1]. Nonetheless, the localization and tracking system is not new.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model Based on Constraint OSELM, Adaptive Weighted SRC and KNN for Large-Scale Indoor Localization

et al. 2019

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In this paper, a novel hybrid model based on the constraint online sequential extreme learning machine (COSELM) classifier with adaptive weighted sparse representation classification (WSRC) and k nearest neighbor (KNN) is proposed for the WiFi-based indoor positioning system. It is referred to as A Fast-Accurate-Reliable Localization System (AFARLS). AFARLS exploits the speed advantage of COSELM to reduce the computational cost, and the accuracy advantage of WSRC to enhance the classification performance, by utilizing KNN as the adaptive sub-dictionary selection strategy. The understanding is that the original extreme learning machine (ELM) is less robust against noise, while sparse representation classification (SRC) and KNN suffer a high computational burden when using the over-complete dictionary. AFARLS unifies their complementary strengths to resolve each other's limitation. In large-scale multibuilding and multi-floor environments, AFARLS estimates a location that considers the building, floor, and position (longitude and latitude) in a hierarchical and sequential approach according to a discriminative criterion to the COSELM output. If the classifier result is unreliable, AFARLS uses KNN to achieve the best relevant sub-dictionary. The sub-dictionary is fed to WSRC to re-estimate the building and the floor, while the position is predicted by the ELM regressor. AFARLS has been verified on two publicly available datasets, the EU Zenodo and the UJIIndoorLoc. The experimental results demonstrate that AFARLS outperforms the state-of-the-art algorithms on the former dataset, and it provides near state-of-the-art performance on the latter dataset. When the size of the dataset increases remarkably, AFARLS shows that it can maintain its real-time high-accuracy performance.INDEX TERMS Constraint online sequential extreme learning machine, k nearest neighbor, weighted sparse representation classification, WiFi-based IPS.

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“…The IoT began with the increasing number of interconnected physical objects providing interactions. The IoT paradigm [4] has a main role as a key facilitator of the integration of various application solutions and communication technologies, such as identification and tracking [5,6], sensor networks, wired and wireless actuators [7], improved communication protocols, and distributed intelligence for objects. According to the Internet Business Solutions Group (IBSG), a milestone of IoT emergence occurred when the Earth's population was exceeded by the number of objects connected to the Internet, which happened in 2008-2009.…”

Section: Introductionmentioning

confidence: 99%

IoT-Based Solid Waste Management Solutions: A Survey

Pardini

Rodrigues

Kozlov

et al. 2019

JSAN

138

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With the increase of population density and the rural exodus to cities, urbanization is assuming extreme proportions and presents a tremendous urban problem related to waste generation. The increase of waste generation has been considered a significant challenge to large urban centers worldwide and represents a critical issue for countries with accelerated population growth in cities. The Internet of Things (IoT) and cloud computing offer an automation possibility through cyberphysical systems that will change the way solid waste management is performed. Considering IoT requirements, a review analysis of waste management models available in the literature is performed in detail in this paper. Then, a deep review is undertaken of the related literature based on IoT infrastructure for efficient handling of waste generated in urban scenarios, focusing on the interaction among concessionaires and waste generators (citizens) from the perspective of a shorter collection time with reduced costs, as well as citizenship promotion. An IoT-based reference model is described, and a comparison analysis of the available solutions is presented, with the goal to highlight the most relevant approaches and identify open research issues on the topic.

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IoT based localization and tracking

Cited by 74 publications

References 8 publications

RcdMathLib: An Open Source Software Library for Computing on Resource-Limited Devices

RcdMathLib: An Open Source Software Library for Computing on Resource-Limited Devices

A Hybrid Model Based on Constraint OSELM, Adaptive Weighted SRC and KNN for Large-Scale Indoor Localization

IoT-Based Solid Waste Management Solutions: A Survey

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