Stacked Auto-Encoder for Scalable Indoor Localization in Wireless Sensor Networks

BelMannoubi, Souad; Touati, Haifa; Snoussi, Hichem

doi:10.1109/iwcmc.2019.8766761

Cited by 14 publications

(10 citation statements)

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“…On the other hand, the AE provides unsupervised learning and can represent sparse and compact problems. Although it is challenging to pre-train with large data, it is one of the most powerful and successful unsupervised learning methods [274]. Furthermore, SAE-based models have been used for video processing on edge [245].…”

Section: Open Issues Challenges and Future Research Directionsmentioning

confidence: 99%

A Survey on Requirements of Future Intelligent Networks: Solutions and Future Research Directions

2022

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The context of this study examines the requirements of Future Intelligent Networks (FIN), solutions, and current research directions through a survey technique. The background of this study is hinged on the applications of Machine Learning (ML) in the networking field. Through careful analysis of literature and real-world reports, we noted that ML has significantly expedited decision-making processes, enhanced intelligent automation, and helped resolve complex problems economically in different fields of life. Various researchers have also envisioned future networks incorporating intelligent functions and operations with the ML. Several efforts have been made to automate individual functions and operations in the networking domain; however, most of the existing ML models proposed in the literature lack several vital requirements. Hence, this study aims to present a comprehensive summary of the requirements of FIN and propose a taxonomy of different network functionalities that needs to be equipped with ML techniques. The core objectives of this study are to provide a taxonomy of requirements envisioned for end-to-end FIN, relevant ML techniques, and their analysis to find research gaps, open issues, and future research directions. The real benefit of machine learning applications in any domain can only be ensured if intelligent capabilities cover all its components. We observed that future generations of networks are heterogeneous, multi-vendor, and multidimensional, and ML can provide optimal results only if intelligent capabilities are used on a holistic scale. Realizing intelligence on a holistic scale is only possible if the ML algorithms can solve heterogeneous problems in a multi-vendor and multidimensional environment. ML models must be reliable and efficient, support distributed learning architecture, and possess the capability to learn and share the knowledge across the network layers and administrative domains to solve issues. Firstly, this study ascertains the requirements of the FIN and proposes their taxonomy through reviews on envisioned ideas by various researchers and articles gathered from reputed conferences and standard developing organizations using keyword queries. Secondly, we have reviewed existing studies on ML applications focusing on coverage, heterogeneity, distributed architecture, and cross-domain knowledge learning and sharing. Our study observed that in the past, ML applications were focused mainly on an individual/isolated level only, and aspects of global and deep holistic learning with cross-layer/domain knowledge sharing with agile ML operations are not explored at large. We recommend that the issues mentioned above be addressed with improved ML architecture and agile operations and propose ML pipeline-based architecture for FIN. The significant contribution of this study is the impetus for researchers to seek ML models suitable for a modular, distributed, multi-domain and multi-layer environment and provide decision-making on a global or holistic rather than individual function level.

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Section: Open Issues Challenges and Future Research Directionsmentioning

confidence: 99%

A Survey on Requirements of Future Intelligent Networks: Solutions and Future Research Directions

2022

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“…Similarly, [74] proposes a multi-output network that uses the encoded features to predict the building, the floor, and the coordinate. An SAE followed by two FC layers and then an argmax to predict the multi-label classification is used in [291]. Ref.…”

Section: Supervisedmentioning

confidence: 99%

A Comprehensive Survey of Machine Learning Based Localization with Wireless Signals

Burghal,

Ravi,

Rao

et al. 2020

Preprint

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The last few decades have witnessed a growing interest in location-based services. Using localization systems based on Radio Frequency (RF) signals has proven its efficacy for both indoor and outdoor applications. However, challenges remain with respect to both complexity and accuracy of such systems. Machine Learning (ML) is one of the most promising methods for mitigating these problems, as ML (especially deep learning) offers powerful practical data-driven tools that can be integrated into localization systems. In this paper, we provide a comprehensive survey of ML-based localization solutions that use RF signals. The survey spans different aspects, ranging from the system architectures, to the input features, the ML methods, and the datasets.A main point of the paper is the interaction between the domain knowledge arising from the physics of localization systems, and the various ML approaches. Besides the ML methods, the utilized input features play a major role in shaping the localization solution; we present a detailed discussion of the different features and what could influence them, be it the underlying wireless technology or standards or the preprocessing techniques. A detailed discussion is dedicated to the different ML methods that have been applied to localization problems, discussing the underlying problem and the solution structure. Furthermore, we summarize the different ways the datasets were acquired, and then list the publicly available ones. Overall, the survey categorizes and partly summarizes insights from almost 400 papers in this field.This survey is self-contained, as we provide a concise review of the main ML and wireless propagation concepts, which shall help the researchers in either field navigate through the surveyed solutions, and suggested open problems.

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“…However, the multi-label classification (MLC) problem still exists and it has recently attracted increasing research sights due to its wide range of applications, such as text classification, 1,2 gene function classification, 3 social network analysis, 4 and image/video annotation. 5 Furthermore, with the rapid increase of development and applications with wireless sensor networks (WSNs), massive data collected from a large number of monitoring objects [6][7][8][9][10][11][12] are analyzed, clustered, and classified with methods like classic K-nearest neighbor (KNN), support vector machine (SVM) algorithms, 9,10 and MLC methods. 11,12 Following are a few examples to illustrate advanced data analysis approaches which are applied to WSN data.…”

Section: Introductionmentioning

confidence: 99%

“…5 Furthermore, with the rapid increase of development and applications with wireless sensor networks (WSNs), massive data collected from a large number of monitoring objects [6][7][8][9][10][11][12] are analyzed, clustered, and classified with methods like classic K-nearest neighbor (KNN), support vector machine (SVM) algorithms, 9,10 and MLC methods. 11,12 Following are a few examples to illustrate advanced data analysis approaches which are applied to WSN data. With a wireless sensor network system set in a room to collect limb motion data, Guraliuc et al 9 use the KNN and SVM algorithms to classify limb movements, aiming to develop a method for patient motion therapy.…”

Section: Introductionmentioning

confidence: 99%

“…Constructing a sensor device over a bed to collect the sleeping posture data of human body, Barsocchi 10 use KNN and SVM algorithms to classify the sleeping postures for bedsore therapy. Belmannoubi et al 11 adopted the MLC method to simplify and reduce the complexity of the classification task in order to improve the accuracy of zone location in a multibuilding, multi-floor indoor environment. Zhang et al 12 applied the MLC method to detect multiple data faults 13 (regarded as multiple labels) simultaneously in sensor networks because it is difficult to build detection model for each fault type.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A novel multi-label classification algorithm based on K-nearest neighbor and random walk

Wang

Wan

et al. 2020

International Journal of Distributed Sensor Networks

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The multi-label classification problem occurs in many real-world tasks where an object is naturally associated with multiple labels, that is, concepts. The integration of the random walk approach in the multi-label classification methods attracts many researchers’ sight. One challenge of using the random walk-based multi-label classification algorithms is to construct a random walk graph for the multi-label classification algorithms, which may lead to poor classification quality and high algorithm complexity. In this article, we propose a novel multi-label classification algorithm based on the random walk graph and the K-nearest neighbor algorithm (named MLRWKNN). This method constructs the vertices set of a random walk graph for the K-nearest neighbor training samples of certain test data and the edge set of correlations among labels of the training samples, thus considerably reducing the overhead of time and space. The proposed method improves the similarity measurement by differentiating and integrating the discrete and continuous features, which reflect the relationships between instances more accurately. A label predicted method is devised to reduce the subjectivity of the traditional threshold method. The experimental results with four metrics demonstrate that the proposed method outperforms the seven state-of-the-art multi-label classification algorithms in contrast and makes a significant improvement for multi-label classification.

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Stacked Auto-Encoder for Scalable Indoor Localization in Wireless Sensor Networks

Cited by 14 publications

References 10 publications

A Survey on Requirements of Future Intelligent Networks: Solutions and Future Research Directions

A Survey on Requirements of Future Intelligent Networks: Solutions and Future Research Directions

A Comprehensive Survey of Machine Learning Based Localization with Wireless Signals

A novel multi-label classification algorithm based on K-nearest neighbor and random walk

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