From Compression of Wearable-based Data to Effortless Indoor Positioning

In recent years, wearable devices have become ever-present in modern society. They are typically defined as small, battery-restricted devices, worn on, in, or in very close proximity to a human body. Their performance is defined by their functionalities as much as by their comfortability and convenience. As such, they need to be compact yet powerful, thus making energy efficiency an extremely important and relevant aspect of the system. The market of wearable devices is nowadays dominated by smartwatches and fitness bands, which are capable of gathering numerous sensor-based data such as temperature, pressure, heart rate, or blood oxygen level, which have to be processed in real-time, stored, or wirelessly transferred while consuming as little energy as possible to ensure long battery life. Implementing compression schemes directly at the wearable device is one of the relevant methods to reduce the volume of data and to minimize the number of required operations while processing them, as raw measurements include plenty of redundancies that can be removed without damaging the useful information itself. This thesis presents a number of contributions in the field of compression of wearable-based data, mainly in areas of lossy compression techniques designated for the time series sensor-based data and positioning. In the scope of this work, two novel time-series compression techniques are proposed, namely Direct Lightweight Temporal Compression (DLTC) and Altered Symbolic Aggregate Approximation (ASAX), which are specifically designed to address relevant challenges of modern wearable systems. As many of the modern wearables also possess localization capabilities critical for navigation, tracking, and monitoring applications, reducing the computational and storage demands for indoor positioning applications is the second addressed challenge. Performing the positioning task quickly and efficiently on all connected devices, including wearables, becomes crucial in industrial applications, eHealth, or security. As the localization technique of choice in Global Navigation Satellite System (GNSS) signal-obscured scenarios, positioning via fingerprinting proves a reliable and efficient solution, while arising new challenges to be solved. Improving the efficiency of the fingerprinting-based system by applying lossy compressions onto the training radio map is realized by proposing, implementing, and evaluating various novel dimensionality-reduction techniques. This thesis proposes Element-Wise cOmpression using K-means (EWOK), a bitlevel compression based on element-wise k-means clustering, radio Map compression Employing Signal Statistics (MESS), a sample-wise compression that extracts signal statistics based on their locations, as well as evaluates feature-wise methods Principal Component Analysis (PCA) and Auto-Encoder (AE) that transform fingerprints into low-dimensional representation. The evaluation in the thesis shows the effectiveness of each compression scheme on 26 different datasets and provides the results achieved by combining the individual schemes together, accomplishing multi-dimensional radio map compression that sustains high positioning accuracy of the dataset, despite manyfold size reduction. The processing requirements of the positioning system are further addressed by proposing a cascade of models that reduces the required search space of the algorithm. By combining numerous Machine Learning (ML) architectures, it is capable of further reducing the positioning time (and thus, positioning effort), while improving the positioning performance. The thesis further includes the introduction of an indoor positioning dataset collected by the author, denoted TUJI 1, a novel performance metric to evaluate the latency caused by the lossy compression, and several crucial adjustments to the distance metric calculations, generalizing their applicability. The thesis provides novel insights into the compression of sensor-based, timeseries data and into reducing the computational effort of the fingerprinting positioning schemes while introducing a relevant number of novel and efficient solutions beyond the State-of-the-Art.Cotutelle -yhteisväitöskirj

IMPLEMENTASI ALGORITMA SAX DAN RANDOM PROJECTION UNTUK TIME SERIES MOTIF DISCOVERY PADA BIG DATA PLATFORM STUDI KASUS: RESONANSI ELEMEN ORBITAL ASTEROID

Fenomena Big Data telah terjadi pada banyak bidang pengetahuan, salah satunya adalah pada bidang astronomi. Data yang memiliki jumlah yang sangat banyak pada bidang astronomi salah satunya adalah data resonansi elemen orbital asteroid. Data tersebut dapat diolah sehingga para ilmuwan dapat mencari mean motion resonance pada suatu partikel asteroid untuk mengetahui pada tahun keberapa asteroid akan beresonansi dengan planet planet tertentu. Untuk itu, penelitian ini membuat sebuah model komputasi untuk mendapatkan mean motion resonance secara cepat dan efektif dengan memodifikasi dan mengimplementasikan algoritma SAX dan algoritma motif discovery random projection pada Big Data Platform yaitu Apache Hadoop dan Apache Spark. Hasil penelitian ini menunjukkan adanya percepatan yang sangat signifikan antara penggunaan stand alone dan penggunaan Big Data platform dengan perancangan 2 skenario. Skenario pertama yaitu penggunaan cluster dengan 4 cores dan beberapa worker node dan skenario kedua yaitu penggunaan cluster dengan 2 worker node dan beberapa jumlah core. Penelitian ini juga membuktikan bahwa model komputasi yang dibangun dibandingkan dengan kelauran software SwiftVis mendapatkan rata-rata akurasi sebesar 83%. The Big Data phenomenon has occurred in many fields of knowledge, one of them is in the field of astronomy. One of data that has a very large amount in the astronomy field is the resonance data of asteroid orbital elements. The data can be processed so that scientists can find the mean motion resonance in an asteroid particle to find out in what year the asteroid will resonate with a particular planet. But now, it cannot be denied that to process the data with a large amount of data will take a lot of time. For this reason, this research makes a computational model to get mean motion resonance quickly and effectively by modifying and implementing the SAX algorithm and motif discovery random projection algorithm on the Big Data platform, using Apache Hadoop and Apache Spark. The results of this study indicate a very significant acceleration between standalone use and the use of Big Data platforms by designing 2 scenarios. The first scenario is the use of clusters with 4 cores and several worker nodes and the second scenario is the use of clusters with 2 worker nodes and a number of cores. This study also proves that the computational model compared to the result from SwiftVis software gets an average accuracy of 83%

Energy-efficient Continuous Context Sensing on Mobile Phones

With the ever increasing adoption of smartphones worldwide, researchers have found the perfect sensor platform to perform context-based research and to prepare for context-based services to be also deployed for the end-users. However, continuous context sensing imposes a considerable challenge in balancing the energy consumption of the sensors, the accuracy of the recognized context and its latency. After outlining the common characteristics of continuous sensing systems, we present a detailed overview of the state of the art, from sensors sub-systems to context inference algorithms. Then, we present the three main contribution of this thesis. The first approach we present is based on the use of local communications to exchange sensing information with neighboring devices. As proximity, location and environmental information can be obtained from nearby smartphones, we design a protocol for synchronizing the exchanges and fairly distribute the sensing tasks. We show both theoretically and experimentally the reduction in energy needed when the devices can collaborate. The second approach focuses on the way to schedule mobile sensors, optimizing for both the accuracy and energy needs. We formulate the optimal sensing problem as a decision problem and propose a two-tier framework for approximating its solution. The first tier is responsible for segmenting the sensor measurement time series, by fitting various models. The second tier takes care of estimating the optimal sampling, selecting the measurements that contributes the most to the model accuracy. We provide near-optimal heuristics for both tiers and evaluate their performances using environmental sensor data. In the third approach we propose an online algorithm that identifies repeated patterns in time series and produces a compressed symbolic stream. The first symbolic transformation is based on clustering with the raw sensor data. Whereas the next iterations encode repetitive sequences of symbols into new symbols. We define also a metric to evaluate the symbolization methods with regard to their capacity at preserving the systems' states. We also show that the output of symbols can be used directly for various data mining tasks, such as classification or forecasting, without impacting much the accuracy, but greatly reducing the complexity and running time. In addition, we also present an example of application, assessing the user's exposure to air pollutants, which demonstrates the many opportunities to enhance contextual information when fusing sensor data from different sources. On one side we gather fine grained air quality information from mobile sensor deployments and aggregate them with an interpolation model. And, on the other side, we continuously capture the user's context, including location, activity and surrounding air quality. We also present the various models used for fusing all these information in order to produce the exposure estimation

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

A Survey on Explainable Anomaly Detection

In the past two decades, most research on anomaly detection has focused on improving the accuracy of the detection, while largely ignoring the explainability of the corresponding methods and thus leaving the explanation of outcomes to practitioners. As anomaly detection algorithms are increasingly used in safety-critical domains, providing explanations for the high-stakes decisions made in those domains has become an ethical and regulatory requirement. Therefore, this work provides a comprehensive and structured survey on state-of-the-art explainable anomaly detection techniques. We propose a taxonomy based on the main aspects that characterize each explainable anomaly detection technique, aiming to help practitioners and researchers find the explainable anomaly detection method that best suits their needs.Comment: Paper accepted by the ACM Transactions on Knowledge Discovery from Data (TKDD) for publication (preprint version

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Jornadas Nacionales de Investigación en Ciberseguridad: actas de las VIII Jornadas Nacionales de Investigación en ciberseguridad: Vigo, 21 a 23 de junio de 2023

Jornadas Nacionales de Investigación en Ciberseguridad (8ª. 2023. Vigo)atlanTTicAMTEGA: Axencia para a modernización tecnolóxica de GaliciaINCIBE: Instituto Nacional de Cibersegurida

A survey on explainable anomaly detection

NWOAlgorithms and the Foundations of Software technolog