Efficient Kernel-Based Subsequence Search for Enabling Health Monitoring Services in IoT-Based Home Setting

This paper presents an efficient approach for subsequence search in data streams. The problem consists of identifying coherent repetitions of a given reference time-series, also in the multivariate case, within a longer data stream. The most widely adopted metric to address this problem is Dynamic Time Warping (DTW), but its computational complexity is a well-known issue. In this paper, we present an approach aimed at learning a kernel approximating DTW for efficiently analyzing streaming data collected from wearable sensors, while reducing the burden of DTW computation. Contrary to kernel, DTW allows for comparing two time-series with different length. To enable the use of kernel for comparing two time-series with different length, a feature embedding is required in order to obtain a fixed length vector representation. Each vector component is the DTW between the given time-series and a set of "basis" series, randomly chosen. The approach has been validated on two benchmark datasets and on a real-life application for supporting self-rehabilitation in elderly subjects has been addressed. A comparison with traditional DTW implementations and other state-of-the-art algorithms is provided: results show a slight decrease in accuracy, which is counterbalanced by a significant reduction in computational costs

Data Mining in Internet of Things Systems: A Literature Review

The Internet of Things (IoT) and cloud technologies have been the main focus of recent research, allowing for the accumulation of a vast amount of data generated from this diverse environment. These data include without any doubt priceless knowledge if could correctly discovered and correlated in an efficient manner. Data mining algorithms can be applied to the Internet of Things (IoT) to extract hidden information from the massive amounts of data that are generated by IoT and are thought to have high business value. In this paper, the most important data mining approaches covering classification, clustering, association analysis, time series analysis, and outlier analysis from the knowledge will be covered. Additionally, a survey of recent work in in this direction is included. Another significant challenges in the field are collecting, storing, and managing the large number of devices along with their associated features. In this paper, a deep look on the data mining for the IoT platforms will be given concentrating on real applications found in the literatur

Data semantic enrichment for complex event processing over IoT Data Streams

This thesis generalizes techniques for processing IoT data streams, semantically enrich data with contextual information, as well as complex event processing in IoT applications. A case study for ECG anomaly detection and signal classification was conducted to validate the knowledge foundation

Trustworthy Wireless Personal Area Networks

In the Internet of Things (IoT), everyday objects are equipped with the ability to compute and communicate. These smart things have invaded the lives of everyday people, being constantly carried or worn on our bodies, and entering into our homes, our healthcare, and beyond. This has given rise to wireless networks of smart, connected, always-on, personal things that are constantly around us, and have unfettered access to our most personal data as well as all of the other devices that we own and encounter throughout our day. It should, therefore, come as no surprise that our personal devices and data are frequent targets of ever-present threats. Securing these devices and networks, however, is challenging. In this dissertation, we outline three critical problems in the context of Wireless Personal Area Networks (WPANs) and present our solutions to these problems. First, I present our Trusted I/O solution (BASTION-SGX) for protecting sensitive user data transferred between wirelessly connected (Bluetooth) devices. This work shows how in-transit data can be protected from privileged threats, such as a compromised OS, on commodity systems. I present insights into the Bluetooth architecture, Intel’s Software Guard Extensions (SGX), and how a Trusted I/O solution can be engineered on commodity devices equipped with SGX. Second, I present our work on AMULET and how we successfully built a wearable health hub that can run multiple health applications, provide strong security properties, and operate on a single charge for weeks or even months at a time. I present the design and evaluation of our highly efficient event-driven programming model, the design of our low-power operating system, and developer tools for profiling ultra-low-power applications at compile time. Third, I present a new approach (VIA) that helps devices at the center of WPANs (e.g., smartphones) to verify the authenticity of interactions with other devices. This work builds on past work in anomaly detection techniques and shows how these techniques can be applied to Bluetooth network traffic. Specifically, we show how to create normality models based on fine- and course-grained insights from network traffic, which can be used to verify the authenticity of future interactions

Napredna (edge computing) softverska arhitektura za upravljanje resursima i unutrašnje pozicioniranje

In Part I, this thesis aims to shed light on IoT and edge com-puting systems and accompanying computing and architectural paradigms, their definition, areas of application, and common use-cases, as well as operational, business, economical, social challenges and benefits. It illustrates modern needs and requests in building IoT systems and current State-of-The-Art (SoTA) approaches to designing them. Additionally, it discusses the security and privacy topics of IoT and edge computing systems. It also encompasses research, design, and implementation of an MQTT-based Resource Management Framework for Edge Com-puting systems that handle: resource management, failover detection and handover administration, logical and physical workload balancing and protection, and monitoring of physical and logical system resources designed for a real-world IoT platform. The thesis offers insights into modern requests for such frameworks, current SoTA approaches, and offer a solution in the form of a software framework, with minimal implementation and communication overhead. In Part II, the thesis elaborates on IPS, their definition, deploy-ment types, commonly used positioning techniques, areas of application, and common use-cases, as well as operational, business, economic, social challenges, and benefits. It specifically discusses designing IPS for the typical IoT infrastructure. It offers insights to modern IPS requests, current SoTA in solving them, and under-line original approaches from this thesis. It elaborates on the research, design and authors’ implementation of an IPS for the IoT – Bluetooth LowEnergyMicrolocation Asset Tracking (BLEMAT), including its software engines (collections of software components) for: indoor positioning, occupancy detection, visualization, pattern discovery and prediction, geofencing, movement pattern detection, visualization, discovery and prediction, social dynamics analysis, and indoor floor plan layout detection.Deo I teze ima je za cilj da rasvetli IoT i edge computing računarske sisteme i prateće računarske paradigme softverskih arhitektura, njihovu definiciju, područja primene i slučajeve uobičajene upotrebe, kao i operativne, poslovne, ekonomske, i socijalne izazove i koristi. Teza ilustruje savremene potrebe i zahtevi u izgradnji IoT sistema i najsavremeniji pristupi u njihovom dizajniranju. Raspravlja se o temama bezbednosti i privatnosti u IoT i edge computing računarskim sistemima. Kao još jedan glavni zadatak, teza je obuhvata istraživanje, dizajn i implementaciju softverske arhitekture za upravljanje resursima zasnovanim na MQTT komunikacionom protokolu za edge computing računarske sisteme koja se bavi: upravljanjem resursima, detekcijom prestanka rada upravljačkih algoritama i administracijom primopredaje tj. transporta upravljačkih algoritama, i logičkim i fizičkim balansiranjem i zaštitom radnog opterećenja sistema. Diskutuju se savremeni zahtevi za takve softverske arhitekture, trenutni pristupi. Na kraju, prikazuje se rešenje sa minimalnim troškovima implementacije i  komunikacije. Deo II teze ima za cilj da objasni sisteme za unutrašnje pozicioniranje, njihovu definiciju, vrste primene, najčešće korišćene tehnike pozicioniranja, područja primene i uobičajene slučajeve upotrebe, kao i operativne, poslovne, ekonomske, i socijalne izazove i koristi. Posebno se diskutuje o dizajniranju ovakvih sistema za tipičnu IoT infrastrukturu. Nudi se uvid u savremene zahteve sisteme za unutrašnje pozicioniranje, trenutne pristupe u rešavanju istih, i naglašeni su originalni pristupe iz ove teze. Dalje je fokus na istraživanju, dizajniranju i implementaciji sistema za unutrašnje pozicioniranje (BLEMAT), uključujući njegove softverske podsisteme (kolekcije softverskih komponenti) za: pozicioniranje u zatvorenom prostoru, detekciju zauzeća prostorija, vizualizaciju, otkrivanje i predviđanje obrazaca kretanja, geofencing, vizualizaciju i analizu društvene dinamike i detekciju rasporeda prostorija unutrašnjeg prostora

Mining sensor data from complex systems

Today, virtually everything, from natural phenomena to complex artificial and physical systems, can be measured and the resulting information collected, stored and analyzed in order to gain new insight. This thesis shows how complex systems often exhibit diverse behavior at different temporal scales, and that data mining methods should be able to cope with the multiple resolutions (scales) at the same time in order to fully understand the data at hand and extract useful information from it. Under these assumptions, we introduce novel data mining and visualization methods for large time series data collected from complex physical systems. In particular, we focus on three fundamental problems: the detection of multi-scale patterns, the recognition of recurrent events, and the interactive visualization of massive time series data. We evaluate our methods on a real-world scenario provided by InfraWatch, a Structural Health Monitoring project centered around the management and analysis of data collected by a large sensor network deployed on a Dutch highway bridge. The application of our methods resulted in the identification of the relevant scales of analysis in the InfraWatch data (and other datasets), the detection of the different recurring motifs and the visualization of terabytes of time series data interactively.STWAlgorithms and the Foundations of Software technolog

Examining sensor-based physical activity recognition and monitoring for healthcare using Internet of Things: A systematic review.

Due to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Monitoring (PARM) have been considered as a key paradigm for smart healthcare. Traditional methods for PARM focus on controlled environments with the aim of increasing the types of identifiable activity subjects complete and improving recognition accuracy and system robustness by means of novel body-worn sensors or advanced learning algorithms. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to open and connected uncontrolled environments by connecting heterogeneous cost-effective wearable devices and mobile apps. Little is currently known about whether traditional PARM technologies can tackle the new challenges of IoT environments and how to effectively harness and improve these technologies. In an effort to understand the use of IoT technologies in PARM studies, this paper will give a systematic review, critically examining PARM studies from a typical IoT layer-based perspective. It will firstly summarize the state-of-the-art in traditional PARM methodologies as used in the healthcare domain, including sensory, feature extraction and recognition techniques. The paper goes on to identify some new research trends and challenges of PARM studies in the IoT environments, and discusses some key enabling techniques for tackling them. Finally, this paper consider some of the successful case studies in the area and look at the possible future industrial applications of PARM in smart healthcare

Crowdfunding Non-fungible Tokens on the Blockchain

Non-fungible tokens (NFTs) have been used as a way of rewarding content creators. Artists publish their works on the blockchain as NFTs, which they can then sell. The buyer of an NFT then holds ownership of a unique digital asset, which can be resold in much the same way that real-world art collectors might trade paintings. However, while a deal of effort has been spent on selling works of art on the blockchain, very little attention has been paid to using the blockchain as a means of fundraising to help finance the artist’s work in the first place. Additionally, while blockchains like Ethereum are ideal for smaller works of art, additional support is needed when the artwork is larger than is feasible to store on the blockchain. In this paper, we propose a fundraising mechanism that will help artists to gain financial support for their initiatives, and where the backers can receive a share of the profits in exchange for their support. We discuss our prototype implementation using the SpartanGold framework. We then discuss how this system could be expanded to support large NFTs with the 0Chain blockchain, and describe how we could provide support for ongoing storage of these NFTs

Fake Malware Generation Using HMM and GAN

In the past decade, the number of malware attacks have grown considerably and, more importantly, evolved. Many researchers have successfully integrated state-of-the-art machine learning techniques to combat this ever present and rising threat to information security. However, the lack of enough data to appropriately train these machine learning models is one big challenge that is still present. Generative modelling has proven to be very efficient at generating image-like synthesized data that can match the actual data distribution. In this paper, we aim to generate malware samples as opcode sequences and attempt to differentiate them from the real ones with the goal to build fake malware data that can be used to effectively train the machine learning models. We use and compare different Generative Adversarial Networks (GAN) algorithms and Hidden Markov Models (HMM) to generate such fake samples obtaining promising results