Design and Evaluation of Compression, Classification and Localization Schemes for Various IoT Applications

Nowadays we are surrounded by a huge number of objects able to communicate, read information such as temperature, light or humidity, and infer new information through ex- changing data. These kinds of objects are not limited to high-tech devices, such as desktop PC, laptop, new generation mobile phone, i.e. smart phone, and others with high capabilities, but also include commonly used object, such as ID cards, driver license, clocks, etc. that can made smart by allowing them to communicate. Thus, the analog world of just a few years ago is becoming the a digital world of the Inter- net of Things (IoT), where the information from a single object can be retrieved from the Internet. The IoT paradigm opens several architectural challenges, including self-organization, self-managing, self-deployment of the smart objects, as well as the problem of how to minimize the usage of the limited resources of each device. The concept of IoT covers a lot of communication paradigms such as WiFi, Radio Frequency Identification (RFID), and Wireless Sensor Network (WSN). Each paradigm can be thought of as an IoT island where each device can communicate directly with other devices. The thesis is divided in sections in order to cover each problem mentioned above. The first step is to understand the possibility to infer new knowledge from the deployed device in a scenario. For this reason, the research is focused on the web semantic, web 3.0, to assign a semantic meaning to each thing inside the architecture. The sole semantic concept is unusable to infer new information from the data gathered; in fact, it is necessary to organize the data through a hierarchical form defined by an Ontology. Through the exploitation of the Ontology, it is possible to apply semantic engine reasoners to infer new knowledge about the network. The second step of the dissertation deals with the minimization of the usage of every node in a WSN. The main purpose of each node is to collect environmental data and to exchange hem with other nodes. To minimize battery consumption, it is necessary to limit the radio usage. Therefore, we implemented Razor, a new lightweight algorithm which is expected to improve data compression and classification by leveraging on the advantages offered by data mining methods for optimizing communications and by enhancing information transmission to simplify data classification. Data compression is performed studying the well-know Vector Quantization (VQ) theory in order to create the codebooks necessary for signal compression. At the same time, it is requested to give a semantic meaning to un- known signals. In this way, the codebook feature is able not only to compress the signals, but also to classify unknown signals. Razor is compared with both state-of-the-art compression and signal classification techniques for WSN . The third part of the thesis covers the concept of smart object applied to Robotic research. A critical issue is how a robot can localize and retrieve smart objects in a real scenario without any prior knowledge. In order to achieve the objectives, it is possible to exploit the smart object concept and localize them through RSSI measurements. After the localization phase, the robot can exploit its own camera to retrieve the objects. Several filtering algorithms are developed in order to mitigate the multi–path issue due to the wireless communication channel and to achieve a better distance estimation through the RSSI measurement. The last part of the dissertation deals with the design and the development of a Cognitive Network (CN) testbed using off the shelf devices. The device type is chosen considering the cost, usability, configurability, mobility and possibility to modify the Operating System (OS) source code. Thus, the best choice is to select some devices based on Linux kernel as Android OS. The feature to modify the Operating System is required to extract the TCP/IP protocol stack parameters for the CN paradigm. It is necessary to monitor the network status in real-time and to modify the critical parameters in order to improve some performance, such as bandwidth consumption, number of hops to exchange the data, and throughput

Processing of Electronic Health Records using Deep Learning: A review

Availability of large amount of clinical data is opening up new research avenues in a number of fields. An exciting field in this respect is healthcare, where secondary use of healthcare data is beginning to revolutionize healthcare. Except for availability of Big Data, both medical data from healthcare institutions (such as EMR data) and data generated from health and wellbeing devices (such as personal trackers), a significant contribution to this trend is also being made by recent advances on machine learning, specifically deep learning algorithms

Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at Scale

Deriving disease subtypes from electronic health records (EHRs) can guide next-generation personalized medicine. However, challenges in summarizing and representing patient data prevent widespread practice of scalable EHR-based stratification analysis. Here we present an unsupervised framework based on deep learning to process heterogeneous EHRs and derive patient representations that can efficiently and effectively enable patient stratification at scale. We considered EHRs of 1,608,741 patients from a diverse hospital cohort comprising of a total of 57,464 clinical concepts. We introduce a representation learning model based on word embeddings, convolutional neural networks, and autoencoders (i.e., ConvAE) to transform patient trajectories into low-dimensional latent vectors. We evaluated these representations as broadly enabling patient stratification by applying hierarchical clustering to different multi-disease and disease-specific patient cohorts. ConvAE significantly outperformed several baselines in a clustering task to identify patients with different complex conditions, with 2.61 entropy and 0.31 purity average scores. When applied to stratify patients within a certain condition, ConvAE led to various clinically relevant subtypes for different disorders, including type 2 diabetes, Parkinson's disease and Alzheimer's disease, largely related to comorbidities, disease progression, and symptom severity. With these results, we demonstrate that ConvAE can generate patient representations that lead to clinically meaningful insights. This scalable framework can help better understand varying etiologies in heterogeneous sub-populations and unlock patterns for EHR-based research in the realm of personalized medicine.Comment: C.F. and R.M. share senior authorshi

Design and Evaluation of Compression, Classification and Localization Schemes for Various IoT Applications

Nowadays we are surrounded by a huge number of objects able to communicate, read information such as temperature, light or humidity, and infer new information through ex- changing data. These kinds of objects are not limited to high-tech devices, such as desktop PC, laptop, new generation mobile phone, i.e. smart phone, and others with high capabilities, but also include commonly used object, such as ID cards, driver license, clocks, etc. that can made smart by allowing them to communicate. Thus, the analog world of just a few years ago is becoming the a digital world of the Inter- net of Things (IoT), where the information from a single object can be retrieved from the Internet. The IoT paradigm opens several architectural challenges, including self-organization, self-managing, self-deployment of the smart objects, as well as the problem of how to minimize the usage of the limited resources of each device. The concept of IoT covers a lot of communication paradigms such as WiFi, Radio Frequency Identification (RFID), and Wireless Sensor Network (WSN). Each paradigm can be thought of as an IoT island where each device can communicate directly with other devices. The thesis is divided in sections in order to cover each problem mentioned above. The first step is to understand the possibility to infer new knowledge from the deployed device in a scenario. For this reason, the research is focused on the web semantic, web 3.0, to assign a semantic meaning to each thing inside the architecture. The sole semantic concept is unusable to infer new information from the data gathered; in fact, it is necessary to organize the data through a hierarchical form defined by an Ontology. Through the exploitation of the Ontology, it is possible to apply semantic engine reasoners to infer new knowledge about the network. The second step of the dissertation deals with the minimization of the usage of every node in a WSN. The main purpose of each node is to collect environmental data and to exchange hem with other nodes. To minimize battery consumption, it is necessary to limit the radio usage. Therefore, we implemented Razor, a new lightweight algorithm which is expected to improve data compression and classification by leveraging on the advantages offered by data mining methods for optimizing communications and by enhancing information transmission to simplify data classification. Data compression is performed studying the well-know Vector Quantization (VQ) theory in order to create the codebooks necessary for signal compression. At the same time, it is requested to give a semantic meaning to un- known signals. In this way, the codebook feature is able not only to compress the signals, but also to classify unknown signals. Razor is compared with both state-of-the-art compression and signal classification techniques for WSN . The third part of the thesis covers the concept of smart object applied to Robotic research. A critical issue is how a robot can localize and retrieve smart objects in a real scenario without any prior knowledge. In order to achieve the objectives, it is possible to exploit the smart object concept and localize them through RSSI measurements. After the localization phase, the robot can exploit its own camera to retrieve the objects. Several filtering algorithms are developed in order to mitigate the multi–path issue due to the wireless communication channel and to achieve a better distance estimation through the RSSI measurement. The last part of the dissertation deals with the design and the development of a Cognitive Network (CN) testbed using off the shelf devices. The device type is chosen considering the cost, usability, configurability, mobility and possibility to modify the Operating System (OS) source code. Thus, the best choice is to select some devices based on Linux kernel as Android OS. The feature to modify the Operating System is required to extract the TCP/IP protocol stack parameters for the CN paradigm. It is necessary to monitor the network status in real-time and to modify the critical parameters in order to improve some performance, such as bandwidth consumption, number of hops to exchange the data, and throughput.In questi ultimi anni siamo circondati da una grande quantita` di oggetti che sono capaci di comunicare tra di loro e leggere in tempo reale grandezze fisiche come temperatura, luce e umidita`. Un passo successivo consistera` nel combinare il contenuto informativo di queste grandezze fisiche per estrarre ulteriore informazione non osservabile analizzando il singolo dato. Questi oggetti non sono solamente hi-tech, come PC da tavolo, PC portatili, tablet, tele- foni mobili di ultima generazione, i.e., smartphone, e altri con elevate capacita`, ma soprat- tutto quegli oggetti di uso comune, come carte d’identita`, patenti di guida, orologi, che possono essere connessi ad Internet semplicemente applicando micro dispositivi di comuni- cazione. Cos`ı, del mondo analogico di qualche anno fa, ci si sta sempre piu` spostando verso il mondo digitale dell’Internet of Things (IoT), dove le informazioni di ogni singolo oggetto possono essere cercate in Internet. Il paradigma IoT pone a diverse sfide architetturali, come auto organizzazione, auto controllo e auto disposizione degli smart object. Inoltre esiste il problema di come minimizzare l’utilizzo delle risorse limitate di ogni dispositivo. Il concetto di IoT ricopre molti paradigmi di comunicazione, che possono essere elencati come reti WiFi, Radio Frequency Identificator (RFID), e reti di sensori senza fili (WSN). Ogni paradigma so- pra elencato puo` essere pensato come un’isola dell’architettura IoT dove ogni dispositivo all’interno di essa e` capace di comunicare con gli altri dispositivi. La tesi e` divisa in diverse sezioni per ricoprire alcune problematiche menzionate poc’anzi. Il primo argomento trattato dalla tesi riguarda la possibilita` di inferire nuova conoscenza dai nodi disposti all’interno di uno scenario. Per questa ragione, la ricerca si e` focalizzata sul web semantico, web 3.0, dove si assegna un significato semantico ad ogni “thing” che si trova all’interno dell’architettura. Il solo concetto di semanticita` e` inutilizzabile ai fini di estrarre nuove informazioni, ma deve essere organizzato secondo una struttura chiamata Ontologia. Con l’ausilio del concetto di ontologia, e` possibile applicare ragionatori onto- logici per l’inferenza di nuova informazione da quella gia` esistente. Il secondo passo della tesi e` stato minimizzare l’utilizzo complessivo di ogni nodo pre- sente all’interno di una WSN. Partendo dall’idea che ogni nodo e` capace di collezionare dati ambientali e scambiare questo tipo d’informazione con altri, questo comporta un con- sumo energetico non “ammissibile” per dispositivi che devono avere un tempo di vita quasi “illimitato”. Un punto chiave per aumentare il tempo di vita e` minimizzare il consumo energetico di ogni dispositivo. Per questo motivo e` stato concepito RAZOR, un algoritmo leggero capace di migliorare la compressione e la classificazione dei dati basandosi sui van- taggi offerti dai metodi di data mining per comunicazioni, ottimizzando e potenziando la trasmissione dell’informazione per la classificazione dei dati. La parte di teoria riguardante la compressione dei dati e` basata sulla quantizzazione vettoriale (VQ) per la creazione di dizionari di codifica necessari a RAZOR nella fase di compressione dei segnali. Allo stesso tempo e` richiesto di dare un significato semantico ai segnali per poi poter classificare segnali sconosciuti. RAZOR e` stato comparato con lo stato dell’arte della compressione e classificazione di segnali. Un terzo passo della tesi riguarda il concetto di smart object applicato al campo della robotica. Un punto cruciale sta nella possibilita` di utilizzare gli smart object per creare un’architettura hardware/software dove non e` necessaria la presenza di un operatore umano per il suo funzionamento. Un esempio puo` essere il riconoscimento e recupero di smart object tramite l’utilizzo di un robot completamente autonomo. Durante l’inizializzazione del sistema, robot e smart objects non avranno nessuna conoscenza dello scenario che li circonda. Un’idea consiste nell’utilizzare dispositivi poco costosi, come i nodi sensore, da applicare ad ogni oggetto e al robot, per poi strutturare la loro capacita` di comunicazione wireless per essere localizzati dal robot. Dopo che gli oggetti sono stati localizzati, il robot utilizza la propria videocamera per riconoscere e recuperare l’oggetto. Per ottenere una localizzazione attendibile tramite misure di RSSI sono stati realizzati e comparati diversi algoritmi di stima e filtraggio per ovviare a problemi di multi–path che si verificano durante la comunicazioni wireless. L’ultima parte della tesi riguarda la progettazione e lo sviluppo di un Cognite Network (CN) testbed realizzato con dispositivi commerciali. La scelta dei dispositivi e` stata fatta considerando determinati punti chiave come: costo, usabilita`, configurabilita`, mobilita` e possibilita` di modificare il Sistema Operativo (OS). Percio ́ la migliore scelta e` stata quella di utilizzare dispositivi basati su kernel Linux come Android OS. Un sistema operativo modificabile e` la chiave per realizzare il CN testbed perche ́ da` la possibilita` di estrarre i parametri dello stack protocollare TCP/IP per migliorarne le prestazioni come consumo della banda, numero di passaggi che un segmento deve fare per arrivare al nodo desti- nazione e il throughtput di una connessione punto a punto

Improving Internet of Things communications through compression and classification

The amount of data produced and exchanged in the Internet of Things is continuously increasing. The associated management costs for information transmission and classification are becoming an almost unbearable burden due to the unprecedented number of data sources and the intrinsic vastness of the dataset. In this paper, we propose a novel lightweight approach capable of alleviating both aspects by leveraging on the advantages offered by classification methods to optimize communications and by enhancing information transmission to simplify data classification. In particular, we propose to adopt Motifs, recurrent features used for signal categorization, in order to compress data streams: in such a way it is possible to achieve compression levels of up to an order of magnitude, while maintaining the signal distortion rate within acceptable bounds and allowing for simple lightweight distributed classification and anomaly detection techniques. We elaborate about data representation and motif extraction methods for constrained devices, proposing a simple and effective solution for the problem. We validate our approach with an extensive simulation campaign thoroughly spanning the system parameter set. This work paves the road ahead for the realization of a universal signal processor for constrained devices in the Internet of Things, which will be capable of appropriately handling any given data while at the same time increasing communication efficiency

An Ontology-Based Framework for Autonomic Classification in the Internet of Things

The advent of ontology systems has brought inference capabilities to database systems. Also, the number of interconnected objects is constantly growing, thus forming a new paradigm for the Internet of the future, where not only will any single device be accessible and usable from anywhere and at anytime, but also, the system will be able to self--organize and adapt to external agents. This paper focuses on the description and the realization of an ontology system for wireless sensors and actuators networks dealing with heterogeneous device integration and composite event detection. The system has been developed with widely accepted tools such as protege and Pellet and has been implemented on the server of a wireless sensor network testbed that features 350 devices and is fully IPv6 compliant. The main features of the proposed system are the complete interoperability thanks to the support of advanced web languages on constrained devices, the capability of classifying any node of the network according to its sensors and its geographic position, and a general method for detecting events and anomalies among the data collected by the network. Finally, the paper provides evidence of the viability of the approach by describing its implementation and showing the results of a first experimental campaign

RAZOR: A Compression and Classification Solution for the Internet of Things

The Internet of Things is expected to increase the amount of data produced and exchanged in the network, due to the huge number of smart objects that will interact with one another. The related information management and transmission costs are increasing and becoming an almost unbearable burden, due to the unprecedented number of data sources and the intrinsic vastness and variety of the datasets. In this paper, we propose RAZOR, a novel lightweight algorithm for data compression and classification, which is expected to alleviate both aspects by leveraging the advantages offered by data mining methods for optimizing communications and by enhancing information transmission to simplify data classification. In particular, RAZOR leverages the concept of motifs, recurrent features used for signal categorization, in order to compress data streams: in such a way, it is possible to achieve compression levels of up to an order of magnitude, while maintaining the signal distortion within acceptable bounds and allowing for simple lightweight distributed classification. In addition, RAZOR is designed to keep the computational complexity low, in order to allow its implementation in the most constrained devices. The paper provides results about the algorithm configuration and a performance comparison against state-of-the-art signal processing techniques

Autonomous robot exploration in smart environments exploiting wireless sensors and visual features

This paper presents a complete solution for the integration of robots and wireless sensor networks in an ambient intelligence scenario. The basic idea consists in shifting from the paradigm of a very skilled robot interacting with standard objects to a simpler robot able to communicate with smart objects, i.e., objects capable of interacting among themselves and with the robots. A smart object is a standard item equipped with a wireless sensor node (or mote ) that provides sensing, communication, and computational capabilities. The mote’s memory is preloaded with object information, as name, size, and visual descriptors of the object. In this paper, we will show how the orthogonal advantages of wireless sensor network technology and of mobile robots can be synergically combined in our approach. We detail the design and the implementation of the interaction of the robot with the smart objects in the environment. Our approach encompasses three main phases: (a) discovery , the robot discovers the smart objects in the area by using wireless communication; (b) mapping , the robot moving in the environment roughly maps the objects in space using wireless communication; (c) recognition , the robot recognizes and precisely locates the smart object of interest by requiring the object to transmit its visual appearance. Hence, the robot matches this appearance with its visual perception and reach the object for fine-grain interaction. Experimental validation for each of the three phases in a real environment is presented

A machine learning-based ETA estimator for Wi-Fi transmissions

Recent advancements related to device to device (D2D) communication make it possible for a transmitting node to dynamically select the interface to be used for data transfers locally, without traversing any network infrastructure. In this scenario, a controller is identified, whose goal is to manage the D2D connection after its establishment. The software defined networking paradigm makes it possible to select this controller node via software: a device becomes the master node of a Wi-Fi-direct network, whereas the remaining units, i.e., the clients, can exchange data with other devices through the master. This paper develops a machine learning-based prediction algorithm for the aforementioned scenario, in which multiple elements, while receiving data from the controller, require an accurate on-the-fly estimation of the remaining transmission time, i.e., the estimated time of arrival. Different machine learning approaches are considered for this task, with the goal of exploiting only the information available at each client, without modifying any standard communication protocol. This information is critical when, for instance, a mobile user needs to decide whether or not to delay a data transfer, based on the load of the network and on the residual time under radio coverage from an access point