In recent times, plasmonic sensing is widely used for detecting tiny particles (micro / nano-scale) and plays a crucial role in diverse application domains such as sustainability, healthcare etc. In current scenario, numerical simulators are used for predicting the optical response of a plasmonic nanostructure by solving Maxwell’s equations. These simulators are highly expensive and extremely time-consuming. We are proposing an alternative of numerical simulators using DL-based generative model to predict optical response against a geometrical structure of plasmonic sensor with reduced computing time. Our method is based on a variant of variational auto-encoder (VAE) constrained to follow the resonance peak of the optical response during training. Our method is capable of predicting the optical response from a diverse set of plasmonic nano-structures. We have demonstrated the performance using publicly available optical ring resonator an H-shaped nano-structure geometry data.

Identification of user transport mode using mobile phone-based sensors is a key component of Intelligent Transportation System. However, collecting labels/annotations while switching multiple transport modes into different journeys is tedious. Also, transport type identification working across cities and countries is a prime need. This paper proposes a method for generalizable journey mode detection without using any annotations during training exploiting unsupervised representation learning. Our method uses commonalities and diversities across various user’s different journeys, to identify user-specific journey segments from either the same or different city/country. This method is also sensitive to preserve privacy, as it does not use GPS information. We propose a multistage unsupervised learning mechanism to form clusters on the learned latent representation using a choice of best distance measure. We also propose an Invariant Auto-Encoded Compact Sequence, which is a learned compact representation encompassing the common encoded latent feature representation across diverse users and cities. We prove with an exhaustive experimental analysis, that our method, is generalizable across varying users and cities using IMU-Accelerometer sensors. We use real-life publicly available transportation datasets captured from two different cities of different countries -Sussex (United Kingdom) and Bologna (Italy), and also in-house data collected from three Indian cities.

Intelligent continuous monitoring of an IoT system to identify the operational changes, encompassing both normal and abnormal scenarios, with drift in sensing device is a challenging problem. It demands capability of learning continuously with multiple interventions or shifts, without forgetting past events information. However, forgetting the past learned knowledge, known as catastrophic forgetting, impacts significantly on the performance of continuous monitoring. In this work, we propose a generative neural network based model to handle various operational changes. Here, one objective is to learn continually by capturing past data distributional knowledge, while adapting new data signatures. Other objective, is to handle changes in data distribution like, to identify drifts, as well as, variations in diverse operational conditions of the system. We have experimented using vibration sensor based public real-world bearing data and performed extensive analysis incorporating synthetic drifts. Proposed method outperforms existing benchmark performances with clear separation of drift in learned representation.

Time Series Classification (TSC) is becoming a challenging and important problem to solve specifically due to the advent of sensor-based applications and Internet of Things (IoT). Residual mapping displays easier and evidently near-optimal learning. In this paper, we extend this notion by incorporating fine-grained refining of the residual learning through augmentation of feature space using gamut of signal processing transformations. Our proposed Sig-R2ResNet refines the learning process by introducing newer representation through signal processing primitives without distorting the residual mapping channel, along with an auto-tuning L1regularization. We further adapt the learning rate decay through learning over the trend of validation loss and modify the network parameter update process of Adam optimizer. The proposed method- Sig-R2ResNet can be viewed as an informal game where training experience is augmented through unsupervised signal processing features while the model growth is controlled by a regularization process and smoother learning convergence is achieved by validation loss dependent learning rate. One of the novelties is that the training signal dynamics control the enhancement of representation complexity, regularization and learning rate adaptation. We perform extensive experiments with diverse datasets from publicly available UCR time series database and demonstrate empirical evidences that our method consistently outperforms the existing benchmark results (creating 59.10% new benchmark results) as well as shows significantly better classification outcome than the current baselines and state-of-the-art algorithms like ResNet, BOSS, COTE.

Time Series Classification (TSC) is a challenging problem owing to the practical constraints of lack of availability of training examples and insufficient sample points in the training instances. In order to ensure the construction of a robust trained model (under practical constraints) to address TSC, we propose DyReg-FResNet, which is a dynamically regularized Residual Network (ResNet), amplified by unsupervised feature space training. We generate signal processing, information theoretic and statistical features to augment the representation learning of the ResNet. The unsupervised features are capable of extracting the morphological and structural characteristics of the time series signals, whereas our proposed dynamic regularizer trades off by reducing substantial variance while not perturbing the bias. The regularization factor is a function of the signal dynamics (in effect, regularization factor is different for different training sets). DyReg-FResNet learns through residual mapping to minimize the exploding or vanishing gradient problems, amplified unsupervised features amplify the representation space by introducing lower level representation to guide the learning towards the basins of attraction of minima and dynamic regularizer minimizes the generalization error. We extensively experiment with publicly available UCR time series datasets. DyReg-FResNet demonstrates extremely superior performance by consistently outperforming the existing benchmark results as well as current state-of-the-art algorithms.

In this paper we propose a novel process flow of a low-cost, non-invasive screening system for identifying Coronary Artery Disease (CAD) patients using a two-stage classification approach. A statistical rule engine is designed based on patient demography and medical history which is applied at the first stage of the proposed classification system. The misclassification error at this stage is reduced at the second stage based on numerical features extracted from multiple cardiovascular signals. Two sets of features are extracted from phonocardiogram (PCG) and photoplethysmogram (PPG) signals, collected from each subject for creating two independent Support Vector Machine (SVM) classifiers. Outcomes of the two classifiers are fused at the decision level for final prediction at second stage based on absolute distance of the test data-point from its respective SVM hyperplane. Results show that the proposed approach achieves sensitivity of 0.92 and specificity of 0.90 in classifying CAD patients on a hospital dataset of 99 subjects including CAD and non-CAD patients.

In this paper, we present completely automated cardiac anomaly detection for remote screening of cardio-vascular abnormality using Phonocardiogram (PCG) or heart sound signal. Even though PCG contains significant and vital cardiac health information and cardiac abnormality signature, the presence of substantial noise does not guarantee highly effective analysis of cardiac condition. Our proposed method intelligently identifies and eliminates noisy PCG signal and consequently detects pathological abnormality condition. We further present a unified model of hybrid feature selection method. Our feature selection model is diversity optimized and cost-sensitive over conditional likelihood of the training and validation examples that maximizes classification model performance. We employ multi-stage hybrid feature selection process involving first level filter method and second level wrapper method. We achieve 85% detection accuracy by using publicly available MIT-Physionet challenge 2016 datasets consisting of more than 3000 annotated PCG signals.

Feature subset selection and identification of appropriate classification method plays an important role to optimize the predictive performance of supervised machine learning system. Current literature makes isolated attempts to optimize the feature selection and classifier identification. However, feature set has an intrinsic relationship with classification technique and together they form a `model' for classification task. In this paper, we propose AutoModeling that finds optimal learning model and jointly optimize the feature and hypothesis space to maximize performance measure objective function. It is an automated framework of selecting the ensemble model {selected feature subset, selected classifier} from a given superset of features and classifiers learned from given training dataset in a computational efficient manner. We introduce novel relax-greedy search with our proposed patience function as a wrapper feature selection that maximizes the predictive performance and eliminates the classical nesting effect. We perform extensive experimentations on different types of publicly available datasets and AutoModeling demonstrates superior performance over relevant state-of-the-art methods, expert-driven manual methods and deep neural networks.

Remote cardiac health management is an important healthcare application. We have developed Heartmate that enables basic screening of cardiac health using low cost sensors or smartphone-inbuilt sensors without manual intervention. It consists of robust denoising algorithm along with effective anomaly analytics for physiological signals. Heartmate identifies and eliminates signal corruption as well as detects cardiac anomaly condition from physiological cardiac signals like heart sound or phonocardiogram (PCG) and photoplethysmogram (PPG).

Healthcare data is quite rich and often contains human survival related information. Analyzing healthcare data is of prime importance particularly considering the immense potential of saving human life and improving quality of life. Furthermore, IoT revolution has redefined modern health care systems and management. IoT offers its greatest promise to deliver excellent progress in healthcare domain. In this talk, proactive healthcare analytics specifically for cardiac disease prevention will be discussed. Anomaly detection plays a prominent role in healthcare analytics. In fact, the anomalous events are to be accurately detected with low false negative alarms often under high noise (low SNR) condition. An exemplary case of smartphone based cardiac anomaly detection will be presented.

In this paper we leverage the power of smartphone to enable proactive in-house heart condition monitoring. We introduce Heart-Trend, a nonparametric model to analyze and detect heart abnormality conditions like arrhythmia from photoplethysmogram (PPG) signal. It does on-demand heart status monitoring using smartphones (can also be implemented in PC/ICU monitors) and facilitates timely detection of heart condition deterioration to permit early diagnosis and prevention of fatal heart diseases. Proposed robust anomaly analytics engine accurately detects the morphological trend to find abnormal heart condition in real time through machine learning based trend prediction. PPG signal is frequently corrupted by ambient noise, and motion artifacts, which lead to high amount of false alarms. We introduce precise denoising technique that identifies and eliminates the corrupted segments of clinical signal to minimize its impact on the decision process and analytics. We demonstrate that Heart-Trend ensures high detection capability with lower false alarm rates.

Sensors play a vital role for realizing the vision of connected smart universe. In this paper, we present a novel sensor agnostic model SensIPro to perform robust unsupervised analysis of sensor data to support scalable analytics, a prime need for Internet of things (IoT). In the context of sensor analytics, outliers contain most delicate information. Analysis of anomaly or outlier is mostly dependent on the application domain as well as signal characteristics. Our proposed sensor analytics model SensIPro automates analysis of outliers based on inferring signal characteristics of diverse sensors from different IoT applications like healthcare, smart energy, smart transport. We apply relevant time-series algorithms using statistical analysis, information theoretic measure for sensor data analytics. We measure similarity/dissimilarity of the time series sensor data and correlate with detected outliers. Our algorithm does not require any prior knowledge of sensor data type and metadata. We present results and analysis based on real life heterogeneous sensor data sets. Obtained results further prove efficacy of the proposed mechanism.

In Internet of Things (IoT), numerous and diverse types of sensors generate a plethora of data that needs to be stored and processed with minimum loss of information. This demands efficient compression mechanisms where loss of information is minimized. Hence data generated by diverse sensors with different signal features require optimum balance between compression gain and information loss. This paper presents a unique analysis of contemporary lossy compression algorithms applied on real field sensor data with different sensor dynamics. The aim of the work is to classify the compression algorithms based on the signal characteristics of sensor data and to map them to different sensor data types to ensure efficient compression. The present work is the stepping stone for a future recommender system to choose the preferred compression techniques for the given type of sensor data.

Detection of normal and anomalous events from sensor signal is a key necessity in today's smart world. Here, we propose a novel mechanism to classify normal and anomalous phenomena by using self-learning of signal, i.e., by discovering its pattern. This is the first step in the long drawn out analysis of signals. We demonstrate a prototype of our proposed method by using a real field quasi-periodic photoplethysmogram (PPG) signal with (or without) motion artifacts, which has an immense impact on cardiac health monitoring, stress, blood pressure, and SPO2 measurement. We have achieved more than 90% accuracy to detect anomalous phenomena in the signal.

Management of bulk sensor data is one of the challenging problems in the development of Internet of Things (IoT) applications. High volume of sensor data induces for optimal implementation of appropriate sensor data compression technique to deal with the problem of energy-efficient transmission, storage space optimization for tiny sensor devices, and cost-effective sensor analytics. The compression performance to realize significant gain in processing high volume sensor data cannot be attained by conventional lossy compression methods, which are less likely to exploit the intrinsic unique contextual characteristics of sensor data. In this paper, we propose SensCompr, a dynamic lossy compression method specific for sensor datasets and it is easily realizable with standard compression methods. Senscompr leverages robust statistical and information theoretic techniques and does not require specific physical modeling. It is an information-centric approach that exhaustively analyzes the inherent properties of sensor data for extracting the embedded useful information content and accordingly adapts the parameters of compression scheme to maximize compression gain while optimizing information loss. Senscompr is successfully applied to compress large sets of heterogeneous real sensor datasets like ECG, EEG, smart meter, accelerometer. To the best of our knowledge, for the first time 'sensor information content'-centric dynamic compression technique is proposed and implemented particularly for IoT-applications and this method is independent to sensor data types.

Sensor nodes are embodiment of IoT systems in microscopic level. As the volume of sensor data increases exponentially, data compression is essential for storage, transmission and in-network processing. The compression performance to realize significant gain in processing high volume sensor data cannot be attained by conventional lossy compression methods. In this paper, we propose ASDC (Adaptive Sensor Data Compression), an adaptive compression scheme that caters various sensor applications and achieve high performance gain. Our approach is to exhaustively analyze the sensor data and adapt the parameters of compression scheme to maximize compression gain while optimizing information loss. We apply robust statistics and information theoretic techniques to establish the adaptivity criteria. We experiment with large sets of heterogeneous sensor datasets to prove the efficacy. Nonlinear lossy compression (Chebyshev) is extensively considered as the standard technique as well as experimental result with frequency domain compression like Discrete Fourier Transform (DFT) is shown as future scope of further improvement.

Smart meter, the important component of smart energy management systems invites intended or unintended, possibly dangerous privacy breaching activities, like in-house activity detection. With the emergence of Non-Intrusive Load Monitoring (NILM), privacy preservation of smart meter data becomes very important for an individual. Emerging solution provides privacy breach minimization by supervised learning through training that incurs higher capex and opex. IoT systems do not consist of human-in-loop. So, involuntary approach of privacy preservation is to be employed. In this paper, we propose a novel solution for addressing the problem of involuntary privacy breaching risk minimization in smart energy management systems. Our proposed solution `Dynamic Privacy Analyzer' scheme is an attempt towards achieving a unique privacy metric that is derived from fundamental principles like robust statistics and information theory. We analyze the performance of our scheme with large set of publicly available real smart meter datasets and evaluate optimality criteria like utility-privacy trade-off. Efficacy of our proposed scheme is demonstrated by minimizing the capability of privacy intruders like NILM. To the best of our knowledge, for the first time the involuntary privacy-aware scheme tailored for IoT system is proposed. Our proposed scheme is generic enough to suit in other IoT applications.

Sensors are one of the primary building blocks of IoT. Owing to close proximity of physical world, sensors often collect sensitive information. Invariably, sensor data has rich information content. Here we propose a novel solution IAS: Information Analytics for Sensors to unlock massive potential of sensor data through information analytics and demonstrate an alerting mechanism based on criticality of sensor information. ECG anomaly detection for healthcare, unusual appliance operation detection from smart energy meter data, bad road condition as well as activity detection from accelerometer data are typical use-case scenarios. We use robust statistical and information theoretic approaches. Our approach is unsupervised and is completely sensor agnostic. This abstract provides overview of design and implementation of our tool IAS along with obtained results tested on publicly available datasets. Last but not the least, IAS validates that outliers contain most delicate information.

The problem of privacy disclosure hinders large number of ubiquitous applications to collect, disseminate and analyze personal data from providing useful and important services. It is understood that sharing private data has high potential for facilitating innumerable benefits as well as inviting intended or unintended malicious activities leading to severe privacy breach. Such privacy breach attacks mostly capture sensitive or broadly the anomalous events. Fine grained, high resolution smart meter energy consumption data contains sensitive house hold activity signature. In this paper, we propose a tool called `Sensitivity Inspector' that detects sensitivity in smart meter data and inculcates privacy awareness among smart meter users, presuming private events are related to anomalous or unusual activities. Specifically, we analyze the sensitive content of smart meter data through robust unsupervised statistical method considering user activity as a piece-wise, stationary, stochastic process with associated uncertainty. We show the efficacy of our scheme under relevant statistical and information theoretic measures. We implement our algorithm and compare sensitivity detection capability with related supervised learning based approach and relevant privacy breaching attack like Non-Intrusive Load Monitoring (NILM).

In this paper we present a unique approach to make use of CoAP (Constrained Application Protocol) [1] from IETF (Internet Engineering Task Force) in a situation aware mode. The protocol adapts its characteristic for resource optimization depending on the indication inferred from sensed data. We consider a use-case for vehicular telemetry using a constrained in-vehicle sensor gateway which posts the vehicular information (accelerometer, GPS, device-identifier, time). In this use-case bandwidth usage is the main concern for the sensor gateway whereas usage of power which is directly impacted by overall bandwidth consumption is a key factor in case of mobile phone used as sensor gateway. We have reduced communication cost and optimized resource usage in terms of energy and bandwidth which are essential for any constrained sensor gateway by adapting characteristics of CoAP as mentioned above. The improvements are established by analyzing the data captured in real field.