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.

IoT Healthcare Analytics: The Importance of Anomaly Detection

https://opus.lib.uts.edu.au/bitstream/10453/117906/4/10.1016j.future.2017.08.035%20am.pdf

A payload-based mutual authentication scheme for Internet of Things

The Internet of Things is a vision that broadens the scope of the internet by incorporating physical objects to identify themselves to the participating entities. This innovative concept enables a physical device to represent itself in the digital world. There are a lot of speculations and future forecasts about the Internet of Things devices. However, most of them are vendor specific and lack a unified standard, which renders their seamless integration and interoperable operations. Another major concern is the lack of security features in these devices and their corresponding products. Most of them are resource-starved and unable to support computationally complex and resource consuming secure algorithms. In this paper, we have proposed a lightweight mutual authentication scheme which validates the identities of the participating devices before engaging them in communication for the resource observation. Our scheme incurs less connection overhead and provides a robust defence solution to combat various types of attacks.

/pdf/a-robust-authentication-scheme-for-observing-resources-in-38tqn84ir9.pdf

A Robust Authentication Scheme for Observing Resources in the Internet of Things Environment

BTRES: Beta-based Trust and Reputation Evaluation System for Wireless Sensor Networks

As a key component of the information sensing and aggregating for big data, cloud computing, and Internet of Things (IoT), the information security in wireless sensor network (WSN) is critical. Due to constrained resources of sensor node, WSN is becoming a vulnerable target to many security attacks. Compared to external attacks, it is more difficult to defend against internal attacks. The former can be defended by using encryption and authentication schemes. However, this is invalid for the latter, which can obtain all keys of the network. The studies have proved that the trust management technology is one of effective approaches for detecting and defending against internal attacks. Hence, it is necessary to investigate and review the attack and defense with trust management. In this paper, the state-of-the-art trust management schemes are deeply investigated for WSN. Moreover, their advantages and disadvantages are symmetrically compared and analyzed in defending against internal attacks. The future directions of trust management are further provided. Finally, the conclusions and prospects are given.

/pdf/trust-based-attack-and-defense-in-wireless-sensor-networks-a-3tgx184dvv.pdf

Trust-Based Attack and Defense in Wireless Sensor Networks: A Survey

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.

Heart-trend: An affordable heart condition monitoring system exploiting morphological pattern

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.

Adaptive Sensor Data Compression in IoT systems: Sensor data analytics based approach

Due to its ad hoc, infrastructure-less self-configuring and collaborating nature, wireless sensor networks (WSNs) are very much vulnerable to different security threats. The goal-oriented nature of many sensing applications results in collaboration. In fact, collaboration helps to improve WSNs efficiency as well as the data-centric behavior of WSNs. Collaboration along with its advantages like efficient packet routing and data forwarding, jamming prevention etc. brings in some security issues which mostly need to be addressed from the trust and reputation analysis of the network. In this paper, a trust and reputation based collaborating computing model is derived which effectively eliminates the malicious behavior of rogue nodes with high probability. The detection of malicious nodes along with trust and reputation analysis of WSN makes this model very much robust and secure. Efficacy of this model is shown through simulation results.

Trust and Reputation Based Collaborating Computing in Wireless Sensor Networks

With the advent and rapid deployment of Internet of Things (IoT), artificial intelligence (AI), powerful smartphones, and wearable sensor devices (e.g., smartwatch), we are entering into the era of automated, remote, on-demand mobile healthcare services. According to the WHO, cardiovascular disease is the modern-day disease. However, prognosis rate of cardiac disease patients can be potentially made high with early detection and diagnosis. In this book chapter, we describe automated cardiac health monitoring system using smartphone and wearable sensors. The main contribution of such mobile applications and systems is to form a connected universe with biomedical sensors, patients, physicians, clinics, hospitals, and other medical service providers and to exploit robust analytics to infer and actuate the appropriate information and formative actions. The powerful anomaly analytics exploit AI, signal processing, and deep learning mechanisms that enable predictive decision-making and facilitate preventive cardiac health screening. The main emphasis is to develop and deploy smart, computationally efficient, rather than human-in-loop, user-friendly, data-driven cardiac healthcare solutions, where patients and healthcare service providers are seamlessly connected. In this book chapter, we discuss about important cardiovascular signals, namely, electrocardiogram (ECG), photoplethysmogram (PPG), and heart sound or phonocardiogram (PCG), and describe their role in the process of developing a mobile-based cardiac care solution. These cardiac marker signals constitute an intelligent and robust feature space for detection of different cardiac abnormalities and diseases like coronary artery disease, cardiac arrhythmia, and others. These sensor signals can be captured by affordable wearable sensors. In order to develop such mobile applications and systems, we need to address different challenges like noisy signal removal and data privacy protection along with providing robust analytics engine.

Automated Cardiac Health Screening Using Smartphone and Wearable Sensors Through Anomaly Analytics

This draft presents a work-in-progress on a challenge-response based
lightweight authentication scheme to mutually authenticate CoAP client
and server for establishing a secure unicast communication channel.
The draft discusses the generic idea behind the proposed
authentication scheme, as well as presents its CoAP specific adoption.
The proposed scheme has low communication overhead and can be a robust
alternative against the usual DTLS based connection initiation scheme
with PSK.

Arijit Ukil

Papers

Heart-trend: An affordable heart condition monitoring system exploiting morphological pattern

Adaptive Sensor Data Compression in IoT systems: Sensor data analytics based approach

Trust and Reputation Based Collaborating Computing in Wireless Sensor Networks

Automated Cardiac Health Screening Using Smartphone and Wearable Sensors Through Anomaly Analytics

Lightweight mutual authentication for CoAP (WIP)