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Double threshold authentication using body area radio channel characteristicsThe demand of portable and body-worn devices for remote health monitoring is ever increasing. One of the major challenges caused by this influx of wireless body area network (WBAN) devices is security of user's extremely vital and personal information. Conventional authentication techniques implemented at upper layers of the Open System Interconnection (OSI) model usually consumes huge amount of power. They also require significant changes at hardware and software levels. It makes them unsuitable for inherently low powered WBAN devices. This letter investigates the usability of a double threshold algorithm as a physical layer security measure in these scenarios. The algorithm is based on the user's behavioral fingerprint extracted from the radio channel characteristics. Effectiveness of this technique is established through experimental measurements considering a variety of common usage scenarios. The results show that this method provides high level of security against false authentication attacks and has great potential in WBANs.
Internet of things for sensing: a case study in the healthcare systemMedical healthcare is one of the fascinating applications using Internet of Things (IoTs). The pervasive smart environment in IoTs has the potential to monitor various human activities by deploying smart devices. In our pilot study, we look at narcolepsy, a disorder in which individuals lose the ability to regulate their sleep-wake cycle. An imbalance in the brain chemical called orexin makes the sleep pattern irregular. This sleep disorder in patients suffering from narcolepsy results in them experience irrepressible sleep episodes while performing daily routine activities. This study presents a novel method for detecting sleep attacks or sleepiness due to immune system attacks and affecting daily activities measured using the S-band sensing technique. The S-Band sensing technique is channel sensing based on frequency spectrum sensing using the orthogonal frequency division multiplexing transmission at a 2 to 4 GHz frequency range leveraging amplitude and calibrated phase information of different frequencies obtained using wireless devices such as card, and omni-directional antenna. Each human behavior induces a unique channel information (CI) signature contained in amplitude and phase information. By linearly transforming raw phase measurements into calibrated phase information, we ascertain phase coherence. Classification and validation of various human activities such as walking, sitting on a chair, push-ups, and narcolepsy sleep episodes are done using support vector machine, K-nearest neighbor, and random forest algorithms. The measurement and evaluation were carried out several times with classification values of accuracy, precision, recall, specificity, Kappa, and F-measure of more than 90% that were achieved when delineating sleep attacks.