Abstract-Implementing requirements verified body worn medical sensors and smart phones, acting as base stations, in Body Sensor Networks (BSNs), is of extreme importance for development of reliable pervasive health monitoring systems (PHMS). Models of BSNs have been used to analyze designs with respect to requirements such as energy consumption, lifetime, and network reliability under dynamic context changes due to user mobility. This paper proposes Health-Dev that takes a high level specification of requirements verified BSN design and automatically generates both the sensor and smart phone code. Case studies related to energy efficiency and mobility aware network reliability show whether the resulting implementation satisfies the requirements set forth in the design phase.
Abstract-Privacy of physiological data collected by a network of embedded sensors on human body is an important issue to be considered. Physiological signal-based security is a light weight solution which eliminates the need for security key storage and complex exponentiation computation in sensors. An important concern is whether such security measures are vulnerable to attacks, where the attacker is in close proximity to a Body Sensor Network (BSN) and senses physiological signals through non-contact processes such as electromagnetic coupling. Recent studies show that when two individuals are in close proximity, the electrocardiogram (ECG) of one person gets coupled to the electroencephalogram (EEG) of the other, thus indicating a possibility of proximity-based security attacks. This paper proposes a model-driven approach to proximity-based attacks on security using physiological signals and evaluates its feasibility. Results show that a proximity-based attack can be successful even without the exact reconstruction of the physiological data sensed by the attacked BSN. Our results show that with a 30 second handshake we can break PSKA with an average probability of 0.3 (0.24 minimum and 0.5 maximum).
With periodic technology advancements and pandemic-like situations, remote patient health monitoring has increased significantly. The Internet of Things (IoT) devices, including wearables, sensors, and actuators deployed on the human body, detect and regulate physiological data. These systems can establish a trigger mechanism in the event of a possible health incident. Health monitoring using IoT devices generates a large amount of data. Several Machine Learning (ML) strategies have been utilized to analyze the collected data and derive precise predictions. The confidentiality of patient data is one of the essential requirements of these systems. It has been discovered that malicious coordination of ML algorithms might result in a massive attack surface, providing cyber- criminals with an accessible platform. Considering these requirements for using IoT systems for health monitoring, Federated Learning (FL) can solve data privacy challenges by training ML models locally on IoT de- vices without any data transfer to the cloud. FL facilitates information sharing among all IoT devices installed in hospitals through collabora- tive ML model training. This article will examine the significance of em- bedding FL into IoT-enabled smart hospitals and future guidelines for accomplishing this. This article discusses identifying rare diseases and critical care, resolving the problem of insufficient patient health data to train ML models, and preserving patient privacy.
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