In this work, we propose Over-The-Air (OTA)- based reconfigurable IoT health-monitoring wearables, which tether wirelessly to a low-power and portable central processing and communication hub (CPH). This hub is responsible for the proper packetization and transmission of the physiological data received from the individual sensors connected to each wearable to a remote server. Each wearable consists of a sensor, a communication adapter, and its power module.We introduce lowpower adapters with each sensor, which facilitates the sensor-CPH linkups and on-demand network parameter reconfigurations. The newly introduced adapter supports the interoperability of heterogeneous sensors by eradicating the need for sensor-specific modules through OTA-based reconfiguration. The reconfiguration feature allows for new sensors to connect to an existing adapter, without changing the hardware units or any external interface. The proposed system is scalable and enables multiple sensors to connect in a network and work in synchronization with the CPH to achieve semantic and device interoperability among the sensors. We test the implementation in real-time using three different health-monitoring sensor types – temperature, pulse oximeter, and ECG. The results of our real-time system evaluation depict that the proposed system is reliable and responsive in terms of the achieved packet delivery ratio, received signal strength, and energy consumption

We propose “DROPS”, a scheme which dynamically selects radio protocols in an energy-constrained wearable IoT healthcare system. We consider the use of multiple radio protocols, which are capable of transmitting a patient’s sensed physiological parameters to the server through Local Processing Units (LPUs). As the health parameters are non-stationary and temporally fluctuating, especially for critical patients, the selection of an appropriate radio protocol is essential to maintain the accuracy and timely delivery of data from the patient to the server. Additionally, the mobility of patients through various locations within the hospital mandates the selection of the best radio protocol among the multiple available ones for each location, to enable data to offload to the remote server. We use single-leader-multiple-follower Stackelberg non-cooperative game to map the strategic interactions between a patient’s LPU and the hospital’s server. “DROPS” dynamically selects the appropriate radio protocol, based on the criticality index of a patient, the reputation of the radio, the Euclidean distance between the radios and the LPU, and the load on the protocol. Results on real-life data and their large-scale emulation show that the data rate increases by almost 78% and throughput by approximately 7%, as compared to existing schemes.

In this paper, we develop and analyze a smart digital stethoscope – SkopEdge – to provide reliable remote e-health monitoring with a minimum delay while enhancing overall network performance. SkopEdge initially records the heart sounds from individuals and then senses the quality of the network. Depending on the network traffic, SkopEdge converts the audio clip into an appropriate format before transferring it to remote locations for estimating the number of heartbeats and storage. Towards this, we formulate the link quality along with SkopEdge’s current configuration as a Markov Decision Process (MDP) with actions as conversion format selection. The remote server then returns the result, which SkopEdge displays on its screen. Real-time implementations show that SkopEdge works efficiently in all network conditions. Further, audio conversions usually degrade the quality of sound, but our proposed system does not change its primary components. Although SkopEdge exhibits an increase in energy consumption by 79% while converting to lower-quality formats, it also reduces the energy consumption by 99% while transmitting the same, which subsequently results in energy savings. Further, we provide an analysis of the estimated heartbeats in an audio clip by SkopEdge.

This paper proposes and evaluates an online architecture for networked Brain-Computer Interfaced (BCI) smart home systems for enabling Ambient Assisted Living (AAL). Our work uses multiple portable, low cost, single channel EEG (Electroencephalograph) systems to achieve mind networked devices (MiND) for home appliance control aiming to enable better accessibility for the infirm and people with mobility disability. We tested our approach on real-life hardware towards the control of devices such as lights, fans, and room temperature conditioning systems and emulated the behavior of the networked hardware for a more substantial number of users on conceptualized scenarios, which will be encountered in implementing this architecture. The NS2 simulation results of the large-scale implementation show promising results and give a precise idea about various essential features such as routing protocols, routing configuration, mode of transmission, attenuation losses, and delays incurred during in-house operation of this architecture.