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The internet of things (IoT) comprises various sensor nodes for monitoring physiological signals, for instance, electrocardiogram (ECG), electroencephalogram (EEG), blood pressure, and temperature, etc., with various emerging technologies such as Wi-Fi, Bluetooth and cellular networks. The IoT for medical healthcare applications forms the internet of medical things (IoMT), which comprises multiple resource-restricted wearable devices for health monitoring due to heterogeneous technological trends. The main challenge for IoMT is the energy drain and battery charge consumption in the tiny sensor devices. The non-linear behavior of the battery uses less charge; additionally, an idle time is introduced for optimizing the charge and battery lifetime, and hence the efficient recovery mechanism. The contribution of this paper is three-fold. First, a novel adaptive battery-aware algorithm (ABA) is proposed, which utilizes the charges up to its maximum limit and recovers those charges that remain unused. The proposed ABA adopts this recovery effect for enhancing energy efficiency, battery lifetime and throughput. Secondly, we propose a novel framework for IoMT based pervasive healthcare. Thirdly, we test and implement the proposed ABA and framework in a hardware platform for energy efficiency and longer battery lifetime in the IoMT. Furthermore, the transition of states is modeled by the deterministic mealy finite state machine. The Convex optimization tool in MATLAB is adopted and the proposed ABA is compared with other conventional methods such as battery recovery lifetime enhancement (BRLE). Finally, the proposed ABA enhances the energy efficiency, battery lifetime, and reliability for intelligent pervasive healthcare.

Ubiquitous navigation requires timely, uninterrupted and accurate estimate of receiver's position at all times, in all environments and for all modes of transportation and it is highly dependent on satellite availability, geometry and accurate positioning estimation. However, the availability, continuity and accuracy of a GNSS can be severely affected in a highly dynamic environment due to blockage, fading and multipath. This results in positioning information inaccurate, unreliable and sometimes unavailable. This paper presents a study on the potential vulnerabilities that can affect a multi-constellation multi-frequency GNSS receiver in low to highly dynamic multipath environments such as clear line-of-sight, partially and highly obstructed environments to characterize the distortions/anomalies which could significantly affect the satellite signals and their impact on positioning and navigation. The multi-constellation multi-frequency GNSS receiver configuration in this paper is set to GPS, GLONASS, Galileo and BeiDou for the first time at full capacity and performance comparison is made with the GPS based on satellite availability, blockage, continuity, precision and accuracy parameters. These parameters are then used in this paper to detect and characterize the type of environment for the multi-constellation GNSS receiver without using any external aiding devices or sensors. Based on environment detection and characterization, a new Adaptive Environment Navigation (AEN) based GNSS receiver design is proposed which can work in real time and has achieved an overall availability and accuracy factor of 94% in highly dynamic multipath/NLOS environment along with a reduction in the blockage coefficient, β, by almost 11% resulting in more accuracy and precision than the standard multi-constellation GNSS receiver where the availability factor was found to be 57% only.

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