In an era dominated by rapid digitalization of sensed data, the secure exchange of sensitive information poses a critical challenge across various sectors. Established techniques, particularly in emerging technologies like the Internet of Things (IoT), grapple with inherent risks in ensuring the confidentiality and integrity of transmitted data. Blockchain technology, known for its decentralized and tamper-resistant characteristics, stands as a reliable solution for secure data exchange. However, the persistent challenge lies in protecting sensitive information amidst evolving digital landscapes. This paper explores the potential of Non-Fungible Tokens (NFTs), a manifestation of blockchain technology, as dynamic carriers for diverse forms of sensitive information among businesses beyond their conventional association with art and collectibles. Our solution leverages the immutable NFT data to serve as a secure data pointer, while the mutable NFT data holds sensitive information protected by steganography. This dual approach ensures both data integrity and authorized access, even in the face of evolving digital threats. A performance analysis validates the efficacy of the approach, highlighting its reliability, robustness, and resistance to potential attacks on hidden information, ensuring secure data transmission across industries.

This paper deals with the use of low earth orbit (LEO) satellite links in long-term monitoring of water, ice and snow levels across wide geographic areas, where no other communication links can be applied. Unlike geostationary satellites, LEO satellites do not maintain the same position relative to the ground station, and transmissions need to be scheduled for satellite overfly periods. For our application, the energy consumption optimization is critical, and we develop a learning approach for scheduling the transmission times from the sensors. Our approach is based on online learning, and is applicable to any LEO satellite transmissions.

This paper introduces a novel respiratory detection system based on diaphragm wall motion tracking using an embedded ultrasound sensory system. We assess the utility and accuracy of this method in evaluating diaphragmatic function and its contribution to respiratory workload. The developed system is able to monitor the diaphragm wall activities when the sensor is placed in the zone of apposition (ZOA). This system allows the direct measurements with only one ultrasound PZT5 piezo transducer. The system both generates pulsed ultrasound waves at 2.2 MHz and amplifies reflected echoes. According to the diaphragmatic motions, the respiratory signals of the proposed system is insensitive to human motion artifacts. Promising results were obtained from six subjects on six different tests with an average sensitivity and specificity of 84% and 93% of respiration detection, respectively. Measurements are referenced to a SPR-BTA commercial spirometer. In this study, we also evaluated inertial and photoplethysmography (PPG) sensors as other conventional methods in this area.