The future of Internet is envisioned as the Internet of Things (IoT), a concept where countless physical objects, many of which possess limited or minimal resources, interact with one another autonomously, eliminating the need for human involvement. In the development of IoT, one of the primary concerns is security and privacy protection of end-users. Ensuring the confidentiality of communication is crucial across a range of applications, particularly in situations where communication devices have various limitations. Traditional encryption algorithms like Rivest, Shamir, Adleman (RSA), and advanced encryption standard (AES) are computationally demanding and necessitate significant memory resources, leading to potential performance drawbacks on IoT devices. Basic encryption methods are susceptible to being compromised easily. Hence, altered editions of the highly efficient lightweight cipher extended tiny encryption algorithm (XTEA) has been proposed in this article. The key scheduling of XTEA has been substituted with the key scheduling algorithm of an ultralightweight block cipher PRESENT and this modified version is named as PREXTEA. Additionally, the integration of key-bit generation mechanism of a lightweight stream cipher TRIVIUM, resulted in an enhanced version of XTEA named as TRIXTEA. Consequently, the strength of the original cipher has been enhanced with improved performance parameters.

Remote healthcare monitoring is a crucial component in the field of medical Internet of Things (IoT), which effectively achieves remote monitoring, collection, and transmission of physiological data by combining AI algorithms with intelligent health monitoring systems to improve people’s quality of life and health. In this work, an AI-enabled scalable smartphone photonic sensing system is developed for remote healthcare monitoring using fiber optic sensors and a smartphone. The smartphone serves as both the light source and interrogator for the system, with the ability to connect to the network for integration with the IoT. Scalability is achieved through a multichannel framework, and by modifying the connector design, the system can incorporate more sensors to monitor multiple physiological parameters in real-time. In addition to acquiring basic respiratory and heartbeat signals and various gait parameters, the system successfully implemented the recognition of various gait patterns and fatigue monitoring using an adapted MobileNetV3 neural network structure. The accuracy of 98.5% for the gait pattern recognition task, and 94% and 95.8% for the mental and muscle fatigue monitoring tasks, respectively, demonstrates the system’s potential as a telemedicine tool. Additionally, the low cost, noninvasiveness, and portability of this innovative sensor system make it highly generalizable.

In poultry farms, the internal environment and movement of birds directly impacts health of birds. Timely analysis of internal environment data is important as it may lead to an unhealthy environment for birds. Traditionally, data analysis techniques were performed on the data collected from the Internet of Things (IoT) devices in the cloud. However, cloudbased solutions are constrained by the lower data bandwidth available in the poultry farms situated in rural areas. Also, IoT devices have limited computational capabilities. The increase in processing capabilities of the IoT device facilitates the data analysis on the device itself termed as edge computing. Hence, an edge-IoT-based model has been proposed to monitor and detect anomalies of the internal environment of the farm. Raspberry Pi 4 is used as an edge device in place of the high-cost edge graphics processing units (GPUs). A light-weight deep learning (DL) algorithm, long short-term memory-based autoencoder has been used for inferences on the multivariate data set acquired from various installed sensors. The proposed model has outperformed several existing methods by achieving an F1 score and Recall of 0.9627 and 0.959, respectively, at edge platforms. The performance of light-weight DL model on edge devices is same as that of original model with inference time of 2-ms per event. This leads to inclusion of Raspberry Pi 4 at edge nodes which can be a new opportunity for low-cost solutions. Furthermore, a novel sound-based architecture is proposed to increase the movement of birds inside the farm that directly improves the health of birds.

Medical Internet of Things technology can effectively enable remote physiological data collection, monitoring, and transmission by integrating flexible sensors, wireless communication, and the human body in a wearable and embedded manner. Herein, a stretchable elastomer optical fiber has been developed and sandwiched with two PMMA optical fibers to form a fully flexible polymer optical fiber sensor. The optical fiber integrated with the system mainly consists of a microcomputer, a light-emitting diode driver, a light-emitting diode light source, a photodiode, and a Bluetooth module. One intelligent wearable photonic sensing system has been developed based on the Beer-Lambert law of the stretchable elastomer optical fiber for remote healthcare monitoring. Benefiting from the use of elastomer polymer materials, the sensing system features a maximum strain of more than 250%, a high tensile strain of up to 100%, and a durability of >500 tests. Also, based on the advantage of elastomer optical fiber, the sensing part can be flexibly pasted on the skin surface as a wearable device for real-time monitoring of multiple physiological parameters. In this study, we successfully realized the monitoring of breathing pattern, heart rate, pulse, facial micro-activity, and joint activity, and the recognition of articulatory activity and knee joint activity using a one-dimensional convolutional neural network, with an accuracy of more than 90% for each activity recognition. Such merits demonstrate its potential as a medical toolkit and indicate promise for remote healthcare monitoring.

The design of the memristor emulator, which has found widespread of applications in analog computation, neuromorphic and biological systems, communications, etc., is much sought for. In this article, a resistorless electronically tunable grounded and floating memristor emulator by employing current-mode (CM) building blocks: operational transconductance amplifier (OTA) and voltage differencing buffered amplifier (VDBA) has been proposed. The proposed emulators can be configured as incremental and decremental operations. The robustness of the proposed emulator has been verified by performing various analyses: nonideal interpretations, process corner variations, temperature fluctuations, and nonvolatility behavior. The layout has been performed, and the theoretical fingerprint characteristics have been simulated and observed using 180-nm CMOS process parameters in the Cadence environment. The pinched hysteresis loop of the proposed memristor emulator maintains up to 5 MHz. The proposed memristor emulator uses less number of circuit components, but it maintains performance in terms of speed and operating range of frequency. Binary frequency shift keying (BFSK) has been performed using the proposed memristor to validate its functionality in the application. This memristor emulator with superior performance can be integrated into the communication system, analog computation, neuromorphic and biological systems, etc.