Stress has become an increasing concern among college students. Passive sensing techniques allow the extraction of stress-related parameters from a student. These techniques use highly resource-intensive machine learning algorithms to predict the stress levels of a student from these parameters. However, the current techniques do not provide any social communication solution for students suffering from stress. In this work, we propose StressAlly, a stress companion recommender system. The system comprises two modules, the stress score predictor and the stress companion recommender. The stress score predictor incorporates edge computing and deploys lightweight in-app inferences. The stress score predictor calculates the stress level in the scale 0–4 in the smartphone using the Artificial Neural Network (ANN) Regressor and sends it to the server. The Stress companion recommender provides similar stress levels of students to each student using the User-User Collaborative Filtering technique. We achieved training MAE and loss of 0.7478 and 1.0298, respectively. We get a test Mean Absolute Error (MAE) of 0.737 on the unseen data. We evaluate the CPU, memory, time delay, and network performance of StressAlly on the server and the Android smartphone. StressAlly utilizes 188 MB memory and 25% CPU on the smartphone.

In this article, we propose and implement i-AVR, an Internet of Things (IoT)-based critical-aware system for point-of-care recommendation during ambulatory in-transits. The delay due to ambulances stuck in traffic congestion, disruptive roadways, and far-away hospitals restrain the smooth ambulance services. Therefore, in order to assist the time-critical scenario of a hospital-bound patient, we consider a guidance system to address the necessity. Moreover, these patients require continuous vitals monitoring, which may vary with the progress of time, to reduce the response time upon reaching the destination. The implemented i-AVR comprises two units: 1) a portable healthcare unit and 2) an android navigation unit. The healthcare unit aims to compute the criticality index of the en-route patient and recommend the nearest healthcare center while the navigation unit recommends the convenient route in case of any anomaly in vitals. We show the effectiveness of i-AVR regarding network performance while highlighting the response time of the system. We observe the system response time for computation in orders of seconds and interunit communication in milliseconds. Eventually, this analysis indicates the effectiveness of i-AVR in providing quick decisions during the time-critical situations. Our implementation provides essential intervention toward IoT-based healthcare technologies.

In social IoMT systems, resource-constrained devices face the challenges of limited computation, bandwidth, and privacy in the deployment of deep learning models. Federated learning (FL) is one of the solutions to user privacy and provides distributed training among several local devices. In addition, it reduces the computation and bandwidth of transferring videos to the central server in camera-based IoMT devices. In this work, we design an edge-based federated framework for such devices. In contrast to traditional methods that drop the resource-constrained stragglers in a federated round, our system provides a methodology to incorporate them. We propose a new phase in the FL algorithm, known as split learning. The stragglers train collaboratively with the nearest edge node using split learning. We test the implementation using heterogeneous computing devices that extract vital signs from videos. The results show a reduction of 3.6 h in the training time of videos using the split learning phase with respect to the traditional approach. We also evaluate the performance of the devices and system with key parameters, CPU utilization, memory consumption, and data rate. Furthermore, we achieve 87.29% and 60.26% test accuracy at the nonstragglers and stragglers, respectively, with a global accuracy of 90.32% at the server. Therefore, FedCare provides a straggler-resistant federated method for a heterogeneous system for social IoMT devices.

In this work, we propose SemBox - Semantic interoperability in a Box, to enable wireless on-the-go communication between heterogeneous wearable health monitoring devices. It can connect wirelessly to the health monitoring devices and receive their data packets. It uses a Mamdani-based fuzzy inference system with data pre-processing to classify the received data packet into one of the classes of the vital parameters. It enables semantic interoperability by labelling and annotating the data packets based on the extracted packet information. We implement SemBox using three different health monitoring wearables, with different keywords used for each vital parameter representation in the data packet. SemBox shows a maximum classification accuracy of 85.71%, with a maximum PDR of 1 at the SemBox with varying device parameters. Overall, SemBox is a potential plug-and-play solution to achieve semantic interoperability and collaboration between heterogeneous health monitoring wearable devices, irrespective of their commercial and proprietary specifications. It is customizable for applications that use multiple heterogeneous devices for collaborative monitoring and decision support. SemBox enables interoperability among health monitoring devices, introduces flexibility and ease the inter-device dynamics in the domain of biomedical research.

Robust and reliable communication systems, whether based on electromagnetic (EM) waves or light, fail to perform under water due to very high attenuation and changing visibility conditions. The present generation of systems designed for underwater communications relies mostly on acoustic waves, typically in the ultrasonic frequency range. In this work, we develop and evaluate a means of implementing underwater acoustic channel-based severely constrained IoT networks using low-cost, off-the-shelf, open hardware electronics and transducers, which can support direct communication between two nodes at a data rate of 2.4 kbps for over 65 m. These nodes can be deployed over much longer distances through multihop relay topologies. Furthermore, we evaluate the efficacy of our system toward supporting multimedia data transmission and even attempt multimedia streaming through our deployed underwater IoT network using video compression and reduced sampling of the video frames. We observe that the system successfully supports multihop network topologies and undertakes multimedia transmission by compromising the quality of the data. The system has a clear tradeoff between data quality, transmission range, and transmission delays.

The atmospheric buoyancy and intangible nature of fatal communicable viruses lead to rapid transmissions among individuals, resulting in global pandemics. Strategic lockdowns and mandatory social distancing are immediate solutions in such scenarios. However, this leads to operational disruptions in education, manufacturing, economy, transportation, governance, and community. Although technological assistance is beneficial in overcoming such issues, the current Internet of Things (IoT) infrastructure has limitations. In this article, we provide a comprehensive review of the possible IoT-based solutions that have the capacity of combating the COVID-19-like viruses. We highlight the societal impacts due to pandemics and identify the specific lacunae in current IoT solutions. We also provide comprehensive detail on how to overcome the challenges along with directions toward the possible technological trends for future research. Compared to existing reviews, our work offers a holistic view of the cause, effects, and the possible solutions that are existing, along with already existing solutions that can be customized to serve the special needs during the pandemic.

In this work, we propose and develop healthcare device interoperability (HeDI)—a system to enable device interoperability in IoT-enabled in-home healthcare monitoring platforms. The system consists of multiple sensors, each connected wirelessly to an edge device, acting as a wireless communication gateway to a remote server. The system initiates information handshaking between the sensor adapters and edge device at the beginning of the operation, which is later used to detect the sensor settings to process the data received from the sensor. The system is scalable and dynamically accommodates multiple sensors without any predefined ontologies at the edge device. The implementation of our system avoids dependencies on a system’s physical ports. The low form factor and wireless connectivity of the adapter make the system portable and convenient for in-home health monitoring. Additionally, the system allows multiple homogeneous sensors to operate at the same time in the same system. We implement and evaluate our system with a 3-lead ECG, pulse, and temperature sensors against two different network configurations—star and mesh. We use the data set generated from our implemented system for performance analysis. The network-level analysis of our system shows an average packet delivery ratio of 0.92 for star network configuration and 0.98 for mesh network configuration, ensuring the reliability of performance and its suitability for healthcare monitoring systems.

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 low-power 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.

In this work, we propose an autonomous and onboard image-based agricultural land demarcation and path-planning system—IDeA ( I oT-Based Autonomous Aerial De marcation and Path Planning for Precision A griculture) with Unmanned Aerial Vehicles (UAVs)—using our advanced UAV-based aerial IoT platform. Our work successfully addresses the problem of onboard and autonomous path planning—which conventional UAV-based systems are not capable of—during stand-alone operations and without preloaded Global Positioning SYstem (GPS) markers for flight path waypoints. Our aerial system visually identifies non-electronically and singularly tagged agricultural plots and assesses the enclosing boundaries of the identified plot. Subsequently, an onboard path planning module autonomously generates GPS waypoints for traversing the identified plot with minimal overlaps and maximal coverage. Our proposed system exhibits an area coverage efficiency of 95.39%, performs pixel-to-GPS coordinate conversion with an accuracy of 90.35%, and has high agricultural potential in applications such as surveying crop health conditions and spraying pesticide/herbicides. The proposed system has massive applications in scenarios requiring aerial detection, demarcation, geographical tagging, and coverage of an area.