Abstract Vehicular sensor networks (VSNs) are expected to revolutionize the transportation systems through automated decision‐making. These networks function on the communication between vehicles, traffic infrastructure, and people. One of the major challenges of VSNs is to deal with heterogeneous traffic due to the diverse nature of information and data generated by different types of sensors and devices within the vehicular environment. To ensure an effective operation of the network, it is crucial to offer a differentiated quality of service to each traffic class. Media access control (MAC) protocols offer an opportunity to provide differentiated channel access to the traffic of different priorities. This work is focused on offering comparison of recent MAC schemes in terms of their performance for heterogeneous traffic generated by VSNs. Two protocols FROG‐MAC and UrgMAC have been selected: FROG‐MAC is developed using a simple concept of packet fragmentation, where the packets of lower priority are fragmented to offer an early transmission opportunity to urgent traffic; on the other hand, urgMAC is designed using advanced cross‐layer techniques of two‐tiered service differentiation mechanism, adaptive data rate adjustment mechanism, urgency‐based contention window size adaptation, traffic type adaptive duty cycle, and multimedia message passing. It has been found that the FROG‐MAC outperforms urgMAC in terms of delay, energy consumption and packet delivery ratio.

A Bayesian game theoretic model is developed to dynamically select channel sensing intervals in a massively dense network of Internet of Things. In such networks, the core objective is to minimize every node's energy consumption while having incomplete information about other nodes actively communicating in the network. Selecting channel sensing intervals in a medium access control (MAC) protocol is absolutely crucial, especially in massively dense networks, and selecting intelligently these intervals can optimize the overall network energy consumption while also minimizing latency during the information transfer. In the proposed model, a sensing interval chosen by a node is dynamically derived using current and previous incoming traffic patterns at other nodes in the vicinity. This paper shows that formulating the problem of channel sensing intervals as a Bayesian game model can extensively improve the performance of a MAC protocol when incorporating information from other nodes within the network.

The ubiquity of sensor technology and the Internet of Things prompted us to propose to develop a real-time digital dashboard to visualize the anxiety risks of populations during a pandemic, as in the case of COVID-19. To this end, here we provide an end-to-end communication architecture to detect physiological data related to heart rate, blood pressure, and SPO2, using wearable sensors and communicate them to remote servers. Based on this collected data, the centralized dashboard will classify in real time the patients of each geographic region involved according to a specific attribute, i.e., normal, mild, moderate, high, severe, or extreme. In addition, we also propose to incorporate the emerging technologies of Space Time Frequency Spreading (STFS) and Space-Time Spreading-Aided Indexed Modulation (STS-IM) for the design of the communication links. It has been found that the integration of STFS and STS-IM promises to reduce the likelihood of data disruption for the proposed architecture.

COVID-19 affects the mental health of many people around the world. In particular, isolation situations due to lockdown have become more challenging for elderly people as they have limited access to technology. At the same time, technologies of remote systems using Internet of Things (IoT) have emerged as a pivotal role for healthcare management and therefore could assist the elderly in managing and improving their mental health and quality of life. In this paper, we suggest the use of wearable devices with health sensors, therapeutic music playback devices, and a cloud-based data collection system as an integrated Internet of Things architecture capable of assessing the level of anxiety and depression of elderly people. Through an accurate monitoring and reporting of the measured temperature, pulse rate, and SpO2, the system is capable of assessing the level of anxiety and depression and triggers the playback of therapeutic music to reduce the level of stress and anxiety. The implementation of the system, using NodeMCU platform, and space time spreading (STS)-aided communication links, emerges as a promising solution to help the healthcare sector and families to manage and reduce the anxiety and depression risks among elderly population.

Today, various mobile applications and wearable devices support the management of diabetes by offering early and remote monitoring facilities. However, most of the available products recommend the activity/exercise level for patients based on standard data about the impact of exercise on calories burnt and blood Glucose levels. There is a risk associated with such products due to lack of customization to the individual patients. In this paper, we propose to use an Internet of Medical Things (IoMT) architecture to predict the level of activity required each day by the patient to maintain the recommended level of blood Glucose. We compare the performance of Artificial Neural Network (ANN) and Support Vector Machine (SVM) for their prediction accuracy. The proposed model takes pre-exercise Glucose level as input parameter and recommends the duration and intensity of the physical activity required by the patient each day. ANN has been observed to perform better for its classification accuracy.

Efficient selection of MAC parameters in Wireless Sensor Networks (WSNs) improves the network performance by reducing energy consumption and increasing network lifetime. This paper describes the importance of efficiently selecting threshold parameters on the performance of MAC protocol for dynamic traffic applications such as eHealtchare. The asynchronous Adaptive & Dynamic Polling MAC protocol (ADP-MAC) has been used for the analysis. ADP-MAC uses the statistical measure of Coefficient of Variation (CV) as a threshold parameter for selecting channel polling interval distribution. Considering the medical applications, Poisson and bursty arrivals have been assumed in this work. Energy & delay performance of ADP-MAC has been studied for the varying values of CV. Experiments have been performed on Mica2 testbed using Avrora emulator. It has been observed that low values of CV results in better energy performance for Poisson traffic, whereas high value should be selected for CBR traffic. The performance of ADP-MAC for bursty arrivals for varying values of CV depicts nearly a constant trend. Based on the analysis presented in this paper, the medical applications of WSN can be facilitated.

Various dynamic MAC layer approaches have been adopted by researchers for achieving better performance of Wireless Sensor Networks (WSNs). Some of these approaches include Dynamic Duty-Cycle (DDC), multi-channel parallel transmissions, Dynamic Channel Polling (DCP) and adaptive traffic rate adjustments. This work provides a comparative analysis of the two schemes 'dynamic duty cycle' and 'dynamic channel polling' by comparing two protocols T-AAD and ADP-MAC taken as a representative protocol from each category respectively. Avrora simulator has been used for conducting experiments on mica-2 testbed, and energy & delay performance for the two schemes has been compared. Results have revealed that ADP-MAC outperforms T-AAD by reducing the transmission energy and latency, and hence it is more suitable for emerging applications of WSN & IoT.