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Non-invasive human vital sign detection has gained significant attention in recent years, with its potential for contactless, long-term monitoring. Advances in radar systems have enabled non-contact detection of human vital signs, emerging as a crucial area of research. The movements of key human organs influence radar signal propagation, offering researchers the opportunity to detect vital signs by analyzing received electromagnetic (EM) signals. In this review, we provide a comprehensive overview of the current stateof- the-art in millimeter-wave (mmWave) sensing for vital sign detection. We explore human anatomy and various measurement methods, including contact and non-contact approaches, and summarize the principles of mmWave radar sensing. To demonstrate how EM signals can be harnessed for vital sign detection, we discuss four mmWave-based vital sign sensing (MVSS) signal models and elaborate on the signal processing chain for MVSS. Additionally, we present an extensive review of deep learning-based MVSS and compare existing studies. Finally, we offer insights into specific applications of MVSS (e.g., biometric authentication) and highlight future research trends in this domain. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are growing rapidly in various fields of commerce, medicine, industrial, agriculture, research, meteorology, etc. that eases complicated tasks. The most active and recent research areas in wireless sensor networks are deployment strategies, energy efficiency and coverage. Besides energy harvesting, network lifetime of the sensors can be increased by decreasing the consumption of energy. This becomes the most challenging areas of utilizing wireless sensor network in practical applications. Deployment in WSNs directly influence the performance of the networks. The usage of sensor nodes in large quantity in the random deployment improves concerns in reliability and scalability. Coverage in wireless sensor networks measures how long the physical space is monitored by the sensors. Barrier coverage is an issue in wireless sensor networks, which is used for security application aims in intruder detection of the protected area. Several ongoing research work focuses on energy efficiency and coverage in wireless sensor networks and numerous schemes, algorithms, methods and architectures have been proposed. Still, there is no comprehensive solution applicable universally. Hence,this work provides with a state-of-the-art of the classification of wireless sensor networks based on different dimensions, such as, types of sensors, deployment strategies, sensing models, coverage and energy efficiency. [ABSTRACT FROM AUTHOR]

In this paper, a novel time-phased directional-sensor network deployment strategy is presented for the mobile-target search problem, e.g., wilderness search and rescue (WiSAR). The proposed strategy uses probabilistic target-motion models combined with a variation of a standard direct search algorithm to plan the optimal locations of directional-sensors which maximize the likelihood of target detection. A linear sensing model is employed as a simplification for directional-sensor network deployment planning, while considering physical constraints, such as on-time sensor deliverability. Extensive statistical simulations validated our method. One such illustrative experiment is included herein to demonstrate the method’s operation. A comparative study was also carried out, whose summary is included in this paper, to highlight the tangible improvement of our approach versus three traditional deployment strategies: a uniform, a random, and a ring-of-fire type deployment, respectively.

In wireless sensor networks (WSNs), deterministic sensing model has been widely studied and explored for its coverage analysis. Boolean sensing model falls under the deterministic category, which considers a fixed sensing radius, although it is not a realistic assumption. In the literature other probabilistic sensing models like Elfes sensing model and shadow fading sensing model are also considered. However, the linking between the Boolean sensing model and probabilistic sensing models has not yet been analyzed. Nowadays, mobile sensor networks (MSNs) are becoming a hot research topic for their diverse area of applications. It comes with many mathematical and analytical complexities for coverage performance analysis due to continuous topological changes. In this paper, we derive the expression of effective sensing radius (ESR) for probabilistic sensing models to make the link between these sensing models. We also extend our study to MSNs for studying performance analysis such as network coverage fraction and intruder detection time by utilizing ESR of probabilistic sensing models. Our proposed ESR is suitable for analyzing and planning of WSNs. [ABSTRACT FROM AUTHOR]

In this paper, a novel time-phased directional-sensor network deployment strategy is presented for the mobile-target search problem, e.g., wilderness search and rescue (WiSAR). The proposed strategy uses probabilistic target-motion models combined with a variation of a standard direct search algorithm to plan the optimal locations of directional-sensors which maximize the likelihood of target detection. A linear sensing model is employed as a simplification for directional-sensor network deployment planning, while considering physical constraints, such as on-time sensor deliverability. Extensive statistical simulations validated our method. One such illustrative experiment is included herein to demonstrate the method's operation. A comparative study was also carried out, whose summary is included in this paper, to highlight the tangible improvement of our approach versus three traditional deployment strategies: a uniform, a random, and a ring-of-fire type deployment, respectively. [ABSTRACT FROM AUTHOR]