Continuous blood pressure (BP) estimation remains a significant challenge in non-contact hemodynamic monitoring, where existing methods struggle to strike a balance between clinical accuracy and usability. We present Physio-BP, a physiologically-guided dual-radar system enabling calibration-free, continuous tracking of systolic (SBP) and diastolic blood pressure (DBP) by integrating spatially resolved vascular dynamics. Unlike conventional single-waveform radar approaches, Physio-BP synchronously captures mechanical signals from the heart and pulse waves from the carotid artery, allowing precise measurement of pulse arrival time (PAT) and inter-beat intervals (IBI). To enhance signal quality, we introduce a novel joint signal selection algorithm that optimizes feature extraction across dual radars, effectively addressing the inherent signal-to-noise ratio (SNR) disparities between the chest and neck monitoring sites. To validate our system, an extensive dataset exceeding 50 hours was collected under diverse conditions, including different seasons, times of day, body postures, and radar devices, ensuring comprehensive coverage. Experimental results on the dataset confirm that Physio-BP enables continuous and precise SBP and DBP tracking, a capability not achieved by current baseline methods, thereby advancing non-contact sensing for hemodynamic monitoring.

Distributed Coordinate Functions (DCF) is the mandatory medium access method for IEEE 802.11 Wireless Local Area Networks (WLANs), which is designed for infrastructure WLANs where all end users connect with Access Point (AP) with single-hop links. DCF doubles its Contention Window (CW) before each retransmission. However, in multi-hops WLANs, a node may encounter different kinds of transmission failures from different transmission directions. In this paper, we will point out that, in some situations doubling CW before retransmitting makes DCF exploit the medium inefficiently. We also propose a method to resolve this problem by employing different backoff mechanisms for different transmission directions. NS-2 simulations demonstrate a significant throughput improvement of our enhanced MAC over the native MAC.

The major objective of an admission control in wireless mesh networks is to prevent excessive real-time traffic from over-utilizing the bandwidth resources. A key component of an admission control is an accurate estimator of the bandwidth available at each node and of the actual bandwidth (including the MAC overhead) required by a new flow. This estimation problem is particularly difficult when node localization information is not available. We propose a novel admission control based on active neighbor bandwidth reservation (AC-ANBR), by which active neighbors periodically broadcast bandwidth reservation information and flow setup requests carry their bandwidth reservation states. Our proposed AC-ANBR accurately estimates the available bandwidth at each node, as well as the bandwidth required by each new flow. The effectiveness of AC-ANBR in multi-hop wireless networks is demonstrated by NS-2 simulations.

Experiments show that IEEE 802.11 DCF system exhibits unstable behavior in the congestion onset load range where the system starts to become saturated. This phenomenon is not well investigated due to the lack of proper models. In this paper, we propose a two-dimensional (2-D) Markov chain model by combining the well-known saturated model with newly created idle states. Closed-form solution to the 2-D Markov chain is derived, and system performances are validated by simulations. With the proposed 2-D model, we are able to characterize the unstable system behavior, and provide insights into 802.11 DCF system dynamics.