We study Compound TCP (C-TCP), the default TCP in the Windows operating system, with Random Exponential Marking (REM) and the widely used Drop-Tail queue policy. The performance metrics we consider are stability of the queue size, queuing delay, link utilization, and packet loss. We analyze the following models: 1) a nonlinear model for C-TCP with Drop-Tail and small buffers; 2) a stochastic variant of REM along with C-TCP; and 3) the original REM proposal as a continuous-time nonlinear model with delayed feedback. We derive conditions to ensure local stability and show that variations in system parameters can induce a Hopf bifurcation, which would lead to the emergence of limit cycles. With Drop-Tail and small buffers, the Compound parameters and the buffer size both play a key role in ensuring stability. In the stochastic variant of REM, larger thresholds for marking/dropping packets can destabilize the system. With the original REM proposal, using Poincaré normal forms and the center manifold analysis, we also characterize the type of the Hopf bifurcation. This enables us to analytically verify the stability of the bifurcating limit cycles. Packet-level simulations corroborate some of the analysis. Some design guidelines to ensure stability and low latency are outlined.

In emergency medicine, patient care is intense and stressful, often requiring paramedics to consult with remote physicians to convey the patient's condition. We present a framework for context-management in telemedicine developed in collaboration between engineers, physicians, and paramedics. We describe a mobile platform and embedded wireless sensors to capture physiological and audio context into a comprehensive patient record, accessible locally and remotely. We describe a first evaluation of this technology by trained paramedics in simulated scenarios and evaluate key aspects of system performance. Early results suggest that wireless sensing can provide reliable and low latency data both locally and to remote physicians. In addition, audio context capture is a promising approach to capturing a comprehensive patient record, with a low rate of medically important errors.