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.

Knowledge of the network path properties such as latency, hop count, loss and bandwidth is key to the performance of overlay networks, grids and P2P applications. Network operators also use these metrics for managing and diagnosing problems in their networks. However, the size of the Internet makes the task of measuring these metrics immensely difficult. A more scalable approach of inference and estimation of these metrics based on partial measurements has been recently adopted. Current inference approaches do not adapt to different network topologies and the evolution of the network over time. In this paper, we propose a novel learning based approach, called Minerva, for the inferencing of inter-node properties. Minerva uses partial measurements to create signature-like profiles for the participating nodes. These signatures are later used as input to a trained Bayesian network module to estimate the different network properties. We have built a system based on our approach and present performance results from real network measurements obtained from the Planet-Lab testbed. The sensitivity of the system to different parameters including training set, measurement overhead, and size of network have also been studied in this paper.

Peer-to-peer networks are mainly unstructured, where no specific topology is imposed on the network during its operations. These networks offer a high degree of resilience against network dynamics disrupting the network's operation. Unstructured networks, based on random connections are limited, however, in the performance and node reachability they can offer to applications. In contrast, structured networks impose predetermined connectivity relationships between nodes in order to offer a guarantee on the diameter between requesting nodes and the requested objects. We observe that neither structured nor unstructured networks can simultaneously offer both good performance and resilience in a single algorithm. To address this challenge, we propose Phenix, a peer-to-peer algorithm that can construct low-diameter resilient topologies. Phenix supports low diameter operations by creating a topology of nodes whose degree distribution follows a power-law, while the implementation of the underlying algorithm is fully distributed requiring no central server, thus, eliminating the possibility of a single point of failure in the system. We present the design and evaluation of the algorithm and show through analysis, simulation, and experimental results obtained from an implementation on the PlanetLab testbed that Phenix is robust to network dynamics such as joins/leaves, node failure and large-scale network attacks, while maintaining low overhead when implemented in an experimental network.

The successful operation of emerging public wireless local-area networks requires flexible network mechanisms that can support differentiated or tiered services for a variety of applications such as bursty transactional Web applications, as well as reservation demanding voice and video applications. Congestion pricing has been proposed as one promising solution to traffic control because it accurately models the cost that each user's traffic imposes on network congestion points. The underlying assumption of congestion pricing is that users will respond to a monetary-based price signal to maximize their own utility. We observe, however, that monetary service charge is largely a business concern and not a traffic control concern because operators and users prefer simple, predictable, and stable service charges such as flat-rate or block-rate charging. Therefore, we argue, that traffic control techniques such as congestion pricing should be decoupled from monetary service charges. Under such a new regime, however, a user's response to a nonmonetary price signal would be quite different from utility maximization, and without the appropriate incentives for users to cooperate such a regime would lead to the "tragedy of the commons" phenomenon, undermining any future differentiated service offerings by wireless Internet service providers. To address this problem, we propose wireless incentive engineering mechanisms for mobile devices (MDs) and access points (APs) that provide incentives for mobile users to cooperatively use different classes of services without relying on monetary service charging. Wireless incentive engineering possesses a number of beneficial properties including minimizing the algorithmic and protocol overhead on MDs and APs, Nash bargaining fairness, and incentive compatibility for mobile users promoting the truthful selection of service class and bandwidth declaration. We use analysis, simulation, and experimental results from a wireless testbed to demonstrate the effectiveness of wireless incentive engineering.

Traffic regulation in public and private wireless LANs face a number of significant challenges, particularly in commercial networks where there is a need for efficient regulation of bursty transactional applications, support for bandwidth reservation services while inhibiting bandwidth hogging by mobile devices, and providing incentives for user cooperation. We take a new approach to solving these problems by applying incentive engineering techniques to wireless access networks. We design two incentive-based allocation service classes: an instantaneous allocation (IA) class, which provides better throughput, and a stable allocation (SA) class, which provides better allocation stability. Our approach possesses a number of beneficial properties including minimizing the algorithmic and protocol overhead on mobile devices, Nash bargaining fairness for the IA service, and incentive compatibility for mobile users promoting the truthful selection of service class and bandwidth declaration. We use analysis, simulation and experimental results from a wireless testbed to demonstrate the effectiveness of wireless incentive engineering.