Victor Shnayder

Simulating the power consumption of large-scale sensor network applications

Developing sensor network applications demands a new set of tools to aid programmers. A number of simulation environments have been developed that provide varying degrees of scalability, realism, and detail for understanding the behavior of sensor networks. To date, however, none of these tools have addressed one of the most important aspects of sensor application design: that of power consumption. While simple approximations of overall power usage can be derived from estimates of node duty cycle and communication rates, these techniques often fail to capture the detailed, low-level energy requirements of the CPU, radio, sensors, and other peripherals. In this paper, we present, a scalable simulation environment for wireless sensor networks that provides an accurate, per-node estimate of power consumption. PowerTOSSIM is an extension to TOSSIM, an event-driven simulation environment for TinyOS applications. In PowerTOSSIM, TinyOS components corresponding to specific hardware peripherals (such as the radio, EEPROM, LEDs, and so forth) are instrumented to obtain a trace of each devices activity during the simulation runPowerTOSSIM employs a novel code-transformation technique to estimate the number of CPU cycles executed by each node, eliminating the need for expensive instruction-level simulation of sensor nodes. PowerTOSSIM includes a detailed model of hardware energy consumption based on the Mica2 sensor node platform. Through instrumentation of actual sensor nodes, we demonstrate that PowerTOSSIM provides accurate estimation of power consumption for a range of applications and scales to support very large simulations.

Sensor networks for emergency response: challenges and opportunities

Sensor networks, a new class of devices has the potential to revolutionize the capture, processing, and communication of critical data for use by first responders. CodeBlue integrates sensor nodes and other wireless devices into a disaster response setting and provides facilities for ad hoc network formation, resource naming and discovery, security, and in-network aggregation of sensor-produced data. We designed CodeBlue for rapidly changing, critical care environments. To test it, we developed two wireless vital sign monitors and a PDA-based triage application for first responders. Additionally, we developed MoteTrack, a robust radio frequency (RF)-based localization system, which lets rescuers determine their location within a building and track patients. Although much of our work on CodeBlue is preliminary, our initial experience with medical care sensor networks raised many exciting opportunities and challenges.

Sensor networks for medical care

Sensor networks have the potential to greatly impact many aspects of medical care. By outfitting patients with wireless, wearable vital sign sensors, collecting detailed real-time data on physiological status can be greatly simplified. However, there is a significant gap between existing sensor network systems and the needs of medical care. In particular, medical sensor networks must support multicast routing topologies, node mobility, a wide range of data rates and high degrees of reliability, and security. This paper describes our experiences with developing a combined hardware and software platform for medical sensor networks, called CodeBlue. CodeBlue provides protocols for device discovery and publish/subscribe multihop routing, as well as a simple query interface that is tailored for medical monitoring. We have developed several medical sensors based on the popular MicaZ and Telos mote designs, including a pulse oximeter, EKG and motion-activity sensor. We also describe a new, miniaturized sensor mote designed for medical use. We present initial results for the CodeBlue prototype demonstrating the integration of our medical sensors with the publish/subscribe routing substrate. We have experimentally validated the prototype on our 30-node sensor network testbed, demonstrating its scalability and robustness as the number of simultaneous queries, data rates, and transmitting sensors are varied. We also study the effect of node mobility, fairness across multiple simultaneous paths, and patterns of packet loss, confirming the system’s ability to maintain stable routes despite variations in node location and

The Advanced Health and Disaster Aid Network: A Light-Weight Wireless Medical System for Triage

Advances in semiconductor technology have resulted in the creation of miniature medical embedded systems that can wirelessly monitor the vital signs of patients. These lightweight medical systems can aid providers in large disasters who become overwhelmed with the large number of patients, limited resources, and insufficient information. In a mass casualty incident, small embedded medical systems facilitate patient care, resource allocation, and real-time communication in the advanced health and disaster aid network (AID-N). We present the design of electronic triage tags on lightweight, embedded systems with limited memory and computational power. These electronic triage tags use noninvasive, biomedical sensors (pulse oximeter, electrocardiogram, and blood pressure cuff) to continuously monitor the vital signs of a patient and deliver pertinent information to first responders. This electronic triage system facilitates the seamless collection and dissemination of data from the incident site to key members of the distributed emergency response community. The real-time collection of data through a mesh network in a mass casualty drill was shown to approximately triple the number of times patients that were triaged compared with the traditional paper triage system.

Decision markets with good incentives

Decision markets both predict and decide the future. They allow experts to predict the effects of each of a set of possible actions, and after reviewing these predictions a decision maker selects an action to perform. When the future is independent of the market, strictly proper scoring rules myopically incentivize experts to predict consistent with their beliefs, but this is not generally true when a decision is to be made. When deciding, only predictions for the chosen action can be evaluated for their accuracy since the other predictions become counterfactuals. This limitation can make some actions more valuable than others for an expert, incentivizing the expert to mislead the decision maker. We construct and characterize decision markets that are --- like prediction markets using strictly proper scoring rules --- myopic incentive compatible. These markets require the decision maker always risk taking every available action, and reducing this risk increases the decision makers worst-case loss. We also show a correspondence between strictly proper decision markets and strictly proper sets of prediction markets, creating a formal connection between the incentives of prediction and decision markets.

Informed Truthfulness in Multi-Task Peer Prediction

The problem of peer prediction is to elicit information from agents in settings without any objective ground truth against which to score reports. Peer prediction mechanisms seek to exploit correlations between signals to align incentives with truthful reports. A long-standing concern has been the possibility of uninformative equilibria. For binary signals, a multi-task mechanism achieves strong truthfulness, so that the truthful equilibrium strictly maximizes payoff. We characterize conditions on the signal distribution for which this mechanism remains strongly-truthful with non-binary signals, also providing a greatly simplified proof. We introduce the Correlated Agreement (CA) mechanism, which handles multiple signals and provides informed truthfulness: no strategy profile provides more payoff in equilibrium than truthful reporting, and the truthful equilibrium is strictly better than any uninformed strategy (where an agent avoids the effort of obtaining a signal). The CA mechanism is maximally strongly truthful, in that no mechanism in a broad class of mechanisms is strongly truthful on a larger family of signal distributions. We also give a detail-free version of the mechanism that removes any knowledge requirements on the part of the designer, using reports on many tasks to learn statistics while retaining epsilon-informed truthfulness.

Truthful prioritization schemes for spectrum sharing

As the rapid expansion of smart phones and associated data-intensive applications continues, we expect to see renewed interest in dynamic prioritization schemes as a way to increase the total utility of a heterogeneous user base, with each user experiencing variable demand and value for access. We adapt a recent sampled-based mechanism for resource allocation to this setting, which is more effective in aligning incentives in a setting with variable demand than an earlier method for pricing network resources due to Varian and Mackie-Mason (1994). Complementing our theoretical analysis, which also considers incentives on the sell-side of the market, we present the results of a simulation study, confirming the effectiveness of our protocol in aligning incentives and boosting welfare.

Eliciting Predictions and Recommendations for Decision Making

When making a decision, a decision maker selects one of several possible actions and hopes to achieve a desirable outcome. To make a better decision, the decision maker often asks experts for advice. In this article, we consider two methods of acquiring advice for decision making. We begin with a method where one or more experts predict the effect of each action and the decision maker then selects an action based on the predictions. We characterize strictly proper decision making, where experts have an incentive to accurately reveal their beliefs about the outcome of each action. However, strictly proper decision making requires the decision maker use a completely mixed strategy to choose an action. To address this limitation, we consider a second method where the decision maker asks a single expert to recommend an action. We show that it is possible to elicit the decision maker’s most preferred action for a broad class of preferences of the decision maker, including when the decision maker is an expected value maximizer.

Truthful prioritization for dynamic bandwidth sharing

We design a protocol for dynamic prioritization of data on shared routers such as untethered 3G/4G devices. The mechanism prioritizes bandwidth in favor of users with the highest value, and is incentive compatible, so that users can simply report their true values for network access. A revenue pooling mechanism also aligns incentives for sellers, so that they will choose to use prioritization methods that retain the incentive properties on the buy-side. In this way, the design allows for an open architecture. In addition to revenue pooling, the technical contribution is to identify a class of stochastic demand models and a prioritization scheme that provides allocation monotonicity. Simulation results confirm efficiency gains from dynamic prioritization relative to prior methods, as well as the effectiveness of revenue pooling.