Maria A. Kazandjieva

A high-resolution human contact network for infectious disease transmission

The most frequent infectious diseases in humans—and those with the highest potential for rapid pandemic spread—are usually transmitted via droplets during close proximity interactions (CPIs). Despite the importance of this transmission route, very little is known about the dynamic patterns of CPIs. Using wireless sensor network technology, we obtained high-resolution data of CPIs during a typical day at an American high school, permitting the reconstruction of the social network relevant for infectious disease transmission. At 94% coverage, we collected 762,868 CPIs at a maximal distance of 3 m among 788 individuals. The data revealed a high-density network with typical small-world properties and a relatively homogeneous distribution of both interaction time and interaction partners among subjects. Computer simulations of the spread of an influenza-like disease on the weighted contact graph are in good agreement with absentee data during the most recent influenza season. Analysis of targeted immunization strategies suggested that contact network data are required to design strategies that are significantly more effective than random immunization. Immunization strategies based on contact network data were most effective at high vaccination coverage.

The β-factor: measuring wireless link burstiness

Measuring 802.15.4 reception in three testbeds, we find that most intermediate links are bursty: they shift between poor and good delivery. We present a metric to measure this link burstiness and name it β. We find that link burstiness affects protocol performance and that β can predict the effects. We show that measuring β allows us to reason about how long a protocol should pause after encountering a packet failure to reduce its transmission cost. We find that using β as a guide to setting a single constant in a standard sensor network data collection protocol reduces its average transmission cost by 15%. In addition to data from 802.15.4 testbeds, we examine traces from 802.11b networks and find β has a broader relevance in the wireless domain.

CTP: An efficient, robust, and reliable collection tree protocol for wireless sensor networks

We describe CTP, a collection routing protocol for wireless sensor networks. CTP uses three techniques to provide efficient, robust, and reliable routing in highly dynamic network conditions. CTPs link estimator accurately estimates link qualities by using feedback from both the data and control planes, using information from multiple layers through narrow, platform-independent interfaces. Second, CTP uses the Trickle algorithm to time the control traffic, sending few beacons in stable topologies yet quickly adapting to changes. Finally, CTP actively probes the topology with data traffic, quickly discovering and fixing routing failures. Through experiments on 13 different testbeds, encompassing seven platforms, six link layers, and multiple densities and frequencies, and detailed observations of a long-running sensor network application that uses CTP, we study how these three techniques contribute to CTPs overall performance.

The case for a network protocol isolation layer

Network protocols are typically designed and tested individually. In practice, however, applications use multiple protocols concurrently. This discrepancy can lead to failures from unanticipated interactions between protocols. In this paper, we argue that sensor network communication stacks should have an isolation layer, whose purpose is to make each protocols perception of the wireless channel independent of what other protocols are running. We identify two key mechanisms the isolation layer must provide: shared collision avoidance and fair channel allocation. We present an example design of an isolation layer that builds on the existing algorithms of grant-to-send and fair queueing. However, the complexities of wireless make these mechanisms insufficient by themselves. We therefore propose two new mechanisms that address these limitations: channel decay and fair cancellation. Incorporating these new mechanisms reduces the increase in end-to-end delivery cost associated with concurrently operating two protocols by more than 60%. The isolation layer improves median protocol fairness from 0.52 to 0.96 in Jains fairness index. Together, these results show that using an isolation layer makes protocols more efficient and robust.

Experiences in measuring a human contact network for epidemiology research

This paper discusses our experience in designing and deploying a 994-node sensor network to measure the social contact network of a high school over one typical day. The system aims to capture interactions of human subjects for the study of infectious disease spread. We describe unique challenges posed by a large-scale network that is heavily affected by humans. We present techniques to address challenges such as frequent node reboots and global timestamps. The end result of the deployment is a dataset of 792 traces which can be used to calculate the school populations contact network and the rough location where interactions occurred.

SWAT: enabling wireless network measurements

Measuring low-level wireless network properties allows researchers to understand how protocols and applications perform in different environments. In this demo, we present SWAT - a software tool that automates gathering and analysis of network measurements. SWAT provides an interface for configuring experimental parameters in a network. It collects raw packet statistics such as the received signal strength and chip error, and provides modules for calculating and visualizing various metrics derived from these statistics.

Granting silence to avoid wireless collisions

We describe grant-to-send, a novel collision avoidance algorithm for wireless mesh networks. Rather than announce packets it intends to send, a node using grant-to-send announces packets it expects to hear others send.

Green enterprise computing data: Assumptions and realities

Until now, green computing research has largely relied on few, short-term power measurements to characterize the energy use of enterprise computing. This paper brings new and comprehensive power datasets through Powernet, a hybrid sensor network that monitors the power and utilization of the IT systems in a large academic building. Over more than two years, we have collected power data from 250+ individual computing devices and have monitored a subset of CPU and network loads. This dense, long-term monitoring allows us to extrapolate the data to a detailed breakdown of electricity use across the buildings computing systems. Our datasets provide an opportunity to examine assumptions commonly made in green computing. We show that power variability both between similar devices and over time for a single device can lead to cost or savings estimates that are off by 15-20%. Extending the coverage of measured devices and the duration (to at least one month) significantly reduces errors. Lastly, our experiences with collecting data and the subsequent analysis lead to a better understanding of how one should go about power characterization studies. We provide several methodology guidelines for future green computing research.

System architecture support for green enterprise computing

This paper proposes a novel system for enterprise computing that reduces energy consumption without sacrificing performance or putting devices to sleep. It uses a hybrid architecture composed of multiple device classes and it runs an application in the most energy-efficient location. Our prototype, called Any-ware, provides desktop-class performance while reducing energy consumption by 75% through a combination of lightweight clients and a small number of servers. Providing such an elastic system invisibly to end users requires solving many challenges, including deciding where to run applications while simultaneously making it appear they all run locally. Anywares hybrid design suggests a new way to think about using the modern spectrum of personal computing devices.

Visualizing sensor network data with Powertron

Powertron is a web-based application that visualizes wireless sensor network deployment data. In this particular demo, we use Powertron to show application-level power data collected from more than 250 sensor nodes. In addition, we expose the routing layer of the deployment by providing real-time interactive visual representation of links, routes, and CTP-related statistics. We hope that the community will have feedback on how such a tool can be extended and generalized to fit a variety of wireless sensor network applications.