Sleep is a basic physiological process essential for good health. However, 40 million people in the U.S. are diagnosed with sleep disorders, with many more undiagnosed. To help address this problem, we developed an application, ShutEye, which provides a peripheral display on the wall-paper of the user's mobile phone to promote awareness about recommended activities that promote good sleep quality. Based on preferences about the user's desired bed-time and activities' for example - consuming caffeine or performing vigorous exercise - ShutEye displays guidance about when engaging in those activities is likely to affect sleep without requiring any explicit interaction from the user. In this paper, we describe ShutEye and results from a four-week field study with 12 participants. Results indicate that a simple, recommendation-based peripheral display can be a very low-effort but still effective method for improving awareness of healthy sleep habits. We also provide recommendations about designing peripheral displays and extend insights for designing health-based mobile applications.

The bedroom environment can have a significant impact on the quality of a person's sleep. Experts recommend sleeping in a room that is cool, dark, quiet, and free from disruptors to ensure the best quality sleep. However, it is sometimes difficult for a person to assess which factors in the environment may be causing disrupted sleep. In this paper, we present the design, implementation, and initial evaluation of a capture and access system, called Lullaby. Lullaby combines temperature, light, and motion sensors, audio and photos, and an off-the-shelf sleep sensor to provide a comprehensive recording of a person's sleep. Lullaby allows users to review graphs and access recordings of factors relating to their sleep quality and environmental conditions to look for trends and potential causes of sleep disruptions. In this paper, we report results of a feasibility study where participants (N=4) used Lullaby in their homes for two weeks. Based on our experiences, we discuss design insights for sleep technologies, capture and access applications, and personal informatics tools.

Anyone within range of an 802.11 wireless network ("Wi-Fi") can use free software to collect the unencrypted web traffic of others on the network. However, many Wi-Fi users are completely unaware of the risk that this creates. This work aims to improve users' awareness about what they expose to others on Wi-Fi networks and provide them with some control. Our system, the Wi-Fi Privacy Ticker, displays information about the exposure of sensitive terms that are sent to and from a user's computer and prevents the unencrypted transmission of terms from the user's computer that she has identified as highly sensitive. In a three-week field study with 17 participants, we found that the Wi-Fi Privacy Ticker improved participants' awareness of the circumstances in which their personal information is transmitted. We show that this heightened awareness contributed to changes in their behavior while on Wi-Fi.

Wi-Fi clients can obtain much better performance at some commercial hot spots than others. Unfortunately, there is currently no way for users to determine which hot spot access points (APs) will be sufficient to run their applications before purchasing access. To address this problem, this paper presents Wifi-Reports, a collaborative service that provides Wi-Fi clients with historical information about AP performance and application support. The key research challenge in Wifi-Reports is to obtain accurate user-submitted reports. This is challenging because two conflicting goals must be addressed in a practical system: preserving the privacy of users' reports and limiting fraudulent reports. We introduce a practical cryptographic protocol that achieves both goals, and address the important engineering challenges in building Wifi-Reports. Using a measurement study of APs in a busy commercial district, we show that Wifi-Reports would improve the performance over previous AP selection approaches in 30-60 percent of locations.

Most sensor network research and software design has been guided by an architectural principle that permits multinode data fusion on small-form-factor, resource-poor nodes, or motes . While we were among the earliest promoters of this approach, through experience we found that this principle leads to fragile and unmanageable systems and explore an alternative. The Tenet architecture is motivated by the observation that future large-scale sensor network deployments will be tiered , consisting of motes in the lower tier and masters , relatively unconstrained 32-bit platform nodes, in the upper tier. Tenet constrains multinode fusion to the master tier while allowing motes to process locally-generated sensor data. This simplifies application development and allows mote-tier software to be reused. Applications running on masters task motes by composing task descriptions from a novel tasklet library. Our Tenet implementation also contains a robust and scalable networking subsystem for disseminating tasks and reliably delivering responses. We show that a Tenet pursuit-evasion application exhibits performance comparable to a mote-native implementation while being considerably more compact. We also present two real-world deployments of Tenet system: a structural vibration monitoring application at Vincent Thomas Bridge and an imaging-based habitat monitoring application at James Reserve, and show that tiered architecture scales network capacity and allows reliable delivery of high rate data. 1

Increasingly, users access online services such as email, e-commerce, and social networking sites via 802.11-based wireless networks. As they do so, they expose a range of personal information such as their names, email addresses, and ZIP codes to anyone within broadcast range of the network. This paper presents results from an exploratory study that examined how users from the general public understand Wi-Fi, what their concerns are related to Wi-Fi use, and which practices they follow to counter perceived threats. Our results reveal that while users understand the practical details of Wi-Fi use reasonably well, they lack understanding of important privacy risks. In addition, users employ incomplete protective practices which results in a false sense of security and lack of concern while on Wi-Fi. Based on our results, we outline opportunities for technology to help address these problems.

Wi-Fi clients can obtain much better performance at some commercial hotspots than at others. Unfortunately, there is currently no way for users to determine which hotspot access points (APs) will be sufficient to run their applications before purchasing access. To address this problem, this paper presents Wifi-Reports, a collaborative service that provides Wi-Fi clients with historical information about AP performance and application support. The key research challenge in Wifi-Reports is to obtain accurate user-submitted reports. This is challenging because two conflicting goals must be addressed in a practical system: preserving the privacy of users' reports and limiting fraudulent reports. We introduce a practical cryptographic protocol that achieves both goals, and we address the important engineering challenges in building Wifi-Reports. Using a measurement study of commercial APs in Seattle, we show that Wifi-Reports would improve performance over previous AP selection approaches in 30%-60% of locations.

We describe the design and implementation of Privacy Oracle, a system that reports on application leaks of user information via the network traffic that they send. Privacy Oracle treats each application as a black box, without access to either its internal structure or communication protocols. This means that it can be used over a broad range of applications and information leaks (i.e., not only Web traffic or credit card numbers). To accomplish this, we develop a differential testing technique in which perturbations in the application inputs are mapped to perturbations in the application outputs to discover likely leaks; we leverage alignment algorithms from computational biology to find high quality mappings between different byte-sequences efficiently. Privacy Oracle includes this technique and a virtual machine-based testing system. To evaluate it, we tested 26 popular applications, including system and file utilities, media players, and IM clients. We found that Privacy Oracle discovered many small and previously undisclosed information leaks. In several cases, these are leaks of directly identifying information that are regularly sent in the clear (without end-to-end encryption) and which could make users vulnerable to tracking by third parties or providers.

We present the design and evaluation of an 802.11-like wireless link layer protocol that obfuscates all transmitted bits to increase privacy. This includes explicit identifiers such as MAC addresses, the contents of management messages, and other protocol fields that the existing 802.11 protocol relies on to be sent in the clear. By obscuring these fields, we greatly increase the difficulty of identifying or profiling users from their transmissions in ways that are otherwise straightforward. Our design, called SlyFi, is nearly as efficient as existing schemes such as WPA for discovery, link setup, and data delivery despite its heightened protections; transmission requires only symmetric key encryption and reception requires a table lookup followed by symmetric key decryption. Experiments using our implementation on Atheros 802.11 drivers show that SlyFi can discover and associate with networks faster than 802.11 using WPA-PSK. The overhead SlyFi introduces in packet delivery is only slightly higher than that added by WPA-CCMP encryption (10% vs. 3% decrease in throughput).

We demonstrate a simple RFID sensor network comprised of an Intel WISP and a commodity UHF RFID reader. WISPs are devices that gather their operating energy from RFID reader transmissions, in the manner of passive RFID tags, and further include sensors, e.g., accelerometers, and provide a very small-scale computing platform. We believe that the small form factor and lack of battery makes the WISP an attractive alternative to motes for many of the original smart dust applications that require very small or long-lived sensors. The Intel WISP that we demonstrate has an ultra-low-power microcontroller, 32K of program space, 8K of flash, and accelerometer and temperature sensors. It harvests power from and communicates sensor data to standard (EPC Class 1 Gen 2) UHF RFID readers with a range of roughly 10 feet. This combination of RFID technology and sensor networks raises many research challenges, such as how to function with intermittent power and how to modify RFID protocols to support sensor queries.

The ubiquity of 802.11 devices and networks enables anyone to track our every move with alarming ease. Each 802.11 device transmits a globally unique and persistent MAC address and thus is trivially identifiable. In response, recent research has proposed replacing such identifiers with pseudonyms (i.e., temporary, unlinkable names). In this paper, we demonstrate that pseudonyms are insufficient to prevent tracking of 802.11 devices because implicit identifiers, or identifying characteristics of 802.11 traffic, can identify many users with high accuracy. For example, even without unique names and addresses, we estimate that an adversary can identify 64 % of users with 90 % accuracy when they spend a day at a busy hot spot. We present an automated procedure based on four previously unrecognized implicit identifiers that can identify users in three real 802.11 traces even when pseudonyms and encryption are employed. We find that the majority of users can be identified using our techniques, but our ability to identify users is not uniform; some users are not easily identifiable. Nonetheless, we show that even a single implicit identifier is sufficient to distinguish many users. Therefore, we argue that design considerations beyond eliminating explicit identifiers (i.e., unique names and addresses), must be addressed in order to prevent user tracking in wireless networks. Categories and Subject Descriptors:

We study the impact on 802.11 networks of RF interference from devices such as Zigbee and cordless phones that increasingly crowd the 2.4GHz ISM band, and from devices such as wireless camera jammers and non-compliant 802.11 devices that seek to disrupt 802.11 operation. Our experiments show that commodity 802.11 equipment is surprisingly vulnerable to certain patterns of weak or narrow-band interference. This enables us to disrupt a link with an interfering signal whose power is 1000 times weaker than the victim's 802.11 signals, or to shut down a multiple AP, multiple channel managed network at a location with a single radio interferer. We identify several factors that lead to these vulnerabilities, ranging from MAC layer driver implementation strategies to PHY layer radio frequency implementation strategies. Our results further show that these factors are not overcome by simply changing 802.11 operational parameters (such as CCA threshold, rate and packet size) with the exception of frequency shifts. This leads us to explore rapid channel hopping as a strategy to withstand RF interference. We prototype a channel hopping design using PRISM NICs, and find that it can sustain throughput at levels of RF interference well above that needed to disrupt unmodified links, and at a reasonable cost in terms of switching overheads.

Most sensor network research and software design has been guided by an architectural principle that permits multi-node data fusion on small-form-factor, resource-poor nodes, or motes. We argue that this principle leads to fragile and unmanageable systems and explore an alternative. The Tenet architecture is motivated by the observation that future large-scale sensor network deployments will be tiered, consisting of motes in the lower tier and masters, relatively unconstrained 32-bit platform nodes, in the upper tier. Masters provide increased network capacity. Tenet constrains multi-node fusion to the master tier while allowing motes to process locally-generated sensor data. This simplifies application development and allows mote-tier software to be reused. Applications running on masters task motes by composing task descriptions from a novel tasklet library. Our Tenet implementation also contains a robust and scalable networking subsystem for disseminating tasks and reliably delivering responses. We show that a Tenet pursuit-evasion application exhibits performance comparable to a mote-native implementation while being considerably more compact.

Small-form-factor, low-power wireless sensors—motes—are convenient to deploy, but lack the bandwidth to capture and transmit raw high-frequency data, such as human voices or neural signals, in real time. Local filtering can help, but we show that the right filter settings depend on changing ambient conditions and network effects such as congestion, which makes them dynamic and unpredictable. Mote collection systems for high-frequency data must support iteratively-tuned, deployment-specific filter settings as well as fast sampling.VANGO, our software system for high-frequency data collection, achieves these goals via integrated processing across network tiers. Bandwidth-limited sensor nodes reduce data in network but rely on microservers, which have greater computational capabilities and a wider scope of observation, to plan how. VANGO provides a cross-platform library for data transformation, measurement, and classification; a fast and low-jitter data acquisition system for motes; and a mechanism to control mote and microserver signal processing. With VANGO we have developed new applications: the first acoustic collection system for motes responsive to changing environmental conditions and user interests, and the first neural spike acquisition application capable of supporting a network of nodes.

Wireless sensor networks enable dense sensing of the environment, offering unprecedented opportunities for observing the physical world. This article addresses two key challenges in wireless sensor networks: in-network storage and distributed search. The need for these techniques arises from the inability to provide persistent, centralized storage and querying in many sensor networks. Centralized storage requires multihop transmission of sensor data to Internet gateways which can quickly drain battery-operated nodes.Constructing a storage and search system that satisfies the requirements of data-rich scientific applications is a daunting task for many reasons: (a) the data requirements may be large compared to available storage and communication capacity of resource-constrained nodes, (b) user requirements are diverse and range from identification and collection of interesting event signatures to obtaining a deeper understanding of long-term trends and anomalies in the sensor events, and (c) many applications are in new domains where a priori information may not be available to reduce these requirements.This article describes a lossy, gracefully degrading storage model . We believe that such a model is necessary and sufficient for many scientific applications since it supports both progressive data collection for interesting events as well as long-term in-network storage for in-network querying and processing. Our system demonstrates the use of in-network wavelet-based summarization and progressive aging of summaries in support of long-term querying in storage and communication-constrained networks. We evaluate the performance of our linux implementation and show that it achieves: (a) low communication overhead for multiresolution summarization, (b) highly efficient drill-down search over such summaries, and (c) efficient use of network storage capacity through load-balancing and progressive aging of summaries.

We study four distributed techniques for computing the area of a region in a sensor network. Area calculation is a fundamental sensor network primitive, and distributed, in-network approaches prove more scalable than centralized collection in terms of energy consumption. The four techniques - Delaunay triangulations, Voronoi diagrams, and two new, simpler algorithms, inverse neighborhood and inverse neighborhood with location - vary in computational complexity, communication cost, and information required from the sensor network. We conclude that when sensors know their physical locations, our simple and efficient inverse-neighborhood approach performs comparably to more systematic, but more expensive, computational geometry algorithms. We also analyze the effects of radio range and deployment density on accuracy, and show that topologies derived from real testbeds behave quite differently from commonly seen random topologies with unit disk connectivity.

Since the inception of sensor networks, in-network processing has been touted as the enabling technology for long-lived deployments. Radio communication is the overriding consumer of energy in such networks. Therefore, data reduction before transmission, either by compression or feature extraction, will directly and significantly increase network lifetime. This paper evaluates a simple temporal compression scheme designed specifically to be used by mica motes for the compaction of microclimate data. The algorithm makes use of the observation that over a small enough window of time, samples of microclimate data are linear. It finds such windows and generates a series of line segments that accurately represent the data. It compresses data up to 20-to-1 while introducing errors in the order of the sensor hardware's specified margin of error. Furthermore, it is simple, consumes little CPU and requires very little storage when compared to other compression techniques. This paper describes the technique and results using a dataset from a one-year microclimate deployment.

We propose a new configuration language, component and service library, and compiler that make it easier to develop efficient sensor network applications. Our goal is the construction of smart application service libraries: high-level libraries that implement concepts like routing trees and periodic sensing, and that combine automatically into efficient programs. Important language features include flexible control over component sharing and transitive arrow connections, which let independently-implemented services knit themselves into integrated control flow paths. Our language, library, and compiler are collectively called SNACK (Sensor Network Application Construction Kit). We describe them, and present and evaluate a simple SNACK-based multihop data collection application. This application uses SNACK language features to provide both simplicity (excluding reusable service definitions, its description is three lines long) and efficiency (it performs comparably to the well-known Surge application).

Wireless sensor networks enable dense sensing of the environment, offering unprecedented opportunities for observing the physical world. Centralized data collection and analysis adversely impact sensor node lifetime. Previous sensor network research has, therefore, focused on in network aggregation and query processing, but has done so for applications where the features of interest are known a priori. When features are not known a priori, as is the case with many scientific applications in dense sensor arrays, efficient support for multi-resolution storage and iterative, drill-down queries is essential.Our system demonstrates the use of in-network wavelet-based summarization and progressive aging of summaries in support of long-term querying in storage and communication-constrained networks. We evaluate the performance of our linux implementation and show that it achieves: (a) low communication overhead for multi-resolution summarization, (b) highly efficient drill-down search over such summaries, and (c) efficient use of network storage capacity through load-balancing and progressive aging of summaries.

Transport protocol design for mobile ad hoc networks is challenging because of unique issues, including mobility-induced disconnection, reconnection, and high out-of-order delivery ratios; channel errors; and network congestion. We describe the design and implementation of a TCP-friendly transport protocol for ad hoc networks. Our key design novelty is to perform multi-metric joint identification for packet and connection behaviors based on end-to-end measurements. Our testbed measurements and ns-2 simulations show a significant performance improvement over standard TCP in ad hoc networks.

The bulk of proposed pervasive computing devices such as PDAs and cellular telephones operate as thin clients within a larger infrastructure. To access services within their local environment, these devices participate in a service discovery protocol which involves a master directory that registers all services available in the local environment. These directories typically are isolated from each other. Devices that move across service discovery domains have no access to information outside their current local domain. In this paper we propose an application-level protocol called VIA that enables data sharing among discovery domains. Each directory maintains a table of active links to other directories that share related information. A set of linked directories forms a data cluster that can be queried by devices for information. The data cluster is distributed, self-organizing, responsive to data mobility, and robust to failures. Using application-defined data schemas, clusters organize themselves into a hierarchy for efficient querying and network resource usage. Through analysis and simulation we describe the behavior of VIA under different workloads and show that the protocol overhead for both maintaining a cluster and handling failures grows slowly with the number of gateways.