Schools in the developing world frequently do not have high bandwidth or reliable connections, limiting their access to web content. As a result, schools are increasingly turning to Offline Educational Resources (OERs), employing purpose-built local hardware to serve content. These approaches can be expensive and difficult to maintain in resource-constrained settings. We present Siskin, an alternative approach that leverages the ubiquity of web browsers to provide a distributed content access cache between user devices on the local network. We demonstrate that this system allows access to web pages offline by identifying the browser as a ubiquitous platform. We build and evaluate a prototype, showing that existing web protocols and infrastructure can be leveraged to create a powerful content cache over a local network.

Micro-aerial vehicle (MAV) swarms are an emerging class of mobile sensing systems. Simulation and staged deployment to prototype testbeds are useful in the early stages of large-scale system design, when hardware is unavailable or deployment at scale is impractical. To faithfully represent the problem domain, a MAV swarm simulator must be able to model the key aspects of the system: actuation, sensing, and communication. We present Simbeeotic, a simulation framework geared toward modeling swarms of MAVs. Simbeeotic enables algorithm development and rapid MAV prototyping through pure simulation and hardware-in-the-loop experimentation. We demonstrate that Simbeeotic provides the appropriate level of fidelity to evaluate prototype systems while maintaining the ability to test at scale.

Micro-aerial vehicle (MAV) swarms are an emerging class of mobile sensing systems. Designing the next generation of such swarms requires the ability to rapidly test algorithms, sensors, and support infrastructure at scale. Simulation is useful in the early stages of such large-scale system design, when hardware is unavailable or deployment at scale is impractical. To faithfully represent the problem domain, an MAV swarm simulator must be able to model all key aspects of the system: actuation, sensing, and communication. Further, it is important to be able to quickly test swarm behavior using different control algorithms in a varied set of environments, and with a variety of sensors.

Research in micro-aerial vehicle (MAV) construction, control, and high-density power sources is enabling swarms of MAVs as a new class of mobile sensing systems. For efficient operation, such systems must adapt to dynamic environments, cope with uncertainty in sensing and control, and operate with limited resources. We propose a novel system architecture based on a hive-drone model that simplifies the functionality of an individual MAV to a sequence of sensing and actuation commands with no in-field communication. This decision simplifies the hardware and software complexity of individual MAVs and moves the complexity of coordination entirely to a central hive computer. We present Karma, a system for programming and managing MAV swarms. Through simulation and testbed experiments we demonstrate how applications in Karma can run on limited resources, are robust to individual MAV failure, and adapt to changes in the environment.

Energy in sensor networks is a distributed, non-transferable resource. Over time, differences in energy availability are likely to arise. Protocols like routing trees may concentrate energy usage at certain nodes. Differences in energy harvesting arising from environmental variations, such as if one node is in the sun and another is in the shade, can produce variations in charging rates and battery levels. Because many sensor network applications require nodes to collaborate -- to ensure complete sensor coverage or route data to the network's edge -- a small set of nodes whose continued operation is threatened by low batteries can have a disproportionate impact on the fidelity provided by the network as a whole. In the most extreme case, the loss of a single sink node may render the remainder of the network unreachable.While previous research has addressed reducing the energy usage of individual nodes, the challenge of collaborative energy management has been largely ignored. We present Integrated Distributed Energy Awareness (IDEA), a sensor network service enabling effective network-wide energy decision making. IDEA integrates into the sensor network application by providing an API allowing components to evaluate their impact on other nodes. IDEA distributes information about each node's load rate, charging rate, and battery level to other nodes whose decisions affect it. Finally, IDEA enables awareness of the connection between the behavior of each node and the application's energy goals, guiding the network toward states that improve performance.This paper describes the IDEA architecture and demonstrates its use through three case studies. Using both simulation and testbed experiments, we evaluate each IDEA application by comparing it to simpler approaches that do not integrate distributed energy awareness. We show that using IDEA can significantly improve performance compared with solutions operating with purely local information.

We present MercuryLive, a web-enhanced extension to a body sensor network platform for continuous home-based body motion sensing, interactive supervised data collection sessions, and long-term activity data analysis. The major goal of MercuryLive is to enable practical long-term health monitoring in a home setting and henceforth reduce the effort and cost for collecting clinically relevant quantitative measures on patients' health conditions during daily activities. MercuryLive contains three tiers: a central web server for streaming and storage of sensor data, a sensor data collection engine, and a user-friendly web-based GUI client. The platform is currently used in clinical studies on Parkinson's disease.

Passive monitoring is an important tool for measuring, troubleshooting, and protecting modern wireless networks. To date, WiFi monitoring has focused primarily on indoor settings or ephemeral outdoor studies though wardriving. We present Argos, the first urban-scale wireless sensor network designed explicitly to support measurement of ambient WiFi traffic across an entire city. Urban-scale wireless monitoring presents unique challenges due to limited packet-capture ability, heterogeneous traffic loads, and limited backhaul capacity between sensor nodes. Argos addresses these through in-network traffic merging and processing, plus an intelligent approach to coordinated channel sampling by multiple sniffers. Argos provides a rich query interface allowing users to study the complex dynamics of ambient wireless traffic. We present a detailed evaluation of a 26-node Argos network deployed on streetlights and rooftops around a city, demonstrating its ability to detect and classify wireless access points and clients; monitor Web page usage; detect malicious traffic; track the mobility of WiFi-equipped public transport vehicles; and fingerprint individual users through 802.11 probe request packets.

Driven by the confluence between the need to collect data about people's physical, physiological, psychological, cognitive, and behavioral processes in spaces ranging from personal to urban and the recent availability of the technologies that enable this data collection, wireless sensor networks for healthcare have emerged in the recent years. In this review, we present some representative applications in the healthcare domain and describe the challenges they introduce to wireless sensor networks due to the required level of trustworthiness and the need to ensure the privacy and security of medical data. These challenges are exacerbated by the resource scarcity that is inherent with wireless sensor network platforms. We outline prototype systems spanning application domains from physiological and activity monitoring to large-scale physiological and behavioral studies and emphasize ongoing research challenges.

Energy in sensor networks is distributed and non-transferable. Over time, differences in energy availability across the network are likely to arise. Protocols such as routing engines can concentrate energy load at certain nodes. Variations in incident sunlight can produce different solar charging rates at different nodes. Because many sensor network applications require nodes collaborate --- to ensure complete sensor coverage or route data to the network's edge --- a small set of nodes threatened by low energy availability can have a disproportionate impact on the entire network. For example, the loss of a single sink node may render the application unable to communicate with all the other nodes.

This paper describes Mercury, a wearable, wireless sensor platform for motion analysis of patients being treated for neuromotor disorders, such as Parkinson's Disease, epilepsy, and stroke. In contrast to previous systems intended for short-term use in a laboratory, Mercury is designed to support long-term, longitudinal data collection on patients in hospital and home settings. Patients wear up to 8 wireless nodes equipped with sensors for monitoring movement and physiological conditions. Individual nodes compute high-level features from the raw signals, and a base station performs data collection and tunes sensor node parameters based on energy availability, radio link quality, and application specific policies.

Networking over UHF white spaces is fundamentally different from conventional Wi-Fi along three axes: spatial variation, temporal variation, and fragmentation of the UHF spectrum. Each of these differences gives rise to new challenges for implementing a wireless network in this band. We present the design and implementation of Net7, the first Wi-Fi like system constructed on top of UHF white spaces. Net7 incorporates a new adaptive spectrum assignment algorithm to handle spectrum variation and fragmentation, and proposes a low overhead protocol to handle temporal variation. builds on a simple technique, called SIFT, that reduces the time to detect transmissions in variable channel width systems by analyzing raw signals in the time domain. We provide an extensive evaluation of the system in terms of a prototype implementation and detailed experimental and simulation results.

This paper presents the results of a pilot study to assess the feasibility of using accelerometer data to estimate the severity of symptoms and motor complications in patients with Parkinson's disease. A support vector machine (SVM) classifier was implemented to estimate the severity of tremor, bradykinesia and dyskinesia from accelerometer data features. SVM-based estimates were compared with clinical scores derived via visual inspection of video recordings taken while patients performed a series of standardized motor tasks. The analysis of the video recordings was performed by clinicians trained in the use of scales for the assessment of the severity of Parkinsonian symptoms and motor complications. Results derived from the accelerometer time series were analyzed to assess the effect on the estimation of clinical scores of the duration of the window utilized to derive segments (to eventually compute data features) from the accelerometer data, the use of different SVM kernels and misclassification cost values, and the use of data features derived from different motor tasks. Results were also analyzed to assess which combinations of data features carried enough information to reliably assess the severity of symptoms and motor complications. Combinations of data features were compared taking into consideration the computational cost associated with estimating each data feature on the nodes of a body sensor network and the effect of using such data features on the reliability of SVM-based estimates of the severity of Parkinsonian symptoms and motor complications.

This paper presents Pixie, a new sensor node operating system designed to support the needs of data-intensive applications. These applications, which include high-resolution monitoring of acoustic, seismic, acceleration, and other signals, involve high data rates and extensive in-network processing. Given the fundamentally resource-limited nature of sensor networks, a pressing concern for such applications is their ability to receive feedback on, and adapt their behavior to, fluctuations in both resource availability and load.

An emerging class of sensor networks focuses on reliable collection of high-resolution signals from across the network. In these applications, the system is capable of acquiring more data than can be delivered to the base station, due to severe limits on radio bandwidth and energy. Moreover, these systems are unable to take advantage of conventional approaches to in-network data aggregation, given the high data rates and need for raw signals. These systems face an important challenge: how to maximize the overall value of the collected data, subject to resource constraints.

We present a critical evaluation of the first known implementation of elliptic curve cryptography over F 2 p for sensor networks based on the 8-bit, 7.3828-MHz MICA2 mote. We offer, along the way, a primer for those interested in the field of cryptography for sensor networks. We discuss, in particular, the decisions underlying our design and alternatives thereto. And we elaborate on the methodologies underlying our evaluation. Through instrumentation of UC Berkeley's TinySec module, we argue that, although symmetric cryptography has been tractable in this domain for some time, there has remained a need, unfulfilled until recently, for an efficient, secure mechanism for distribution of secret keys among nodes. Although public-key infrastructure has been thought impractical, we show, through analysis of our original implementation for TinyOS of point multiplication on elliptic curves, that public-key infrastructure is indeed viable for TinySec keys' distribution, even on the MICA2. We demonstrate that public keys can be generated within 34 seconds and that shared secrets can be distributed among nodes in a sensor network within the same time, using just over 1 kilobyte of SRAM and 34 kilobytes of ROM. We demonstrate that communication costs are minimal, with only 2 packets required for transmission of a public key among nodes. We make available all of our source code for other researchers to download and use. And we discuss recent results based on our work that corroborate and improve upon our conclusions.

The goal of this project is to develop wireless sensors and analysis methods to monitor patients with various motor dysfunctions. We are currently targeting two specific applications: facilitating medication titration in patients with Parkinson's disease and assessing motor recovery in stroke survivors undergoing rehabilitation. In our vision, the treatment and rehabilitation hospital of the future will allow clinicians to continuously monitor motor activity in patients via miniature sensor technology in order to better design interventions on an individual basis. Two key points toward developing the tools necessary to achieve continuous monitoring of motor function are (1) development of a robust and deployable wearable wireless network of sensors and (2) the development of analysis techniques to derive clinically relevant information from miniature sensor data.

The development of high-level programming environments is essential if wireless sensor networks are to be accessible to non-experts. In this paper, we present the Regiment system, which consists of a high-level language for spatiotemporal macroprogramming, along with a compiler that translates global programs into node-level code. In Regiment, the programmer views the network as a set of spatially-distributed data streams. The programmer can manipulate sets of these streams that may be defined by topological or geographic relationships between nodes. Regiment provides a rich set of primitives for processing data on individual streams, manipulating regions, performing aggregation over a region, and triggering new computation within the network.

A great deal of recent work has investigated new programming abstractions and models for sensor networks. However, the complexity of such systems demands a great deal of effort to develop appropriate compilers and runtime platforms to achieve good performance. We will demonstrate Flask [5], a new programming platform for sensor networks that decouples the design of a high-level programming environment from the the low-level details of generating per-node code and an efficient runtime system.

To use their pool of resources efficiently, distributed stream-processing systems push query operators to nodes within the network. Currently, these operators, ranging from simple filters to custom business logic, are placed manually at intermediate nodes along the transmission path to meet application-specific performance goals. Determining placement locations is challenging because network and node conditions change over time and because streams may interact with each other, opening venues for reuse and repositioning of operators. This paper describes a stream-based overlay network (SBON), a layer between a stream-processing system and the physical network that manages operator placement for stream-processing systems. Our design is based on a cost space, an abstract representation of the network and on-going streams, which permits decentralized, large-scale multi-query optimization decisions. We present an evaluation of the SBON approach through simulation, experiments on PlanetLab, and an integration with Borealis, an existing stream-processing engine. Our results show that an SBON consistently improves network utilization, provides low stream latency, and enables dynamic optimization at low engineering cost.

There is widespread agreement that a higher level programming model for sensor networks is needed. A variety of models have been developed, but the community is far from consensus. We propose an intermediate language to speed up the exploration of this design space. Our language, called the token machine language (TML) can be targeted by compilers for higher level systems. TML provides a layer of abstraction for a lower-level runtime environment, such as TinyOS. TML is intended to capture coordinated activity in a sensor network. Notable features of TML are its atomic action model of concurrency, and its unification of communication, control, and storage around the concept of a token. Tokens are small objects, typically under a hundred bytes, and can be disseminated across the network. A token causes computation upon its arrival at a site by invoking a token handler. The effect of the computation is to atomically change the token's own state as well as the state of shared variables at the site.

No abstract available.

Synchronicity is a useful abstraction in many sensor network applications. Communication scheduling, coordinated duty cycling, and time synchronization can make use of a synchronicity primitive that achieves a tight alignment of individual nodes' firing phases. In this paper we present the Reachback Firefly Algorithm (RFA), a decentralized synchronicity algorithm implemented on TinyOS-based motes. Our algorithm is based on a mathematical model that describes how fireflies and neurons spontaneously synchronize. Previous work has assumed idealized nodes and not considered realistic effects of sensor network communication, such as message delays and loss. Our algorithm accounts for these effects by allowing nodes to use delayed information from the past to adjust the future firing phase. We present an evaluation of RFA that proceeds on three fronts. First, we prove the convergence of our algorithm in simple cases and predict the effect of parameter choices. Second, we leverage the TinyOS simulator to investigate the effects of varying parameter choice and network topology. Finally, we present results obtained on an indoor sensor network testbed demonstrating that our algorithm can synchronize sensor network devices to within 100 μsec on a real multi-hop topology with links of varying quality.

We developed and deployed a wireless sensor network for monitoring seismoacoustic activity at Volcán Reventador, Ecuador. Wireless sensor networks are a new technology and our group is among the first to apply them to monitoring volcanoes. The small size, low power, and wireless communication capabilities can greatly simplify deployments of large sensor arrays and are very attractive for this application domain. This project is a follow-on to our previous infrasonic sensor network deployed at Volcán Tungurahua, also in Ecuador, in July 2004 [1].

The growing wireless sensor network research community lacks a standard method for evaluating hardware platforms. Traditional benchmark suites do not sufficiently address the needs of sensor network designers. This work provides motivation for a benchmark suite and details an approach for benchmarking TinyOS compatible hardware. To aid the development of future hardware architectures, we propose the creation of a standard single node benchmark suite, based on both real applications and "stressmarks." We present sample benchmark results and call for further work in this area.

We present the first known implementation of elliptic curve cryptography over F/sub 2p/ for sensor networks based on the 8-bit, 7.3828-MHz MICA2 mote. Through instrumentation of UC Berkeley's TinySec module, we argue that, although secret-key cryptography has been tractable in this domain for some time, there has remained a need for an efficient, secure mechanism for distribution of secret keys among nodes. Although public-key infrastructure has been thought impractical, we argue, through analysis of our own implementation for TinyOS of multiplication of points on elliptic curves, that public-key infrastructure is, in fact, viable for TinySec keys' distribution, even on the MICA2. We demonstrate that public keys can be generated within 34 seconds, and that shared secrets can be distributed among nodes in a sensor network within the same, using just over 1 kilobyte of SRAM and 34 kilobytes of ROM.

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.

We present nesC, a programming language for networked embedded systems that represent a new design space for application developers. An example of a networked embedded system is a sensor network, which consists of (potentially) thousands of tiny, low-power "motes," each of which execute concurrent, reactive programs that must operate with severe memory and power constraints.nesC's contribution is to support the special needs of this domain by exposing a programming model that incorporates event-driven execution, a flexible concurrency model, and component-oriented application design. Restrictions on the programming model allow the nesC compiler to perform whole-program analyses, including data-race detection (which improves reliability) and aggressive function inlining (which reduces resource consumption).nesC has been used to implement TinyOS, a small operating system for sensor networks, as well as several significant sensor applications. nesC and TinyOS have been adopted by a large number of sensor network research groups, and our experience and evaluation of the language shows that it is effective at supporting the complex, concurrent programming style demanded by this new class of deeply networked systems.

Accurate and scalable simulation has historically been a key enabling factor for systems research. We present TOSSIM, a simulator for TinyOS wireless sensor networks. By exploiting the sensor network domain and TinyOS's design, TOSSIM can capture network behavior at a high fidelity while scaling to thousands of nodes. By using a probabilistic bit error model for the network, TOSSIM remains simple and efficient, but expressive enough to capture a wide range of network interactions. Using TOSSIM, we have discovered several bugs in TinyOS, ranging from network bit-level MAC interactions to queue overflows in an ad-hoc routing protocol. Through these and other evaluations, we show that detailed, scalable sensor network simulation is possible.

We argue that existing OS designs are ill-suited for the needs of Internet service applications. These applications demand massive concurrency (supporting a large number of requests per second) and must be well-conditioned to load (avoiding degradation of performance and predictability when demand exceeds capacity). The transparency and virtualization provided by existing operating systems leads to limited concurrency and lack of control over resource usage. We claim that Internet services would be far better supported by operating systems by reconsidering the role of resource virtualization. We propose a new design for server applications, the staged event-driven architecture (SEDA). In SEDA, applications are constructed as a set of event driven stages separated by queues. We present the SEDA architecture and its consequences for operating system design.

We propose a new design for highly concurrent Internet services, which we call the staged event-driven architecture (SEDA). SEDA is intended to support massive concurrency demands and simplify the construction of well-conditioned services. In SEDA, applications consist of a network of event-driven stages connected by explicit queues. This architecture allows services to be well-conditioned to load, preventing resources from being overcommitted when demand exceeds service capacity. SEDA makes use of a set of dynamic resource controllers to keep stages within their operating regime despite large fluctuations in load. We describe several control mechanisms for automatic tuning and load conditioning, including thread pool sizing, event batching, and adaptive load shedding. We present the SEDA design and an implementation of an Internet services platform based on this architecture. We evaluate the use of SEDA through two applications: a high-performance HTTP server and a packet router for the Gnutella peer-to-peer file sharing network. These results show that SEDA applications exhibit higher performance than traditional service designs, and are robust to huge variations in load.

Implementing efficient communication and I/O mechanisms in Java requires both fast access to low-level system resources (such as network and raw disk interfaces) and direct manipulation of memory regions external to the Java heap (such as communication and I/O buffers). Java native methods are too expensive to perform these operations and raise serious protection concerns. We present Jaguar, a new mechanism that provides Java applications with efficient access to system resources while retaining the protection of the Java environment. This is accomplished through compile-time translation of certain Java bytecodes to inlined machine code segments. We demonstrate the use of Jaguar through a Java interface to the VIA fast communications layer, which achieves nearly identical performance to that of C, and Pre-Serialized Objects, a mechanism which greatly reduces the cost of Java object serialization. Copyright © 2000 John Wiley & Sons, Ltd.

Fast Ethernet and ATM are two attractive network technologies for interconnecting workstation clusters for parallel and distributed computing. This paper compares network interfaces with and without programmable co-processors for the two types of networks using the U-Net communication architecture to provide low-latency and high-bandwidth communication. U-Net provides protected, user-level access to the network interface and offers application-level round-trip latencies as low as 60 /spl mu/sec over Fast Ethernet and 90 /spl mu/sec over ATM. The design of the network interface and the underlying network fabric have a large bearing on the U-Net design and performance. Network interfaces with programmable co-processors can transfer data directly to and from user space while others require aid from the operating system kernel. The paper provides detailed performance analysis of U-Net for Fast Ethernet and ATM, including application-level performance on a set of Split-C parallel benchmarks. These results show that high-performance computing is possible on a network of PCs connected via Fast Ethernet.