Recent works have shown the usefulness of network and application layer protocols that connect low-power sensor nodes directly to multiple applications and users on the Internet. We propose a security solution for this scenario. While previous works have provided security support for various communication patterns in sensor networks, such as among nodes, from nodes to a base station, and from users to nodes, the security of communication from sensor nodes to multiple users has not been sufficiently addressed. Specifically, we explore this design space and develop a security solution, named Secure Tiny Web Service, for efficient authentication of data sent by a resource-constrained sensor node to multiple users, using digital signatures. We investigate the resource overheads in communication and computation of four suitable signature schemes—the Elliptic Curve Digital Signature Algorithm, the (elliptic curve) Schnorr signature, and the Boneh–Lynn–Shacham and Zhang–Safavi-Naini–Susilo short signature schemes. We implement these schemes on two popular sensor node architectures (based on AVR ATmega128L and MSP430 processors with 802.15.4 radios) and experimentally characterize relevant trade-offs.

Embedded systems with heterogeneous processors extend the energy/timing trade-off flexibility and provide the opportunity to fine tune resource utilization for particular applications. In this paper, we present a resource model that considers the time and energy costs of run-time mode switching, which considerably improves the accuracy of existing models. Given an application, the software partitioning problem then becomes an optimization over energy cost given deadline constraints, which can be formulate as an integer linear programming (ILP) problem. We apply the resource modeling and software partitioning techniques to a multimodule embedded sensing device, the mPlatform, and present a case study of configuring the platform for a real-time sound source localization application on a stack of MSP430 and ARM7 processor based sensing and processing boards.

We study the problem of concurrently supporting multiple radios with different capabilities and interfaces on a single sensor node platform. Through a detailed experimental study on hardware multi-radio platforms, using the two representative radio technologies 802.15.4 and 802.11, we identify bottlenecks and design tradeoffs that are usually overlooked and that, as we show, have a significant impact on the sensor network's performance and energy efficiency. Our findings are threefold. We show that a proper pairing of processor and radio is crucial for taking the full advantage of the energy efficiency of higher bandwidth radios. The processor/radio pairing affects the energy balance of a sensor node, thus making the design of dynamic switching among multiple radios more challenging. Second, we demonstrate and quantify the impact of network traffic on energy consumption of a sensor node while varying network parameters, and illustrate the deficiency of existing energy-optimizing protocols. Our results indicate that by properly adjusting network parameters, such as packet size and transmission period, energy savings of up to 50% can be achieved under heavy network traffic conditions when a CSMA-based MAC is used. We conclude by presenting a set of guidelines for designing and implementing energy efficient multi-radio platforms.

There are many scenarios where interoperability is required for sensor devices. We demonstrate one approach to achieve interoperability: using web services. Hosting a Web service challenges the battery- life, bandwidth, and processing power constraints of low power sensor nodes. We demonstrate a lightweight implementation on MSP430 based sensor nodes with 802.15.4 radios. The implementation allows standards compliant web service clients to use the sensors but minimizes code size and energy at the sensor nodes. It allows sensor nodes to enter sleep modes. We prototype an example application for a home sensor network along with two types of sensor nodes required for it. We also show how our system enables sensor nodes to be used easily from applications written in high level languages using existing development tools.

We present a web service based approach to enable an evolutionary sensornet system where additional sensor nodes may be added after the initial deployment. The functionality and data provided by the new nodes is exposed in a structured manner, so that multiple applications may access them. The result is a highly inter-operable system where multiple applications can share a common evolving sensor substrate. A key challenge in using web services on resource constrained sensor nodes is the energy and bandwidth overhead of the structured data formats used in web services. Our work provides a detailed evaluation of the overheads and presents an implementation on a representative sensor platform with 48k of ROM, 10k of RAM and a 802.15.4 radio. We identify design choices that optimize the web service operation on resource constrained sensor nodes, including support for low latency messaging and sleep modes, quantifying trade-offs between the design generality and resource efficiency. We also prototyped an example application, for home energy management, demonstrating how evolutionary sensor networks can be supported with our approach.

Mobile phones have two sensors: a camera and a microphone. The widespread and ubiquitous nature of mobile phones around the world makes it attractive to build a large-scale sensor network using the phones as its sensor nodes. There are several interesting challenges in realizing such a system, such as providing efficient methods for the sensor nodes to make their data available to the network, allowing the sensor network applications to access the data from potentially disconnected and highly mobile devices, ensuring that privacy constraints are met, and allowing application developers to program the sensor network as required to build new applications. We demonstrate an initial system prototype that addresses some of these concerns.

No abstract available.

CarTel is a mobile sensor computing system designed to collect, process, deliver, and visualize data from sensors located on mobile units such as automobiles. A CarTel node is a mobile embedded computer coupled to a set of sensors. Each node gathers and processes sensor readings locally before delivering them to a central portal, where the data is stored in a database for further analysis and visualization. In the automotive context, a variety of on-board and external sensors collect data as users drive.CarTel provides a simple query-oriented programming interface, handles large amounts of heterogeneous data from sensors, and handles intermittent and variable network connectivity. CarTel nodes rely primarily on opportunistic wireless (e.g., Wi-Fi, Bluetooth) connectivity to the Internet, or to "data mules" such as other CarTel nodes, mobile phone flash memories, or USB keys-to communicate with the portal. CarTel applications run on the portal, using a delay-tolerant continuous query processor, ICEDB, to specify how the mobile nodes should summarize, filter, and dynamically prioritize data. The portal and the mobile nodes use a delay-tolerant network stack, CafNet, to communicat.CarTel has been deployed on six cars, running on a small scale in Boston and Seattle for over a year. It has been used to analyze commute times, analyze metropolitan Wi-Fi deployments, and for automotive diagnostics.

We propose a reusable data management system, called CarTel, for querying and collecting data from intermittently connected devices. CarTel provides a simple, incrementally-deployable platform for developing automobile-based sensor applications. Our platform provides a dynamic query system that allows both continuous (standing) and one-shot geo-spatial queries over car position, speed, and sensory data as well as a both a low-cost/high-bandwidth substrate for communicating with a large network of mobile devices.

We study the problem of tracking a moving device under two indoor location architectures: an active mobile architecture and a passive mobile architecture. In the former, the infrastructure has receivers at known locations, which estimate distances to a mobile device based on an active transmission from the device. In the latter, the infrastructure has active beacons that periodically transmit signals to a passively listening mobile device, which in turn estimates distances to the beacons. Because the active mobile architecture receives simultaneous distance estimates at multiple receivers from the mobile device, it is likely to perform better tracking than the passive mobile system in which the device obtains only one distance estimate at a time and may have moved between successive estimates. However, an passive mobile system scales better with the number of mobile devices and puts users in control of whether their whereabouts are tracked.We answer the following question: How do the two architectures compare in tracking performance? We find that the active mobile architecture performs better at tracking, but that the passive mobile architecture has acceptable performance; moreover, we devise a hybrid approach that preserves the benefits of the passive mobile architecture while simultaneously providing the same performance as an active mobile system, suggesting a viable practical solution to the three goals of scalability, privacy, and tracking agility.