Determining the mode of transport of an individual is an important element of contextual information. In particular, we focus on differentiating between different forms of motorized transport such as car, bus, subway etc. Our approach uses location information and features derived from transit route information (schedule information, not real-time) published by transit agencies. This enables no up-front training or learning of routes and can be deployed instantly to a new place since most transit agencies publish this information. Combined with motion detection using phone accelerometers, we obtain a classification accuracy of around 90% on 50+ hours of car and transit data.

In emergency medicine, patient care is intense and stressful, often requiring paramedics to consult with remote physicians to convey the patient's condition. We present a framework for context-management in telemedicine developed in collaboration between engineers, physicians, and paramedics. We describe a mobile platform and embedded wireless sensors to capture physiological and audio context into a comprehensive patient record, accessible locally and remotely. We describe a first evaluation of this technology by trained paramedics in simulated scenarios and evaluate key aspects of system performance. Early results suggest that wireless sensing can provide reliable and low latency data both locally and to remote physicians. In addition, audio context capture is a promising approach to capturing a comprehensive patient record, with a low rate of medically important errors.

As wireless devices and sensors are increasingly deployed on people, researchers have begun to focus on wireless body-area networks. Applications of wireless body sensor networks include healthcare, entertainment, and personal assistance, in which sensors collect physiological and activity data from people and their environments. In these body sensor networks, quality of service is needed to provide reliable data communication over prioritized data streams. This article proposes BodyQoS, the first running QoS system demonstrated on an emulated body sensor network. BodyQoS adopts an asymmetric architecture, in which most processing is done on a resource-rich aggregator, minimizing the load on resource-limited sensor nodes. A virtual MAC is developed in BodyQoS to make it radio-agnostic, allowing a BodyQoS to schedule wireless resources without knowing the implementation details of the underlying MAC protocols. Another unique property of BodyQoS is its ability to provide adaptive resource scheduling. When the effective bandwidth of the channel degrades due to RF interference or body fading effect, BodyQoS adaptively schedules remaining bandwidth to meet QoS requirements. We have implemented BodyQoS in NesC on top of TinyOS, and evaluated its performance on MicaZ devices. Our system performance study shows that BodyQoS delivers significantly improved performance over conventional solutions in combating channel impairment.

Event-driven sensor networks operate under an idle or light load and then suddenly become active in response to a detected or monitored event. The transport of event impulses is likely to lead to varying degrees of congestion in the network depending on the distribution and rate of packet sources in the network. It is during these periods of event impulses that the likelihood of congestion is greatest and the information in transit of most importance to users. To address this challenge we propose an energy-efficient congestion control scheme for sensor networks called CODA (COngestion Detection and Avoidance) that comprises three mechanisms: (i) receiver-based congestion detection; (ii) open-loop hop-by-hop backpressure; and (iii) closed-loop multisource regulation. We present the detailed design, implementation, and evaluation of CODA using simulation and experimentation. We define three important performance metrics (i.e., energy tax, fidelity penalty, and power) to evaluate the impact of CODA on the performance of sensing applications. We discuss the performance benefits and practical engineering challenges of implementing CODA in an experimental sensor network testbed based on Berkeley motes using CSMA. Simulation results indicate that CODA significantly improves the performance of data dissemination applications such as directed diffusion by mitigating hotspots, and reducing the energy tax and fidelity penalty on sensing applications. We also demonstrate that CODA is capable of responding to a number of congestion scenarios that we believe will be prevalent as the deployment of these networks accelerates.

As wireless devices and sensors are increasingly deployed on people, researchers have begun to focus on wireless body-area networks. Applications of wireless body sensor networks include healthcare, entertainment, and personal assistance, in which sensors collect physiological and activity data from people and their environments. In these body sensor networks, quality of service is needed to provide reliable data communication over prioritized data streams. This paper proposes BodyQoS, the first running QoS system demonstrated on an emulated body sensor network. BodyQoS adopts an asymmetric architecture, in which most processing is done on a resource rich aggregator, minimizing the load on resource limited sensor nodes. A virtual MAC is developed in BodyQoS to make it radio-agnostic, allowing a BodyQoS to schedule wireless resources without knowing the implementation details of the underlying MAC protocols. Another unique property of BodyQoS is its ability to provide adaptive resource scheduling. When the effective bandwidth of the channel degrades due to RF interference or body fading effect, BodyQoS adaptively schedules remaining bandwidth to meet QoS requirements. We have implemented BodyQoS in NesC on top of TinyOS, and evaluated its performance on MicaZ devices. Our system performance study shows that BodyQoS delivers significantly improved performance over conventional solutions in combating channel impairment.

Many sensor network deployments are heterogeneous: a large number of regular nodes perform sensing, while some nodes have better energy resources and/or more computational capacity. Whereas the effect of heterogeneous nodes on network reliability and lifetime has been studied [3], we here focus on exploiting heterogeneity for security. For security and privacy, the identity of nodes must be authenticated and keys have to be distributed. In our lightweight end-middle-end security framework, every node has a public/private key pair, but only resource-rich gateway nodes use public key cryptography to compute digital signatures. Since gateway nodes vouch for regular nodes, regular nodes can use symmetric cryptography until a gateway is reached. On regular nodes, this reduces energy consumption and processing delays by more than a factor of 3000. Re-encryption on the gateways is typically not the bottleneck, since gateways are often line-powered and typically more than 100 times faster than regular nodes (e.g. Stargate vs. Mica). An additional advantage of our end-middle-end architecture is that only gateways are affected by changes in user privileges.

No abstract available.

There is a critical need for new thinking regarding overload traffic management in sensor networks. It has now become clear that experimental sensor networks (e.g., mote networks) and their applications commonly experience periods of persistent congestion and high packet loss, and in some cases even congestion collapse. This significantly impacts application fidelity measured at the physical sinks, even under light to moderate traffic loads, and is a direct product of the funneling effect ; that is, the many-to-one multihop traffic pattern that characterizes sensor network communications. Existing congestion control schemes are effective at mitigating congestion through rate control and packet drop mechanisms, but do so at the cost of significantly reducing application fidelity measured at the sinks. To address this problem we propose to exploit the availability of a small number of all wireless, multiradio virtual sinks that can be randomly distributed or selectively placed across the sensor field. Virtual sinks are capable of siphoning off data events from regions of the sensor field that are beginning to show signs of high traffic load. In this paper, we present the design, implementation, and evaluation of Siphon , a set of fully distributed algorithms that support virtual sink discovery and selection, congestion detection, and traffic redirection in sensor networks. Siphon is based on a Stargate implementation of virtual sinks that uses a separate longer range radio network (based on IEEE 802.11) to siphon events to one or more physical sinks, and a short-range mote radio to interact with the sensor field at siphon points. Results from analysis, simulation and an experimental 48 Mica2 mote testbed show that virtual sinks can scale mote networks by effectively managing growing traffic demands while minimizing any negative impact on application fidelity. Additionally, we show the scheme is competitive with respect to energy consumption compared to a network composed of only motes.

No abstract available.

There is a critical need for new thinking regarding overload traffic management in sensor networks. It has now become clear that experimental sensor networks (e.g., mote networks) and their applications commonly experience periods of persistent congestion and high packet loss, and in some cases even congestion collapse. This significantly impacts application fidelity measured at the physical sinks, even under light to moderate traffic loads, and is a direct product of the funneling effect; that is, the many-to-one multi-hop traffic pattern that characterizes sensor network communications. Existing congestion control schemes are effective at mitigating congestion through rate control and packet drop mechanisms, but do so at the cost of significantly reducing application fidelity measured at the sinks. To address this problem we propose to exploit the availability of a small number of all wireless, multi-radio virtual sinks that can be randomly distributed or selectively placed across the sensor field. Virtual sinks are capable of siphoning off data events from regions of the sensor field that are beginning to show signs of high traffic load. In this paper, we present the design, implementation, and evaluation of Siphon, a set of fully distributed algorithms that support virtual sink discovery and selection, congestion detection, and traffic redirection in sensor networks. Siphon is based on a Stargate implementation of virtual sinks that uses a separate longer-range radio network (based on IEEE 802.11) to siphon events to one or more physical sinks, and a short-range mote radio to interact with the sensor field at siphon points. Results from analysis, simulation and an experimental 48 Mica2 mote testbed show that virtual sinks can scale mote networks by effectively managing growing traffic demands while minimizing the impact on application fidelity.

There is a growing need to support reliable data communications in sensor networks that are capable of supporting new applications, such as, assured delivery of high-priority events to sinks, reliable control and management of sensor networks, and remotely programming/retasking sensor nodes over-the-air. We present the design, implementation, and evaluation of pump slowly, fetch quickly (PSFQ), a simple, scalable, and robust transport protocol that is customizable to meet the needs of emerging reliable data applications in sensor networks. PSFQ represents a simple approach because it makes minimum assumptions about the underlying routing infrastructure, it is scalable and energy- efficient because it supports minimum signaling, thereby reducing the communication cost for data reliability, and importantly, it is robust because it is responsive to a wide range of operational error conditions found in sensor network, allowing for the successful operation of the protocol even under highly error-prone conditions. The key idea that underpins the design of PSFQ is to distribute data from a source node by pacing data at a relatively slow speed ("pump slowly"), but allowing nodes that experience data loss to fetch (i.e., recover) any missing segments from their local immediate neighbors aggressively ("fetch quickly"). We present the design and implementation of PSFQ, and evaluate the protocol using the ns-2 simulator and an experimental wireless sensor testbed based on Berkeley motes and the TinyOS operating system. We show that PSFQ can outperform existing related techniques and is highly responsive to the various error conditions experienced in sensor networks. The source code for PSFQ is freely available for experimentation.

No abstract available.

No abstract available.

Event-driven sensor networks operate under an idle or light load and then suddenly become active in response to a detected or monitored event. The transport of event impulses is likely to lead to varying degrees of congestion in the network depending on the sensing application. It is during these periods of event impulses that the likelihood of congestion is greatest and the information in transit of most importance to users. To address this challenge we propose an energy efficient congestion control scheme for sensor networks called CODA (COngestion Detection and Avoidance) that comprises three mechanisms: (i) receiver-based congestion detection; (ii) open-loop hop-by-hop backpressure; and (iii) closed-loop multi-source regulation. We present the detailed design, implementation, and evaluation of CODA using simulation and experimentation. We define two important performance metrics (i.e., energy tax and fidelity penalty) to evaluate the impact of CODA on the performance of sensing applications. We discuss the performance benefits and practical engineering challenges of implementing CODA in an experimental sensor network testbed based on Berkeley motes using CSMA. Simulation results indicate that CODA significantly improves the performance of data dissemination applications such as directed diffusion by mitigating hotspots, and reducing the energy tax with low fidelity penalty on sensing applications. We also demonstrate that CODA is capable of responding to a number of congestion scenarios that we believe will be prevalent as the deployment of these networks accelerates.