Lama Nachman

Bio: Lama Nachman is an academic researcher from Intel. The author has contributed to research in topics: Computer science & Wireless sensor network. The author has an hindex of 28, co-authored 107 publications receiving 3292 citations. Previous affiliations of Lama Nachman include KAIST & University of Wisconsin-Madison.

PIPENET: A Wireless Sensor Network for Pipeline Monitoring

Abstract: US water utilities are faced with mounting operational and maintenance costs as a result of aging pipeline infrastructures. Leaks and ruptures in water supply pipelines and blockages and overflow events in sewer collectors cost millions of dollars a year, and monitoring and repairing this underground infrastructure presents a severe challenge. In this paper, we discuss how wireless sensor networks (WSNs) can increase the spatial and temporal resolution of operational data from pipeline infrastructures and thus address the challenge of near real-time monitoring and eventually control. We focus on the use of WSNs for monitoring large diameter bulk-water transmission pipelines. We outline a system, PipeNet, we have been developing for collecting hydraulic and acoustic/vibration data at high sampling rates as well as algorithms for analyzing this data to detect and locate leaks. Challenges include sampling at high data rates, maintaining aggressive duty cycles, and ensuring tightly time- synchronized data collection, all under a strict power budget. We have carried out an extensive field trial with Boston Water and Sewer Commission in order to evaluate some of the critical components of PipeNet. Along with the results of this preliminary trial, we describe the results of extensive laboratory experiments which are used to evaluate our analysis and data processing solutions. Our prototype deployment has led to the development of a reusable, field-reprogrammable software infrastructure for distributed high-rate signal processing in wireless sensor networks, which we also describe.

Design and deployment of industrial sensor networks: experiences from a semiconductor plant and the north sea

Abstract: Sensing technology is a cornerstone for many industrial applications. Manufacturing plants and engineering facilities, such as shipboard engine rooms, require sensors to ensure product quality and efficient and safe operation. We focus on one representative application, preventative equipment maintenance, in which vibration signatures are gathered to predict equipment failure. Based on application requirements and site surveys, we develop a general architecture for this class of industrial applications. This architecture meets the application's data fidelity needs through careful state preservation and over-sampling. We describe the impact of implementing the architecture on two sensing platforms with differing processor and communication capabilities. We present a systematic performance comparison between these platforms in the context of the application. We also describe our experience and lessons learned in two settings: in a semiconductor fabrication plant and onboard an oil tanker in the North Sea. Finally, we establish design guidelines for an ideal platform and architecture for industrial applications. This paper includes several unique contributions: a study of the impact of platform on architecture, a comparison of two deployments in the same application class, and a demonstration of application return on investment.

Audible list traversal

Abstract: Many embodiments may comprise logic such as hardware and/or code to implement user interface for traversal of long sorted lists, via audible mapping of the lists, using sensor based gesture recognition, audio and tactile feedback and button selection while on the go. In several embodiments, such user interface modalities are physically small in size, enabling a user to be truly mobile by reducing the cognitive load required to operate the device. For some embodiments, the user interface may be divided across multiple worn devices, such as a mobile device, watch, earpiece, and ring. Rotation of the watch may be translated into navigation instructions, allowing the user to traverse the list while the user receives audio feedback via the earpiece to describe items in the list as well as audio feedback regarding the navigation state. Many embodiments offer the user a simple user interface to traverse the list without visual feedback.

Adjustment of temporal acoustical characteristics

Abstract: Embodiments may be a standalone module or part of mobile devices, desktop computers, servers, stereo systems, or any other systems that might benefit from condensed audio presentations of item structures such as lists or tables. Embodiments may comprise logic such as hardware and/or code to adjust the temporal characteristics of items comprising words. The items maybe included in a structure such as a text listing or table, an audio listing or table, or a combination thereof, or may be individual words or phrases. For instance, embodiments may comprise a keyword extractor to extract keywords from the items and an abbreviations generator to generate abbreviations based upon the keywords. Further embodiments may comprise a text-to-speech generator to generate audible items based upon the abbreviations to render to a user while traversing the item structure.

E-Gesture: a collaborative architecture for energy-efficient gesture recognition with hand-worn sensor and mobile devices

Abstract: Gesture is a promising mobile User Interface modality that enables eyes-free interaction without stopping or impeding movement. In this paper, we present the design, implementation, and evaluation of E-Gesture, an energy-efficient gesture recognition system using a hand-worn sensor device and a smartphone. E-gesture employs a novel gesture recognition architecture carefully crafted by studying sporadic occurrence patterns of gestures in continuous sensor data streams and analyzing the energy consumption characteristics of both sensors and smartphones. We developed a closed-loop collaborative segmentation architecture, that can (1) be implemented in resource-scarce sensor devices, (2) adaptively turn off power-hungry motion sensors without compromising recognition accuracy, and (3) reduce false segmentations generated from dynamic changes of body movement. We also developed a mobile gesture classification architecture for smartphones that enables HMM-based classification models to better fit multiple mobility situations.

X-MAC: A Short Preamble MAC Protocol for Duty-Cycled Wireless Sensor Networks ; CU-CS-1008-06

Abstract: In this paper we present X-MAC, a low power MAC protocol for wireless sensor networks (WSNs). Standard MAC protocols developed for duty-cycled WSNs such as BMAC, which is the default MAC protocol for TinyOS, employ an extended preamble and preamble sampling. While this “low power listening” approach is simple, asynchronous, and energy-efficient, the long preamble introduces excess latency at each hop, is suboptimal in terms of energy consumption, and suffers from excess energy consumption at nontarget receivers. X-MAC proposes solutions to each of these problems by employing a shortened preamble approach that retains the advantages of low power listening, namely low power communication, simplicity and a decoupling of transmitter and receiver sleep schedules. We demonstrate through implementation and evaluation in a wireless sensor testbed that X-MAC’s shortened preamble approach significantly reduces energy usage at both the transmitter and receiver, reduces per-hop latency, and offers additional advantages such as flexible adaptation to both bursty and periodic sensor data sources.

X-MAC: a short preamble MAC protocol for duty-cycled wireless sensor networks

Abstract: In this paper we present X-MAC, a low power MAC protocol for wireless sensor networks (WSNs). Standard MAC protocols developed for duty-cycled WSNs such as BMAC, which is the default MAC protocol for TinyOS, employ an extended preamble and preamble sampling. While this "low power listening" approach is simple, asynchronous, and energy-efficient, the long preamble introduces excess latency at each hop, is suboptimal in terms of energy consumption, and suffers from excess energy consumption at nontarget receivers. X-MAC proposes solutions to each of these problems by employing a shortened preamble approach that retains the advantages of low power listening, namely low power communication, simplicity and a decoupling of transmitter and receiver sleep schedules. We demonstrate through implementation and evaluation in a wireless sensor testbed that X-MAC's shortened preamble approach significantly reduces energy usage at both the transmitter and receiver, reduces per-hop latency, and offers additional advantages such as flexible adaptation to both bursty and periodic sensor data sources.

Intelligent Automated Assistant

Abstract: An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

CarTel: a distributed mobile sensor computing system

Abstract: 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.