An UWB relative location system

Correal, N.S.; Kyperountas, S.; Shi, Qicai; Welborn, Matthew

doi:10.1109/uwbst.2003.1267871

Cited by 52 publications

(29 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Detection technologies typically include cameras [47], CO2 sensors [48], cellular phone control-channel traffic sensors [49], humidity sensors [50], infrared (IR) sensors [51], light sensors [52], motion sensors [53], radio frequency identification (RFID) [54], sound sensors [55], switch door sensors [56], telephone sensors [57], temperature sensors [50], ultra-wideband (UWB) [58], wireless sensor networks (WSN) [59], and Wi-Fi infrastructures [60]. These detection technologies can be divided to two main groups [61]: (1) precise technologies with incomplete coverage (e.g., cameras); and (2) imprecise technologies with full coverage (e.g., Wi-Fi infrastructures).…”

Section: Monitoring Occupant-specific Energy Consumptionmentioning

confidence: 99%

A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

2015

View full text Add to dashboard Cite

Abstract:Buildings currently account for 30-40 percent of total global energy consumption. In particular, commercial buildings are responsible for about 12 percent of global energy use and 21 percent of the United States' energy use, and the energy demand of this sector continues to grow faster than other sectors. This increasing rate therefore raises a critical concern about improving the energy performance of commercial buildings. Recently, researchers have investigated ways in which understanding and improving occupants' energy-consuming behaviors could function as a cost-effective approach to decreasing commercial buildings' energy demands. The objective of this paper is to present a detailed, up-to-date review of various algorithms, models, and techniques employed in the pursuit of understanding and improving occupants' energy-use behaviors in commercial buildings. Previous related studies are introduced and three main approaches are identified: (1) monitoring occupant-specific energy consumption; (2) Simulating occupant energy consumption behavior; and (3) improving occupant energy consumption behavior. The first approach employs intrusive and non-intrusive load-monitoring techniques to estimate the energy use of individual occupants. The second approach models diverse characteristics related to occupants' energy-consuming behaviors in order to assess and predict such characteristics' impacts on the energy performance of commercial buildings; this approach mostly utilizes agent-based modeling techniques to simulate actions and interactions between occupants and their built environment. The third approach employs occupancy-focused interventions to change OPEN ACCESSEnergies 2015, 8 10997 occupants' energy-use characteristics. Based on the detailed review of each approach, critical issues and current gaps in knowledge in the existing literature are discussed, and directions for future research opportunities in this field are provided.

show abstract

Section: Monitoring Occupant-specific Energy Consumptionmentioning

confidence: 99%

A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

2015

View full text Add to dashboard Cite

show abstract

“…Range measurement accuracy improves at higher SNR [Caffery Jr. and Stuber 1998;Kim et al 2002], thus imposing a tradeoff between energy cost and accuracy. There is also a tradeoff between device cost and range accuracy: using ultrawideband (UWB) [Fleming and Kushner 1995;Correal et al 2003] or hybrid ultrasound/RF techniques [Girod et al 2002] can achieve accuracies on the order of centimeters, but at the expense of high device and energy costs. Alternatively, very inexpensive wireless devices can measure RF RSS just by listening to network packet traffic, but range estimates from RSS incur significant errors due to channel fading.…”

Section: Sensor Localization Requirementsmentioning

confidence: 99%

Distributed weighted-multidimensional scaling for node localization in sensor networks

Costa

Patwari

Hero

2006

ACM Trans. Sen. Netw.

521

460

View full text Add to dashboard Cite

Accurate, distributed localization algorithms are needed for a wide variety of wireless sensor network applications. This article introduces a scalable, distributed weighted-multidimensional scaling (dwMDS) algorithm that adaptively emphasizes the most accurate range measurements and naturally accounts for communication constraints within the sensor network. Each node adaptively chooses a neighborhood of sensors, updates its position estimate by minimizing a local cost function and then passes this update to neighboring sensors. Derived bounds on communication requirements provide insight on the energy efficiency of the proposed distributed method versus a centralized approach. For received signal-strength (RSS) based range measurements, we demonstrate via simulation that location estimates are nearly unbiased with variance close to the Cramér-Rao lower bound. Further, RSS and time-of-arrival (TOA) channel measurements are used to demonstrate performance as good as the centralized maximum-likelihood estimator (MLE) in a real-world sensor network.

show abstract

“…[17], mostly based either on scene analysis (e.g. using motion capture systems) or on triangularization (of RF and ultrasound [18] or wireless communication interfaces [19]). In this section, we describe the various systems we used, and analyze their interests and limitations.…”

Section: A Scale Abilitymentioning

confidence: 99%

ARUM: A cooperative middleware and an experimentation platform for mobile systems

Killijian

Roy

Séverac

2010

2010 IEEE 6th International Conference on Wireless and Mobile Computing, Networking and Communications

View full text Add to dashboard Cite

Abstract-In this paper, we present a middleware architecture for dependable mobile systems and an experimentation platform for its evaluation. The proposed architecture includes three building blocks tailored for mobile cooperative applications: a Proximity Map, a Trust and Cooperation Oracle, and a Cooperative Data Backup service. To illustrate our platform, we developed a Distributed Black-box application, whose aim is to record critical data while tolerating the failure of a node, and implemented a hardware evaluation platform of mobile systems for experimenting with the application. We provide here some insights on the development of the platform, focusing on wireless communication.

show abstract

An UWB relative location system

Cited by 52 publications

References 4 publications

A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

Distributed weighted-multidimensional scaling for node localization in sensor networks

ARUM: A cooperative middleware and an experimentation platform for mobile systems

Contact Info

Product

Resources

About