An IR local positioning system for smart items and devices

Aitenbichler, Erwin; Mühlhäuser, Max

doi:10.1109/icdcsw.2003.1203576

Cited by 70 publications

(46 citation statements)

References 5 publications

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“…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

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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.

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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

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“…While there is no doubt that the implementation of the IR positioning system at room level is at a very low cost, its coverage range and accuracy are limited (Aitenbichler & Muhlhauser, 2003). For a large space implementation, an IR system would need several receivers to improve accuracy (Mautz, 2012).…”

Section: Challenges and Drawbacks Of Irmentioning

confidence: 99%

A State-of-the-Art Survey of Indoor Positioning and Navigation Systems and Technologies

Sakpere

Oshin

Mlitwa

2017

SACJ

148

129

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The research and use of positioning and navigation technologies outdoors has seen a steady and exponential growth. Based on this success, there have been attempts to implement these technologies indoors, leading to numerous studies. Most of the algorithms, techniques and technologies used have been implemented outdoors. However, how they fare indoors is different altogether. Thus, several technologies have been proposed and implemented to improve positioning and navigation indoors. Among them are Infrared (IR), Ultrasound, Audible Sound, Magnetic, Optical and Vision, Radio Frequency (RF), Visible Light, Pedestrian Dead Reckoning (PDR)/Inertial Navigation System (INS) and Hybrid. The RF technologies include Bluetooth, Ultra-wideband (UWB), Wireless Sensor Network (WSN), Wireless Local Area Network (WLAN), Radio-Frequency Identification (RFID) and Near Field Communication (NFC). In addition, positioning techniques applied in indoor positioning systems include the signal properties and positioning algorithms. The prevalent signal properties are Angle of Arrival (AOA), Time of Arrival (TOA), Time Difference of Arrival (TDOA) and Received Signal Strength Indication (RSSI), while the positioning algorithms are Triangulation, Trilateration, Proximity and Scene Analysis/ Fingerprinting. This paper presents a state-of-the-art survey of indoor positioning and navigation systems and technologies, and their use in various scenarios. It analyses distinct positioning technology metrics such as accuracy, complexity, cost, privacy, scalability and usability. This paper has profound implications for future studies of positioning and navigation.

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“…Effective localization techniques using infrared [1] or RF and ultrasound [2] can achieve high localization accuracy within centimeters. However, these approaches need extra deployments which increase system cost.…”

Section: Introductionmentioning

confidence: 99%

The collocation of measurement points in large open indoor environment

Sheng

Mao

et al. 2015

2015 IEEE Conference on Computer Communications (INFOCOM)

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Abstract-With the pervasion of mobile devices, crowdsourcing based received signal strength (RSS) fingerprint collection method has drawn much attention to facilitate the indoor localization since it is effective and requires no pre-deployment. However, in large open indoor environment like museums and exhibition centres, RSS measurement points cannot be collocated densely, which degrades localization accuracy. This paper focuses on measurement point collocation in different cases and their effects on localization accuracy. We first study two simple preliminary cases under assumption that users are uniformly distributed: when measurement points are collocated regularly, we propose a collocation pattern which is most beneficial to localization accuracy; when measurement points are collocated randomly, we prove that localization accuracy is limited by a tight bound. Under the general case that users are distributed asymmetrically, we show the best allocation scheme of measurement points: measurement point density ρ is proportional to (cµ) 2/3 in every part of the region, where µ is user density and c is a constant determined by the collocation pattern. We also give some guidelines on collocation choice and perform extensive simulations to validate our assumptions and results.

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An IR local positioning system for smart items and devices

Cited by 70 publications

References 5 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

A State-of-the-Art Survey of Indoor Positioning and Navigation Systems and Technologies

The collocation of measurement points in large open indoor environment

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