A system for LEASE: location estimation assisted by stationary emitters for indoor RF wireless networks

By Parameshwaran Krishnan, Anjur Sundaresan Krishnakumar, Wen-Hua Ju, Colin L. Mallows, Sachin Ganu. 2004

378 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

No need to war-drive: unsupervised indoor localization (2012, background): “Some techniques have bypasses the calibration effort through deployment of additional infrastructure, e.g., Cricket [10], Lease [4], PAL [8], Pinpoint [15].”
The limits of localization using signal strength: a comparative study (2004, background): “11 and signal strength are closely related to ours [10], [11], [12], [13], [14], [15], [16], [17].”
Wireless sensor network localization techniques (2007, methodology): “The map is obtained either offline by a priori measurements or online using sniffing devices [44] deployed at known locations.”
Signal processing techniques in network-aided positioning: a survey of state-of-the-art positioning designs (2005, background): “To ease the burden of system management (provisioning, security, deployment, and maintenance), many enterprises prefer client-assisted and infrastructure-based deployments in which simple sniffers monitor client activity and measure the signal strength of transmissions received from clients [ 23 ].”
Transferring Localization Models over Time (2008, neutral): “2003) and LEASE (Krishnan et al. 2004) utilized a large number of hardware equipments, including stationary emitters and sniffers, to obtain up-to-date RSS values for updating the radio maps.”
Kernel-Based Positioning in Wireless Local Area Networks (2007, background): “dependence on prior topological information, assumption of isotropic RSS contours [23], [24], and invariance to receiver orientation [25].”
Adaptive Localization in a Dynamic WiFi Environment through Multi-view Learning (2007, methodology): “The LEASE system (Krishnan et al. 2004) utilizes different hardware systems to solve this problem.”
Dynamic, Interactive Documents for Teaching Statistical Practice (2007, neutral): “We have created a brief research article based on several articles on the topic (King et al., 2006; Krishnan et al., 2004; Madigan et al., 2006; Youssef & Agrawala, 2004).”
You are facing the Mona Lisa: spot localization using PHY layer information (2012, methodology): “Horus [4] and LEASE [19] propose enhancements to RADAR by exploiting the structure of RSSI.”
Challenges: device-free passive localization for wireless environments (2007, background): “This allows the system to provide the user with its location and other services related to the estimated location [6,16].”
The Horus WLAN location determination system (2005, neutral): “WLAN location determination is an active research area [5, 6, 8, 9, 12, 13, 15, 17, 20–22, 29–31, 33, 34].”
Adaptive Temporal Radio Maps for Indoor Location Estimation (2005, background): “In this section, we describe our experimental setup and the noisy characteristics of wireless channel which makes the problem of location determination a challenging task.”
Localization Algorithms and Strategies for Wireless Sensor Networks: Monitoring and Surveillance Techniques for Target Tracking (2009, methodology): “The algorithms selected here span broad techniques that proved practical and useful from surveying the references (Elnahrawy et al., 2004; Bahl et al., 2000; Youssef et al., 2003; Youssef et al., 2004; Battiti et al., 2002; Moore et al., 2004; Fang et al., 2005; Savvides et al., 2001; Lorincz et al.”
Zee: zero-effort crowdsourcing for indoor localization (2012, background): “There have also been localization systems based on a deployment of RF transmitters and sniffers [15] or RFID [18].”