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Ayyalasomayajula, Roshan; Vasisht, Deepak; Bharadia, Dinesh

doi:10.1145/3281411.3281428

Cited by 74 publications

(7 citation statements)

References 30 publications

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“…The concern is however the applicability of such a solution since collecting CSI requires special Wi‐Fi chip or SDR, which raises the bar for crowd‐sensing. But since unlabeled data can be used, the bar is still lower than those recent analytical‐based methods 37–45 …”

Section: Discussionmentioning

confidence: 98%

DRVAT: Exploring RSSI series representation and attention model for indoor positioning

Sun

Tao

et al. 2021

Int J of Intelligent Sys

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Although Bluetooth Low Energy (BLE) fingerprinting localization has become a hot research topic with encouraging results, it is difficult to predict the location depending on a short duration received signal strength indication (RSSI) sequence in realistic scenarios due to the severe fluctuation of RSSI. We introduce a new perspective to view the indoor positioning problem by radio map fingerprint. We argue that even though beacons may be independently deployed, the RSSI series bear certain spatial relation because of their copresence in the same physical space. The latent relation implicitly conveyed by the coexistence of their signals at various indoor locations. Unlike existing approaches that try to find a direct mapping between sensed signals and the corresponding location, we explore the spatial relation of beacons from the input data to estimate location. We propose a deep learning localization system, termed DRVAT, which is based on the distributed representation vector (DRV) and selfattention (AT) among the pairs of MAC-RSSI. First, we obtain DRVs which represent dense features in low dimensionality through pre-training on all MAC-RSSIs. Then we exploit self-attention mechanism to learn the latent spatial relation of beacons. Finally, MAC-RSSIs labeled with locations are used to fine-tune the model for estimating location. Localization accuracy results

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Section: Discussionmentioning

confidence: 98%

DRVAT: Exploring RSSI series representation and attention model for indoor positioning

Sun

Tao

et al. 2021

Int J of Intelligent Sys

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“…These experiments demonstrate the potential for automated collection, for example, by pulling an electromagnet behind a tractor in agricultural deployments. Our onboard radio could also be used for localization and presents opportunities to use recent advances in the sensor networking communities (45,46). An alternative method that is more attractive for remote and difficult terrain would be to incorporate biodegradable materials in the design of our microfliers (47).…”

Section: Discussionmentioning

confidence: 99%

Solar-powered shape-changing origami microfliers

Johnson,

Arroyos,

Ferran

et al. 2023

Sci. Robot.

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Using wind to disperse microfliers that fall like seeds and leaves can help automate large-scale sensor deployments. Here, we present battery-free microfliers that can change shape in mid-air to vary their dispersal distance. We designed origami microfliers using bistable leaf-out structures and uncovered an important property: A simple change in the shape of these origami structures causes two dramatically different falling behaviors. When unfolded and flat, the microfliers exhibit a tumbling behavior that increases lateral displacement in the wind. When folded inward, their orientation is stabilized, resulting in a downward descent that is less influenced by wind. To electronically transition between these two shapes, we designed a low-power electromagnetic actuator that produces peak forces of up to 200 millinewtons within 25 milliseconds while powered by solar cells. We fabricated a circuit directly on the folded origami structure that includes a programmable microcontroller, a Bluetooth radio, a solar power–harvesting circuit, a pressure sensor to estimate altitude, and a temperature sensor. Outdoor evaluations show that our 414-milligram origami microfliers were able to electronically change their shape mid-air, travel up to 98 meters in a light breeze, and wirelessly transmit data via Bluetooth up to 60 meters away, using only power collected from the sun.

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“…As such, we chose K = 7 as a trade-off between accuracy and complexity of the setup. The selected unique combinations using K = 7 and R = 3 from all 7 anchors yielded anchor combinations: [1,3,5], [2,4,6], [3,5,7], [4,6,1], [5,7,2], [6, 1, 3], [7,2,4].…”

Section: A Proposed Anchor-subset Methodsmentioning

confidence: 99%

“…Typically, these systems rely on received signal strength indicators (RSSIs) to estimate the distance, which results in a 2 − 3 m error (the "positioning accuracy") due to the presence of non-line-of-sight (NLOS) and multi-path fading effects [2]. More recently, phase-based receivers have been proposed to offer higher accuracy, but these solutions require more expensive hardware and antenna systems [3]. Ultra Wideband (UWB) positioning systems are gaining a lot of attention by the research community and are appearing in consumer products e.g., Apple and Samsung mobile phones.…”

Section: Introductionmentioning

confidence: 99%

Ultra Wideband (UWB) Localization Using Active CIR-Based Fingerprinting

et al. 2023

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Indoor positioning systems using Ultra Wideband (UWB) achieve high positioning accuracy (<30 cm). However, traditional localization approaches require many packet exchanges (e.g. two-way ranging) or challenging clock synchronization (e.g. time difference of arrival). To remedy this, we propose active fingerprinting using the channel impulse response (CIR) from a single UWB packet received at each UWB anchor. The proposed neural network anchor-subset selection method with Savitzky-Golay filter achieves a low mean absolute error (20.9−87.0 cm), in contrast to signal strength based fingerprinting approaches that realize accuracies of 2−3 m. Finally, with CIR interpolation the data collection overhead is reduced.

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BLoc

Cited by 74 publications

References 30 publications

DRVAT: Exploring RSSI series representation and attention model for indoor positioning

DRVAT: Exploring RSSI series representation and attention model for indoor positioning

Solar-powered shape-changing origami microfliers

Ultra Wideband (UWB) Localization Using Active CIR-Based Fingerprinting

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