Techniques of Indoor Positioning Systems (IPS): A Survey

Alkhawaja, Fares; Jaradat, Mohammad A.; Romdhane, Lotfi

doi:10.1109/icaset.2019.8714291

Cited by 24 publications

(10 citation statements)

References 21 publications

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“…Utilized to enhance data imputation using a statistical model to model the sensory information [64,67] Can handle unknown correlations; Tolerant [68,69] Limited to linear estimators; Computation complexity is high [65] Estimation Low [70] Probabilistic Methods…”

Section: Methodsmentioning

confidence: 99%

Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review

Fayyad

Jaradat

Gruyer

et al. 2020

Sensors

303

130

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Autonomous vehicles (AV) are expected to improve, reshape, and revolutionize the future of ground transportation. It is anticipated that ordinary vehicles will one day be replaced with smart vehicles that are able to make decisions and perform driving tasks on their own. In order to achieve this objective, self-driving vehicles are equipped with sensors that are used to sense and perceive both their surroundings and the faraway environment, using further advances in communication technologies, such as 5G. In the meantime, local perception, as with human beings, will continue to be an effective means for controlling the vehicle at short range. In the other hand, extended perception allows for anticipation of distant events and produces smarter behavior to guide the vehicle to its destination while respecting a set of criteria (safety, energy management, traffic optimization, comfort). In spite of the remarkable advancements of sensor technologies in terms of their effectiveness and applicability for AV systems in recent years, sensors can still fail because of noise, ambient conditions, or manufacturing defects, among other factors; hence, it is not advisable to rely on a single sensor for any of the autonomous driving tasks. The practical solution is to incorporate multiple competitive and complementary sensors that work synergistically to overcome their individual shortcomings. This article provides a comprehensive review of the state-of-the-art methods utilized to improve the performance of AV systems in short-range or local vehicle environments. Specifically, it focuses on recent studies that use deep learning sensor fusion algorithms for perception, localization, and mapping. The article concludes by highlighting some of the current trends and possible future research directions.

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Section: Methodsmentioning

confidence: 99%

Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review

Fayyad

Jaradat

Gruyer

et al. 2020

Sensors

303

130

View full text Add to dashboard Cite

show abstract

“…Another disadvantage of this technology is the high computational requirements that make it difficult to integrate into smartphones or wearables. These systems also require LOS, limiting their coverage to only a few meters [83].…”

Section: H Visual Systemsmentioning

confidence: 99%

Room-Level Localization System Based on LoRa Backscatters

2021

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The aim of this paper is to propose a novel room-level localization approach to locate LoRa backscatter devices, which can be easily embedded into wearable devices or smartphones. The advantages of this system lie in its series of low-cost, low-power, low-complexity and long-range features. LoRa backscattering operates by alternatively connecting an antenna, through a switch, to two loads with high reflection coefficients and opposite phase. The result is a frequency shift of the LoRa incident signal equal to the backscatter switching frequency. The localization system comprises several LoRa receivers distributed among the rooms, a LoRa transmitter located at a central point and the backscatter device, which is carried or worn by a subject. The position of the LoRa backscatter device can be determined by comparing the received signal strength between all receivers. In order to improve the accuracy of the system, different machine learning classifiers were compared. System performance was tested in a real-life scenario, achieving an accuracy up to 89.7% using linear discriminant analysis (LDA). INDEX TERMS Backscatter communications, LoRa, localization, wireless sensor networks, RFID, zeropower sensor, internet of things (IoT).

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“…To obtain the other party's position, currently, there are mainly two solutions: (1) Waiting for response: With the help of indoor positioning techniques [2] and location sharing services [3], one could share its location to the other and wait for the response. However, that relies heavily on the responder being able to respond to this kind of location sharing.…”

Section: Related Workmentioning

confidence: 99%

Blockchain‐based indoor location paging and answering service with truncated‐geo‐indistinguishability

Yang

Liu

et al. 2021

IET Blockchain

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Indoor location-based services have numerous applications built on indoor navigation. Over the years, researchers have proposed various indoor navigation methods to find a certain point of interest. This paper proposes location paging and answering service (LPAS) for finding out someone in an indoor environment . However, it may face serious problems of privacy disclosure of the clients' indoor location data . To ensure security, privacy and convenience, the authors leverage blockchain to model the indoor environment, and build blockchain-based indoor LPAS (BILPAS) on blockchain by embedding our three-way-handshake Diffie-Hellman Key Agreement procedures. Our BIL-PAS can automatically establish a secure and privacy communication tunnel between any two users, thus greatly liberating participants from unnecessary self-determining interactions . On the premise that the certain person can also be found, the authors introduce Truncated-Geo-Indistinguishability (TGeoI) to perturb the actual location within a limited, valid, user-defined distance, thereby allowing users to obtain reasonable location privacy. The authors have put a strong emphasis in the formal treatment of the reasonable privacy guarantees, giving a rigorous definition of TGeoI and providing a comparison with Geo-Indistinguishability that the authors' method satisfies such property. Then, the authors present the BILPAS prototype and implement it on Hyperledger Fabric. Finally, the authors conduct the security and privacy analysis and evaluate the time performance. The experiment results validate the reasonable privacy, high security and convenience of the authors' framework.

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Techniques of Indoor Positioning Systems (IPS): A Survey

Cited by 24 publications

References 21 publications

Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review

Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review

Room-Level Localization System Based on LoRa Backscatters

Blockchain‐based indoor location paging and answering service with truncated‐geo‐indistinguishability

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