A location-based smart shopping system with IoT technology

Rezazadeh, Javad; Sandrasegaran, Kumbesan; Kong, Xiaoying

doi:10.1109/wf-iot.2018.8355175

Cited by 41 publications

(14 citation statements)

References 12 publications

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“…Motivated by the explosive growth of the applications requiring location information like mobile marketing and emer-gency services [13], device positioning has been considered as an enabling technology and essential service for future IoT networks [14]. There are some existing technologies which have the capability of device positioning, including the Global Navigation Satellite Systems (GNSS) positioning [15], the positioning technologies using multiple-input-multiple-output systems (referred to below as "MIMO positioning") and the observed time difference of arrival (OTDoA) positioning [16], [17], but most of them are not suitable for IoT networks.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Data Collection and Device Positioning in UAV-Assisted IoT

Wang

Liu

et al. 2020

IEEE Internet Things J.

269

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The Internet-of-Things (IoT) will significantly change both industrial manufacturing and our daily lives. Data collection and three-dimensional (3D) positioning of IoT devices are two indispensable services of such networks. However, in conventional networks, only terrestrial base stations (BSs) are used to provide these two services. On the one hand, this leads to high energy consumption for devices transmitting at cell edges. On the other hand, terrestrial BSs are relatively close in height, resulting in poor performance of device positioning in elevation. Due to their high maneuverability and flexible deployment, unmanned aerial vehicles (UAVs) could be a promising technology to overcome the above shortcomings. In this paper, we propose a novel UAV-assisted IoT network, in which a low-altitude UAV platform is employed as both a mobile data collector and an aerial anchor node to assist terrestrial BSs in data collection and device positioning. We aim to minimize the maximum energy consumption of all devices by jointly optimizing the UAV trajectory and devices' transmission schedule over time, while ensuring the reliability of data collection and required 3D positioning performance. This formulation is a mixed-integer non-convex optimization problem, and an efficient differential evolution (DE) based method is proposed for solving it. Numerical results demonstrate that the proposed network and optimization method achieve significant performance gains in both energy efficient data collection and 3D device positioning, as compared with a conventional terrestrial IoT network.

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Section: Introductionmentioning

confidence: 99%

Energy-Efficient Data Collection and Device Positioning in UAV-Assisted IoT

Wang

Liu

et al. 2020

IEEE Internet Things J.

269

View full text Add to dashboard Cite

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“…IoT technology allows for connecting sensors and intelligent hardware to the internet, it is a growing ubiquitous concept that has impacted considerable aspects of human life [1][2][3]. IoT-based service has been applied in the novel retail shopping fields, thereby offering a set of interactive shopping solutions [4,5]. The customers can complete the whole shopping process by self-service, and the whole consumption process appears to be naturally smooth.…”

Section: Introductionmentioning

confidence: 99%

Design of Smart Unstaffed Retail Shop Based on IoT and Artificial Intelligence

Zou

et al. 2020

IEEE Access

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Unstaffed retail shops have emerged recently and been noticeably changing our shopping styles. In terms of these shops, the design of vending machine is critical to user shopping experience. The conventional design typically uses weighing sensors incapable of sensing what the customer is taking. In the present study, a smart unstaffed retail shop scheme is proposed based on artificial intelligence and the internet of things, as an attempt to enhance the user shopping experience remarkably. To analyze multiple target features of commodities, the SSD (300×300) algorithm is employed; the recognition accuracy is further enhanced by adding sub-prediction structure. Using the data set of 18, 000 images in different practical scenarios containing 20 different type of stock keeping units, the comparison experimental results reveal that the proposed SSD (300×300) model outperforms than the original SSD (300×300) in goods detection, the mean average precision of the developed method reaches 96.1% on the test dataset, revealing that the system can make up for the deficiency of conventional unmanned container. The practical test shows that the system can meet the requirements of new retail, which greatly increases the customer flow and transaction volume.

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“…Unfortunately, the ubiquitous Global Navigation Satellite System (GNSS) cannot be used as a positioning system in greenhouses due to large indoor positioning errors [10]. Indoor positioning is an important part of Internet of Things (IoT) and plays an important role in improving most services in 2 of 15 IoT [11,12]. Moreover, many of the other commercialized indoor positioning systems use Ultra-Wide Band (UWB) signals that only have a 100 mm accuracy [13], and can interfere with wireless systems in Industrial Scientific Medical and Mobile cellular bands [14].…”

Section: Introductionmentioning

confidence: 99%

A Noise Tolerant Spread Spectrum Sound-Based Local Positioning System for Operating a Quadcopter in a Greenhouse

Huang

Tsay

Shiigi

et al. 2020

Sensors

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Quadcopters are beginning to play an important role in precision agriculture. In order to localize and operate the quadcopter automatically in complex agricultural settings, such as a greenhouse, a robust positioning system is needed. In previous research, we developed a spread spectrum sound-based local positioning system (SSSLPS) with a 20 mm accuracy within a 30 × 30 m greenhouse area. In this research, a noise tolerant SSSLPS was developed and evaluated. First, the acoustic noise spectrum emitted by the quadcopter was documented, and then the noise tolerance properties of SSSounds were examined and tested. This was done in a greenhouse with a fixed quadcopter (9.75 N thrust) with the positioning system mounted on it. The recorded quadcopter noise had a broadband noise compared to the SSSound. Taking these SSSound properties into account, the noise tolerance of the SSSLPS was improved, achieving a positioning accuracy of 23.2 mm and 31.6 mm accuracy within 12 × 6 m for both Time-division Multiple Access (TDMA) and Frequency-division Multiple Access (FDMA) modulation. The results demonstrate that the SSSLPS is an accurate, robust positioning system that is noise tolerant and can used for quadcopter operation even within a small greenhouse.

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A location-based smart shopping system with IoT technology

Cited by 41 publications

References 12 publications

Energy-Efficient Data Collection and Device Positioning in UAV-Assisted IoT

Energy-Efficient Data Collection and Device Positioning in UAV-Assisted IoT

Design of Smart Unstaffed Retail Shop Based on IoT and Artificial Intelligence

A Noise Tolerant Spread Spectrum Sound-Based Local Positioning System for Operating a Quadcopter in a Greenhouse

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