New Approach of Indoor and Outdoor Localization Systems

<p class="Abstract">Precision agriculture (PA) offers the opportunity for farmers to improve both efficiency in managing resources and optimisation of process inputs, thus increasing their whole farm’s profitability. Despite these well-known benefits, the adoption of PA technologies (PATs) is still challenging due to socio-economic barriers and unique characteristics of the farms: cropping systems, technical developments, field sizes and farm scale. The economic aspect is undoubtedly one of the most important aspects to consider before adopting PATs. In most of the cases, farmers are reluctant to introduce precision farming systems since the costs and uncertainty about the profitability and advantages need to be addressed. This study aims to explore how PATs could affect the profitability of a representative Italian farm specialising in the production of cereals, making this a case study. In detail, an economic analysis was applied to determine the profitability of the farm, which showed that the adoption of PAT’s increased the yield of durum and soft wheat and significantly reduced the cost of mechanical operations and technical means. Therefore, the potential gains from the adoption of PATs challenges policymakers to design targeted interventions which could encourage their uptake. This paper is an extended version of the original contribution presented to the 2019 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) in Portici, Italy.</p>

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“…PA applications can be classified into three categories, taking into account the different degree of accuracy in the positioning systems [21]:…”

Section: Precision Agriculture Techniques and Technologiesmentioning

confidence: 99%

Measuring a farm's profitability after adopting precision agriculture technologies: A case study from Italy

et al. 2020

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A Precise Tracking Algorithm Using PDR and Wi-Fi/iBeacon Corrections for Smartphones

Nguyen

Shin

2021

IEEE Access

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Considering smartphone-based indoor localization and tracking, combination of Wi-Fi fingerprinting and pedestrian dead reckoning (PDR) becomes prevalent due to wide deployment of indoor Wi-Fi access points and easily access of inertial measurement units (IMUs) on smartphones. Since Wi-Fi fingerprinting depends on the similarities between online and offline received signal strengths, it suffers from the fluctuation of radio signal measurements. Meanwhile, the PDR relies on the IMUs, which undergo accumulated errors in long walking distance. In this paper, we aim to improve the indoor tracking accuracy, while filling up the gap between two popular Android and iOS platforms. In particular, we propose a hybrid tracking algorithm that integrates the PDR with Wi-Fi/iBeacon signal features. There are three key points in our paper. First, we process Wi-Fi/iBeacon signals to build conversion functions to convert signals to related proximity distances. Besides, in order to support mobile tracking, we introduce a placement methodology for iBeacon installation for a specific region of interest. Second, a mobile smartphone uses an improved PDR to localize itself, while incorporating Wi-Fi/iBeacon data for estimating a starting point and correcting its location along the walking path. Finally, we build an iOS app to implement the proposed scheme and display visual tracking results. Experiment results show that our proposed scheme is more robust and accurate compared to the conventional schemes.INDEX TERMS smartphone based indoor localization; pedestrian dead reckoning; iBeacon placement methodology; Wi-Fi proximity

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Smartphone-Based Indoor Tracking in Multiple-Floor Scenarios

Nguyen

Kim³

et al. 2021

IEEE Access

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With the rapid development of location-based services (LBSs), efficient and mobile-friendly localization algorithms should be designed for users to deliver a reliable LBS. In this paper, we present an algorithm with the corresponding smartphone app that enables users to calculate their locations based on representative infrastructures, such as nearby Wi-Fi access points and Bluetooth low energy (BLE) beacons subject to low-cost, rapid system deployment, and competitive location accuracy. Working under indoor multiple-floor scenarios, our app has three prominent features for estimating user locations. First, we establish a feature identifier to detect the current floor and the feasible area in which the user may walk. Second, owing to the structures of the indoor environment and the presence of different obstacles, the unpredictable variation of the received signal strength (RSS) in indoor environments is considered in the RSS-distance relationship to provide accurate location estimates. Third, with the prevalence of smartphones, we extract smartphone-inertial measurement units to learn users' behavior preferences, while collecting reference signals (e.g., Wi-Fi/BLE readings) along the pathway and input to the tracking algorithm. Then, the user's current location is displayed on the app. With this solution, we can provide an accurate location estimate with relatively low computational complexity regarding mobile device capability, while reducing labor costs from traditional fingerprint deployments. Finally, we test our tracking app in real-time multiple floor scenarios and evaluate the collected tracking data. Experimental results show that our proposed scheme achieves an average localization accuracy of more than 80% within a 2-m error bound in multiple-floor scenarios, while all areas (i.e., corridors, rooms, and stairs) were successfully identified.

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New Approach of Indoor and Outdoor Localization Systems

Cited by 14 publications

References 25 publications

Measuring a farm's profitability after adopting precision agriculture technologies: A case study from Italy

Measuring a farm's profitability after adopting precision agriculture technologies: A case study from Italy

A Precise Tracking Algorithm Using PDR and Wi-Fi/iBeacon Corrections for Smartphones

Smartphone-Based Indoor Tracking in Multiple-Floor Scenarios

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