In recent years, location-based services have been receiving increasing attention because of their great development prospects. Researchers from all over the world have proposed many solutions for indoor positioning over the past several years. However, owing to the dynamic and complex nature of indoor environments, accurately and efficiently localising targets in indoor environments remains a challenging problem. In this paper, we propose a novel indoor positioning algorithm based on the received signal strength indication and pedestrian dead reckoning. In order to enhance the accuracy and reliability of our proposed probabilistic position selection algorithm in mixed line-of-sight (LOS) and non-line-of-sight (NLOS) environments, a low-complexity identification approach is proposed to identify the change in the channel situation between NLOS and LOS. Numerical experiment results indicate that our proposed algorithm has a higher accuracy and is less impacted by NLOS errors than other conventional methods in mixed LOS and NLOS indoor environments.

https://www.mdpi.com/2220-9964/7/6/232/pdf

A RSSI/PDR-Based Probabilistic Position Selection Algorithm with NLOS Identification for Indoor Localisation

The interior space of large-scale buildings, such as hospitals, with a variety of departments, is so complicated that people may easily lose their way while visiting. Difficulties in wayfinding can cause stress, anxiety, frustration and safety issues to patients and families. An indoor navigation system including route planning and localization is utilized to guide people from one place to another. The localization of moving subjects is a critical-function component in an indoor navigation system. Pedestrian dead reckoning (PDR) is a technology that is widely employed for localization due to the advantage of being independent of infrastructure. To improve the accuracy of the localization system, combining different technologies is one of the solutions. In this study, a multi-sensor fusion approach is proposed to improve the accuracy of the PDR system by utilizing a light sensor, Bluetooth and map information. These simple mechanisms are applied to deal with the issue of accumulative error by identifying edge and sub-edge information from both Bluetooth and the light sensor. Overall, the accumulative error of the proposed multi-sensor fusion approach is below 65 cm in different cases of light arrangement. Compared to inertial sensor-based PDR system, the proposed multi-sensor fusion approach can improve 90% of the localization accuracy in an environment with an appropriate density of ceiling-mounted lamps. The results demonstrate that the proposed approach can improve the localization accuracy by utilizing multi-sensor data and fulfill the feasibility requirements of localization in an indoor navigation system.

Multi-sensor fusion approach for improving map-based indoor pedestrian localization

The weighted K-nearest neighbor algorithm (WKNN) is easily implemented, and it has been widely applied. In the large-scale positioning regions, using all fingerprint data in matching calculations would lead to high computation expenses, which is not conducive to real-time positioning. Due to signal instability, irrelevant fingerprints reduce the positioning accuracy when performing the matching calculation process. Therefore, selecting the appropriate fingerprint data from the database more quickly and accurately is an urgent problem for improving WKNN. This paper proposes an improved Bluetooth indoor positioning method using a dynamic fingerprint window (DFW-WKNN). The dynamic fingerprint window is a space range for local fingerprint data searching instead of universal searching, and it can be dynamically adjusted according to the indoor pedestrian movement and always covers the maximum possible range of the next positioning. This method was tested and evaluated in two typical scenarios, comparing two existing algorithms, the traditional WKNN and the improved WKNN based on local clustering (LC-WKNN). The experimental results show that the proposed DFW-WKNN algorithm enormously improved both the positioning accuracy and positioning efficiency, significantly, when the fingerprint data increased.

https://www.mdpi.com/1424-8220/20/24/7269/pdf

An Improved Bluetooth Indoor Positioning Method Using Dynamic Fingerprint Window.

The digital world offers ample availability of data, both historic and real-time. While this capability has the potential for better decision making, the contrary can be the case for a human actuator. Information overflow causes mental overload rather than empowerment of choice. In the context of traditional supermarket shopping, for example, customers are exposed to unstructured and complex product information including ingredients, nutrition facts, product labels, and more. Processing all this information in the context of multiple sustainability aspects requires expert knowledge. On the other hand, the rise of digitalization and the Internet of Things can be used to assist and empower customers during this shopping process. However, an integrated solution is required to provide a high grade of usability and crucial complexity reduction for customers. Therefore, we outline an IoT decision support system that assists customers on the sales floor and enables better decision making according to personal preferences and sustainable consumption. It integrates an indoor localization system, a product information database, and a ranking system considering the individual shopping preferences, where the latter is specified by the customer within an interactive smartphone application. The discussed IoT decision support system was deployed and tested in two retail stores. Its interaction with non-expert test participants was observed over months, and the results are summarized in this contribution.

/pdf/augmented-shopping-experience-for-sustainable-consumption-15urqo7lze.pdf

Augmented Shopping Experience for Sustainable Consumption Using the Internet of Thing

Multi-sensor integrated navigation/positioning systems using data fusion: From analytics-based to learning-based approaches

Indoor localization becomes a research focus in recent years since. Smartphone-based pedestrian dead reckoning (PDR) is one of the widely-adopted localization techniques with limiting problems such as the drift of inertial sensors. Bluetooth Low Energy (BLE) has better performance result which makes it an auxiliary tool for PDR to correct errors. But BLE fingerprint sampling and calibrating are time-consuming and labor-intensive. In this paper, a Support Vector Machine (SVM) classification algorithm based crowdsourcing method is developed and applied to generate BLE landmarks instead of manual work leveraging smartphone sensors and uploaded BLE signals. A particle filter is also used to fusion PDR and landmarks detection results for better localization performance. The experiments show that the proposed fusion algorithm achieved the accuracy of 3.15 m at 90% of the time with dense landmarks (1 landmark per 5 m), which performs 51.76% better than 6.53 m from PDR algorithm. With sparse landmarks (1 landmark per 15 m), the proposed fusion algorithm achieved the accuracy of 3.26 m at 90% of the time. The proposed tracking system using smartphone inertial sensors and BLE beacons can be a promising methodology in practical usages.

Jinglong Li

Papers

An indoor localization system by fusing smartphone inertial sensors and bluetooth low energy beacons