Graph-based metadata modeling in indoor positioning systems

Abstract

Modeling and persistence of different data structures in indoor positioning systems is a requirement for providing a large number of specialized location-based services. Collection and diversification of indoor positioning systems’ metadata are important to understand the context of the system’s operation to create a positive feedback improvement loop. While metadata from a residential building’s indoor positioning system operational context benefits the system (i.e. through occupancy patterns extraction that drive resource utilization strategies), it can also benefit the tenants’ well-being or drive other decisions through observing social dynamics. Observation of social relationships in residential buildings is rarely addressed due to highly stochastic movement patterns of tenants. In this article we have proposed a set of graph-based approaches for modeling social behavior data: modeling of tenants’ movement paths and detecting the existence of patterns, modeling of tenants’ social relationships (frequency, quality) as well as detecting social communities and tracking their evolution. We have tested our approaches on a real-world private residential building resulting in multidisciplinary implications and applications connecting the fields of IoT and indoor positioning to behavioral sciences. Finally, we provide public, high-quality positioning and occupancy datasets and open-source code for reproducing experiments on the observed residential building.

Introduction

With the uprising of the Internet of Things (IoT), buildings (public, commercial, residential) were the main focus of innovative automated solutions. Resource usage efficiency attracts the most attention from both research and industry communities [24], resulting in the creation of smart Building Management Systems (BMSs) with functionalities steered towards optimizing how resources are utilized. While feeding different contextual metadata benefits the indoor positioning system’s (IPS) efficiency (increase overall accuracy, derive occupancy or movement patterns to create energy-saving strategies, etc.), the idea behind BMSs is also to address the well-being (social, comfort, etc.) of tenants.

IPSs are gaining the attention of the research community and industry that try to solve challenges in the domain of smart spaces. Data obtained from indoor positioning systems provide insightful information about the usage of the observed space. Other information, derived from positioning data, can be considered as metadata in IPS. Metadata further enhances positioning data by providing contextual insight, such as floor or room information, space occupancy, energy consumption, and similar.

It can be challenging to derive positioning/occupancy without applying some intrusive, high cost-inducing approaches like video surveillance, dedicated sensory systems, and wearable devices [29]. To achieve efficient real-time monitoring these approaches might be more acceptable, but come at the expense of high cost, privacy issues, and reduced scalability. IPS based on WiFi or Bluetooth are inexpensive and scale well. Additionally, WiFi-based IPS are easily deployed on existing infrastructures. Privacy of positioning information in such systems is preserved by of-loading location computation functions to most convenient devices (e.g. in edge computing IPS location information does not leave the local network). Through privacy intrusion may be minimized in such systems, it is not eliminated; hence, privacy remains a challenge [35]. Fortunately, modern mobile users are always carrying their wireless devices (smartphones, wearables), and through them are in continuous communication with the networking infrastructure inside and around a building. Thus, by observing the physical dispersion and continuous movements of such mobile devices, the behavioral characteristics of users of the indoor space can be inferred.

Characterizing social behavior inside a network of people is a challenging task. For public/company buildings it is more straightforward since there are established assumptions/patterns of typical behavior (e.g. daily routines of employees, restricted movement rules for visitors, meal breaks at certain times, etc.). Such assumptions cannot be made inside a residential building since tenants’ behavior is highly stochastic – movement patterns of tenants inside private residential buildings are rather inconsistent, without periodicity and ad-hoc, as opposed to public/company buildings where the allocation of employees and movement patterns are rather consistent. Therefore it is harder to model and extrapolate useful information from private residential buildings. Furthermore, observing the formation and evolution of social groups, which is a highly relevant problem in social networks, is significantly harder to maintain for such ad-hoc, stochastic networks modeling human social behavior.

By modeling social behavior inside residential buildings, IPS can enhance the underlying BMS with the information enabling them to animate and support meaningful interaction between proximate users, network serendipitous social encounters, and to seamlessly integrate events with the way interaction takes place in the observed social network. BMSs can also use this information to drive business decisions such as building a common entertainment room, organizing indoor social events, targeted community marketing (e.g. offer event tickets), etc.

All of the above-mentioned challenges in modeling social behavior for IPS are addressed with Bluetooth Low Energy Microlocation Asset Tracking system (BLEMAT). BLEMAT is a semi space-agnostic, a context-aware edge computing system that performs real-time indoor positioning, smoothing and filtering, fingerprinting, and floor plan layout detection supplemented with various complex data analytics and forecasting tasks. Additionally, BLEMAT is equipped with occupancy detection, neural network-based forecasting, and patterns extraction algorithms [26], [27].

In this article we are proposing enhancements to BLEMAT, a series of novel graph-based approaches for modeling social behavior data in indoor spaces. Our approach to modeling of tenants’ movement paths and detecting the existence of patterns is transforming graphs to sentences to apply less time-consuming similarity and pattern extraction algorithms. In our approach to modeling of tenants’ social relationships, we add several relevant vertices/edges attributes such as frequency, quality, and consistency of detected relationship. On social relationship graphs, we showcase two distinctive approaches to the detection of social communities and offer comparison metrics and discussion. For the time-continuous detection of social communities, we provide a novel approach to track their evolution (major events that lead to communities merging or dissolution) by vertex label propagation.

Finally, datasets, source code, and original high-resolution figures from data analysis performed in this article are published as open-source on Zenodo [36]. Datasets from this research are a contribution to quality real-world positioning datasets, and hopefully, they will be used by other researchers to test approaches from this article, as well as other approaches. As most of the publicly available positioning and occupancy datasets come from public places (universities, shopping malls, etc.), a quality dataset from a regular residential building is an important addition to the research field of IPS.

The rest of the article is structured as follows. Section 2 elaborates on related work in three IPS-related research areas: metadata modeling approaches, movement patterns exploration, and social networks and communities. Section 3 will underline our contributions to IPS metadata modeling through BLEMAT and present theoretical foundations for two types of positioning data graphs we use in the rest of the paper. In Section 4, we present the experimental environment (real-world BLEMAT deployment site) that is referenced throughout the article and the occupancy metadata collection workflow. Section 5 presents and elaborates on the practical usage of the graph described in Section 3 inside the BLEMAT IPS. Section 6 is the largest section, and it presents in detail the performed experiments on the observed deployment site by exploring tenant movement patterns and exploring tenant social relationships existence, quality, and grouping. Section 7 discusses the efficiency, novelty, and usability of the presented results. Finally, Section 8 contains conclusive and future-work remarks.