Wi-Fi Logs Research Articles

Floor-Level Occupancy Estimation of a Multi-Story Building Using Coarse Wi-Fi Data

In recent years, there has been an extraordinary increase in wireless capable devices and network infrastructure, which spawned a corresponding rise in data produced from the interactions of these technologies. Mobile devices constantly roam, leading to a perpetual dialog between a mobile device and wireless access points. This dialogue generates a continuous stream of device-specific data, including but not limited to a device’s media access control address, time of access, and received signal strength. Given the knowledge of the access point’s location and received signal strength, it is possible to infer the position of user devices and estimate their mobility and occupancy. This article presents two methods for accurately measuring floor-level occupancy in a multi-story building at Texas State University using coarse Wi-Fi log data. The first method employs a static filter, while the second incorporates user-role data and user location to create a dynamic filter. Quantitative methods are used to evaluate these filters against field-collected reference data and existing internal people-counting sensors. Our results demonstrate that the dynamic filter, leveraging variable thresholds, provides a more accurate estimation of occupancy compared to the fixed 5-min static filter which consistently overestimated occupancy. This research sheds light on the potential of dynamic filters derived from user-role data for precise floor-level occupancy estimations, with implications for various applications.

Leveraging WiFi network logs to infer student collocation and its relationship with academic performance

A comprehensive understanding of collocated social interactions can help campuses and organizations better support their community. Universities could determine new ways to conduct classes and design programs by studying how students have collocated in the past. However, this needs data that describe large groups over a long period. Harnessing user devices to infer collocation, while tempting, is challenged by privacy concerns, power consumption, and maintenance issues. Alternatively, embedding new sensors across the entire campus is expensive. Instead, we investigate an easily accessible data source that can retroactively depict multiple users on campus over a semester, a managed WiFi network. Despite the coarse approximations of collocation provided by WiFi network logs, we demonstrate that leveraging such data can express meaningful outcomes of collocated social interaction. Since a known outcome of collocating with peers is improved performance, we inspected if automatically–inferred collocation behaviors can indicate the individual performance of project group members on a campus. We studied 163 students (in 54 project groups) over 14 weeks. After describing how we determine collocation with the WiFi logs, we present a study to analyze how collocation within groups relates to a student’s final score. We found that modeling collocation behaviors showed a significant correlation (Pearson’sr =0.24) with performance (better than models of peer feedback or individual behaviors). These findings emphasize that it is feasible and valuable to characterize collocated social interactions with archived WiFi network logs. We conclude the paper with a discussion of applications for repurposing WiFi logs to describe collocation, along with privacy considerations, and directions for future work.

Exploring 3D spatial morphology using multilayered space syntax, network science and wi-fi log data

Spatial morphology of 2D space has been well studied at the scales of building, community, and city in space syntax literature. Space syntax decomposes continuous geographic space into a set of unique axial lines and represents them as a spatial network to analyze spatial morphology. However, 3D spatial morphology remains largely unexplored, partially due to limited data access and methodological constraints. Motivated by the multilayered network literature in network science, this work extends the conventional 2D space syntax axial network into a multilayered axial network to explore the spatial morphology of a university campus. Scaling properties are discovered from several aspects of the constructed spatial network (e.g., degree, local integration). The relationship between spatial morphology and route-based pedestrian flow is evaluated using a large Wi-Fi log dataset and multiple regression analysis. We found a significant correlation between the two, with a correlation coefficient of 0.756 and r2 of 0.571. Four network features (i.e., local depth, geometric length, eigenvector, and betweenness), are found to significantly shape the pedestrian flow. The results of community detection show the effectiveness of multilayered space syntax analysis in depicting functional areas of campus, despite the complex nature of 3D space. Whilst a limitation of this research is the need to account for the 3D angular route preferences when characterizing the morphology of campus and the flow estimated based on the shortest routes using the Dijkstra algorithm.

A Study of the Ambient Noise in the Public Space on Campus and the Correlation Between the Campus Crowds’ Ambient Noise and the WiFi Log

Urban noise is becoming more serious and increasingly concerning environmental problems. This has led to numerous studies on traffic noise. However, not many studies have been done on noise from a human perspective as they go about their daily life. In another aspect, using of the crowd-sourcing platform is on the rise as the usage of personal devices (smartphones) and the deployment of Internet-of-thing increases. Thus, a large pool of data collected via mobile applications enables users to measure the environmental factor directly and provide immediate feedback for and community’s greater good. In this paper, we utilize the crowd-sourcing platform to collect noise data by volunteers to study the noise level in a campus environment, in open common areas which are frequented by students. We are able to map out the noise across the campus from the perspective of the students. The noise level increase through the day as the student gather around popular open spaces. Our study shows that the sound level on campus is due mainly to human and mechanical noise. By combining the noise data with WiFi log data, we were able to show a good correlation between sound level and human density in an area

Auxiliary-LSTM based floor-level occupancy prediction using Wi-Fi access point logs

Smart city concepts have gained increased traction over the years. The advances in technology such as the Internet of things (IoT) networks and their large-scale implementation has facilitated data collection, which is used to obtain valuable insights towards managing, improving, and planning for services. One key component in this process is the understanding of human mobility behaviour. Traditional data collection methods such as surveys and GPS data have been extensively used to study human mobility. However, a key concern with such data is the protection of user privacy. This study aims to overcome those concerns using Wi-Fi access point logs and demonstrate their utility by creating building occupancy prediction models using advanced machine learning techniques. The floor level occupancy counts and auxiliary variable for a campus building are extracted from the Wi-Fi logs. They are used to develop specifications of Long-Short Term Memory network (LSTM), Auxiliary LSTM (Aux-LSTM), Autoregressive Integrated Moving Average (ARIMA), and Multi-layer Perceptron (MLP) models. The LSTM performed better than the other models and can efficiently capture peak values. Aux-LSTM was shown to increase the reliability in prediction and applicability in the context of facilities management. Results show the effectiveness of the Wi-Fi dataset in capturing trends, providing supplementary information, and highlight the ability of LSTM to adequately model time-series data.

WIFI LOG-BASED STUDENT BEHAVIOR ANALYSIS AND VISUALIZATION SYSTEM

Abstract. Student behavior research can improve learning efficiency, provide decision evidences for infrastructure management. Existing campus-scale behavioral analysis work have not taken into account the students characteristics and spatiotemporal pattern. Moreover, the visualization methods are weak in wholeness, intuitiveness and interactivity perspectives. In this paper, we design a geospatial dashboard-based student behavior analysis and visualization system considering students characteristics and spatiotemporal pattern. This system includes four components: user monitoring, data mining analysis, behavior prediction and spatiotemporal visualization. Furthermore, a deep learning model based on LSTNet to predict student behaviour. Our work takes WiFi log data of a university in Beijing as dataset. The results show that this system can identify student behavior patterns at a finer granularity by visualization method, which is helpful in improving learning and living efficiency.

Dynamic network modelling with similarity based aggregation algorithm

Proper modelling of complex systems allows hidden knowledge discovery that cannot be explored using traditional methods. One of the techniques for such modelling is dynamic networks. In this work, we aim to develop a methodology for extracting proper dynamic networks. We concentrate on two fundamentally interconnected problems: first, determining the appropriate window size for dynamic network snapshots; and second, obtaining a proper dynamic network model. For the former problem, we propose Jaccard similarity and its statistical significance based compression ratio, and for the latter, we propose an aggregation approach that extracts dynamic networks with snapshots of varying duration. The aggregation algorithm compresses the system information when there is repetition and takes snapshots when there is a significant structural change. The experiments are realised on four simple or complex data sets by comparing our proposal with baseline approaches. We used well-known Enron emails as simple set and Haggle Infocomm, MIT Reality Mining, and Sabanci Wi-Fi logs as complex data sets. These complex sets like Wi-Fi or Bluetooth connections which are known to be noisy, making analysis difficult show the proximity of system objects. The experimental results show that the proposed methodology can be used to find not only significant time points in simple Enron emails, but also circadian rhythms with their time intervals that reveal the life-cycle of connected areas from complex Wi-Fi logs or bluetooth connections. According to testing on four real-world data sets, both compression ratios and the aggregation process enable the extraction of dynamic networks with reduced noise, are easy to comprehend, and appropriately reflect the characteristics of the system.

Optimizing intra-facility crowding in Wi-Fi environments using continuous-time Markov chains

Various measures have been devised to reduce crowdedness and alleviate the transmission of COVID-19. In this study, we propose a method for reducing intra-facility crowdedness based on the usage of Wi-Fi networks. We analyze Wi-Fi logs generated continually in vast quantities in the ever-expanding wireless network environment to calculate the transition probabilities between the nodes and the mean stay time at each node. Subsequently, we model this data as a continuous-time Markov chain to determine the variance of the stationary distribution, which is used as a metric of intra-facility crowdedness. Therefore, we solved the optimization problem by using stay rate as a parameter and developed a numerical solution to minimize the intra-facility crowdedness. The optimization results demonstrate that the intra-facility crowding is reduced by approximately 30%. This solution can practically reduce intra-facility crowdedness as it adjusts people’s stay times without making any changes to their movements. We categorized Wi-Fi users into a set of classes using the k-means method and documented the behavioral characteristics of each class to help implement class-specific measures to reduce intra-facility crowdedness, thus enabling facility managers to implement effective countermeasures against crowdedness based on the circumstances. We present a detailed description of our computing environment and workflow used for the basic analysis of vast quantities of Wi-Fi logs. We believe this research will be useful for analysts and facility operators because we have used general-purpose data for analysis.

What Could Ambient Noise around Campus Tell Us? A Study on Campus Crowd Noise

Urban noise is becoming more serious and increasingly concerning environmental problems. This has led to numerous study on traffic noise. However, not much studies has been done on the noise from a human perspective as they go about their daily life. In this paper we report on the study of noise level in a campus environment, in open common areas which are frequented by students. Using crowd sourcing noise data by volunteers, we are able to map out the noise across the campus from the perspective of the students. The noise level increase through the day as the student gather around popular open spaces. Our study shows that the sound level on campus are due mainly to human and mechanical noise. By mashing the noise data with Wi-Fi log data, we were able to show the good correlation between sound level and human density in an area.

LADM-IndoorGML for exploring user movements in evacuation exercise

The users' movements in the indoor environments differ based on the condition of the environments. During an indoor emergency, an efficient evacuation is required to help the users to move to the safe areas. Many types of incidents could impact the movements of users and this requires studying the behavior of the people during the evacuation. The reaction of the users to the incidents could affect the evacuation procedures and that could lead to several types of injuries or death. Each user understands and perceives the indoor environment differently and this plays a critical role in the evacuation. Furthermore, the users of the indoor environments have different rights to access the indoor spaces, which affects the movements of the users during an incident. This paper aims to support the evacuation of a building (educational building) in a crisis by using the integrated model of LADM-IndoorGML and the representation of the 3D model of the building. This research is presenting the initial assessment based on real world application. To reflect evacuation cases, we extended the conceptual model of LADM-IndoorGML to define the access rights for users of indoor environments during crisis. An evacuation exercise has been held at the Faculty of Applied Science at TU Delft to study the access rights during an incident. During the evacuation, Wi-Fi data has been collected for the users of the building for further analysis. A 3D model has been built for the Faculty of Applied Science to analyze the movement of the users. The collected data of the Wi-Fi access points have been structured and imported into the freeware database PostgreSQL/PostGIS. Furthermore, the geometry of 3D model was used to visualize the users’ movements as individuals and groups of users according to their connection to Wi-Fi access. Appropriate visualization has been created using QGIS. This paper demonstrates the entire process of analysis and visualization of users’ movements based on the Wi-Fi logs by using the extended LADM-IndoorGML. The outcome of the research has showed that the results for individual users and group users attached to the same access point differs. The study has also exhibited the importance of the time resolution on Monitoring the movements of a single user or group of users. The completed study clearly demonstrates that with the proposed extension, the integrated LADM-IndoorGML model is able to support the decision-making process during an incident in educational building.

On construction of a network log management system using ELK Stack with Ceph

A log management system is essential for the networks administrator. With a log management tool, we can collect, store, analyze, archive, and finally dispose of the log information. In this paper, we propose the architecture model of a log management system using ELK Stack with Ceph to provide a safe network, good Wi-Fi signal strength, and adequate backup data mechanism. In this case, we use our campus data of Wi-Fi log and NetFlow log. First, we collect and store data of our Wi-Fi log using Filebeats tool, and then, we use Elasticsearch, Logstash, and Kibana Stack to visualize the Wi-Fi log data. Second, we collect and store our NetFlow log using NFDUMP, and then, we also use ELK Stack to visualize the NetFlow log data. Third, we integrate the Wi-Fi log and NetFlow log data in one architecture using a distributed storage Ceph file system (CephFS). Moreover, we also compare the performance of RADOS Gateway and CephFS for better storage mechanism.

On the semantic annotation of Wi-Fi SSID logs in mobile applications

Research on mining data collected from smartphones has received tremendous interests in the past few years. While significant research efforts have been made on mining various smartphone data, such as GPS trajectories, app usage logs, and accelerator readings, the issue of mining Wi-Fi SSID (Service Set IDentifier) logs was not well explored. The SSID of a Wi-Fi access point is normally a human-readable string, which is typically named by the owner of the Wi-Fi network. Extracting and leveraging information encoded in SSIDs are crucial for a better understanding of the smartphone data. In this study, we investigate the problem of inferring location type of a given SSID, i.e., associating an SSID with a location type, such as a workplace or a shop, where the Wi-Fi access point is installed. This paper reports our findings of leveraging human behavior patterns and spatial correlation among Wi-Fi access points to infer the location type of an SSID. Extensive experiments using real Wi-Fi logs collected from the recruited participants are conducted to evaluate the performance of the proposed schemes, and the experiment results demonstrate the effectiveness of the schemes.

Location-Aware POI Recommendation for Indoor Space by Exploiting WiFi Logs

Indoor shopping trajectories provide us with a new approach to understanding user’s behaviour pattern in urban shopping mall, which can be derived from user-generated WiFi logs using indoor localization technology. In this paper, we propose a location-aware Point-of-Interest (POI) recommendation service in urban shopping mall that offers a user a set of indoor POIs by considering both personal interest and location preference. The POI recommendation service cannot only improve user’s shopping experience but also help the store owner better understand user’s shopping preference and intent. Specifically, the proposed method consists of two phases: offline modelling and online recommendation. The offline modelling phase is designed to learn user preference by mining his/her historical shopping trajectories. The online recommendation phase automatically produces top-k recommended POIs based on the learnt preference. To demonstrate the utility of our proposed approach, we have performed a comprehensive experiment evaluation on a real-world dataset collected by 468 users over 33 days. The experimental results show that the proposed recommendation service achieves much better recommendation performance than several existing benchmark methods.

A Framework for Enabling User Preference Profiling through Wi-Fi Logs

Nowadays, mobile devices have become a ubiquitous medium supporting various forms of functionality and are widely accepted for commons. In this study, we investigate using Wi-Fi logs from a mobile device to discover user preferences. The core ideas are two folds. First, every Wi-Fi access point is with a network name, normally a human-readable string, called SSID (Service Set Identifier). Since SSIDs are often with semantics, from which we can infer the place where the user stayed. Second, a Wi-Fi log is produced when the user is near a Wi-Fi access point. A high frequency of a consecutively observed SSID implies a long stay duration at a place. To the best of our knowledge, our work is the first attempting to understand users from the collected Wi-Fi logs from mobile devices. However, Wi-Fi logs are essentially of various information types and with noises. How to assess the information types, eliminate irrelevant information, and clean up the noises within partial-informative SSIDs are therefore keys for profiling user preferences over Wi-Fi logs. In this paper, we propose a data cleaning and information enrichment framework for enabling the user preference understanding through collected Wi-Fi logs, and introduce a data clean framework for cleaning, correcting, and refining Wi-Fi logs. In addition, a comprehensive experiment with data collected from users is made to verify the effectiveness of the proposed techniques for cleaning noisy Wi-Fi data for user preferences profiling. The experiment results demonstrate the effectiveness of the proposed framework for profiling user preferences through Wi-Fi logs.