Wi-Fi Data Research Articles

Identifying tourism attractiveness based on intra-destination tourist behaviour: evidence from Wi-Fi data

ABSTRACT Knowledge of tourism attractiveness is crucial for tourists to choose preferred destinations and helps tourism managers configure attractive resources to better meet tourist demands. However, small-scale (e.g. intra-destination) studies of tourism attractiveness are scarce, despite their unique significance in understanding tourist demand at the destination level. Based on time geography theory, this study proposes a new method called the stay-time-based tourism attractiveness index (STTAI), which can be used to identify tourism attractiveness within a destination using Wi-Fi data. Specifically, we tracked Wi-Fi probe requests from 670,000 travellers over a month in the Shichahai scenic area, a famous cultural tourism destination in Beijing. After segmenting tourists from the Wi-Fi data, we employed the STTAI to identify locations with frequent and extended tourist stays as activity stations with high tourism attractiveness. Further, this study examined the environmental impact on identified tourism attractiveness using regression analysis.

Evaluating the Effectiveness of a Codeveloped e-Mental Health Intervention for University Students: Protocol for a Randomized Controlled Trial.

University life typically occurs during a period of life transition, where the incidence of mental health and substance use problems and disorders peaks. However, relatively few students obtain effective treatment and support. e-Interventions have proven effective in improving the psychological outcomes of university students and have the potential to provide scalable services that can easily integrate into existing models of care. Minder is a mobile app codeveloped with university students that offers users a collection of evidence-based interventions tailored to help university students maintain their mental health and well-being and manage their substance use. This paper describes the protocol for a randomized controlled trial (RCT) that aims to assess the effectiveness of the Minder app in improving the mental health and substance use outcomes of university students. This study is a 2-arm, parallel assignment, single-blinded, 30-day RCT with 1 intervention group and 1 waitlist control group. Overall, 1496 (748 per trial arm) university students from the University of British Columbia Vancouver Campus (N=54,000) who are aged ≥17 years, have a smartphone with Wi-Fi or cellular data, and speak English will be recruited via a variety of web-based and offline strategies. Participants will be randomized into the intervention or control group after completing a baseline survey. Those randomized into the intervention group will gain immediate access to the Minder app and will be assessed at 2 weeks and 30 days. Those randomized into the control group will be given access to the app content after their follow-up assessment at 30 days. The primary outcomes are measured from baseline to follow-up at 30 days and include changes in general anxiety symptomology, depressive symptomology, and alcohol consumption risk measured by the General Anxiety Disorder 7-Item scale, Patient Health Questionnaire 9-Item scale, and US Alcohol Use Disorders Identification Test-Consumption Scale, respectively. Secondary outcomes include measures related to changes in the frequency of substance use, mental well-being, self-efficacy in managing mental health and substance use, readiness to change, and self-reported use of mental health services and supports (including referral) from baseline to follow-up at 30 days. Trial recruitment and data collection began in September 2022, and the completion of data collection for the trial is anticipated by June 2023. As of May 10, 2023, a total of 1425 participants have been enrolled. The RCT described in this protocol paper will assess whether the Minder app is effective in improving the mental health and substance use outcomes of a general population of Canadian university students. Additional secondary outcome research aims to explore additional outcomes of interest for further research and better understand how to support students' general mental well-being. ClinicalTrials.gov NCT05606601; https://clinicaltrials.gov/ct2/show/NCT05606601. DERR1-10.2196/49364.

Prototyping Apps for the Management of Sleep, Fatigue, and Behavioral Health in Austere Far-Forward Environments: Development Study.

Military service inherently includes frequent periods of high-stress training, operational tempo, and sustained deployments to austere far-forward environments. These occupational requirements can contribute to acute and chronic sleep disruption, fatigue, and behavioral health challenges related to acute and chronic stress and disruption of team dynamics. To date, there is no centralized mobile health platform that supports self- and supervised detection, monitoring, and management of sleep and behavioral health issues in garrison and during and after deployments. The objective of this study was to adapt a clinical decision support platform for use outside clinical settings, in garrison, and during field exercises by medics and soldiers to monitor and manage sleep and behavioral health in operational settings. To adapt an existing clinical decision support digital health platform, we first gathered system, content, and context-related requirements for a sleep and behavioral health management system from experts. Sleep and behavioral health assessments were then adapted for prospective digital data capture. Evidence-based and operationally relevant educational and interventional modules were formatted for digital delivery. These modules addressed the management and mitigation of sleep, circadian challenges, fatigue, stress responses, and team communication. Connectivity protocols were adapted to accommodate the absence of cellular or Wi-Fi access in deployed settings. The resulting apps were then tested in garrison and during 2 separate field exercises. Based on identified requirements, 2 Android smartphone apps were adapted for self-monitoring and management for soldiers (Soldier app) and team supervision and intervention by medics (Medic app). A total of 246 soldiers, including 28 medics, received training on how to use the apps. Both apps function as expected under conditions of limited connectivity during field exercises. Areas for future technology enhancement were also identified. We demonstrated the feasibility of adapting a clinical decision support platform into Android smartphone-based apps to collect, save, and synthesize sleep and behavioral health data, as well as share data using adaptive data transfer protocols when Wi-Fi or cellular data are unavailable. The AIRE (Autonomous Connectivity Independent System for Remote Environments) prototype offers a novel self-management and supervised tool to augment capabilities for prospective monitoring, detection, and intervention for emerging sleep, fatigue, and behavioral health issues that are common in military and nonmilitary high-tempo occupations (eg, submarines, long-haul flights, space stations, and oil rigs) where medical expertise is limited.

Wi-Fi Data Analysis based on Machine Learning

This study proposes using machine learning to improve Wi-Fi network security. As Wi-Fi networks spread from industrial to residential areas, the necessity for strong security has risen. The rise of smart networking, especially in the IoT, has created data security and vulnerability issues. A unique method that uses machine learning to detect abnormalities and probable security breaches in Wi-Fi networks addresses these difficulties. We gather, preprocess, and analyse network data to create a complete dataset. This dataset trains machine learning algorithms to identify and classify network anomalies. Using agile methods, data mining, and machine learning algorithms, we created a Wi-Fi network intrusion detection system (WNIDS) that can detect diverse network threats. The proposed WNIDS contains two linked stages with specific machine learning models. These algorithms accurately classify network data as normal or attack specific. Our technology protects against malicious attacks and provides a robust Wi-Fi network for users across domains by incorporating machine learning. Modern network security dangers were fully understood by surveys and data analysis. The WNIDS was implemented and deployed through a structured system development life cycle. This tool eliminates network weaknesses and advances distant enterprises, offering safe and smooth access for consumers globally.

Automatic Site Survey for RSS-Based Wi-Fi Positioning Using Unlabeled Data

Preparing a database of Wi-Fi access points (APs) is essential for deploying Wi-Fi-based positioning solutions. This study proposes an automated method to discover APs installed in an indoor site by exploiting unlabeled data provided by mobile users. Furthermore, the positions of these APs are estimated by comparing the shapes of two trajectories, which are obtained using the provided Wi-Fi and sensor data. The effectiveness of the proposed method is verified with excessive experimental campaigns, including an extreme case scenario, where the positions of 60 APs are obtained from only three APs with known positions.

RSSI based Device-free Human Identification

Researchers have explored many methods and techniques for human detection and identification in diverse contexts. One such approach is studying variations of Radio Frequency (RF) signals (e.g., Received Signal Strength Indicator (RSSI)). RSSI based techniques have been widely used in human detection, but not for unique human identification due to its noisy nature. This research focused on investigating the possibilities and limitations of device-free human identification using Wi-Fi RSSI data with machine learning-based classification techniques. To inspect the characteristics of Wi-Fi RSSI data, the authors have conducted statistical analysis techniques. A Kalman filter was applied to minimize the noise in Wi-Fi RSSI data, followed by a feature extraction process. Furthermore, the authors have conducted multiple research experiments in different configurations of receivers and participant numbers. The experimental results show that the human identification accuracy level increases with the number of receivers used for the data collection. Moreover, the authors have identified that human identification accuracy can be further improved by leveraging proper noise reduction methods and feature extraction processes. For a Kalman filter applied and feature-extracted Wi-Fi RSSI dataset of 20 people, the Support Vector Machine (SVM) - Radial Kernel model recorded the highest average accuracy of 99.58%.

PO-03-078 IS BLUETOOTH HOME MONITORING REALLY THE BEST OPTION FOR AN URBAN POPULATION?

CIED companies are developing Bluetooth (BT) technology for remote monitoring & are advertising adherence rate as high as 95%. For Medtronic (MDT) BT compatible CIEDs, the patient has the option of choosing BT or traditional RF monitor. For Abbott (ABT) BT devices the patient must use a BT monitor- either their own or an ABT provided smart phone. Describe remote monitoring compliance in a real world urban population. Patients implanted with MDT or ABT CIED between 8/1/2020 and 6/1/2022 were reviewed. Demographics, remote follow-up compliance, & device specific information was collected. Categorical variables were compared using Chi-squared test, continuous variable using student T-test. We excluded patients who did not receive a monitor due to supply chain issues. Data is presented in Figure 1. Pairing and providing a traditional monitor at the time of implant had the highest compliance, as is done with ABT CIEDs (ABT PM 95.8% & ABT ICD 93.2% transmissions received). Patients implanted with CIEDs that require BT (ABT ICD) using either their own or company provided smart device had the second-best compliance 86.2%. When the only option is BT, data showed older and non-English speaking patients had lower compliance. Patients with a CIED capable of either BT or traditional monitor (MDT PMs & ICDs), who are given a traditional monitor had follow up 76% PM and 81.6% ICD, & BT had follow up 85% PM and 72.7% ICD. When patients are given the opportunity to decide which type of monitor they are more comfortable with, the compliance was better with BT. Older & non-English speaking patients were less likely to have BT capabilities. With the increasing prevalence of smart phones, many patients already own a BT capable device. BT monitoring, however, requires Wi-Fi or cellular data access in order to transmit; as opposed to traditional monitors that use cellular network. BT must remain on at all times & the app must remain open, resulting in battery depletion. If the monitoring app is closed or BT/Wi-Fi turned off transmissions will not be sent. In our urban clinic many patients do not have access to a smart phone or iPad with BT technology. Even if the company provides a BT enabled monitor, some patients are uncomfortable, hesitant, or fear the device will be stolen. Our data showed pairing & providing a traditional monitor at implant or allowing the patient to decide monitor type based on their existing capability results in the improved remote monitoring compliance.

Elastic buildings: Calibrated district-scale simulation of occupant-flexible campus operation for hybrid work optimization

Before 2020, the way occupants utilized the built environment had been changing slowly towards scenarios in which occupants have more choice and flexibility in where and how they work. The global COVID-19 pandemic accelerated this phenomenon rapidly through lockdowns and hybrid work arrangements. Many occupants and employers are considering keeping some of these flexibility-based strategies due to their benefits and cost impacts.This paper explores how demand-driven control strategies in the built environment might support the transition to increased workplace flexibility by simulating various scenarios related to the operational technologies and policies of a real-world campus using a district-scale City Energy Analyst (CEA) model that is calibrated with measured energy demand data and occupancy profiles extracted from WiFi data. These scenarios demonstrate the energy impact of ramping building operations up and down more rapidly and effectively to the flex-based work strategies that may solidify. The scenarios show a 5–15% decrease in space cooling demand due to occupant absenteeism of 25–75% if centralized building system operation is in place, but as high as 17–63% if occupancy-driven building controls are implemented. The paper discusses technologies and strategies that are important in this paradigm shift of operations.

The potential of Wi-Fi data to estimate bus passenger mobility

Last decades have been marked by deep socio-economic transformations, an uneven evolution of transport demand in main urban areas and the emergence of new and more sustainable modes of transportation (carpooling, self-services bicycles). These changes have strongly impacted the interaction between service supply and demand in the transport industry. In this context, passive data as Wi-Fi and Bluetooth become a key source of information to understand individual mobility behaviors and ensure the sustainable development of transport infrastructures. In this paper, we present a framework that uses disruptive technology to collect passive data in buses, continuously and at a lower cost than traditional mobility surveys. Previous research, conducted over a more limited spatial and temporal framework, uses filtering methods, which do not allow the results to be replicated. This study uses artificial intelligence to sort transmitted signals, get transit ridership and build Origin–Destination matrices. Its originality consists in providing a concrete, automatic and replicable method to transport operators. The comparison of the results with other data sources confirms the relevance of the presented algorithms in demand forecasting. Therefore, our findings provide interesting insights for data-driven decision making and service quality management in urban public transport.

Sample Survey of Passenger Traffic by Analysing Wi-Fi Data in Moscow Transport Hub. Part 2

In modern, rapidly developing cities of the world, building an urban transport model requires traffic data. The lack of those data does not allow making timely management decisions on distribution of passenger flows, namely within transport flows. Currently, there are various methods and systems for counting passenger flows, such as the manual staff counts, survey and counted ticketed entries methods, and various automated technology-based systems. However, those well-known methods have their drawbacks. For this reason, the task to search for alternative methods and data sources for the study of passenger flows remains relevant. This article is based on the updated results of the study recently conducted by the author during preparation of his master’s thesis. During the study and developing previous author’s papers, data on connections of passengers to Wi-Fi routers were chosen as a data source. Since this phase of the study was conducted on the territory of Moscow transport hub, in metro and on Moscow Central Diameters (MCD), where the cars are equipped with great number of Wi-Fi routers, with free connection and Internet access, it has increased the sample Wi-Fi data array significantly. The objective of the study was to study the possibility of processing Wi-Fi data obtained from Wi-Fi scanners as a passenger flow analysis tool. The study has revealed that, on average, up to 40 % of passengers in metro and MCD cars on the studied lines use the WI-FI module turned on in their mobile devices. The results of the study have confirmed that Wi-Fi data can be used as a tool for passenger traffic analysis, but at the same time revealed the necessity to integrate them with other data sources, as well as the strong dependence of the result of Wi-Fi data processing on the technical features of the Wi-Fi scanner and its location in the vehicle during experiments. This issue contains the second part of the article.

Artificial Intelligence and Internet of Things-Based Leak Detection Method for the Water Supply Network

The good management and safe operation of the urban water supply network are of great significance to residents’ lives and industrial production. In view of the difficulties in supervision and leakage location of the urban water supply network, based on the technology of Internet of things and artificial intelligence algorithm, a leakage detection method of the urban water supply network is proposed. First of all, low-power, low-cost terminal detection equipment and gateway monitoring equipment are developed for remote data transmission through WiFi or cellular data networks. The data organization, storage, release and control are realized by using the data center software platform. Second, the leakage location model of the water supply network is established by using remote pressure monitoring data, and the accurate location of pipe network leakage is realized. Based on ALO and PSO optimization algorithms, the water supply network in an industrial area of a city in China is solved. Finally, the performance of the two optimization algorithms is compared and analyzed. The results show that the designed intelligent monitoring system of the water supply network can monitor the pipe network well. In addition, on the problem of leakage detection, the ALO algorithm is superior to the PSO algorithm in terms of optimization ability and search efficiency. The leakage monitoring method of water supply networks proposed in this study can provide a reference for the design and management of urban water supply networks.

Methodological Proposal for the Analysis of Urban Mobility Using Wi-Fi Data and Artificial Intelligence Techniques: The Case of Palma

Knowing and modeling mobility in smart city spaces is important for both planning and managing city resources. The optimization of public resources and the improvement of their management are some of the main concerns in the development of sustainable urban development policies. This study proposes the application of several artificial intelligence methodologies to support mobility planning based on data provided by public Wi-Fi infrastructures in the city. Considering that Wi-Fi networks provide high-frequency data about the devices under their coverage radius, three classification techniques are proposed: by frequency of occurrence of the devices, by estimation of the mode of transport, and by estimation of the most common travel routes. As a case study, the city of Palma (Mallorca, Spain), an international tourist destination where mobility is of singular importance, is selected. This study shows the results obtained from a Wi-Fi network with wide coverage that is integrated into the urban space. It provides novel and updatable information on the mobility model of the city by taking advantage of public high-frequency monitoring resources.

Sample Survey of Passenger Traffic by Analysing Wi-Fi Data in Moscow Transport Hub. Part 1

In modern, rapidly developing cities of the world, building an urban transport model requires traffic data. The lack of those data does not allow making timely management decisions on distribution of passenger flows, namely within transport flows. Currently, there are various methods and systems for counting passenger flows, such as the manual staff counts, survey and counted ticketed entries methods, and various automated technology-based systems. However, those well-known methods have their drawbacks.For this reason, the task to search for alternative methods and data sources for the study of passenger flows remains relevant.This article is based on the updated results of the study recently conducted by the author during preparation of his master’s thesis. During the study and developing previous author’s papers, data on connections of passengers to Wi-Fi routers were chosen as a data source. Since this phase of the study was conducted on the territory of Moscow transport hub, in metro and on Moscow Central Diameters (MCD), where the cars are equipped with great number of Wi-Fi routers, with free connection and Internet access, it has increased the sample Wi-Fi data array significantly.The objective of the study was to study the possibility of processing Wi-Fi data obtained from Wi-Fi scanners as a passenger flow analysis tool.The study has revealed that, on average, up to 40% of passengers in metro and MCD cars on the studied lines use the WI-FI module turned on in their mobile devices.The results of the study have confirmed that Wi-Fi data can be used as a tool for passenger traffic analysis, but at the same time revealed the necessity to integrate them with other data sources, as well as the strong dependence of the result of Wi-Fi data processing on the technical features of the Wi-Fi scanner and its location in the vehicle during experiments. You can find the first part of the article in the issue.

USING THE HOME ASSISTANT DIGITAL PLATFORM TO CONTROL THE ELECTRICAL INSTALLATION

The relevance of this topic is justified by the fact that nowadays digital technologies represent a kind of integration platform for interaction, which involves the introduction of digital technologies into all spheres of human life. The Home Assistant digital platform deployed on a personal computer (server) through a special Virtual Box program was chosen for the study. The purpose of the study is to demonstrate the use of digital technologies based on the Home Assistant open platform, which provides control of the electrical installation. It was found that this platform makes it possible to create automations for responding to entities, which are understood as: events (including virtual ones), states and information. These entities are not associated with any device. For their development, they used a trigger that starts automation, a condition and an action. The type of action was chosen to call the Notify.Events service, which allowed receiving notifications about various events occurring. The use of Home Assistant for the centralized management of wireless elements of various companies has been investigated. It turned out to be the most convenient to manage electrical installations from one place in Lovelace, where a plan of a production facility with smart devices is added. In this context, the combination of the powerful Home Assistant platform and the ESP Home software application, specialized microcontrollers with support for Wi-Fi networks and data transfer protocols, provides the creation of Internet devices for controlling electrical relays that control the corresponding electrical installation. With the help of the proposed system, a number of tasks are solved, which will enable the use of Home Assistant in the future. In particular: service availability; get rid of the need to contact the cloud server; support of various manufacturing plants and a huge number of integrations; the ability to send notifications to the messenger.

UTMInDualSymFi: A Dual-Band Wi-Fi Dataset for Fingerprinting Positioning in Symmetric Indoor Environments

Recent studies on indoor positioning using Wi-Fi fingerprinting are motivated by the ubiquity of Wi-Fi networks and their promising positioning accuracy. Machine learning algorithms are commonly leveraged in indoor positioning works. The performance of machine learning based solutions are dependent on the availability, volume, quality, and diversity of related data. Several public datasets have been published in order to foster advancements in Wi-Fi based fingerprinting indoor positioning solutions. These datasets, however, lack dual-band Wi-Fi data within symmetric indoor environments. To fill this gap, this research work presents the UTMInDualSymFi dataset, as a source of dual-band Wi-Fi data, acquired within multiple residential buildings with symmetric deployment of access points. UTMInDualSymFi comprises the recorded dual-band raw data, training and test datasets, radio maps and supporting metadata. Additionally, a statistical radio map construction algorithm is presented. Benchmark performance was evaluated by implementing a machine-learning-based positioning algorithm on the dataset. In general, higher accuracy was observed, on the 5 GHz data scenarios. This systematically collected dataset enables the development and validation of future comprehensive solutions, inclusive of novel preprocessing, radio map construction, and positioning algorithms.

Secure Industrial IoT Systems via RF Fingerprinting Under Impaired Channels With Interference and Noise

Industrial IoT-enabled critical infrastructures are susceptible to cyber attacks due to their mission-critical deployment. To ensure security by design, radio frequency (RF)-based security is considered an effective way for wirelessly monitored or actuated critical infrastructures. For this purpose, this paper presents a novel augmentation-driven deep learning approach to analyze unique transmitter fingerprints and determine the legitimacy of a user device or transmitter. An RF fingerprinting model is susceptible to various channel and environmental conditions that impact the learning performance of a machine/deep learning model. As data gathering cannot always be considered a feasible alternative, efficient solutions that can tackle the impact of varying propagation channels on learning performance are emergent. This work aims to shed light on the RF fingerprinting problem from a different angle when 4G, 5G, and WiFi data samples are collected from different transmitters by proposing a fine-grained augmentation approach to improve the learning performance of a deep learning model. This work also proposes an enhanced classifier structure following the fine-grained augmentation approach. Results of experiments, conducted on the POWDER dataset, demonstrate promising RF fingerprinting performance when training data are augmented in a waveform-specific fine-grained manner. Thus, the RF identification accuracy can be boosted to 97.84% on unseen RF data instances from our previously published work where we had achieved an accuracy of 87.94% using tapped delay line (TDL)/clustered delay line (CDL)-based augmentation approach. The paper also presents a sensitivity analysis of the fine-grained approach concerning different signal-to-noise-ratio (SNR), signal-to-interference-ratio (SIR) levels (20 dB and 30 dB), and signal-to-interference-plus-noise-ratio (SINR) levels (15 dB, 25 dB). The sensitivity analysis exhibits that it achieves 85.78% accuracy at 20 dB SIR on both Day 1 (train) and Day 2 (test) data. In addition, it achieves 92.37% accuracy even at 20 dB SNR on Day 2 data from POWDER dataset. Furthermore, it achieves 84.95% accuracy at 15 dB SINR on Day 2 data. Hence, these results exhibit the resiliency of the fine-grained augmentation approach against interference and noise.

SleepMore

The availability of commercial wearable trackers equipped with features to monitor sleep duration and quality has enabled more useful sleep health monitoring applications and analyses. However, much research has reported the challenge of long-term user retention in sleep monitoring through these modalities. Since modern Internet users own multiple mobile devices, our work explores the possibility of employing ubiquitous mobile devices and passive WiFi sensing techniques to predict sleep duration as the fundamental measure for complementing long-term sleep monitoring initiatives. In this paper, we propose SleepMore, an accurate and easy-to-deploy sleep-tracking approach based on machine learning over the user's WiFi network activity. It first employs a semi-personalized random forest model with an infinitesimal jackknife variance estimation method to classify a user's network activity behavior into sleep and awake states per minute granularity. Through a moving average technique, the system uses these state sequences to estimate the user's nocturnal sleep period and its uncertainty rate. Uncertainty quantification enables SleepMore to overcome the impact of noisy WiFi data that can yield large prediction errors. We validate SleepMore using data from a month-long user study involving 46 college students and draw comparisons with the Oura Ring wearable. Beyond the college campus, we evaluate SleepMore on non-student users of different housing profiles. Our results demonstrate that SleepMore produces statistically indistinguishable sleep statistics from the Oura ring baseline for predictions made within a 5% uncertainty rate. These errors range between 15-28 minutes for determining sleep time and 7-29 minutes for determining wake time, proving statistically significant improvements over prior work. Our in-depth analysis explains the sources of errors.

Cross-Technology Communication for Heterogeneous Wireless Devices Through Symbol-Level Energy Modulation

The coexistence of heterogeneous devices in wireless networks brings a new topic on cross-technology communication (CTC) to improve the coexistence efficiency and boost collaboration among these devices. Current advances on CTC mainly fall into two categories, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">physical-layer CTC</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">packet-level energy modulation</i> (PLEM). The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">physical-layer CTC</i> achieves a high CTC data rate, but with channel incompatible to commercial devices, making it hard to be deployed in current wireless networks. PLEM is channel and physical layer compatible, but with two main drawbacks of the low CTC data rate and MAC incompatibility, which will induce severe interference to the other devices’ normal data transmissions. In this paper, we propose symbol-level energy modulation (SLEM), the first CTC method that is fully compatible with current devices in both channel and the physical/MAC layer processes, having the ability to be deployed in commercial wireless networks smoothly. SLEM inserts extra bits to WiFi data bits to generate the transmitting bits, so as to adjust the energy levels of WiFi symbols to deliver CTC information. We make theoretical analysis to figure out the performance of both CTC and WiFi transmissions. We also conduct experiments to demonstrate the feasibility of SLEM and its performance under different network situations.

Simplicial temporal networks from Wi-Fi data in a university campus: The effects of restrictions on epidemic spreading

Wireless networks are commonly used in public spaces, universities, and public institutions and provide accurate and easily accessible information to monitor the mobility and behavior of users. Following the application of containment measures during the recent pandemic, we analyzed extensive data from the Wi-Fi network in a university campus in Italy during three periods, corresponding to partial lockdown, partial opening, and almost complete opening. We measured the probability distributions of groups and link activations at Wi-Fi access points, investigating how different areas are used in the presence of restrictions. We ranked the hotspots and the area they cover according to their crowding and to the probability of link formation, which is the relevant variable in determining potential outbreaks. We considered a recently proposed epidemic model on simplicial temporal networks, and we used the measured distributions to infer the change in the reproduction number in the three phases. Our data show that additional measures are necessary to limit the spread of epidemic in the total opening phase due to the dramatic increase in the number of contacts.

Building occupancy modelling at the district level: A combined copula-nested hazard-based approach

Planning and managing an energy system in a district require a comprehensive understanding and accurate modelling of people's occupancy and circulation among multiple buildings. Due to the lack of occupancy modelling tools for district scale analysis, energy models still use simplified occupancy patterns provided in building codes and standards. However, the simplified information restricts the reflection of complex occupancy patterns driven by urban heterogeneity. This paper fills this research gap and presents a hazard-based model combined with nested copula dependence to describe the complex occupants' interactions between buildings in a district, enabling the characterisation of irregular occupancy patterns in special cases. The proposed model is calibrated using Wi-Fi authentication data from the Imperial College London (UK) South Kensington campus and is validated using the following days of the same data by evaluating the performance of predicted occupancy patterns both on average and day by day. The validation results demonstrate that the model can accurately capture the effects of the urban environment on occupancy duration and choice of transition within a district. Mean Absolute Percentage Errors (MAPEs) of average-pattern predictions are between 7% and 16% for most buildings, though a bit lower in accuracy for the Library and Food Hall predictions with MAPEs of 32%–36%. We also discuss the contributions of the proposed occupancy model to potential future applications, including efficient building space use, local energy planning and management.