Group-In: Group Inference from Wireless Traces of Mobile Devices

This paper proposes Group-In, a wireless scanning system to detect static or mobile people groups in indoor or outdoor environments. Group-In collects only wireless traces from the Bluetooth-enabled mobile devices for group inference. The key problem addressed in this work is to detect not only static groups but also moving groups with a multi-phased approach based only noisy wireless Received Signal Strength Indicator (RSSIs) observed by multiple wireless scanners without localization support. We propose new centralized and decentralized schemes to process the sparse and noisy wireless data, and leverage graph-based clustering techniques for group detection from short-term and long-term aspects. Group-In provides two outcomes: 1) group detection in short time intervals such as two minutes and 2) long-term linkages such as a month. To verify the performance, we conduct two experimental studies. One consists of 27 controlled scenarios in the lab environments. The other is a real-world scenario where we place Bluetooth scanners in an office environment, and employees carry beacons for more than one month. Both the controlled and real-world experiments result in high accuracy group detection in short time intervals and sampling liberties in terms of the Jaccard index and pairwise similarity coefficient.Comment: This work has been funded by the EU Horizon 2020 Programme under Grant Agreements No. 731993 AUTOPILOT and No.871249 LOCUS projects. The content of this paper does not reflect the official opinion of the EU. Responsibility for the information and views expressed therein lies entirely with the authors. Proc. of ACM/IEEE IPSN'20, 202

Smart luminaire positioning and lighting control in collaborative spaces

Abstract. Smart lighting systems have become more common as they provide energy savings with various occupancy detection methods and better lighting control opportunities for users. This thesis explores two aspects of these smart lighting systems, configuration and control, by utilizing an ActiveAhead controlled smart luminaire installation at the University of Oulu. Smart luminaire identification is a common configuration task that needs to performed before being able to control the individual luminaires and can be especially tedious with large installations. However, this task can be partly automated by positioning the smart luminaires based on passive infrared (PIR) sensors or the received signal strength indicators (RSSI) the luminaires broadcast with Bluetooth Low Energy (BLE) advertisements. For PIR sensor-based positioning, a centroid-based method is presented and evaluated with two datasets reflecting a typical and optimal scenarios of triggering the sensors. For RSSI-based positioning, a log-distance path loss distance estimation with mean squared error (MSE) based position optimization is presented and evaluated. Moreover, relevant literature concerning the RSSI-based device positioning is discussed. Second, the design, implementation and evaluation of a lighting control prototype for collaborative spaces are presented. The prototype uses near-field communication (NFC) tags to indicate the user position and to initiate a lighting preference input to an Android application. The user preferences are transmitted to a local server responsible for the control logic and communication with the luminaires. The potential conflicts between users are resolved with distance weighted preference averaging, which makes the prototype especially convenient for cases where the users share the surrounding luminaires with others. Furthermore, related smart lighting control systems are compared.Älyvalaisinten paikantaminen ja valaistuksen säätö yhteistyötiloissa. Tiivistelmä. Älykkäät valaistusjärjestelmät ovat yleistyneet mahdollistaen energiansäästöt useilla läsnäolon tunnistusratkaisuilla ja paremmat valaistuksen säätömahdollisuudet käyttäjille. Tämä työ käsittelee älyvalaistusjärjestelmiä kahdesta näkökulmasta hyödyntäen ActiveAhead älyvalaisinasennusta Oulun yliopistossa. Älyvalaisinten paikan tunnistaminen on yleinen konfigurointivaihe ennen kuin yksittäisiä valaisimia on mahdollista säätää ja se voi osoittautua erityisen työlääksi suurissa asennuksissa. Tämä vaihe on kuitenkin mahdollista automatisoida paikantamalla älyvalot hyödyntäen PIR-liiketunnistimia tai vastaanotetun signaalin voimakkuutta (RSSI), joita valaisimet lähettävät matalanenergian (BLE) Bluetoothin mainosviesteillä. PIR-liiketunnistimiin pohjautuvaan paikantamiseen esitellään painopisteeseen perustuva metodi, joka myös evaluoidaan kahdella datasetillä, jotka kuvaavat yleistä ja optimaalista PIR-liiketunnistimien laukaisua. RSSI pohjaiseen paikantamiseen esitellään ja arvioidaan metodi, joka hyödyntää logaritmisen signaalin vaimenemisen etäisyys-mallia ja keskimääräiseen neliövirheeseen perustuvaa paikan optimointia. Lisäksi esitellään käytettyjä menetelmiä RSSI-pohjaiseen paikantamiseen. Toiseksi esitellään yhteisöllisiin työtiloihin tarkoitetun valaistuksensäätöprotyypin suunnittelu, toteutus ja evaluointi. Prototyyppi hyödyntää NFC (near field communication) tarroja käyttäjän sijainnin ilmaisuun ja valaistuspreferenssien syöttämisen osoittamiseen Android sovellukselle. Käyttäjäpreferenssit välitetään paikalliselle palvelimelle, joka vastaa ohjauslogiikasta ja viestinnästä valaisimien kanssa. Mahdolliset konfliktit käyttäjien välillä ratkaistaan etäisyydellä painotetulla keskiarvolla, mikä tekee prototyypistä kätevän erityisesti tilanteisiin missä käyttäjät jakavat ympäröivät valaisimet toistensa kanssa. Lisäksi vertaillaan muita älykkäitä järjestelmiä valaistuksen säätämiseen

ItsBlue: A Distributed Bluetooth-Based Framework for Intelligent Transportation Systems

Inefficiency in transportation networks is having an expanding impact, at a variety of levels. Transportation authorities expect increases in delay hours and in fuel consumption and, consequently, the total cost of congestion. Nowadays, Intelligent Transportation Systems (ITS) have become a necessity in order to alleviate the expensive consequences of the rapid demand on transportation networks. Since the middle of last century, ITS have played a significant role in road safety and comfort enhancements. However, the majority of state of the art ITS are suffering from several drawbacks, among them high deployment costs and complexity of maintenance. Over the last decade, wireless technologies have reached a wide range of daily users. Today\u27s Mobile devices and vehicles are now heavily equipped with wireless communication technologies. Bluetooth is one of the most widely spread wireless technologies in current use. Bluetooth technology has been well studied and is broadly employed to address a variety of challenges due to its cost-effectiveness, data richness, and privacy perverseness, yet Bluetooth utilization in ITS is limited to certain applications. However, Bluetooth technology has a potential far beyond today\u27s ITS applications. In this dissertation, we introduce itsBlue, a novel Bluetooth-based framework that can be used to provide ITS researchers and engineers with desired information. In the itsBlue framework, we utilize Bluetooth technology advantages to collect road user data from unmodified Bluetooth devices, and we extract a variety of traffic statistics and information to satisfy ITS application requirements in an efficient and cost-effective way. The itsBlue framework consists of data collection units and a central computing unit. The itsBlue data collection unit features a compact design that allows for stationary or mobile deployment in order to extend the data collection area. Central computing units aggregate obtained road user data and extract a number of Bluetooth spatial and temporal features. Road users’ Bluetooth features are utilized in a novel way to determine traffic-related information, such as road user context, appearance time, vehicle location and direction, etc. Extracted information is provided to ITS applications to generate the desired transportation services. Applying such a passive approach involves addressing several challenges, like discovering on-board devices, filtering out data received from vehicles out of the target location, or revealing vehicle status and direction. Traffic information provided by the itsBlue framework opens a wide to the development of a wide range of ITS applications. Hence, on top of the itsBlue framework, we develop a pack of intersection management applications that includes pedestrians’ volume and waiting times, as well as vehicle queue lengths and waiting times. Also, we develop a vehicle trajectory reconstruction application. The itsBlue framework and applications are thoroughly evaluated by experiments and simulations. In order to evaluate our work, we develop an enhanced version of the UCBT Network Simulator 2 (NS-2). According to evaluation outcomes, itsBlue framework and applications evaluations show promising results. For instance, the evaluation results show that the itsBlue framework has the ability to reveal road user context with accuracy exceeding 95% in 25s

Study of Bluetooth Low Energy as a Contact Tracing Technology

In recent months we have seen how Bluetooth Low Energy has become, due to the epidemiological situation, the most used technology for contact tracing. With this in mind, the objective of this project is to test and contrast the robustness and functionalities offered by Bluetooth Low Energy for contact tracing. And overall to see the accuracy and capabilities it can offer. To study digital contact tracing techniques using sensing devices we will integrate Internet of Things elements into the Digital Contact Tracing architecture. We will not only evaluate the proximity between two people for their contagion, but we will also add other factors, such as air quality through Internet of Things network nodes. Secondly, we are trying to determine as reliably and accurately as possible, within the limitations of Bluetooth, the accuracy between the transmitter and the receiver. For this we will first present a new architecture for digital contact tracing, and some of its adaptations. Along with the adaptation of an Internet of Things node to add the ability to communicate via Bluetooth Low Energy and the advantages to be gained in this case. Along with these modifications we will try to apply a solution that does not represent a large increase in the cost of implementation. We will then evaluate the accuracy and reliability of Bluetooth Low Energy for determining distances between a transmitter and receiver. In addition to determining the contact risk between two people for the possibility of contact, we also evaluate the quality of the signal between contacts in several scenarios. With this we can evaluate how accurate is the communication through our Internet of Things node and other Bluetooth Low Energy elements. We will then evaluate the mechanisms and the accuracy we can obtain for determining the contact risk in two ways. First, we will try to calculate the risk based on the relationship between the signal strength and the distance between the transmitter and the receiver. Secondly, we will try to classify contact risk based on distance ranges and apply a Machine Learning classifier to determine the approximate distance between the transmitter and the receiver. With all this added information we will be able to evaluate and determine conclusively if BLE is a potential technology for digital contact tracing protocols. And also if adding Internet of Things elements to the Digital Contact tracing architecture is an option to improve the determination of the contagion risk

New Contact Sensorization Smart System for IoT e-Health Applications Based on IBC IEEE 802.15.6 Communications

[EN] This paper proposes and demonstrates the capabilities of a new sensorization system that monitors skin contact between two persons. Based on the intrabody communication standard (802.15.6), the new system allows for interbody communication, through the transmission of messages between di erent persons through the skin when they are touching. The system not only detects if there has been contact between two persons but, as a novelty, is also able to identify the elements that have been in contact. This sensor will be applied to analyze and monitor good follow-up of hand hygiene practice in health care, following the ¿World Health Organization Guidelines on Hand Hygiene in Health Care¿. This guide proposes specific recommendations to improve hygiene practices and reduce the transmission of pathogenic microorganisms between patients and health-care workers (HCW). The transmission of nosocomial infections due to improper hand hygiene could be reduced with the aid of a monitoring system that would prevent HCWs from violating the protocol. The cutting-edge sensor proposed in this paper is a crucial innovation for the development of this automated hand hygiene monitoring system (AHHMS).This research was funded by the Spanish Ministerio de Economia y Competitividad, grant number DPI2016-80303-C2-1-P.Hernández, D.; Ors Carot, R.; Capella Hernández, JV.; Bonastre Pina, AM.; Campelo Rivadulla, JC. (2020). New Contact Sensorization Smart System for IoT e-Health Applications Based on IBC IEEE 802.15.6 Communications. Sensors. 20(24):1-17. https://doi.org/10.3390/s20247097S117202

Minimal Infrastructure Radio Frequency Home Localisation Systems

The ability to track the location of a subject in their home allows the provision of a number of location based services, such as remote activity monitoring, context sensitive prompts and detection of safety critical situations such as falls. Such pervasive monitoring functionality offers the potential for elders to live at home for longer periods of their lives with minimal human supervision. The focus of this thesis is on the investigation and development of a home roomlevel localisation technique which can be readily deployed in a realistic home environment with minimal hardware requirements. A conveniently deployed Bluetooth ® localisation platform is designed and experimentally validated throughout the thesis. The platform adopts the convenience of a mobile phone and the processing power of a remote location calculation computer. The use of Bluetooth ® also ensures the extensibility of the platform to other home health supervision scenarios such as wireless body sensor monitoring. Central contributions of this work include the comparison of probabilistic and nonprobabilistic classifiers for location prediction accuracy and the extension of probabilistic classifiers to a Hidden Markov Model Bayesian filtering framework. New location prediction performance metrics are developed and signicant performance improvements are demonstrated with the novel extension of Hidden Markov Models to higher-order Markov movement models. With the simple probabilistic classifiers, location is correctly predicted 80% of the time. This increases to 86% with the application of the Hidden Markov Models and 88% when high-order Hidden Markov Models are employed. Further novelty is exhibited in the derivation of a real-time Hidden Markov Model Viterbi decoding algorithm which presents all the advantages of the original algorithm, while producing location estimates in real-time. Significant contributions are also made to the field of human gait-recognition by applying Bayesian filtering to the task of motion detection from accelerometers which are already present in many mobile phones. Bayesian filtering is demonstrated to enable a 35% improvement in motion recognition rate and even enables a floor recognition rate of 68% using only accelerometers. The unique application of time-varying Hidden Markov Models demonstrates the effect of integrating these freely available motion predictions on long-term location predictions

Applications of Context-Aware Systems in Enterprise Environments

In bring-your-own-device (BYOD) and corporate-owned, personally enabled (COPE) scenarios, employees’ devices store both enterprise and personal data, and have the ability to remotely access a secure enterprise network. While mobile devices enable users to access such resources in a pervasive manner, it also increases the risk of breaches for sensitive enterprise data as users may access the resources under insecure circumstances. That is, access authorizations may depend on the context in which the resources are accessed. In both scenarios, it is vital that the security of accessible enterprise content is preserved. In this work, we explore the use of contextual information to influence access control decisions within context-aware systems to ensure the security of sensitive enterprise data. We propose several context-aware systems that rely on a system of sensors in order to automatically adapt access to resources based on the security of users’ contexts. We investigate various types of mobile devices with varying embedded sensors, and leverage these technologies to extract contextual information from the environment. As a direct consequence, the technologies utilized determine the types of contextual access control policies that the context-aware systems are able to support and enforce. Specifically, the work proposes the use of devices pervaded in enterprise environments such as smartphones or WiFi access points to authenticate user positional information within indoor environments as well as user identities

Estimating Transit Ridership Patterns through Automated Data Collection Technology: A Case Study in San Luis Obispo, California

Public transportation offers a crucial solution to the travel demand in light of national and global economic, energy, and environmental challenges. If implemented effectively, public transit offers an affordable, convenient, and sustainable transportation mode. Implementation of new technologies for information-harvesting may lead to more effective transit operations. This study examines the potential of automated data collection technologies to analyzing and understand the origin-destination flow patterns, which is essential for transit route planning and stop location placement. This thesis investigates the collection and analysis of data of passengers onboard San Luis Obispo Transit buses in February and March 2017 using Bluetooth (BT) and automatic passenger counter (APC) data. Five BlueMAC detectors were placed on SLO Transit buses to collect Bluetooth data. APC data was obtained from San Luis Obispo Transit. The datasets were used to establish a data processing method to exclude invalid detections, to identify and process origin and destination trips of passengers, and to make conclusions regarding passenger behavior. The filtering methods were applied to the Bluetooth data to extract counts of unique passenger information and to compare the filtered data to the ground-truth APC data. The datasets were also used to study the San Luis Obispo Downtown Farmer’s Market and its impact on transit ridership demand. The investigation revealed that after carefully employing the filters on BT data there were no consistent patterns in differences between unique passenger counts obtained from APC data and the BT data. As a result, one should be careful in employing BT data for transit OD estimation. Not every passenger enables Bluetooth or owns a Bluetooth device, so relying on the possession of Bluetooth-enabled devices may not lead to a random sample, resulting in misleading travel patterns. Based on the APC data, it was revealed that transit ridership is 40% higher during the days during which Higuera Street in Downtown San Luis Obispo is used for Farmer’s Market – a classic example of tactical urbanism. Increase in transit ridership is one of the aspects of tactical urbanism that may be further emphasized. With rapidly-evolving data collection technologies, transit data collection methods could expand beyond the traditional onboard survey. The lessons learned from this study could be expanded to provide a robust and detailed data source for transit operations and planning

Advanced Trends in Wireless Communications

Physical limitations on wireless communication channels impose huge challenges to reliable communication. Bandwidth limitations, propagation loss, noise and interference make the wireless channel a narrow pipe that does not readily accommodate rapid flow of data. Thus, researches aim to design systems that are suitable to operate in such channels, in order to have high performance quality of service. Also, the mobility of the communication systems requires further investigations to reduce the complexity and the power consumption of the receiver. This book aims to provide highlights of the current research in the field of wireless communications. The subjects discussed are very valuable to communication researchers rather than researchers in the wireless related areas. The book chapters cover a wide range of wireless communication topics

Achieving multi-user capabilities through an indoor positioning system based on BLE beacons

The multiple user challenge is one of the issues that need to be addressed in order to facilitate the adoption of intelligent environments in everyday activities. The development of multi-user capabilities in smart homes is closely related to the creation of effective indoor positioning systems. This research work reports on the development and evaluation of an indoor positioning system that allows multi-user management in a smart home environment. The design of the BLE based system is presented, as well as its implementation and evaluation in the Smart Spaces Lab at Middlesex University. The validation of the system is shown as a case study in which it is used to develop multi-user capabilities in two context-aware systems of the laboratory. Video demonstrations are provided to illustrate the multi-user capabilities that were developed in the validation