Survey and Systematization of Secure Device Pairing

Secure Device Pairing (SDP) schemes have been developed to facilitate secure communications among smart devices, both personal mobile devices and Internet of Things (IoT) devices. Comparison and assessment of SDP schemes is troublesome, because each scheme makes different assumptions about out-of-band channels and adversary models, and are driven by their particular use-cases. A conceptual model that facilitates meaningful comparison among SDP schemes is missing. We provide such a model. In this article, we survey and analyze a wide range of SDP schemes that are described in the literature, including a number that have been adopted as standards. A system model and consistent terminology for SDP schemes are built on the foundation of this survey, which are then used to classify existing SDP schemes into a taxonomy that, for the first time, enables their meaningful comparison and analysis.The existing SDP schemes are analyzed using this model, revealing common systemic security weaknesses among the surveyed SDP schemes that should become priority areas for future SDP research, such as improving the integration of privacy requirements into the design of SDP schemes. Our results allow SDP scheme designers to create schemes that are more easily comparable with one another, and to assist the prevention of persisting the weaknesses common to the current generation of SDP schemes.Comment: 34 pages, 5 figures, 3 tables, accepted at IEEE Communications Surveys & Tutorials 2017 (Volume: PP, Issue: 99

Learning spatiotemporal patterns for monitoring smart cities and infrastructure

Recent advances in the Internet of Things (IoT) have changed the way we interact with the world. The ability to monitor and manage objects in the physical world electronically makes it possible to bring data-driven decision making to new realms of city infrastructure and management. Large volumes of spatiotemporal data have been collected from pervasive sensors in both indoor and outdoor environments, and this data reveals dynamic patterns in cities, infrastructure, and public property. In light of the need for new approaches to analysing such data, in this thesis, we propose present relevant data mining techniques and machine learning approaches to extract knowledge from spatiotemporal data to solve real-world problems. Many challenges and problems are under-addressed in smart cities and infrastructure monitoring systems such as indoor person identification, evaluation of city regions segmentation with parking events, fine collection from cars in violations, parking occupancy prediction and airport aircraft path map reconstruction. All the above problems are associated with both spatial and temporal information and the accurate pattern recognition of these spatiotemporal data are essential for determining problem solutions. Therefore, how to incorporate spatiotemporal data mining techniques, artificial intelligence approaches and expert knowledge in each specific domain is a common challenge. In the indoor person identification area, identifying the person accessing a secured room without vision-based or device-based systems is very challenging. In particular, to distinguish time-series patterns on high-dimensional wireless signal channels caused by different activities and people, requires novel time-series data mining approaches. To solve this important problem, we established a device-free system and proposed a two-step solution to identify a person who has accessed a secure area such as an office. Establishing smart parking systems in cities is a key component of smart cities and infrastructure construction. Many sub-problems such as parking space arrangements, fine collection and parking occupancy prediction are urgent and important for city managers. Arranging parking spaces based on historical data can improve the utilisation rate of parking spaces. To arrange parking spaces based on collected spatiotemporal data requires reasonable region segmentation approaches. Moreover, evaluating parking space grouping results needs to consider the correlation between the spatial and temporal domains since these are heterogeneous. Therefore, we have designed a spatiotemporal data clustering evaluation approach, which exploits the correlation between the spatial domain and the temporal domain. It can evaluate the segmentation results of parking spaces in cities using historical data and similar clustering results that group data consisting of both spatial and temporal domains. For fine collection problem, using the sensor instrumentation installed in parking spaces to detect cars in violation and issue infringement notices in a short time-window to catch these cars in time is significantly difficult. This is because most cars in violation leave within a short period and multiple cars are in violation at the same time. Parking officers need to choose the best route to collect fines from these drivers in the shortest time. Therefore, we proposed a new optimisation problem called the Travelling Officer Problem and a general probability-based model. We succeeded in integrating temporal information and the traditional optimisation algorithm. This model can suggest to parking officers an optimised path that maximise the probability to catch the cars in violation in time. To solve this problem in real-time, we incorporated the model with deep learning methods. We proposed a theoretical approach to solving the traditional orienteering problem with deep learning networks. This approach could improve the efficiency of similar urban computing problems as well. For parking occupancy prediction, a key problem in parking space management is with providing a car parking availability prediction service that can inform car drivers of vacant parking lots before they start their journeys using prediction approaches. We proposed a deep learning-based model to solve this parking occupancy prediction problem using spatiotemporal data analysis techniques. This model can be generalised to other spatiotemporal data prediction problems also. In the airport aircraft management area, grouping similar spatiotemporal data is widely used in the real world. Determining key features and combining similar data are two key problems in this area. We presented a new framework to group similar spatiotemporal data and construct a road graph with GPS data. We evaluated our framework experimentally using a state-of-the-art test-bed technique and found that it could effectively and efficiently construct and update airport aircraft route map. In conclusion, the studies in this thesis aimed to discover intrinsic and dynamic patterns from spatiotemporal data and proposed corresponding solutions for real-world smart cities and infrastructures monitoring problems via spatiotemporal pattern analysis and machine learning approaches. We hope this research will inspire the research community to develop more robust and effective approaches to solve existing problems in this area in the future

An intelligent system for interactive visits in smart places

In this work, we designed and developed a prototype as an integrated system to provide visitors or users of complex buildings, such as museums, hospitals, shopping malls, amongst others, relevant information in an interactive and comfortable way while keeping it simple and affordable for the building managers. To achieve this, a mobile application was developed that allows its users to access information from several Smart Places, in a single mobile application. This application has a generic and common interface to be used by different types of Smart Places and provides multi-language information, to the end-users, that can be defined and translated by each Smart Place. To make this possible, the prototype architecture was designed providing the Smart Places independence since they have their own database and can manage their information. Furthermore, the system uses an Indoor Location and Tracking System taking advantage of BLE devices and Bluetooth capabilities of mobile devices to provide the relevant and interactive information based on the end-user location within the Smart Place. This prototype is also composed of an open-source Content Management System (based on Drupal 8), that allows the system operators to upload and manage the information provided to the end-users. Since the design and construction of most modern buildings go through a BIM process, our Content Management System provides a way to upload information extracted from BIM tools through CSV files. The resulting system is affordable for Smart Places while being comfortable and interactive for the users.Neste trabalho, foi desenhado e desenvolvido um protótipo sendo um sistema integrado de forma a proporcionar aos visitantes ou utilizadores de edifícios complexos, tais como museus, hospitais, centros comerciais, entre outros, informação relevante de forma interactive e confortável, mantendo-o simples e acessível para os gestores de edifícios. Para tal, foi desenvolvida uma aplicação móvel que permite aos seus utilizadores aceder à informação de vários Smart Places, numa única aplicação. Esta tem uma interface genérica e comum para ser utilizada por diferentes tipos de Smart Places e disponibiliza informação multilingue, aos utilizadores, podendo ser definida e traduzida por cada Smart Place. Para que isto seja possível, a arquitetura do protótipo foi desenhada de forma a garantir a independência dos Smart Places, uma vez que estes têm a sua própria base de dados e podem gerir a sua informação. Além disso, o sistema utiliza um Indoor Location and Tracking System, aproveitando os dispositivos BLE e os recursos Bluetooth dos dispositivos móveis, para fornecer as informações relevantes e interativas baseadas na localização do utilizador dentro do Smart Place. Este protótipo também é composto por um Content Management System de código aberto (baseado no Drupal 8), que permite aos operadores do sistema carregar e gerir as informações fornecidas aos utilizadores finais. Como o planeamento e construção dos edifícios mais modernos passam por um processo de BIM, o Content Management System permite carregar informações extraídas de ferramentas BIM através de ficheiros CSV. O sistema resultante é acessível para Smart Places mantendo-se confortável e interativo para os utilizadores

A model personal energy meter

Every day each of us consumes a significant amount of energy, both directly through transport, heating and use of appliances, and indirectly from our needs for the production of food, manufacture of goods and provision of services. This dissertation investigates a personal energy meter which can record and apportion an individual's energy usage in order to supply baseline information and incentives for reducing our environmental impact. If the energy costs of large shared resources are split evenly without regard for individual consumption each person minimises his own losses by taking advantage of others. Context awareness offers the potential to change this balance and apportion energy costs to those who cause them to be incurred. This dissertation explores how sensor systems installed in many buildings today can be used to apportion energy consumption between users, including an evaluation of a range of strategies in a case study and elaboration of the overriding principles that are generally applicable. It also shows how second-order estimators combined with location data can provide a proxy for fine-grained sensing. A key ingredient for apportionment mechanisms is data on energy usage. This may come from metering devices or buildings directly, or from profiling devices and using secondary indicators to infer their power state. A mechanism for profiling devices to determine the energy costs of specific activities, particularly applicable to shared programmable devices is presented which can make this process simpler and more accurate. By combining crowdsourced building-inventory information and a simple building energy model it is possible to estimate an individual's energy use disaggregated by device class with very little direct sensing. Contextual information provides crucial cues for apportioning the use and energy costs of resources, and one of the most valuable sources from which to infer context is location. A key ingredient for a personal energy meter is a low cost, low infrastructure location system that can be deployed on a truly global scale. This dissertation presents a description and evaluation of the new concept of inquiry-free Bluetooth tracking that has the potential to offer indoor location information with significantly less infrastructure and calibration than other systems. Finally, a suitable architecture for a personal energy meter on a global scale is demonstrated using a mobile phone application to aggregate energy feeds based on the case studies and technologies developed

Design and Implementation of a Bluetooth 4.0 LE Infrastructure for Mobile Devices

The new Bluetooth 4.0 low energy (Bluetooth Smart) specification provides developers a means to create unique and never before seen systems. Using low energy beacons, new systems are able to deliver location based information to the user, whenever it comes into close proximity of a certain beacon. BLExpo, a beacon based service developed for an enhanced user experience at expo’s, relays location sensitive information saved on a server to the consumer, depending on his location, is described in this thesis in detail. Adapting the design of the service, a vast variety of different systems can emerge, and existing ones improved, by supplying beacons. A brief look at how Bluetooth low energy can make our lives more convenient is provided, as well as the issues that might arise when dealing with beacon based systems. Alternative designs are taken into account by describing systems that rely purely on Bluetooth low energy for both event triggering, and information exchange. Since every system mentioned in this thesis uses Bluetooth LE as the underlaying technology, it is also introduced and a brief overview is provided

Wearable Bluetooth Sensors for Capturing Relational Variables and Temporal Variability in Relationships: A Construct Validation Study

The advent of wearable sensor technologies has the potential to transform organizational research by offering the unprecedented opportunity to collect continuous, objective, highly granular data over extended time periods. Recent evidence has demonstrated the potential utility of Bluetooth-enabled sensors, specifically, in identifying emergent networks via colocation signals in highly controlled contexts with known distances and groups. Although there is proof of concept that wearable Bluetooth sensors may be able to contribute to organizational research in highly controlled contexts, to date there has been no explicit psychometric construct validation effort dedicated to these sensors in field settings. Thus, the two studies described here represent the first attempt to formally evaluate longitudinalBluetooth data streams generated in field settings, testing their ability to (a) show convergent validity with respect to traditional self-reports of relational data; (b) display discriminant validitywith respect to qualitative differences in the nature of alternative relationships (i.e., advice vs. friendship); (c) document predictive validity with respect to performance; (d) decompose variance in network-related measures into meaningful within- and between-unit variability over time; and (e) complement retrospective self-reports of time spent with different groups where there is a “ground truth” criterion. Our results provide insights into the validity of Bluetooth signals with respect to capturing variables traditionally studied in organizational science and highlight how the continuous data collection capabilities made possible by wearable sensors can advance research far beyond that of the static perspectives imposed by traditional data collection strategies

A Survey on Multi-Resident Activity Recognition in Smart Environments

Human activity recognition (HAR) is a rapidly growing field that utilizes smart devices, sensors, and algorithms to automatically classify and identify the actions of individuals within a given environment. These systems have a wide range of applications, including assisting with caring tasks, increasing security, and improving energy efficiency. However, there are several challenges that must be addressed in order to effectively utilize HAR systems in multi-resident environments. One of the key challenges is accurately associating sensor observations with the identities of the individuals involved, which can be particularly difficult when residents are engaging in complex and collaborative activities. This paper provides a brief overview of the design and implementation of HAR systems, including a summary of the various data collection devices and approaches used for human activity identification. It also reviews previous research on the use of these systems in multi-resident environments and offers conclusions on the current state of the art in the field.Comment: 16 pages, to appear in Evolution of Information, Communication and Computing Systems (EICCS) Book Serie

Intelligent home automation security system based on novel logical sensing and behaviour prediction

The thesis, Intelligent Home Automation Security System Based on Novel Logical Sensing and Behavior Prediction, was designed to enhance authentication, authorization and security in smart home devices and services. The work proposes a three prong defensive strategy each of which are analyzed and evaluated separately to drastically improve security. The Device Fingerprinting techniques proposed, not only improves the existing approaches but also identifies the physical device accessing the home cybernetic and mechatronic systems using device specific and browser specific parameters. The Logical Sensing process analyses home inhabitant actions from a logical stand point and develops sophisticated and novel sensing techniques to identify intrusion attempts to a home’s physical and cyber space. Novel Behavior prediction methodology utilizes Bayesian networks to learn normal user behavior which is later compared to distinguish and identify suspicious user behaviors in the home in a timely manner. The logical sensing, behavior prediction and device fingerprinting techniques proposed were successfully tested, evaluated and verified in an actual home cyber physical system. The algorithms and techniques proposed in the thesis can be easily modified and adapted into many practical applications in Industrial Internet of Things, Industry 4.0 and cyber-physical systems.Thesis (PhD)--University of Pretoria, 2017.Electrical, Electronic and Computer EngineeringPhDUnrestricte