MSc THESIS - Occupancy Detection in Indoor Environments Based on Wi-Fi Measurements and Machine Learning Methods

Οι κινητές συσκευές γίνονται σήμερα σημαντικά μέρος της καθημερινότητάς μας λόγω των δυνατοτήτων ασύρματης επικοινωνίας που έχουν επιτρέψει μια σειρά από υπηρεσίες υψηλού επιπέδου. Αυτός ο εξοπλισμός Wi-Fi στέλνει συνεχώς πακέτα που δηλώνονται ως αιτήματα ανίχνευσης που μπορούν να καταγραφούν χρησιμοποιώντας ασύρματα sniffer. Σε αυτή τη διατριβή, προσπαθήσαμε να λύσουμε το πρόβλημα της αξιοποίησης μιας τέτοιας μεθοδολογίας για την ολοκλήρωση της εκτίμησης της πληρότητας λαμβάνοντας υπόψη πόσα άτομα υπάρχουν σε έναν συγκεκριμένο χώρο. Αρχικά, συζητήσαμε τη συλλογή πακέτων αιτημάτων ανίχνευσης Wi-Fi χρησιμοποιώντας τη συσκευή Raspberry Pi και την ανάλυσή τους με εργαλεία ανάλυσης πακέτων. Χειριστήκαμε τη συλλογή δεδομένων σε διαφορετικά περιβάλλοντα, εύρη σε διαφορετικές πυκνότητες επιπέδων και χρησιμοποιήσαμε την κάμερα κινητής τηλεφωνίας ως βασική τιμή αλήθειας. Στη συνέχεια, παρουσιάσαμε πώς μπορούμε να χρησιμοποιήσουμε διευθύνσεις MAC και πληροφορίες επιπέδου ισχύος για την πρόβλεψη εσωτερικού χώρου στο προτεινόμενο μοντέλο γραμμικής παλινδρόμησης κορυφογραμμής χρησιμοποιώντας διαφορετικές προσεγγίσεις. Παρουσιάσαμε ένα φθηνό και ακριβές μοντέλο εκτίμησης πληρότητας που βασίζεται στην καταγραφή των συσκευών των χρηστών πλαισίων Wi-Fi. Το μοντέλο εφαρμόζεται σε υλικό χαμηλού κόστους και χρησιμοποιεί ένα μοντέλο εποπτευόμενης εκμάθησης για να ταιριάζει σε διαφορετικά περιβάλλοντα. Τα πειράματα τέτοιων εκτιμήσεων εσωτερικού χώρου έχουν εφαρμοστεί σε διαφορετικά σενάρια για να καταδειχθεί η εγκυρότητα της προτεινόμενης λύσης και να αξιολογηθούν τα αποτελέσματά της. Τα αποτελέσματα προσδιορίζουν ότι οι κινητές συσκευές έχουν καλές δυνατότητες πρόβλεψης του αριθμού των ατόμων στο χώρο.Mobile devices are currently significantly becoming part of our daily lives due to the wireless communication capabilities that have enabled a series of high-level services. These Wi-Fi equipment are continuously sending packets stated as probe requests that can be captured using wireless sniffers. In this thesis, we tried to solve the problem of exploiting such a methodology to complete occupancy estimation by considering how many people exist in a specific space. At first, we discussed collecting Wi-Fi probe request packets using the Raspberry Pi device and analysing them with packet analyzer tools. We operated data collection in different environments, ranges in different level densities and used the mobile camera as the ground truth value. Afterwards, we represented how we can use MAC addresses and power level information for indoor prediction in the proposed linear ridge regression model using different approaches. We introduced a cheap and precise occupancy estimation model based on the capture of Wi-Fi frames user’s devices. The model is applied on low-cost hardware and utilized a supervised learning model to fit different environments. The experiments of such indoor estimations have been implemented in different scenarios to demonstrate the validity of the proposed solution and evaluate its results of it. The outcomes specify that mobile devices have good potential for predicting of the number of people in the space

Inferring Activities of Daily Living of Home-Care Patients Through Wearable and Ambient Sensing

There is an increasing demand for remote healthcare systems for single person households as it facilitates independent living in a smart home setting. Much research effort has been invested to develop such systems to monitor and infer if the person is able to perform their routine activities on a daily basis. In this research study, two different methods have been proposed for recognizing activities of daily life (ADL) using wearable and ambient sensing respectively. The thesis presents a novel algorithm for near real-time recognition of low-level micro-activities and their associated zone of occurrence within the house by using just the wearable as the lone sensor data. This is achieved by gathering location information of the target person using a wearable beacon embedded with magnetometer and inertial sensors. A hybrid three-tier approach is adopted where the main intention is to map the location of a person performing an activity with pre-defined house landmarks and zones in the offline labeled database. Experimental results demonstrate that it is possible to achieve centimeter-level accuracy for recognition of micro-activities and a classification accuracy of 85% for trajectory prediction. Furthermore, addi-tional tests were carried out to assess whether increased antenna gain improves the ranking accuracy of the fingerprinting method adopted for location estimation. The thesis explores another method using ambient sensors for activity recognition by integrating stream reasoning, ontological modeling and probabilistic inference using Markov Logic Networks. The incoming sensor data stream is analyzed in real time by exploring semantic relationships, location context and temporal rea-soning between individual events using a stream-processing engine. Experimental analysis of the proposed method with two real-world datasets shows improvement in recognizing complex activities carried out in a smart home environment. An average F-measure score of 92.35% and 85.75% was achieved for recognition of interwoven activities using this method

Energy Data Analytics for Smart Meter Data

The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal

Safety Return on Investment (ROI): The Broader Adoption of Rotorcraft CFIT-Avoidance Technology

This dissertation provided a method of estimating the potential return on investment (ROI) that could be achieved if operators were to adopt the readily available controlled flight into terrain (CFIT) avoidance technology more broadly. Previous research explored the costs and benefits of different safety initiatives but did not evaluate from an operators’ perspective. For the operators, a private ROI that excludes societal costs and benefits was therefore considered the suitable metric. For the rotorcraft industry, the ROI estimation methodology was not readily available, and this study sought to fill that gap. The purpose of this study was to estimate the potential ROI by determining the costs associated with the outcomes of CFIT-accidents, the costs of adopting the technology, the current accident rate, the benefits expressed as costs avoided through a reduction in the number of accidents, and application of the appropriate ROI formula. The dissertation was conducted as a mixed method study that used qualitative data from historical CFIT-related accident reports to identify the accident outcomes and estimate the associated accident costs plus the available quantitative data to estimate the CFIT-avoidance technology adoption costs. The accident cost categories were based on categories used in airline research and modified for the rotorcraft industry. Using the formula, ROI = Net benefits divided by safety technology adoption costs, ROI values were generated in multiple iterations of the Monte Carlo simulation. The net benefits were evaluated as the difference between the potential accident costs avoided with a reduction in CFIT accidents and the technology adoption costs. The simulation results for the three rotorcraft categories showed that the turbinesingle would experience the highest ROI, followed by the piston category and the twinturbines. When all rotorcraft categories were considered, the ROI was positive but could turn negative if the technology adoption costs grew by a factor of more than three. The broad range in the ROI values for both the piston and single-turbine categories were largely driven by the high variation of the individual cost categories, especially the direct costs: occupant death and injuries, aircraft damage, and leasing costs. From the results of the study, it was recommended that CFIT-avoidance technology should be more broadly adopted by piston and single-turbine rotorcraft operators. For twin-turbines, the adoption should be evaluated against the impact of the regulatory changes for helicopter air ambulance (HAA) operations, which may reduce the number of accidents and generate a positive ROI before further action from operators. Future research should focus on validating the methodology by using it as a starting point for evaluating the ROI for safety initiatives that have already been implemented, whether technology or operational programs. The industry should also improve the methodology by defining or proposing better processes for estimating rotorcraft accident costs, especially indirect costs estimated to be the of the same magnitude as the direct costs. The rotorcraft industry should find ways to make costs data, such as accident investigation costs, more accessible in order to apply the ROI estimation methodology to achieve more accurate results

Intelligent Buildings in Smart Grids: A Survey on Security and Privacy Issues Related to Energy Management

During the last decade, the smart grid (SG) concept has started to become a reality, mainly thanks to the technical progress achieved in telecommunications, informatics and power electronics, among other domains, leading to an evolution of the traditional electrical grid into an intelligent one. Nowadays, the SG can be seen as a system of smart systems that include cyber and physical parts from different technologies that interact with each other. In this context, intelligent buildings (IBs) constitute a paradigm in which such smart systems are able to guarantee the comfort of residents while ensuring an appropriate tradeoff of energy production and consumption by means of an energy management system (EMS). These interconnected EMSs remain the objective of potential cyber-attacks, which is a major concern. Therefore, this paper conducts a survey, from a multidisciplinary point of view, of some of the main security and privacy issues related to IBs as part of the SG, including an overview of EMS, smart meters, and the main communication networks employed to connect IBs to the overall SG. Future research directions towards a security enhancement from both technical and human perspectives are also provided

Let's track! strategies to establish active people tracking in workplaces

The action research component is conducted by developing a system that delivers insights into teamwork dynamics, as revealed by tracking the social network interactions that occur within collaborative work environments. I constructed a working prototype that utilised an indoor people tracking system that captures people's movements as they operate within their workspace. It is capable of simultaneously monitoring the progress of multiple cohabitating project teams. Focusing on providing context specific insights, I designed a flexible behaviour model that constructed customised social networks to extract interactions of interest from the tracked data. The visually rich analysis reporting that was layered with contextual cues enabled quick cognition by the intended viewer. The targeted user covers all levels of the organisation from project collaborators to the support personnel and upper management. With this setup, everyone can participate in a data-supported reflective learning process. The original contribution of my research is two-fold. Firstly, the people tracking system and analytics I developed demonstrated the technical capability to provide real time insights to workspace design, project management and human resource management applications. Secondly, through reference to my three case studies, I argue that a user-centric approach is critical for the successful integration and adaptation of people tracking systems and analytics into real world workplace practices

Self-Defense Against Robots and Drones

Robots can pose-or can appear to pose-a threat to life, property, and privacy. May a landowner legally shoot down a trespassing drone? Can she hold a trespassing autonomous car as security against damage done or further torts? Is the fear that a drone may be operated by a paparazzo or Peeping Tom sufficient grounds to disable or interfere with it? How hard may you shove if the office robot rolls over your foot? This Article addresses all those issues and one more. what rules and standards we could put into place to make the resolution of those questions easier and fairer to all concerned The default common-law legal rules governing each of these perceived threats are somewhat different, although reasonableness always plays an important role in defining legal rights and options. In certain cases-drone overflights, autonomous cars-national,s tate, and even local regulations may trump the common law. Because it is in most cases obvious that humans can use force to protect themselves against actual physical attack, this Article concentrates on the more interesting cases of (1) robot (and especially drone) trespass and (2) responses to perceived threats other than physical attack by robots-notably the risk that the robot (or drone) may be spying-perceptions which may not always be justified, but which sometimes may nonetheless be considered reasonable in law

An exploration of virtual criminal investigations in Ghana : legal issues and challenges

The widespread cybercrime has caused changes and brought about a need for new investigative skills, laws and enforcement procedures to attack these obstacles. Since technological crimes committed through the information superhighway or the internet is evolving very rapidly, efficacious enforcement of cybercrime is becoming extremely challenging. Cybercrime is both a national and international issue and local legislation alone cannot be able to combat the menace. Digital evidence permeates every aspect of the average person's life in today's society and no matter what you are doing these days, a digital footprint is probably being created and contains some type of digital evidence that can be recovered through digital forensic investigation It requires stringent laws, skilled personnel, well-established institutions, and transnational response. To efficaciously combat cybercrime, countries, states or governments must establish an independent anti-cybercrime unit and design national guidelines for digital evidence collections to combat the canker. This thesis, therefore, presents an examination of the virtual crime or cybercrime investigation challenges and legal issues on electronic evidence in Ghana. The study examines the existing cybercrime laws and practices in Ghana and makes a comparative study from other jurisdictions. Also, the study draws a survey from the international legal framework on cybercrime and electronic evidence on various methods and procedures that can be used to conduct digital forensic search and seizure of electronic evidence and investigation when cybercrimes occur. Recommendations were made which include formulation of stringent laws, establishing the national Cybercrime investigation Strategy and policies, the establishment of national guidelines for digital evidence collections, develop anti-cybercrime tool-kit for the collection of digital evidence, the establishment of digital forensic training institutions in all regions of Ghana for hands-on skilled based training for law enforcement officers and judges to ensure efficiency in the process of digital forensic investigation and prosecution of cybercrimes in Ghana are given.Police PracticeD. Phil. (Criminal Justice