Today, the Internet of Things is widely used in various fields, such as factories, public facilities, and even homes. The use of the Internet of Things involves a large number of sensor devices that collect various types of data in real time, such as machine voltage, current, and temperature. These devices will generate a large amount of streaming sensor data. These data can be used to make the data analysis, which can discover hidden relation such as monitoring operating status of a machine, detecting anomalies and alerting the company in time to avoid significant losses. Therefore, the application of anomaly detection in the field of data mining is very extensive. This paper proposes an anomaly detection method based on multiple streaming sensor data and performs anomaly detection on three data sets which are from the real company. First, this project proposes the state transition detection algorithm, state classification algorithm, and the correlation analysis method based on frequency. Then two algorithms were implemented in Python, and then make the correlation analysis using the results from the system to find some possible meaningful relations which can be used in the anomaly detection. Finally, calculate the accuracy and time complexity of the system, and then evaluated its feasibility and scalability. From the evaluation result, it is concluded that the method

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During and after earthquakes, occupants inside a damaged building should be evacuated rapidly and safely whereas related units outside the buildings (e.g. first responders) should know the current condition of the building. Obviously, this information should be as accurate as possible and accessed timely in order to speed up the evacuation. Unfortunately, absence of such information during evacuation and emergency response operations results in increased number of casualties. Hence, there arises a need for an approach to make rapid damage and blockage assessment in buildings possible. This study focuses on sensor-based, real-time blockage assessment of buildings during earthquakes and it is based on the idea that / the blocked units of a building (e.g. corridors) can be assessed with the help of different types of sensors. The number and locations of these sensors are arranged in such a way that it becomes possible to picture the current condition of the building. Sensors utilized in this study can be listed as accelerometer, ultrasonic range finder, gyro sensor, closed cable circuit and video camera. The research steps of this thesis include (1) examination of the damage indicators which can cause blockage, (2) assessment of the monitoring devices, (3) expression of the conducted experimental studies in order to assess blokage condition of a corridor unit, (4) proposing an sensor fusion approach, and (5) presentation of the performed case study as an implementation of the blockage assessment. The findings of this research can be made use of in future studies on sensor-based blockage assessment.

This thesis introduces the idea of an interactive web-based visualization tool to support inquiry-based science learning. The problem that occurs when the teachers and students are discussing the collected data is that they are lacking a tool to display such large quantities of data. It is often hard to fully understand such data. This education tool makes use of different visualization approaches in order to support students while getting insights from their collected data. In this thesis I proposed and implemented an interactive web-based visualization tool that was used at a prototype level during the educational activities. The requirements and user needs led the development of this prototype. Requirement elicitations have been done as a part of the research project conducted by CeLeKT.   For the development of this tool, it was necessary for the input of the teachers and students in order to get an understanding of the requirements. The initial inquiry of the teachers and students show the necessity and usefulness of an interactive web-based visualization tool to support learning practices.

Global position system (GPS) is widely used in land vehicles but suffers deterioration in its accuracy in urban canyons; mostly due to satellite signal blockage and signal multipath. To obtain accurate, reliable, and continuous positioning solutions, GPS is usually augmented with inertial sensors, including accelerometers and gyroscopes to monitor both translational and rotational motions of a moving vehicle. Due to space and cost requirements, micro-electro-mechanical-system (MEMS) inertial sensors, which are typically inexpensive are presently utilized in land vehicles for various reasons and can be used for integration with GPS for navigation purposes. Kalman filtering (KF) usually used to performs this integration. However, the complex error characteristics of these MEMS based sensors lead to divergence of the positioning solution. Furthermore, the residual GPS pseudorange correlated errors are always ignored, thus reducing the GPS overall positioning accuracy. This thesis targets enhancing the performance of integrated MEMS based INS/GPS navigation systems through exploring new non-linear modelling approaches that can deal with the non-linear and correlated parts of INS and GPS errors. The research approach in this thesis relies on reduced inertial sensor systems (RISS) incorporating single axis gyroscope, vehicle odometer, and accelerometers is considered for the integration with GPS in one of two schemes; either loosely-coupled where GPS position and velocity are used for the integration or tightly-coupled where GPS pseudorange and pseudorange rates are utilized. A new method based on parallel cascade identification (PCI) is developed in this research to enhance the performance of KF by modelling azimuth errors for the RISS/GPS loosely-coupled integration scheme. In addition, PCI is also utilized for the modelling of residual GPS pseudorange correlated errors. This thesis develops a method to augment a PCI – based model of GPS pseudorange correlated errors to a tightly-coupled KF. In order to take full advantage of the PCI based models, this thesis explores the Particle filter (PF) as a non-linear integration scheme that is capable of accommodating the arbitrary sensor characteristics, motion dynamics, and noise distributions. The performance of the proposed methods is examined through several road test experiments in land vehicles involving different types of inertial sensors and GPS receivers. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2012-07-31 16:09:16.559

The rapid growth of sensor data can potentially enable a better awareness of the environment for humans. In this regard, interpretation of data needs to be human-understandable. For this, data interpretation may include semantic annotations that hold the meaning of numeric data. This thesis is about bridging the gap between quantitative data and qualitative knowledge to enrich the interpretation of data. There are a number of challenges which make the automation of the interpretation process non-trivial. Challenges include the complexity of sensor data, the amount of available structured knowledge and the inherent uncertainty in data. Under the premise that high level knowledge is contained in ontologies, this thesis investigates the use of current techniques in ontological knowledge representation and reasoning to confront these challenges. Our research is divided into three phases, where the focus of the first phase is on the interpretation of data for domains which are semantically poor in terms of available structured knowledge. During the second phase, we studied publicly available ontological knowledge for the task of annotating multivariate data. Our contribution in this phase is about applying a diagnostic reasoning algorithm to available ontologies. Our studies during the last phase have been focused on the design and development of a domain-independent ontological representation model equipped with a non-monotonic reasoning approach with the purpose of annotating time-series data. Our last contribution is related to coupling the OWL-DL ontology with a non-monotonic reasoner. The experimental platforms used for validation consist of a network of sensors which include gas sensors whose generated data is complex. A secondary data set includes time series medical signals representing physiological data, as well as a number of publicly available ontologies such as NCBO Bioportal repository.

Online monitoring supports plant reliability and performance management by providing real time information about the condition of equipment. However, the intricate geometries and harsh operating environment of coal fired power plant boilers inhibit the ability to do online measurements of all process related variables. A low-cost alternative lies in the possibility of using knowledge about boiler operation to extract information about its condition from standard online process measurements. This approach is evaluated with the aim of enhancing online condition monitoring of a boiler’s convective pass heat exchanger network by respectively using a soft sensor and a data-driven method. The soft sensor approach is based on a one-dimensional thermofluid process model which takes measurements as inputs and calculates unmeasured variables as outputs. The model is calibrated based on design information. The data-driven method is one developed specifically in this study to identify unique fault signatures in measurement data to detect and quantify changes in unmeasured variables. The fault signatures are initially constructed using the calibrated one-dimensional thermofluid process model. The benefits and limitations of these methods are compared at the hand of a case study boiler. The case study boiler has five convective heat exchanger stages, each composed of four separate legs. The data-driven method estimates the average conduction thermal resistance of individual heat exchanger legs and the flue gas temperature at the inlet to the convective pass. In addition to this, the soft sensor estimates the average fluid variables for individual legs throughout the convective pass and therefore provides information better suited for condition prognosis. The methods are tested using real plant measurements recorded during a period which contained load changes and on-load heat exchanger cleaning events. The cleaning event provides some basis for validating the results because the qualitative changes of some unmeasured monitored variables expected during this event are known. The relative changes detected by both methods are closely correlated. The data-driven method is computationally less expensive and easily implementable across different software platforms once the fault signatures have been obtained. Fault signatures are easily trainable once the model has been developed. The soft sensors require the continuous use of the modelling software and will therefore be subject to licencing constraints. Both methods offer the possibility to enhance the monitoring resolution of modern boilers without the need to install any additional measurements. Implementation of these monitoring frameworks can provide a simple and low-cost contribution to optimized boiler performance and reliability management.

The global micromobility market is a fast growing market valued at USD 40.19 Billion in 2020. As the market grows, it is of great importance for companies to gain market shares in order to stay competitive and be the first choice within micromobility services. This can be achieved by, e.g., offering a safe micromobility service, for both riders and other road users. With state-of-the-art technology, accident prevention and preventing misuse of scooters and cities’ infrastructure is achievable. This study is conducted in collaboration with Voi Technology, a Swedish micromobility company that is committed to eliminate all serious injuries and fatalities in their value chain by 2030. Given such an ambition, the aim of the thesis is to evaluate the possibility of using unsupervised machine learning for anomaly detection with sensor data, to distinguish abnormal and normal riding behaviours. The study evaluates two machine learning algorithms; isolation forest and artificial neural networks, namely autoencoders. Beyond assessing the models ability to detect abnormal riding behaviours in general, they are evaluated based on their ability to find certain behaviours. By simulating different abnormal riding behaviours, model evaluation can be performed. The data preparation performed for the models include transforming the time series data into non-overlapping windows of a specific size containing descriptive statistics. The result obtained shows that finding a one-size-fits all type of anomaly detection model did not work as desired for either the isolation forest or the autoencoder. Further, the result indicate that one of the abnormal riding behaviours appears to be easier to distinguish, which motivates evaluating models created with the aim of distinguishing that specific behaviour. Hence, a simple moving average is also implemented to explore the performance of a very basic forecasting method. For this method, a similar data transformation as previously described is not performed as it utilises a sliding window of specific size, which is run on a single feature corresponding to an entire scooter ride. The result show that it is possible to isolate one type of abnormal riding behaviour using the autoencoder model. Additionally, the simple moving average model can also be utilised to detect the behaviour in question. Out of the two models, it is recommended to deploy a simple moving average due to its simplicity. / Den globala mikromobilitetsmarknaden är en snabbt växande marknad som år 2020 värderades till 40,19 miljarder USD. I takt med att marknaden växer så ökar också kraven bland företag att erbjuda produkter och tjänster av hög kvalitet, för att  erhålla en stark position på marknaden, vara konkurrenskraftiga och förbli ett förstahandsval hos sina kunder. Detta kan uppnås genom att bland annat erbjuda mikromobilitetstjänster som är säkra, för både föraren och andra trafikanter. Med hjälp av den senaste tekniken kan olyckor förebyggas och skadligt bruk av skotrar och städers infrastruktur förhindras. Följande studie utförs i samarbete med Voi Technology, ett svenskt mikromobilitetsföretag som har åtagit sig ansvaret att eliminera samtliga allvarliga skador och dödsfall i deras värdekedja till och med år 2030. I linje med en sådan ambition, är syftet med avhandlingen att utvärdera möjligheten att använda oövervakad maskininlärning för anomalidetektering bland sensordata, för att särskilja onormala och normala körbeteenden. Studien utvärderar två maskininlärningsalgoritmer; isolation forest och artificiella neurala nätverk, mer specifikt autoencoders. Utöver att bedöma modellernas förmåga att upptäcka onormala körbeteenden i allmänhet, utvärderas modellerna utifrån deras förmåga att hitta särskilda körbeteenden. Genom att simulera olika onormala körbeteenden kan modellerna evalueras. Dataförberedelsen som utförs för modellerna inkluderar omvandling av den råa tidsseriedatan till icke överlappande fönster av specifik storlek, bestående av beskrivande statistik. Det erhållna resultatet visar att varken isolation forest eller autoencodern presterar som förväntat samt att önskan om att hitta en generell modell som klarar av att detektera anomalier av olika karaktär inte verkar uppfyllas. Vidare indikerar resultatet på att ett visst onormalt körbeteende verkar enklare att särskilja än resterande, vilket motiverar att utvärdera modeller skapade i syfte att detektera det specifika beteendet. Följaktligen implementeras därför ett glidande medelvärde för att utforska prestandan hos en mycket grundläggande prediktionsmetod. För denna metod utförs inte den tidigare nämnda datatransformationen eftersom metoden använder ett glidande medelvärde som appliceras på en variabel tillhörande en fullständig åktur.  Följande analys visar att autoencoder modellen klarar av att urskilja denna typ av onormalt körbeteende. Resultatet visar även att ett glidande medelvärde klarar av att detektera körbeteendet i fråga. Av de två modellerna rekommenderas en implementering av ett glidande medelvärdet på grund av dess enkelhet.

Predicting events based on available data is an effective way to protect human lives. Issuing health alert based on prediction of environmental pollution, executing timely evacuation of people from vulnerable areas based on prediction of natural disasters are the application areas of sensor data stream where accurate and timely prediction is crucial to safeguard people and assets. Thus, prediction accuracy plays a significant role to take precautionary measures and minimize the extent of damage. Belief rule-based Expert System (BRBES) is a rule-driven approach to perform accurate prediction based on knowledge base and inference engine. It outperforms other such knowledge-driven approaches, such as, fuzzy logic, Bayesian probability theory in terms of dealing with uncertainties. On the other hand, Deep Learning is a data-driven approach which belongs to Artificial Intelligence (AI) domain. Deep Learning discovers hidden data pattern by performing analytics on huge amount of data. Thus, Deep Learning is also an effective way to predict events based on available data, such as, historical data and sensor data streams. Integration of Deep Learning with BRBES can improve prediction accuracy further as one can address the inefficiency of the other to bring down error gap. We have taken air pollution prediction as the application area of our proposed integrated approach. Our combined approach has shown higher accuracy than relying only on BRBES and only on Deep Learning. / <p>This is a Master Thesis Report as part of degree requirement of Erasmus Mundus Joint Master Degree (EMJMD) in Pervasive Computing and Communications for Sustainable Development (PERCCOM).</p>

Indiana University-Purdue University Indianapolis (IUPUI) / Indoor tracking is a challenging problem. The level of accepted error is on a much smaller scale than that of its outdoor counterpart. While the global positioning system has become omnipresent, and a widely accepted outdoor tracking system it has limitations in indoor environments due to loss or degradation of signal. Many attempts have been made to address this challenge, but currently none have proven to be the de-facto standard. In this thesis, we introduce the concept of opportunistic tracking in which tracking takes place with whatever sensing infrastructure is present – static or mobile, within a given indoor environment. In this approach many of the challenges (e.g., high cost, infeasible infrastructure deployment, etc.) that prohibit usage of existing systems in typical application domains (e.g., asset tracking, emergency rescue) are eliminated. Challenges do still exist when it comes to provide an accurate positional estimate of an entities location in an indoor environment, namely: sensor classification, sensor selection, and multi-sensor data fusion. We propose an enhanced tracking framework that through the infusion of QoS-based selection criteria of trust and reliability we can improve the overall accuracy of the tracking estimate. This improvement is predicated on the introduction of learning techniques to classify sensors that are dynamically discovered as part of this opportunistic tracking approach. This classification allows for sensors to be properly identified and evaluated based upon their specific behavioral characteristics through performance evaluation. This in-depth evaluation of sensors provides the basis for improving the sensor selection process. A side effect of obtaining this improved accuracy is the cost, found in the form of system runtime. This thesis provides a solution for this tradeoff between accuracy and cost through an optimization function that analyzes this tradeoff in an effort to find the optimal subset of sensors to fulfill the goal of tracking an object as it moves indoors. We demonstrate that through this improved sensor classification, selection, data fusion, and tradeoff optimization we can provide an improvement, in terms of accuracy, over other existing indoor tracking systems.