Temporal Clustering for Behavior Variation and Anomaly Detection from Data Acquired Through IoT in Smart Cities

In this chapter, we propose a methodology for behavior variation and anomaly detection from acquired sensory data, based on temporal clustering models. Data are collected from five prominent European smart cities, and Singapore, that aim to become fully “elderly-friendly,” with the development and deployment of ubiquitous systems for assessment and prediction of early risks of elderly Mild Cognitive Impairments (MCI) and frailty, and for supporting generation and delivery of optimal personalized preventive interventions that mitigate those risks, utilizing smart city datasets and IoT infrastructure. Low level data collected from IoT devices are preprocessed as sequences of activities, with temporal and causal variations in sequences classified as normal or anomalous behavior. The goals of proposed methodology are to (1) recognize significant behavioral variation patterns and (2) support early identification of pattern changes. Temporal clustering models are applied in detection and prediction of the following variation types: intra-activity (single activity, single citizen) and inter-activity (multiple-activities, single citizen). Identified behavioral variations and anomalies are further mapped to MCI/frailty onset behavior and risk factors, following the developed geriatric expert model

Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives

Enormous amounts of data are being produced everyday by sub-meters and smart sensors installed in residential buildings. If leveraged properly, that data could assist end-users, energy producers and utility companies in detecting anomalous power consumption and understanding the causes of each anomaly. Therefore, anomaly detection could stop a minor problem becoming overwhelming. Moreover, it will aid in better decision-making to reduce wasted energy and promote sustainable and energy efficient behavior. In this regard, this paper is an in-depth review of existing anomaly detection frameworks for building energy consumption based on artificial intelligence. Specifically, an extensive survey is presented, in which a comprehensive taxonomy is introduced to classify existing algorithms based on different modules and parameters adopted, such as machine learning algorithms, feature extraction approaches, anomaly detection levels, computing platforms and application scenarios. To the best of the authors' knowledge, this is the first review article that discusses anomaly detection in building energy consumption. Moving forward, important findings along with domain-specific problems, difficulties and challenges that remain unresolved are thoroughly discussed, including the absence of: (i) precise definitions of anomalous power consumption, (ii) annotated datasets, (iii) unified metrics to assess the performance of existing solutions, (iv) platforms for reproducibility and (v) privacy-preservation. Following, insights about current research trends are discussed to widen the applications and effectiveness of the anomaly detection technology before deriving future directions attracting significant attention. This article serves as a comprehensive reference to understand the current technological progress in anomaly detection of energy consumption based on artificial intelligence.Comment: 11 Figures, 3 Table

Simplified Video Surveillance Framework for Dynamic Object Detection under Challenging Environment

An effective video surveillance system is highly essential in order to ensure constructing better form of video analytics. Existing review of literatures pertaining to video analytics are found to directly implement algorithms on the top of the video file without much emphasis on following problems i.e. i) dynamic orientation of subject, ii)poor illumination condition, iii) identification and classification of subjects, and iv) faster response time. Therefore, the proposed system implements an analytical concept that uses depth-image of the video feed along with the original colored video feed to apply an algorithm for extracting significant information about the motion blob of the dynamic subjects. Implemented in MATLAB, the study outcome shows that it is capable of addressing all the above mentioned problems associated with existing research trends on video analytics by using a very simple and non-iterative process of implementation. The applicability of the proposed system in practical world is thereby proven

Malfunction diagnosis in industrial process systems using data mining for knowledge discovery

The determination of abnormal behavior at process industries gains increasing interest as strict regulations and highly competitive operation conditions are regularly applied at the process systems. A synergetic approach in exploring the behavior of industrial processes is proposed, targeting at the discovery of patterns and implement fault detection (malfunction) diagnosis. The patterns are based on highly correlated time series. The concept is based on the fact that if independent time series are combined based on rules, we can extract scenarios of functional and non-functional situations so as to monitor hazardous procedures occurring in workplaces. The selected methods combine and apply actions on historically stored, experimental data from a chemical pilot plant, located at CERTH/CPERI. The implementation of the clustering and classification methods showed promising results of determining with great accuracy (97%) the potential abnormal situations