Behavioural pattern identification and prediction in intelligent environments

In this paper, the application of soft computing techniques in prediction of an occupant's behaviour in an inhabited intelligent environment is addressed. In this research, daily activities of elderly people who live in their own homes suffering from dementia are studied. Occupancy sensors are used to extract the movement patterns of the occupant. The occupancy data is then converted into temporal sequences of activities which are eventually used to predict the occupant behaviour. To build the prediction model, different dynamic recurrent neural networks are investigated. Recurrent neural networks have shown a great ability in finding the temporal relationships of input patterns. The experimental results show that non-linear autoregressive network with exogenous inputs model correctly extracts the long term prediction patterns of the occupant and outperformed the Elman network. The results presented here are validated using data generated from a simulator and real environments

When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

Probabilistic Analysis of Temporal and Sequential Aspects of Activities of Daily Living for Abnormal Behaviour Detection

This paper presents a probabilistic approach for the identification of abnormal behaviour in Activities of Daily Living (ADLs) from dense sensor data collected from 30 participants. The ADLs considered are related to preparing and drinking (i) tea, and (ii) coffee. Abnormal behaviour identified in the context of these activities can be an indicator of a progressive health problem or the occurrence of a hazardous incident. The approach presented considers the temporal and sequential aspects of the actions that are part of each ADL and that vary between participants. The average and standard deviation for the duration and number of steps of each activity are calculated to define the average time and steps and a range within which a behaviour could be considered as normal for each stage and activity. The Cumulative Distribution Function (CDF) is used to obtain the probabilities of abnormal behaviours related to the early and late completion of activities and stages within an activity in terms of time and steps. Analysis shows that CDF can provide precise and reliable results regarding the presence of abnormal behaviour in stages and activities that last over a minute or consist of many steps. Finally, this approach could be used to train machine learning algorithms for abnormal behaviour detection.status: publishe

A Comparative Analysis of Human Behavior Prediction Approaches in Intelligent Environments

Behavior modeling has multiple applications in the intelligent environment domain. It has been used in different tasks, such as the stratification of different pathologies, prediction of the user actions and activities, or modeling the energy usage. Specifically, behavior prediction can be used to forecast the future evolution of the users and to identify those behaviors that deviate from the expected conduct. In this paper, we propose the use of embeddings to represent the user actions, and study and compare several behavior prediction approaches. We test multiple model (LSTM, CNNs, GCNs, and transformers) architectures to ascertain the best approach to using embeddings for behavior modeling and also evaluate multiple embedding retrofitting approaches. To do so, we use the Kasteren dataset for intelligent environments, which is one of the most widely used datasets in the areas of activity recognition and behavior modeling.This work was carried out with the financial support of FuturAAL-Ego (RTI2018-101045-A-C22) and FuturAAL-Context (RTI2018-101045-B-C21) granted by Spanish Ministry of Science, Innovation and Universities

Leveraging the heterogeneity of the internet of things devices to improve the security of smart environments

The growing number of devices that are being incorporated into the Internet of Things (IoT) environments leads to a wider presence of a variety of sensors, making these environments heterogeneous. However, the lack of standard input interfaces in such ecosystems poses a challenge in securing them. Among other existing vulnerabilities, the most prevalent are the lack of adequate access control mechanisms and the exploitation of cross-channel interactions between smart devices. In order to tackle the first challenge, I propose a novel behavioral biometric system based on naturally occurring interactions with objects in smart environments. This system is designed to reduce the reliance on existing app-based authentication mechanisms of current smart home platforms and it leverages existing heterogeneous IoT devices to both identify and authenticate users without requiring any hardware modifications of existing smart home devices. To be able to collect the data and evaluate this system, I introduce an end-to-end framework for remote experiments. Such experiments play an important role across multiple fields of studies, from medical science to engineering, as they allow for better representation of human participants and more realistic experimental environments, and ensure research continuity in exceptional circumstances, such as nationwide lockdowns. Yet cyber security has few standards for conducting experiments with human participants, let alone in a remote setting. This framework systematizes design and deployment practices while preserving realistic, reproducible data collection and the safety and privacy of participants. Using this methodology, I conduct two experiments. The first one is a multi-user study taking place in six households composed of 25 participants. The second experiment involves 13 participants in a company environment and is used to study mimicry attacks on the biometric system proposed in this thesis. I demonstrate that this system can identify users in multi-user environments with an accuracy of at least 98% for a single object interaction without requiring any sensors on the object itself. I also show that it can provide seamless and unobtrusive authentication while remaining highly resistant to zero-effort, video, and in-person observation-based mimicry attacks. Even when at most 1% of the strongest type of mimicry attacks are successful, this system does not require the user to take out their phone to approve legitimate transactions in more than 80% of cases for a single interaction. This increases to 92% of transactions when interactions with more objects are considered. To mitigate the second vulnerability, where an attacker exploits multiple heterogeneous devices in a chain such that each one triggers the next, I propose a novel approach that uses only dynamic analysis to examine such interactions in smart ecosystems. I use real-time device data to generate a knowledge graph that models the interactions between devices and enables the system to identify attack chains and vulnerable automations. I evaluate this approach in a smart home environment with 8 devices and 10 automations, with and without the presence of an active user. I demonstrate that such a system can accurately detect 10 cross-channel interactions that lead to 30 different cross-channel interaction chains in the unoccupied environment and 6 such interactions that result in 13 interaction chains in the occupied environment

Ambient Intelligence As The Bridge To The Future of Pervasive Computing

One prediction about this future of pervasive technology is that people will carry the tools needed to interface with technological resources sprinkled through out the environment. A problem with this vision is the dark side of the network effect: early adopters will end up carrying around interfaces for technology that largely does not yet exist, and building managers will question the value of installing technology with features that almost no one will be able to use. An intermediate solution is that certain buildings with specific needs for efficiency or security (such as hospitals) may become smart, with technology insinuated into particular spaces. Since many, or even most of the people in these spaces will not have the technology to interface directly with the new pervasive resources, we must think of the interaction idiom as initially being closer to the notion of smart environments. These environments will have to sense, interpret, and facilitate the actions of the inhabitants, possibly with very little help from technology attached to the people involved, or even their cooperation. We survey a body of work on perceptual tools for smart buildings, built on the sensor network model, and focused on the idea that statistical methods and population dynamics can provide valuable information even in situations where detection of individual instances of behavior may be difficult to detect. These are some of the tools which will fuel the building optimization applications that will justify the efforts of early adopters to build smart buildings studded with pervasive technology

A data-driven situation-aware framework for predictive analysis in smart environments

In the era of Internet of Things (IoT), it is vital for smart environments to be able to efficiently provide effective predictions of user’s situations and take actions in a proactive manner to achieve the highest performance. However, there are two main challenges. First, the sensor environment is equipped with a heterogeneous set of data sources including hardware and software sensors, and oftentimes complex humans as sensors, too. These sensors generate a huge amount of raw data. In order to extract knowledge and do predictive analysis, it is necessary that the raw sensor data be cleaned, understood, analyzed, and interpreted. Second challenge refers to predictive modeling. Traditional predictive models predict situations that are likely to happen in the near future by keeping and analyzing the history of past user’s situations. Traditional predictive analysis approaches have become less effective because of the massive amount of data that both affects data processing efficiency and complicates the data semantics. In this study, we propose a data-driven, situation-aware framework for predictive analysis in smart environments that addresses the above challenges