Modelling and verification of ambient systems using petri nets

PhD ThesisThe expeditious development of technology in the past decades re- sulted in the introduction of concurrent systems that incorporate both ubiquitous and pervasive computing, the ambient systems. These sys- tems are named after their ability to be completely embedded in the environment in which they operate and interact with the users, in a silent and non distracting way, facilitating the completion of their tasks. Hence, there is a growing need to introduce and develop formal tech- niques for computational models capable of faithfully modelling the behaviour of these systems. One way of capturing the intricate be- haviours of the ambient systems is to use Petri nets, which are a modelling language that is used for the representation and analysis of concurrent systems. Within the domain of rigorous system design, veri cation of systems e ectively checks and guarantees the correctness of the examined mod- els with respect to the speci cation. This work investigates the modelling and the analysis of ambient sys- tems using Petri nets. To examine the modelling of these systems, their taxonomy into Ambient Guidance Systems and Ambient Infor- mation Systems is carried out and a case study is used for the mod- elling of each category. To model ambient systems, the step-modelling approach and a vari- ant class of Coloured Petri Nets, the Ambient Petri Nets (APNs), are introduced. Step modelling approach focuses on the interaction be- tween the system and the user and Ambient Petri Nets is a class of nets with colour-sensitive inhibitor arcs that is used especially for the structural and behavioural representation of ambient systems. For the modelling of general ambient systems, the compositionality of the Ambient Petri Nets is used. To verify the correctness of the produced Ambient Petri Nets models, the introduction of the Transformed Ambient Petri Nets class that has no colour-sensitive inhibitor arcs is required since Charlie and generally most of the existing veri cation tools do not support the analysis of inhibitor nets. To address this problem, a construction is de ned to translate the Ambient Petri Nets into Transformed Ambient Petri Nets. Afterwards, the Step Transition Systems are used to prove the behavioural equivalence of the nets that are associated through the construction. Subsequently, the Transformed Ambient Petri Nets models of the cho- sen case studies are veri ed against model checking and qualitative properties. For the rst category, Computation Tree Logic (CTL) is used to check the models against important properties of the ambient systems that are related to their features and their general function- ing. Finally, qualitative properties consider fundamental structural and behavioural properties of Petri nets that provide useful outcome about the systems under consideration

A survey of petri nets slicing

Petri nets slicing is a technique that aims to improve the verification of systems modeled in Petri nets. Petri nets slicing was first developed to facilitate debugging but then used for the alleviation of the state space explosion problem for the model checking of Petri nets. In this article, different slicing techniques are studied along with their algorithms introducing: i) a classification of Petri nets slicing algorithms based on their construction methodology and objective (such as improving state space analysis or testing), ii) a qualitative and quantitative discussion and comparison of major differences such as accuracy and efficiency, iii) a syntactic unification of slicing algorithms that improve state space analysis for easy and clear understanding, and iv) applications of slicing for multiple perspectives. Furthermore, some recent improvements to slicing algorithms are presented, which can certainly reduce the slice size even for strongly connected nets. A noteworthy use of this survey is for the selection and improvement of slicing techniques for optimizing the verification of state event models

IoT-based Activities of Daily Living for Abnormal Behaviour Detection: Privacy Issues and Potential Countermeasures

Activities of Daily Living (ADL) systems have been playing an important role in assessing and monitoring the quality of life of elderly people for many years. With the recent advancement and integration of Internet of Things (IoT) devices within the ADL systems, the number and quality of services offered has increased significantly. One of these vital services is abnormal behaviour detection based on the data collected from IoT devices within smart homes. However, the IoT data collected could have enormous privacy implications on smart home users if the data is not handled properly. We address this issue by analysing a generic ADL system for abnormal behaviour detection, including its entities and their interactions. We highlight three major privacy issues: (i) identity privacy, (ii) data confidentiality, and (iii) metadata data leakage. These issues are particularly relevant to ADL systems and we propose potential countermeasures to tackle them. Finally, we sketch a privacy-preserving version of a state-of-the-art ADL system to demonstrate the effectiveness of our proposed countermeasures, before suggesting future research directions

Conditional Random Field Feature Generation of Smart Home Sensor Data using Random Forests

A typical approach to building a feature set for a conditionalrandom field model is to build a large set of conjunctions of atomic tests, all of which adhere to a small number of relatively simple templates. Building more complex features in this way can be difficult, as the more complex templates needed to do this can result in a combinatoric explosion in the number of features. We use the inherent instability of decision trees to produce a small set of more complex conjunctions that are particularly suitable for the problem to be solved, using the same techniques used in generating random forest ensemble classifiers, andbuild a CRF on these features. We apply this method to an activityrecognition problem on a dataset from the CASAS smart home project, in which we predict activities of daily living from sensor activations

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

Modelling Activities of Daily Living with Petri nets

Modelling Activities of Daily Living (ADLs) is an important step in the process to design and implement reliable sensor systems that effectively monitor the activities of the ageing population. Once modelled, unusual activities may be detected that have the potential of impacting upon a person's well-being. The use of Petri nets to model ADLs is considered in this research as a means to capture the intricate behaviours of ambient systems. To our best knowledge there has not been extensive work in the related literature, hence the novelty of this work. The ADLs considered in the developed Petri net model are: (i) preparing tea, (ii) preparing coffee, and (iii) preparing pasta. The first two ADLs listed are deemed to have many occurrences during a typical day of an elderly person. The third activity is representative of activities that involve cooking. Hence, abnormal behaviour detected in the context of these activities can be an indicator of a progressive health problem or the occurrence of a hazardous incident. The completion and non- completion of activities are considered in the developed Petri net model and are also formally verified. The description of the sensor system of the kitchen ADLs, its Petri net model and verification results are presented. Results show that the Petri net modelling of ADLs can reliably and effectively reflect the real behaviour of the examined system detecting all the activities of the users that can exhibit both their normal and abnormal behaviour

Fusion of Unobtrusive Sensing Solutions for Home-Based Activity Recognition and Classification using Data Mining Models and Methods

This paper proposes the fusion of Unobtrusive Sensing Solutions (USSs) for human Activity Recognition and Classification (ARC) in home environments. It also considers the use of data mining models and methods for cluster-based analysis of datasets obtained from the USSs. The ability to recognise and classify activities performed in home environments can help monitor health parameters in vulnerable individuals. This study addresses five principal concerns in ARC: (i) users’ privacy, (ii) wearability, (iii) data acquisition in a home environment, (iv) actual recognition of activities, and (v) classification of activities from single to multiple users. Timestamp information from contact sensors mounted at strategic locations in a kitchen environment helped obtain the time, location, and activity of 10 participants during the experiments. A total of 11,980 thermal blobs gleaned from privacy-friendly USSs such as ceiling and lateral thermal sensors were fused using data mining models and methods. Experimental results demonstrated cluster-based activity recognition, classification, and fusion of the datasets with an average regression coefficient of 0.95 for tested features and clusters. In addition, a pooled Mean accuracy of 96.5% was obtained using classification-by-clustering and statistical methods for models such as Neural Network, Support Vector Machine, K-Nearest Neighbour, and Stochastic Gradient Descent on Evaluation Test

Probabilistic Analysis of Abnormal Behaviour Detection in Activities of Daily Living

This paper presents a probabilistic approach for the identification of abnormal behaviour in Activities of Daily Living (ADLs) from sensor data collected from 30 participants. The ADLs considered are: (i) preparing and drinking tea, and (ii) preparing and drinking 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 aspect of the sequences of actions that are part of each ADL and that vary between participants. The average and standard deviation for the durations of each action were calculated to define an average time and a range in which a behaviour could be considered as normal for each stage and activity. The Cumulative Distribution Function (CDF) was used to obtain the probabilities of abnormal behaviours related to the early and late completion of activities and stages within an activity. The data analysis show that CDF can provide accurate and reliable results regarding the presence of abnormal behaviour in stages and activities that last over a minute. Finally, this approach could be used to train machine learning algorithms for the abnormal behaviour detection

Fusion of Unobtrusive Sensing Solutions for Home-Based Activity Recognition and Classification Using Data Mining Models and Methods

From MDPI via Jisc Publications RouterHistory: accepted 2021-09-24, pub-electronic 2021-09-29Publication status: PublishedFunder: interreg VA; Grant(s): IVA5034This paper proposes the fusion of Unobtrusive Sensing Solutions (USSs) for human Activity Recognition and Classification (ARC) in home environments. It also considers the use of data mining models and methods for cluster-based analysis of datasets obtained from the USSs. The ability to recognise and classify activities performed in home environments can help monitor health parameters in vulnerable individuals. This study addresses five principal concerns in ARC: (i) users’ privacy, (ii) wearability, (iii) data acquisition in a home environment, (iv) actual recognition of activities, and (v) classification of activities from single to multiple users. Timestamp information from contact sensors mounted at strategic locations in a kitchen environment helped obtain the time, location, and activity of 10 participants during the experiments. A total of 11,980 thermal blobs gleaned from privacy-friendly USSs such as ceiling and lateral thermal sensors were fused using data mining models and methods. Experimental results demonstrated cluster-based activity recognition, classification, and fusion of the datasets with an average regression coefficient of 0.95 for tested features and clusters. In addition, a pooled Mean accuracy of 96.5% was obtained using classification-by-clustering and statistical methods for models such as Neural Network, Support Vector Machine, K-Nearest Neighbour, and Stochastic Gradient Descent on Evaluation Test