CONFIDENCE-BASED DECISION-MAKING SUPPORT FOR MULTI-SENSOR SYSTEMS

We live in a world where computer systems are omnipresent and are connected to more and more sensors. Ranging from small individual electronic assistants like smartphones to complex autonomous robots, from personal wearable health devices to professional eHealth frameworks, all these systems use the sensors’ data in order to make appropriate decisions according to the context they measure. However, in addition to complete failures leading to the lack of data delivery, these sensors can also send bad data due to influences from the environment which can sometimes be hard to detect by the computer system when checking each sensor individually. The computer system should be able to use its set of sensors as a whole in order to mitigate the influence of malfunctioning sensors, to overcome the absence of data coming from broken sensors, and to handle possible conflicting information coming from several sensors. In this thesis, we propose a computational model based on a two layer software architecture to overcome this challenge. In a first layer, classification algorithms will check for malfunctioning sensors and attribute a confidence value to each sensor. In the second layer, a rule-based proactive engine will then build a representation of the context of the system and use it along some empirical knowledge about the weaknesses of the different sensors to further tweak this confidence value. Furthermore, the system will then check for conflicting data between sensors. This can be done by having several sensors that measure the same parameters or by having multiple sensors that can be used together to calculate an estimation of a parameter given by another sensor. A confidence value will be calculated for this estimation as well, based on the confidence values of the related sensors. The successive design refinement steps of our model are shown over the course of three experiments. The first two experiments, located in the eHealth domain, have been used to better identify the challenges of such multi-sensor systems, while the third experiment, which consists of a virtual robot simulation, acts as a proof of concept for the semi-generic model proposed in this thesis

Principles and design of global proactive scenarios over a network of proactive engines

This thesis examines Global Proactive Scenarios (GPaSs) in the context of proactive computing. GPaSs are Proactive Scenarios (PaSs) that dynamically collect information, provide strategies for cooperative reasoning and support collective decision making [1]. More precisely we will extract properties from GPaSs and define them. Based on these properties we will then create templates for GPaSs, which will help to facilitate and standardize the creation of future GPaSs. The applicability of these templates is showed through the design of GPaSs for three example applications and finally we will implement one of these applications as a proof of concept example to showcase the usage of the templates in the real world

A rule-based approach for self-optimisation in autonomic eHealth systems

Advances in machine learning techniques in recent years were of great benefit for the detection of diseases/medical conditions in eHealth systems, but only to a limited extend. In fact, while for the detection of some diseases the data mining techniques were performing very well, they still got outperformed by medical experts in about half of the tests done. In this paper, we propose a hybrid approach, which will use a rule-based system on top of the machine learning techniques in order to optimise the results of conflict handling. The goal is to insert the knowledge from medical experts in order to optimise the results given by the classification techniques. Possible positive and negative effects will be discussed

Proactive Model for Handling Conflicts in Sensor Data Fusion Applied to Robotic Systems

Robots have to be able to function in a multitude of different situations and environments. To help them achieve this, they are usually equipped with a large set of sensors whose data will be used in order to make decisions. However, the sensors can malfunction, be influenced by noise or simply be imprecise. Existing sensor fusion techniques can be used in order to overcome some of these problems, but we believe that data can be improved further by computing context information and using a proactive rule-based system to detect potentially conflicting data coming from different sensors. In this paper we will present the architecture and scenarios for a generic model taking context into account

Using Hidden Markov Models and Rule-based Sensor Mediation on Wearable eHealth Devices

Improvements in sensor miniaturization allow wearable devices to provide more functionality while also being more comfortable for users to wear. The Samsung Simband©, for example, has 6 different sensors Electrocardiogram (ECG), Photoplethysmogram (PPG), Galvanic Skin Response (GSR), Bio-Impedance (Bio-Z), Accelerometer and a thermometer as well as a modular sensor hub to easily add additional ones. This increased number of sensors for wearable devices opens new possibilities for a more precise monitoring of patients by integrating the data from the different sensors. This integration can be influenced by failing or malfunctioning sensors and noise. In this paper, we propose an approach that uses Hidden Markov Models (HMM) in combination with a rule-based engine to mediate among the different sensors’ data in order to allow the eHealth system to compute a diagnosis on the basis of the selected reliable sensors. We also show some preliminary results about the accuracy of the first stage of the proposed model

Conflict handling for autonomic systems

Technological advances in recent years lead to the miniaturization of a whole arsenal of different sensors. They can be used to offer new services in eHealth applications, smart homes, robotics or smart cities. With the increasing diversity and the cooperation needed between these sensors in order to provide the best possible services to the user the systems that use the data coming from these sensors need to be able to handle conflicting information and thus also conflicting actions. In this paper we propose an approach that uses Hidden Markov Models in a first step to analyse the incoming data and in a second step uses a rule engine in order to handle the occurring conflicts

Enhancing Mobile Devices with Cooperative Proactive Computing

Abstract-With the increasing popularity of smartphones and with the fact that they are connected to the Internet most of the time, people manage to stay online everywhere they go. They can access online services remotely at any time they want, using their mobile devices. However, in order to make the best out of these circumstances, the users have to use sophisticated mobile applications. These applications do not have to only address key aspects like collaboration and cooperation between various devices but have to deal also with the involvement of the users in order to achieve the desired outcome. The main contribution of this paper is to present a solution, i.e., Proactive Engine for Mobile Devices (PEMD), together with its implementation for Androidbased systems, for enhancing mobile devices with proactive properties. The model serves as a basis for developing smart applications that are able to perform complex real-world tasks. Furthermore, it provides a method for achieving cooperation, coordination and collaboration of multiple smart devices. Finally, we provide the performance experiments and we discuss the results and the effects of using PEMD on different devices

SilentMeet - A Prototype Mobile Application for Real-Time Automated Group-Based Collaboration

Today’s growing world of mobile devices offers all the necessary elements for developing collaborative mobile applications. However, this brings new challenges like how to handle the high complexity of efficient collaborative mechanisms or automatize part of the user’s interaction with the applications, as too many actions are required from the users in order to perform even the most basic operations. This paper describes an experimental mobile application, i.e., SilentMeet, that uses a rule-based middleware architecture for mobile devices and a new technique for exchanging information, for coordinating and for taking distributed decisions. More precisely, the application is designed to detect, based on collaboration, possible meetings or events with more than 2 participants and automatically switch the smartphone into silent mode. The goal of SilentMeet can be divided into 2 two main parts: 1) to develop a collaborative application with the help of rule-based systems; and 2) implement and evaluate Global Proactive Scenarios (GPaSs) in a real-case example

A Context-Aware Collaborative Mobile Application for Silencing the Smartphone during Meetings or Important Events

This study describes a mobile application, i.e., Silent-Meet, that uses group-driven collaboration and location-based collaboration for automatically switching smartphones into silent mode during meetings or important events. More precisely, for the first step of the collaboration, a partial agreement algorithm will be used for establishing if a meeting is confirmed by its participants and, for the second round, confirming if the meeting will take place, based on the location of the participants. The application tries to avoid those cases when a meeting is accepted but the participants are not coming to the meeting or when participants do not reply to the meeting invitations but they are still attending the meeting. SilentMeet uses a new technique for exchanging information, for coordinating and for taking distributed decisions, called Global Proactive Scenarios (GPaSs). For executing GPaSs, a rule-based middleware architecture for mobile devices is utilised. GPaSs and the middleware architecture allow developers of collaborative applications to define the actions of their applications in a structured way without having to take care of the communication and coordination of the mobile devices. Also, there is no need for developing a server-side application; all the logic is integrated into GPaSs. Apart the main goal of the application, which is to silence mobile phones during meetings, there are three secondary objectives: a) to provide an collaborative application capable of acquiring contextual information from various devices, b) to check if it is possible to achieve collective reasoning using a rule-based middleware architecture for mobile devices, and c) to validate GPaSs in a real-case example

Enhancing Mobile Devices with Cooperative Proactive Computing

With the increasing popularity of smartphones and with the fact that they are connected to the Internet most of the time, people manage to stay online everywhere they go. They can access online services remotely at any time they want, using their mobile devices. However, in order to make the best out of these circumstances, the users have to use sophisticated mobile applications. These applications do not have to only address key aspects like collaboration and cooperation between various devices but have to deal also with the involvement of the users in order to achieve the desired outcome. The main contribution of this paper is to present a solution, i.e., Proactive Engine for Mobile Devices (PEMD), together with its implementation for Android based systems, for enhancing mobile devices with proactive roperties. The model serves as a basis for developing smart applications that are able to perform complex real-world tasks. Furthermore, it provides a method for achieving cooperation, coordination and collaboration of multiple smart devices. Finally, we provide the performance experiments and we discuss the results and the effects of using PEMD on different devices