A Role-Based Approach for Orchestrating Emergent Configurations in the Internet of Things

The Internet of Things (IoT) is envisioned as a global network of connected things enabling ubiquitous machine-to-machine (M2M) communication. With estimations of billions of sensors and devices to be connected in the coming years, the IoT has been advocated as having a great potential to impact the way we live, but also how we work. However, the connectivity aspect in itself only accounts for the underlying M2M infrastructure. In order to properly support engineering IoT systems and applications, it is key to orchestrate heterogeneous 'things' in a seamless, adaptive and dynamic manner, such that the system can exhibit a goal-directed behaviour and take appropriate actions. Yet, this form of interaction between things needs to take a user-centric approach and by no means elude the users' requirements. To this end, contextualisation is an important feature of the system, allowing it to infer user activities and prompt the user with relevant information and interactions even in the absence of intentional commands. In this work we propose a role-based model for emergent configurations of connected systems as a means to model, manage, and reason about IoT systems including the user's interaction with them. We put a special focus on integrating the user perspective in order to guide the emergent configurations such that systems goals are aligned with the users' intentions. We discuss related scientific and technical challenges and provide several uses cases outlining the concept of emergent configurations.Comment: In Proceedings of the Second International Workshop on the Internet of Agents @AAMAS201

An optimization model for group formation in project-based learning

We propose an optimization model to tackle the problem of determining how projects are assigned to student groups based on a bidding procedure. In order to improve student experience in project-based learning we resort to actively involving them in a transparent and unbiased project allocation process. To evaluate our work, we collected information about the students\u27 own views on how our approach influenced their level of learning and overall learning experience and provide a detailed analysis of the results. The results of our evaluation show that the large majority of students (i.e., 91%) increased or maintained their satisfaction ratings with the proposed procedure after the assignment was concluded, as compared to their attitude towards the process before the project assignment occurred

Integration of Smart Home Technologies for District Heating Control in Pervasive Smart Grids

Pervasive technologies permeating our immediate surroundings provide a wide variety of low-cost means of sensing and actuating in our environment. This paper presents an approach for leveraging insights onto the lifestyle and routines of the users in order to control heating in a smart home through the use of individual climate zones, while ensuring system efficiency at a grid-level scale. Organizing smart living spaces into controllable individual climate zones allows us to exert a more fine-grained level of control. Thus, the system can benefit from a higher degree of freedom to adjust the heat demand according to the system objectives. Whereas district heating planing is only concerned with balancing heat demand among buildings, we extend the reach of these systems inside the home through the use of pervasive sensing and actuation. That is to say, we bridge the gap between traditional district heating systems and pervasive technologies in the home designed to maintain the thermal comfort of the user, in order to increase efficiency. The objective is to automate heating based on the user's preferences and behavioral patterns. The control scheme proposed applies a learning algorithm to take advantage of the sensing data inside the home in combination with an optimization procedure designed to trade-off the discomfort undertaken by the user and heating supply costs. We report on preliminary simulation results showing the effectiveness of our approach and describe the setup of our forthcoming field study

An investigation of transfer learning for deep architectures in group activity recognition

Pervasive technologies permeating our immediate surroundings provide a wide variety of means for sensing and actuating in our environment, having a great potential to impact the way we live, but also how we work. In this paper, we address the problem of activity recognition in office environments, as a means for inferring contextual information in order to automatically and proactively assists people in their daily activities. To this end we employ state-of-the-art image processing techniques and evaluate their capabilities in a real-world setup. Traditional machine learning is characterized by instances where both the training and test data share the same distribution. When this is not the case, the performance of the learned model is deteriorated. However, often times, the data is expensive or difficult to collect and label. It is therefore important to develop techniques that are able to make the best possible use of existing data sets from related domains, relative to the target domain. To this end, we further investigate in this work transfer learning techniques in deep learning architectures for the task of activity recognition in office settings. We provide herein a solution model that attains a 94% accuracy under the right conditions

An Investigation of Context-Aware Object Detection based on Scene Recognition

Visual scene understanding for humans entails identifying regions of interest and then reasoning about information gained through their contextual analysis. Attempts to reproduce this process using Computer Vision techniques play an important role in improving the performance of Object Detection and general scene understanding algorithms. In this paper, we augment the training algorithm of an Object Detection model by integrating contextual information in the form of ‘scene labels’, through various methods to identify the superior approach. Several deep learning models were implemented and evaluated; results show that the Contextually Aware Object Detection Model performed the best, producing the highest classification accuracy of 75.14% and mean bounding box IoU of 0.69 on the constructed dataset. This was achieved by virtue of an auxiliary scene classification model used to make image scene predictions during the training phase

Multimodal Deep Learning for Group Activity Recognition in Smart Office Environments

Deep learning (DL) models have emerged in recent years as the state-of-the-art technique across numerous machine learning application domains. In particular, image processing-related tasks have seen a significant improvement in terms of performance due to increased availability of large datasets and extensive growth of computing power. In this paper we investigate the problem of group activity recognition in office environments using a multimodal deep learning approach, by fusing audio and visual data from video. Group activity recognition is a complex classification task, given that it extends beyond identifying the activities of individuals, by focusing on the combinations of activities and the interactions between them. The proposed fusion network was trained based on the audio-visual stream from the AMI Corpus dataset. The procedure consists of two steps. First, we extract a joint audio-visual feature representation for activity recognition, and second, we account for the temporal dependencies in the video in order to complete the classification task. We provide a comprehensive set of experimental results showing that our proposed multimodal deep network architecture outperforms previous approaches, which have been designed for unimodal analysis, on the aforementioned AMI dataset

Reducing Labeling Costs in Alzheimer’s Disease Diagnosis: A Study of Semi-Supervised and Active Learning with 3D Medical Imaging

Alzheimer’s Disease (AD) is a neurodegenerative condition that is the most common cause of dementia. While there is no cure, its early detection is crucial for effective medical intervention. Deep learning models trained on brain Magnetic Resonance Imaging (MRI) scans have shown promise in this regard, but obtaining annotations for medical imaging data is expensive. In this study, we explore three network training approaches that aim to minimize labeling costs – Active Learning (AL), Semi-Supervised Learning (SSL), and Semi-Supervised Active Learning (SSAL). These were applied to train a 3D subject-level convolutional neural network to diagnose AD using 3D brain MRI scans. Our results confirm the significant impact of the annotation budget and the initial training set on model performance. We observe that all approaches consistently outperform random sampling. Uncertainty-based AL achieves comparable performance to the traditional supervised baseline using only 30 percent of the annotated data. Representative AL and joint SSAL outperform the traditional supervised baseline using 30 percent of the annotated data, with the latter showing robustness even with a restricted initial training set

ECOOP : Applying Dynamic Coalition Formation to the Power Regulation Problem in Smart Grids

In this work, we focus on one particular area of the smart grid, namely, the challenges faced by distribution network operators in securing the balance between supply and demand in the intraday market, as a growing number of load-controllable devices and small-scale, intermittent generators coming from renewables are expected to pervade the system. We introduce a multiagent design to facilitate coordinating the various actors in the grid. The underpinning of our approach consists of an online cooperation scheme, ECOOP, where agents learn a prediction model regarding potential coalition partners and so can respond in an agile manner to situations that are occurring in the grid, by means of negotiating and formulating speculative solutions, with respect to the estimated behavior of the system. We provide a computational characterization for our solution in terms of complexity, as well as an empirical analysis against real consumption data sets, based on the macro-model of the Australian energy market, showing a performance improvement of about 17%

Natural Language Understanding for Multi-Level Distributed Intelligent Virtual Sensors

In this paper we address the problem of automatic sensor composition for servicing human-interpretable high-level tasks. To this end, we introduce multi-level distributed intelligent virtual sensors (multi-level DIVS) as an overlay framework for a given mesh of physical and/or virtual sensors already deployed in the environment. The goal for multi-level DIVS is two-fold: (i) to provide a convenient way for the user to specify high-level sensing tasks; (ii) to construct the computational graph that provides the correct output given a specific sensing task. For (i) we resort to a conversational user interface, which is an intuitive and user-friendly manner in which the user can express the sensing problem, i.e., natural language queries, while for (ii) we propose a deep learning approach that establishes the correspondence between the natural language queries and their virtual sensor representation. Finally, we evaluate and demonstrate the feasibility of our approach in the context of a smart city setup

End-to-end anytime solution for appliance recognition based on high-resolution current sensing with few-shot learning

With the steady rise of home and building automation management system, it is becoming paramount to gain access to information that reflects consumption patterns with devicelevel granularity. Various application-level services can then makes use of this data for monitoring and controlling purposes in an efficient manner. In this paper we report on the design and development of an Internet of Things (IoT) end-to-end solution for electric appliance recognition that can operate in real-time and entails low hardware cost. For the task of identifying various appliance signatures we also provide a comparative analysis, where on the one hand, we investigate the suitability of several machine learning approaches given publicly available datasets, that generally provide months worth of data with a relatively low sampling frequency. On the other hand, we proceed to evaluate their discriminative effectiveness for our particular scenario, where the goal is to provide rapid identification of the appliance signature in real-time based on a reduced training dataset (few-shot learning). This is particularly important in the context of appliance recognition, where due to the high variance in consumption patterns within each class, in order to achieve high accuracy, data points often need to be collected for each individual appliance or device that would need to be later identified. Clearly, this data collection process is often expensive and difficult to perform, especially in large-scale settings, hence few-shot learning is key. Besides presenting our end-to-end IoT solution that meets the abovementioned desiderata, the paper also provides an analysis of the computational demand of such an approach with regard to cost and real-time performance, which is often critical to low-powered IoT solutions. (C) 2020 The Authors. Published by Elsevier B.V