[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
Trends and Advances in Ambient Intelligence for the Internet of...
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Trends and Advances in Ambient Intelligence for the Internet of Things

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (31 October 2019) | Viewed by 20692

Special Issue Editors

Prof. Dr. Nick Bassiliades

E-Mail Website
Guest Editor
School of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: smart grids; electric vehicles; multi-agent systems; information integration
Special Issues, Collections and Topics in MDPI journals
Dr. Antonis Bikakis

E-Mail Website
Guest Editor
Associate Professor, Department of Information Studies, University College London, Gower Street, London WC1E 6BT, UK
Interests: knowledge representation; nonmonotonic reasoning; argumentation; semantic web; ambient intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Dimitris Vrakas

E-Mail Website
Guest Editor
Assistant Professor, Department of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: artificial intelligence; automated planning; semantic web services; sensor networks; problem solving; knowledge representation; parallel systems and parallel search algorithms

Special Issue Information

Dear Colleagues,

Owing mainly to the recent advances in wireless communication and embedded computing technologies, billions of devices, from tiny wireless sensors to powerful data centre nodes, are now connected to each other over the Internet, forming what is known as the Internet of Things (IoT). At the same time, ambient intelligence (AmI), a new paradigm of human–computer interaction, powered by the developments in artificial intelligence, promises to transform our living environments into intelligent spaces that will support us in our everyday tasks and activities in an adaptive, seamless and unobtrusive fashion. Leveraging the enhanced connectivity of the Internet of Things and the growing capabilities of ambient intelligence technologies will lead to the development of a new generation of more advanced and larger-scale solutions for several application domains such as healthcare, education, culture, transportation, energy management, emergency services and others.

In order to maximize the benefits that the technology has to offer, one has to tailor ambient intelligence methodologies, systems and techniques in a way that realizes the notion of “from data to intelligent action”. Data from IoT sensors need to be analysed in an intelligent way, enabling reasoning about goals and actions to be efficiently communicated to the IoT actuators in a resilient and versatile scheme that realizes the smart environment vision.

The aim of this Special Issue is to present the latest trends, research solutions, developments and applications that combine methods, techniques and technologies from the Internet of Things and ambient intelligence, demonstrating the potential of combining these two lines of research.

Prof. Dr. Nick Bassiliades
Prof. Dr. Antonis Bikakis
Prof. Dr. Dimitris Vrakas
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Internet of Things

Web of Things

Ambient Intelligence

Ambient Computing

Ubiquitous Computing, Pervasive Computing

Mobile Computing

Disappearing computer

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

44 pages, 10027 KiB  
Article
Ambient Intelligence in the Living Room
by Asterios Leonidis, Maria Korozi, Vassilis Kouroumalis, Evangelos Poutouris, Evropi Stefanidi, Dimitrios Arampatzis, Eirini Sykianaki, Nikolaos Anyfantis, Evangelos Kalligiannakis, Vassilis C. Nicodemou, Zinovia Stefanidi, Emmanouil Adamakis, Nikos Stivaktakis, Theodoros Evdaimon and Margherita Antona
Sensors 2019, 19(22), 5011; https://doi.org/10.3390/s19225011 - 16 Nov 2019
Cited by 22 | Viewed by 7204
Abstract
The emergence of the Ambient Intelligence (AmI) paradigm and the proliferation of Internet of Things (IoT) devices and services unveiled new potentials for the domain of domestic living, where the line between “the computer” and the (intelligent) environment becomes altogether invisible. Particularly, the [...] Read more.
The emergence of the Ambient Intelligence (AmI) paradigm and the proliferation of Internet of Things (IoT) devices and services unveiled new potentials for the domain of domestic living, where the line between “the computer” and the (intelligent) environment becomes altogether invisible. Particularly, the residents of a house can use the living room not only as a traditional social and individual space where many activities take place, but also as a smart ecosystem that (a) enhances leisure activities by providing a rich suite of entertainment applications, (b) implements a home control middleware, (c) acts as an intervention host that is able to display appropriate content when the users need help or support, (d) behaves as an intelligent agent that communicates with the users in a natural manner and assists them throughout their daily activities, (e) presents a notification hub that provides personalized alerts according to contextual information, and (f) becomes an intermediary communication center for the family. This paper (i) describes how the “Intelligent Living Room” realizes these newly emerged roles, (ii) presents the process that was followed in order to design the living room environment, (iii) introduces the hardware and software facilities that were developed in order to improve quality of life, and (iv) reports the findings of various evaluation experiments conducted to assess the overall User Experience (UX). Full article
(This article belongs to the Special Issue Trends and Advances in Ambient Intelligence for the Internet of Things)
►▼ Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) The 3D representation of the living room as it resulted from the prototyping phase; (<b>b</b>) The current “Intelligent Living Room” setup.</p> Full article ">Figure 2
<p>(<b>a</b>) SmartSofa artefact; (<b>b</b>) 3D printed cases for installing the sensors into the sofa.</p> Full article ">Figure 3
<p>AugmenTable consists of a projector embedded on the ceiling above the coffee table, and a Kinect sensor installed on top of the TV.</p> Full article ">Figure 4
<p>The AmI-Solertis Hybrid Communication protocol.</p> Full article ">Figure 5
<p>AmI TV instances: (<b>a</b>) Home Screen; (<b>b</b>) TV application; (<b>c</b>) Movies application; (<b>d</b>) Music application; (<b>e</b>) Images application; (<b>f</b>) News application.</p> Full article ">Figure 6
<p>(<b>a</b>) Instance of a popup notification displayed on the AmITV artefact; (<b>b</b>) The environment assists in reducing the stress of the user (CaLmi).</p> Full article ">Figure 7
<p>Chatbot application: (<b>a</b>) A message is decomposed into smaller chunks permitting the user to disapprove each one of them; (<b>b</b>) The user can correct a specific part of the rule(s), instead of having to repeat the complete conversation.</p> Full article ">
32 pages, 2435 KiB  
Article
Robot Assistance in Dynamic Smart Environments—A Hierarchical Continual Planning in the Now Framework
by Helen Harman, Keshav Chintamani and Pieter Simoens
Sensors 2019, 19(22), 4856; https://doi.org/10.3390/s19224856 - 7 Nov 2019
Cited by 8 | Viewed by 4266
Abstract
By coupling a robot to a smart environment, the robot can sense state beyond the perception range of its onboard sensors and gain greater actuation capabilities. Nevertheless, incorporating the states and actions of Internet of Things (IoT) devices into the robot’s onboard planner [...] Read more.
By coupling a robot to a smart environment, the robot can sense state beyond the perception range of its onboard sensors and gain greater actuation capabilities. Nevertheless, incorporating the states and actions of Internet of Things (IoT) devices into the robot’s onboard planner increases the computational load, and thus can delay the execution of a task. Moreover, tasks may be frequently replanned due to the unanticipated actions of humans. Our framework aims to mitigate these inadequacies. In this paper, we propose a continual planning framework, which incorporates the sensing and actuation capabilities of IoT devices into a robot’s state estimation, task planing and task execution. The robot’s onboard task planner queries a cloud-based framework for actuators, capable of the actions the robot cannot execute. Once generated, the plan is sent to the cloud back-end, which will inform the robot if any IoT device reports a state change affecting its plan. Moreover, a Hierarchical Continual Planning in the Now approach was developed in which tasks are split-up into subtasks. To delay the planning of actions that will not be promptly executed, and thus to reduce the frequency of replanning, the first subtask is planned and executed before the subsequent subtask is. Only information relevant to the current (sub)task is provided to the task planner. We apply our framework to a smart home and office scenario in which the robot is tasked with carrying out a human’s requests. A prototype implementation in a smart home, and simulator-based evaluation results, are presented to demonstrate the effectiveness of our framework. Full article
(This article belongs to the Special Issue Trends and Advances in Ambient Intelligence for the Internet of Things)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Grey boxes indicate actions which have not yet been executed; blue shows the actions currently being executed and green indicates executed actions. These figures describe the general concept of hierarchical planning in the now, introduced by Kaelbling and Lozano-Pérez [<a href="#B29-sensors-19-04856" class="html-bibr">29</a>].</p> Full article ">Figure 2
<p>During planning, external module calls check if any device is capable of performing an action by calling a service running in the Capability Checker, which queries the cloud for capable devices and caches the results. The Context Monitor queries this when a plan has been generated, to know which device(s) should perform which action. The plan (containing the list of actual device) is sent to the cloud, which is monitoring the state of the environment and the devices. Any Internet of Things (IoT) data relevant to the robot’s plan is sent back to the robot and inserted into the robot’s knowledge base. If this information will cause the robot’s plan to fail, its current action is cancelled forcing the state to be (re-)estimated and replanning to be performed.</p> Full article ">Figure 3
<p>The continual planner calls the State Estimators (i.e., Static State Estimators and Dynamic State Estimators) and Domain Enricher to generate the PDDL problem and domain based on observations from the robot and smart environment. The TFD/M planner generates a task plan that contains primitive actions, to be executed by the robot and IoT actuators and composite actions. Composite action executors contain an instance of the the continual planner, which is configured with the relevant State Estimators, Action Executors and domain file.</p> Full article ">Figure 4
<p>Outline of what we define action, capability, device and robot as. PDDL actions require a capability and devices along with what they are capable of are defined in an ontology.</p> Full article ">Figure 5
<p>Functionality of the types of Action Executors. Grey boxes indicate types of plug-ins, white indicates classes/instances and yellow shows files.</p> Full article ">Figure 6
<p>All actions definitions are contained within a single domain file. When planning everything upfront, the planner must make an assumption on the most likely request and include all actions needed to fulfil that request. In all figures the following words are abbreviated: doorway is shortened to d, waypoint to w, room to r and floor to f. In all proceeding figures blue boxes (with downwards arrows) indicate composite actions and green is used for primitive actions.</p> Full article ">Figure 7
<p>Action definitions split-up into two domain files; and executed actions when the state and actions are split up by key concepts.</p> Full article ">Figure 8
<p>Action definitions split-up into three domain files. Executed actions when repeated groups of actions are separated.</p> Full article ">Figure 9
<p>Domain files and executed actions for when we have split-up knowledge about what floor the robot is on. The fully hierarchical approach.</p> Full article ">Figure 10
<p>Real world tests. The full list of PDDL executed actions are given in Listings 3 and 4. Doorway has been shortened to d and room to r.</p> Full article ">Figure 11
<p>Gazebo simulated world used for the experiments. We use an extended version of the world originally created by Speck et al. [<a href="#B3-sensors-19-04856" class="html-bibr">3</a>]. Waypoints (e.g., <tt>w1</tt>) that are used during the different experiments have been indicated on the map. For all experiments there are also waypoints either side of doorways.</p> Full article ">Figure 12
<p>Planning times for planning everything upfront (red line) versus using a hierarchy (green line). For each result three random rooms were selected as goal locations and all experiments were ran 5 times.</p> Full article ">
25 pages, 2938 KiB  
Article
A Multi-Protocol IoT Platform Based on Open-Source Frameworks
by Charilaos Akasiadis, Vassilis Pitsilis and Constantine D. Spyropoulos
Sensors 2019, 19(19), 4217; https://doi.org/10.3390/s19194217 - 28 Sep 2019
Cited by 35 | Viewed by 8198
Abstract
Internet of Things (IoT) technologies have evolved rapidly during the last decade, and many architecture types have been proposed for distributed and interconnected systems. However, most systems are implemented following fragmented approaches for specific application domains, introducing difficulties in providing unified solutions. However, [...] Read more.
Internet of Things (IoT) technologies have evolved rapidly during the last decade, and many architecture types have been proposed for distributed and interconnected systems. However, most systems are implemented following fragmented approaches for specific application domains, introducing difficulties in providing unified solutions. However, the unification of solutions is an important feature from an IoT perspective. In this paper, we present an IoT platform that supports multiple application layer communication protocols (Representational State Transfer (REST)/HyperText Transfer Protocol (HTTP), Message Queuing Telemetry Transport (MQTT), Advanced Message Queuing Protocol (AMQP), Constrained Application Protocol (CoAP), and Websockets) and that is composed of open-source frameworks (RabbitMQ, Ponte, OM2M, and RDF4J). We have explored a back-end system that interoperates with the various frameworks and offers a single approach for user-access control on IoT data streams and micro-services. The proposed platform is evaluated using its containerized version, being easily deployable on the vast majority of modern computing infrastructures. Its design promotes service reusability and follows a marketplace architecture, so that the creation of interoperable IoT ecosystems with active contributors is enabled. All the platform’s features are analyzed, and we discuss the results of experiments, with the multiple communication protocols being tested when used interchangeably for transferring data. Developing unified solutions using such a platform is of interest to users and developers as they can test and evaluate local instances or even complex applications composed of their own IoT resources before releasing a production version to the marketplace. Full article
(This article belongs to the Special Issue Trends and Advances in Ambient Intelligence for the Internet of Things)
►▼ Show Figures

Figure 1

Figure 1
<p>Platform components</p> Full article ">Figure 2
<p>Database model.</p> Full article ">Figure 3
<p>Composition of the face-counting service.</p> Full article ">Figure 4
<p>Composition of the fall-detection and route-monitoring service.</p> Full article ">Figure 5
<p>End-to-end message delays vs. batch message traffic size.</p> Full article ">Figure 6
<p>Parallel clients: 30, publish topics per client: 2, frequency of batch messaging: 1 s, batch message size: 50 B.</p> Full article ">Figure 7
<p>Without AuthN/AuthZ; parallel clients: 30, publish topics per client: 2, frequency of batch messaging: 1 s, batch message size: 50 B.</p> Full article ">Figure 8
<p>Parallel clients: 30, publish topics per client: 2, frequency of batch messaging: 1 s, batch message size: 50 B.</p> Full article ">
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop