Special Issue "Algorithm Engineering for Collective Ambient Intelligence"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: 15 March 2020

Special Issue Editors

Guest Editor
Prof. Ioannis Chatzigiannakis

Department of Computer, Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, via Ariosto 25, 00185 Rome, Italy
Website | E-Mail
Interests: distributed algorithms; population protocols; dynamic networks; algorithmic engineering; Internet of Things
Guest Editor
Prof. Vassilis-Javed Khan

Assistant Professor at Industrial Design Department, Eindhoven University of Technology, 5612 AZ Eindhoven, Netherlands
Website | E-Mail
Interests: crowdsourcing; human–computer interaction; mobile computing; interaction design; experience sampling
Guest Editor
Prof. Irene Mavrommati

Assistant Professor at School of Applied Arts, Hellenic Open University, Patra 263 35, Greece
Website | E-Mail
Interests: AmI systems interaction; human–computer interaction; user-centered design; end-user development; IoT
Guest Editor
Prof. Kostas Stathis

Department of Computer Science, Royal Holloway University of London, Egham TW20 0EX, UK
Website | E-Mail
Interests: artificial intelligence; multi-agent systems; logic programming

Special Issue Information

Dear Colleagues,

We are going through a new phase of computing evolution that involves processing capabilities in everything: objects, places, people, and processes. Devices, networks, and cloud services are being merged with people and their activities—individuals or groups—to provide new and exciting possibilities in everything we do. Such highly interconnected computational components act autonomously and intelligently through the use of software agents that seamlessly integrate humans in the loop, creating new opportunities for intelligent systems where humans and agents interact continuously. In crowd-powered systems, traditional algorithmic computing gives way to data flowing through machines and exchanged with people. The notion of crowd-algorithms is a consequence of such systems. Decisions are being made based on what data is telling us. New technologies allow the interpretation of data without demanding our full attention. New methods enable the integration of data with our daily activities without requiring us to deviate from our usual behavior.

The aim of this Special Issue is to bring together researchers and practitioners from the fields of science, engineering, and design working towards the vision of Collective Ambient Intelligence. Current work on Ambient Intelligence is focusing more on how individual systems/devices are offering smart services individually but are ignoring the collective (set of) devices that we use and carry around us and how information from them could be coordinated to be combined to achieve common goals. Of particular interest is the engineering of algorithms for behaviors of agents that can then also be used as managers for larger-scale coordination algorithms that exhibit organization and distribution of responsibility, suitable for complex problems and collective goals. It is evident that the realization of the vision of Collective Ambient Intelligence involves significant developments in terms of algorithm engineering, from the organization, exchange, and analysis of data; to interconnecting smart sensors and intelligent devices with groups of peoples; to pilots reporting recent developments in real-world deployments; to examining new issues of ethics, privacy, and security.

Relevant research topics include, but are not limited to:

- Methodologies for studying, analyzing, and engineering algorithms for Collective Ambient Intelligence;

- Algorithm engineering for data management and knowledge extraction in Collective Ambient Intelligence;

- Machine learning, data mining and big data in Collective Ambient Intelligence;

- Artificial intelligence and collaboration in Collective Ambient Intelligence;

- Security issues in Collective Ambient Intelligence;

- Privacy-enhanced, privacy-preserving, and privacy-by-design in Collective Ambient Intelligence;

- Revolutionary designs and new paradigms for real-world Collective Ambient Intelligence;

- Visualization issues for big data used for Collective Ambient Intelligence;

- Multimodal human-computer interaction in Collective Ambient Intelligence;

- Innovative usages of virtual, mixed and augmented reality in Collective Ambient Intelligence;

- Symmetric interaction in real and virtual worlds in Collective Ambient Intelligence;

- Robotic companions in Collective Ambient Intelligence;

- Pilot applications of Collective Ambient Intelligence;

- Citizen engagement and participation in Collective Ambient Intelligence;

- Maintenance issues in Collective Ambient Intelligence applications;

- Conception and evaluation of crowd-algorithms in Collective Ambient Intelligence.

Prof. Ioannis Chatzigiannakis
Prof. Vassilis-Javed Khan
Prof. Irene Mavrommati
Prof. Kostas Stathis
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 papers will be 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. Algorithms is an international peer-reviewed open access monthly 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 1000 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.

Published Papers (1 paper)

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Research

Open AccessArticle Fog-Computing-Based Heartbeat Detection and Arrhythmia Classification Using Machine Learning
Algorithms 2019, 12(2), 32; https://doi.org/10.3390/a12020032
Received: 10 January 2019 / Revised: 25 January 2019 / Accepted: 30 January 2019 / Published: 2 February 2019
PDF Full-text (2450 KB) | HTML Full-text | XML Full-text
Abstract
Designing advanced health monitoring systems is still an active research topic. Wearable and remote monitoring devices enable monitoring of physiological and clinical parameters (heart rate, respiration rate, temperature, etc.) and analysis using cloud-centric machine-learning applications and decision-support systems to predict critical clinical states. [...] Read more.
Designing advanced health monitoring systems is still an active research topic. Wearable and remote monitoring devices enable monitoring of physiological and clinical parameters (heart rate, respiration rate, temperature, etc.) and analysis using cloud-centric machine-learning applications and decision-support systems to predict critical clinical states. This paper moves from a totally cloud-centric concept to a more distributed one, by transferring sensor data processing and analysis tasks to the edges of the network. The resulting solution enables the analysis and interpretation of sensor-data traces within the wearable device to provide actionable alerts without any dependence on cloud services. In this paper, we use a supervised-learning approach to detect heartbeats and classify arrhythmias. The system uses a window-based feature definition that is suitable for execution within an asymmetric multicore embedded processor that provides a dedicated core for hardware assisted pattern matching. We evaluate the performance of the system in comparison with various existing approaches, in terms of achieved accuracy in the detection of abnormal events. The results show that the proposed embedded system achieves a high detection rate that in some cases matches the accuracy of the state-of-the-art algorithms executed in standard processors. Full article
(This article belongs to the Special Issue Algorithm Engineering for Collective Ambient Intelligence)
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