E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Special Issue "Sensors and Wireless Sensor Networks for Novel Concepts of Things, Interfaces and Applications in Smart Spaces"

Quicklinks

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

Deadline for manuscript submissions: closed (30 April 2012)

Special Issue Editors

Guest Editor
Dr. Boon-Chong Seet

Department of Electrical & Electronic Engineering
Website | E-Mail
Fax: +64 9 921 9973
Interests: ad-hoc, mesh, and sensor networks; smart environments and ambient Intelligence; smart textile technologies; radio frequency identification
Guest Editor
Dr. Ana M. Bernardos

Department of Signal, Systems and Radiocommunications, Telecommunications School, Universidad Politécnica de Madrid, Av. Complutense 30, 28040, Madrid, Spain
E-Mail
Phone: +349145335353
Fax: +34 913365876
Interests: wireless sensor networks; data fusion; pervasive computing; sensors for healthcare; context management; positioning systems; activity recognition

Special Issue Information

Dear Colleagues,

This special issue will publish selected papers from The 4th International Workshop on Sensor Networks and Ambient Intelligence (SeNAmI 2012), and invited papers related to the topics within this workshop. The fourth edition of SeNAmI aims at bringing together researchers from academia and industry to present and discuss recent trends and advances in supportive technologies for ambient intelligence. In this edition, it is particularly focused on advances on novel concepts of Things, Interfaces and Applications in Smart Spaces. The conference website is at http://www.grpss.ssr.upm.es/senami2012/

Not being restrictive to, suitable topics include:

  • Architectures for handling smart items in responsive spaces
  • Context management for interaction in smart spaces
  • Reasoning and learning techniques for interaction in smart spaces
  • Cyber-physical systems for human-centric applications: tangible interfaces and smart objects
  • Sensors and Wireless Sensor Networks for the Internet of Things
  • Sensors management for interaction Advanced (mobile) ubiquitous applications
  • Privacy, security and trust management for interaction in smart spaces
  • Prototypes, testbeds, and real-world deployments
  • Field evaluation of user experience

Dr. Dr. Ana M. Bernardos
Dr. Boon-Chong Seet
Guest Editors

Published Papers (6 papers)

View options order results:
result details:
Displaying articles 1-6
Export citation of selected articles as:

Research

Open AccessArticle Framework for End-User Programming of Cross-Smart Space Applications
Sensors 2012, 12(11), 14442-14466; doi:10.3390/s121114442
Received: 16 September 2012 / Revised: 16 October 2012 / Accepted: 24 October 2012 / Published: 29 October 2012
Cited by 4 | PDF Full-text (977 KB) | HTML Full-text | XML Full-text
Abstract
Cross-smart space applications are specific types of software services that enable users to share information, monitor the physical and logical surroundings and control it in a way that is meaningful for the user’s situation. For developing cross-smart space applications, this paper makes two
[...] Read more.
Cross-smart space applications are specific types of software services that enable users to share information, monitor the physical and logical surroundings and control it in a way that is meaningful for the user’s situation. For developing cross-smart space applications, this paper makes two main contributions: it introduces (i) a component design and scripting method for end-user programming of cross-smart space applications and (ii) a backend framework of components that interwork to support the brunt of the RDFScript translation, and the use and execution of ontology models. Before end-user programming activities, the software professionals must develop easy-to-apply Driver components for the APIs of existing software systems. Thereafter, end-users are able to create applications from the commands of the Driver components with the help of the provided toolset. The paper also introduces the reference implementation of the framework, tools for the Driver component development and end-user programming of cross-smart space applications and the first evaluation results on their application. Full article
Open AccessArticle Ontological Representation of Light Wave Camera Data to Support Vision-Based AmI
Sensors 2012, 12(9), 12126-12152; doi:10.3390/s120912126
Received: 2 May 2012 / Revised: 31 July 2012 / Accepted: 21 August 2012 / Published: 5 September 2012
PDF Full-text (2389 KB) | HTML Full-text | XML Full-text
Abstract
Recent advances in technologies for capturing video data have opened a vast amount of new application areas in visual sensor networks. Among them, the incorporation of light wave cameras on Ambient Intelligence (AmI) environments provides more accurate tracking capabilities for activity recognition. Although
[...] Read more.
Recent advances in technologies for capturing video data have opened a vast amount of new application areas in visual sensor networks. Among them, the incorporation of light wave cameras on Ambient Intelligence (AmI) environments provides more accurate tracking capabilities for activity recognition. Although the performance of tracking algorithms has quickly improved, symbolic models used to represent the resulting knowledge have not yet been adapted to smart environments. This lack of representation does not allow to take advantage of the semantic quality of the information provided by new sensors. This paper advocates for the introduction of a part-based representational level in cognitive-based systems in order to accurately represent the novel sensors’ knowledge. The paper also reviews the theoretical and practical issues in part-whole relationships proposing a specific taxonomy for computer vision approaches. General part-based patterns for human body and transitive part-based representation and inference are incorporated to an ontology-based previous framework to enhance scene interpretation in the area of video-based AmI. The advantages and new features of the model are demonstrated in a Social Signal Processing (SSP) application for the elaboration of live market researches. Full article
Open AccessArticle A Semantic Autonomous Video Surveillance System for Dense Camera Networks in Smart Cities
Sensors 2012, 12(8), 10407-10429; doi:10.3390/s120810407
Received: 2 May 2012 / Revised: 21 July 2012 / Accepted: 26 July 2012 / Published: 2 August 2012
Cited by 8 | PDF Full-text (1030 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a proposal of an intelligent video surveillance system able to detect and identify abnormal and alarming situations by analyzing object movement. The system is designed to minimize video processing and transmission, thus allowing a large number of cameras to be
[...] Read more.
This paper presents a proposal of an intelligent video surveillance system able to detect and identify abnormal and alarming situations by analyzing object movement. The system is designed to minimize video processing and transmission, thus allowing a large number of cameras to be deployed on the system, and therefore making it suitable for its usage as an integrated safety and security solution in Smart Cities. Alarm detection is performed on the basis of parameters of the moving objects and their trajectories, and is performed using semantic reasoning and ontologies. This means that the system employs a high-level conceptual language easy to understand for human operators, capable of raising enriched alarms with descriptions of what is happening on the image, and to automate reactions to them such as alerting the appropriate emergency services using the Smart City safety network. Full article
Figures

Open AccessArticle A Distributed Reasoning Engine Ecosystem for Semantic Context-Management in Smart Environments
Sensors 2012, 12(8), 10208-10227; doi:10.3390/s120810208
Received: 9 June 2012 / Revised: 12 July 2012 / Accepted: 26 July 2012 / Published: 30 July 2012
Cited by 4 | PDF Full-text (755 KB) | HTML Full-text | XML Full-text
Abstract
To be able to react adequately a smart environment must be aware of the context and its changes. Modeling the context allows applications to better understand it and to adapt to its changes. In order to do this an appropriate formal representation method
[...] Read more.
To be able to react adequately a smart environment must be aware of the context and its changes. Modeling the context allows applications to better understand it and to adapt to its changes. In order to do this an appropriate formal representation method is needed. Ontologies have proven themselves to be one of the best tools to do it. Semantic inference provides a powerful framework to reason over the context data. But there are some problems with this approach. The inference over semantic context information can be cumbersome when working with a large amount of data. This situation has become more common in modern smart environments where there are a lot sensors and devices available. In order to tackle this problem we have developed a mechanism to distribute the context reasoning problem into smaller parts in order to reduce the inference time. In this paper we describe a distributed peer-to-peer agent architecture of context consumers and context providers. We explain how this inference sharing process works, partitioning the context information according to the interests of the agents, location and a certainty factor. We also discuss the system architecture, analyzing the negotiation process between the agents. Finally we compare the distributed reasoning with the centralized one, analyzing in which situations is more suitable each approach. Full article
Figures

Open AccessArticle Model-Driven Methodology for Rapid Deployment of Smart Spaces Based on Resource-Oriented Architectures
Sensors 2012, 12(7), 9286-9335; doi:10.3390/s120709286
Received: 2 May 2012 / Revised: 22 June 2012 / Accepted: 27 June 2012 / Published: 6 July 2012
Cited by 3 | PDF Full-text (1334 KB) | HTML Full-text | XML Full-text
Abstract
Advances in electronics nowadays facilitate the design of smart spaces based on physical mash-ups of sensor and actuator devices. At the same time, software paradigms such as Internet of Things (IoT) and Web of Things (WoT) are motivating the creation of technology to
[...] Read more.
Advances in electronics nowadays facilitate the design of smart spaces based on physical mash-ups of sensor and actuator devices. At the same time, software paradigms such as Internet of Things (IoT) and Web of Things (WoT) are motivating the creation of technology to support the development and deployment of web-enabled embedded sensor and actuator devices with two major objectives: (i) to integrate sensing and actuating functionalities into everyday objects, and (ii) to easily allow a diversity of devices to plug into the Internet. Currently, developers who are applying this Internet-oriented approach need to have solid understanding about specific platforms and web technologies. In order to alleviate this development process, this research proposes a Resource-Oriented and Ontology-Driven Development (ROOD) methodology based on the Model Driven Architecture (MDA). This methodology aims at enabling the development of smart spaces through a set of modeling tools and semantic technologies that support the definition of the smart space and the automatic generation of code at hardware level. ROOD feasibility is demonstrated by building an adaptive health monitoring service for a Smart Gym. Full article
Figures

Open AccessArticle Detecting Service Chains and Feature Interactions in Sensor-Driven Home Network Services
Sensors 2012, 12(7), 8447-8464; doi:10.3390/s120708447
Received: 2 May 2012 / Revised: 4 June 2012 / Accepted: 18 June 2012 / Published: 25 June 2012
Cited by 6 | PDF Full-text (136 KB) | HTML Full-text | XML Full-text
Abstract
Sensor-driven services often cause chain reactions, since one service may generate an environmental impact that automatically triggers another service. We first propose a framework that can formalize and detect such service chains based on ECA (event, condition, action) rules. Although the service chain
[...] Read more.
Sensor-driven services often cause chain reactions, since one service may generate an environmental impact that automatically triggers another service. We first propose a framework that can formalize and detect such service chains based on ECA (event, condition, action) rules. Although the service chain can be a major source of feature interactions, not all service chains lead to harmful interactions. Therefore, we then propose a method that identifies feature interactions within the service chains. Specifically, we characterize the degree of deviation of every service chain by evaluating the gap between expected and actual service states. An experimental evaluation demonstrates that the proposed method successfully detects 11 service chains and 6 feature interactions within 7 practical sensor-driven services. Full article
Figures

Journal Contact

MDPI AG
Sensors Editorial Office
St. Alban-Anlage 66, 4052 Basel, Switzerland
sensors@mdpi.com
Tel. +41 61 683 77 34
Fax: +41 61 302 89 18
Editorial Board
Contact Details Submit to Sensors
Back to Top