Flexible Simulation of Ubiquitous Computing Environments

Campuzano, Francisco; García-Valverde, Teresa; García-Sola, Alberto; Botía, Juan A.

doi:10.1007/978-3-642-19937-0_24

Cited by 16 publications

(4 citation statements)

References 7 publications

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“…The experiments are made, firstly, using a simulator of intelligent and ubiquitous environment, UbikSim [7], and then, we report our experiments in real world settings. In the following subsections, we will describe these experiments and show the feasibility of the proposal.…”

Section: Experiments and Resultsmentioning

confidence: 99%

An adaptive learning fuzzy logic system for indoor localisation using Wi-Fi in Ambient Intelligent Environments

García-Valverde

García-Sola

Gómez‐Skarmeta

et al. 2012

2012 IEEE International Conference on Fuzzy Systems

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One of the important requirements for Ambient Intelligent Environments (AIEs) is the ability to localise the whereabouts of the user in the AIE to address her/his needs. The outdoor localisation means (like GPS systems) cannot be used in indoor environments. The majority of non intrusive and non camera based indoor localisation systems require the installation of extra hardware such as ultra sound emitters/antennas, RFID antennas, etc. In this paper, we will propose a novel fuzzy logic based indoor localisation system which is based on the WiFi signals which are free to receive and they are available in abundance in the majority of domestic spaces. The proposed system receives WiFi signals from a big number of existing WiFi Access Points (up to 170 Access Points) with no prior knowledge of the access points locations and the environment. The proposed system is able to adapt online incrementally in a lifelong learning mode to deal with the uncertainties and changing conditions facing unknown indoor structures with a few days of calibration at zero-cost deployment with high accuracy. The proposed system was tested in simulated and real environments where the system has given high accuracy (that outperformed the existing techniques) to detect the user in the given AIE and the system was able also to adapt its behaviour to changes in the AIE or the WiFi signals.

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Section: Experiments and Resultsmentioning

confidence: 99%

An adaptive learning fuzzy logic system for indoor localisation using Wi-Fi in Ambient Intelligent Environments

García-Valverde

García-Sola

Gómez‐Skarmeta

et al. 2012

2012 IEEE International Conference on Fuzzy Systems

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“…UbikSim is a simulator for ubiquitous computing that aims to reduce the features of services experiments and applications for treatment appertain related to both the physical environment and users. It proposes substantial techniques for implementing new sensor configurations, e.g., type of event detection or the required range of coverage (Campuzano, 2011;UbikSim, 2017). 51.…”

Section: Specific-purpose Simulatorsmentioning

confidence: 99%

Ubiquitous sensor network simulation and emulation environments: A survey

Sharif

Sadeghi‐Niaraki

2017

Journal of Network and Computer Applications

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Recent human effort has been directed at expanding pervasive smart environments. For this, ubiquitous computing technology is introduced to provide all users with any service, anytime, anywhere, with any device, and under any network. However, high cost, long time consumption, extensive effort, and in some cases irrevocability are the main challenges and difficulties for developing ubiquitous systems. Therefore, one solution is to initially simulate, analyze, and validate practices prior to deploying sensing and computational devices in the real world. Simulation, as a performance evaluation technique, has attracted attentions due to its speed, cost-effectiveness, repeatability, scalability, flexibility, and ease of implementation. Moreover, emulation, as a hybrid method, not only offers most simulation advantages but also benefits from tight control of implementation, as well as a certain degree of realistic results. Both simulators and emulators are significant tools for enhancing the understanding of ubiquitous sensor networks (USNs) through testing and analyzing several scenarios prior to actual sensor placements. In this regard, this paper surveys 130 simulation and emulation environments and frameworks, which were originally designed and adapted for USN. Of these 130, the 22 that have been widely used, regularly updated, and well supported by their developers are compared based on multifarious criteria. Finally, several studies that had favorably compared the performance of simulators and/or emulators are examined. We believe the present research findings will be helpful for students and researchers to pick an appropriate simulator/emulator, and for software developers and those who are keen on producing their own environment.

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“…presence, open door, pressure sensors) [14,25], sensors integrated in mobile phones or PDAs [29,40], etc. Some scenarios in which these sensors are used to monitor the user and from which behavior patterns can be learned are intelligent environments [8], human robot interaction [6] or Ambient Assisted Living (AAL) [10].…”

Section: Related Workmentioning

confidence: 99%

“…One of them is the validation of smart environments [10], e.g. CMHBs previously produced by CHROMUBE were used prior to the pilot experience of the Necesity system.…”

Section: Introductionmentioning

confidence: 99%

Generation of human computational models with machine learning

Campuzano

García-Valverde

Botía

et al. 2015

Information Sciences

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Services in smart environments pursue to increase the quality of people's lives. The most important issues when developing this kind of environments is testing and validating such services. These tasks usually imply high costs and annoying or unfeasible real-world test-ing. In such cases, artificial societies may be used to simulate the smart environment (i.e. physical environment, equipment and humans). With this aim, the CHROMUBE methodol-ogy guides test engineers when modeling human beings. Such models reproduce behaviors which are highly similar to the real ones. Originally, these models are based on automata whose transitions are governed by random variables. Automaton's structure and the prob-ability distribution functions of each random variable are determined by a manual test and error process. In this paper, it is presented an alternative extension of this methodology which avoids the said manual process. It is based on learning human behavior patterns automatically from sensor data by using machine learning techniques. The presented approach has been tested on a real scenario, where this extension has given highly accurate human behavior models.

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Flexible Simulation of Ubiquitous Computing Environments

Cited by 16 publications

References 7 publications

An adaptive learning fuzzy logic system for indoor localisation using Wi-Fi in Ambient Intelligent Environments

An adaptive learning fuzzy logic system for indoor localisation using Wi-Fi in Ambient Intelligent Environments

Ubiquitous sensor network simulation and emulation environments: A survey

Generation of human computational models with machine learning

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