Simulation of Smart Environments

Armac,; Retkowitz,

doi:10.1109/perser.2007.4283924

Cited by 22 publications

(14 citation statements)

References 8 publications

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“…Furthermore, we implemented our own RMI-like communication over JXTA, called "SimpleRMI". Our eHome prototype includes the eHomeSimulator [8], a 2D environment which enables to simulate smart environments with different kinds of sensors and devices as well as user behavior. We have tested our results with several personal services such as Lighting, Music, TV, Temperature, and Wake-up.…”

Section: Methodsmentioning

confidence: 99%

Privacy-Friendly Smart Environments

Armac

Panchenko

Pettau

et al. 2009

2009 Third International Conference on Next Generation Mobile Applications, Services and Technologies

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In this paper we describe our approach on protecting user privacy in smart environments, particularly smart homes, which we call eHomes. These are environments with devices such as sensors, computational units, actors, which are seamlessly integrated in the environment, and objects we use in our everyday life. In order to provide more convenience to its users such environments can be personalized.As these environments become ubiquitous, thus supporting mobility of the users, new privacy threats arise. These are based on the digital traces and personal information which is left while visiting different environments. We provide a practical approach to minimize these traces and information disclosure by applying negotiation, identity management, and anonymous credentials. Also, we discuss the protection of eHomes from malicious users.

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Section: Methodsmentioning

confidence: 99%

Privacy-Friendly Smart Environments

Armac

Panchenko

Pettau

et al. 2009

2009 Third International Conference on Next Generation Mobile Applications, Services and Technologies

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“…An approach based on the Delaunay Triangulation has been used in [3] to solve the problem of coverage holes and plan the position of the sensors in an environment with obstacles. Similarly, some existing testbeds for wireless sensors and smart environments [6], [7], [8], despite their practical usefulness, are limited to test in 2D space. In [13], a new set of optimal deployment patterns is proposed to address the mutation phenomenon occurring for certain ratios of r c /r s .…”

Section: Related Workmentioning

confidence: 99%

“…Then, the per-cost volume pc for each deployable point is computed and the deployable point with the highest per-cost volume is selected and a sensor node is deployed at the point (lines: [6][7][8][9][10][11][12][13][14][15][16].…”

Section: Mobile K-coverage Algorithmmentioning

confidence: 99%

“…Numerous existing literatures have addressed this problem and some heuristic methods were studied either by reducing the 3D geometry to the 2D [1], [2], [3], [4], [5], [6], [7], [8] or by neglecting the obstacles existing in the target space [9], [10], [11], [12]. However, abstraction of the target space may lead to some deployment errors caused by the inaccuracy of the obtained results.…”

Section: Introductionmentioning

confidence: 99%

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Cost-efficient sensor deployment in indoor space with obstacles

Kouakou

Yasumoto

Yamamoto

et al. 2012

2012 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)

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In this paper, we tackle the problem to achieve kcoverage of a target indoor space with obstacles, that is, any point in the target monitoring area has a line-of-sight to and is located in the sensing range rs of at least k sensors. We propose heuristic algorithms for computing a deployment pattern achieving the kcoverage in an arbitrary 3D target space with stationary and mobile obstacles, while minimizing the overall deployment cost. For the case with only stationary obstacles, we propose a greedy algorithm that puts sensor nodes one by one on a grid point of the deployable area in the descending order of the cost-performance value (i.e., how many monitoring points are covered by putting a sensor at the deployment point per unit deployment cost). In order to extend the algorithm for the case with a mobile obstacle, we define mobile k-coverage that guarantees k-coverage of a target space for arbitrary position of a mobile obstacle, then provide a sufficient condition for the mobile k-coverage: the halfsphere of radius rs centered at a monitoring point bounded by the vertical plane containing the point includes at least k sensor nodes. Based on this condition, we propose a heuristic algorithm that puts at least one sensor node in each π k+1 spherical wedge for each monitoring point. With the proposed algorithms, we have computed the sensor nodes position in an existing indoor environment and confirmed that the obtained WSN deployment in the real space accurately achieves k-coverage.

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“…• The environmental model can act as a test environment, as well as a simulation and demonstration tool for smart homes [1]. • The model can be utilized to supporting the designer to make the necessary design decisions for the instrumentation of the environment, particularly which sensors and actuators to use and where to place them [2].…”

Section: Motivation and Introductionmentioning

confidence: 99%

SmartCase: A Smart Home Environment in a Suitcase

Frey

Bergweiler

Alexandersson

et al. 2011

2011 Seventh International Conference on Intelligent Environments

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Typically, intelligent environments include a heterogeneous set of devices and services offering complex functionalities. To establish a real assistive intelligent environment the available functionalities, their interdependencies and possible side effects must be comprehensible for the end-users and for the diverse set of developers, architects and craftsmen involved in the development process. In this paper, we present a detailed small-scale model of an instrumented two-rooms apartment and show how the model can be utilized for designing, evaluating and controlling intelligent environments. Furthermore, we present an architectural foundation based on Universal Remote Console technology and discuss how to synchronize the model with the real environments.

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Simulation of Smart Environments

Cited by 22 publications

References 8 publications

Privacy-Friendly Smart Environments

Privacy-Friendly Smart Environments

Cost-efficient sensor deployment in indoor space with obstacles

SmartCase: A Smart Home Environment in a Suitcase

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