Ambient Intelligence Services in IoT Environments

Korzun, Dmitry; Balandina, Ekaterina; Kashevnik, Alexey; Balandin, Sergey; Viola, Fabio

doi:10.4018/978-1-5225-8973-0

Cited by 36 publications

(22 citation statements)

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“…The conclusions of the statistical survey showed that the level of achieved ambient intelligence of the enterprise is predicted by the extent to which the company analyzes data that are the basis for decision-making: standardized β = 0.222, t (3, 205) = 3.304, p = 0.001, the enterprise's management attitude towards integration of innovative pervasive technologies into business processes: standardized β = 0.279, t (3,205)…”

Section: Resultsmentioning

confidence: 99%

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Predictors of Ambient Intelligence: An Empirical Study in Enterprises in Slovakia

Bolek¹,

Romanová²

2020

Electronics

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Integration of innovative pervasive technologies into the surrounding environment creates ambient intelligence which has become the subject of several scientific and academic considerations. In particular, the pervasive technology is based on Internet of Things (IoT) and Industrial Internet of Things (IIoT). Ambient intelligence may be viewed from several perspectives: a technological, multi-aspect and visionary point of view. Several predictors influence the level of ambient intelligence. The aim is to identify significant predictors influencing ambient intelligence and simultaneously determine their impact on the achieved level of ambient intelligence of the company. In the research methodology, we acknowledge the research presumptions, the research question and research hypotheses, the measurement instrument, obtained data and methods used in accordance with the research model. The scientific article presents the results of an empirical study conducted on 206 enterprises, focusing on the examining of the influence of selected predictors on the level of ambient intelligence in enterprises in the Slovak Republic. It examines not only the influence of selected predictors on the level of ambient intelligence, but also their mutual interaction. We found that these activities even build ambient intelligence on their own, although in interaction with each other, their effect on the level of ambient intelligence begins to fade. Therefore, it is necessary to opt for their combination appropriately. Finally, we present the limits of the research and suggestions on further directions of research in this field.

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Section: Resultsmentioning

confidence: 99%

“…It provides a general framework for the future of the European information society and its long-term vision. Ambient intelligence services in IoT environments is an emerging research area that can change the way that technology and services are perceived by the users [3].…”

Section: Introductionmentioning

confidence: 99%

Predictors of Ambient Intelligence: An Empirical Study in Enterprises in Slovakia

Bolek¹,

Romanová²

2020

Electronics

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“…In [69], [70], [71], [72], [73], and [74], researchers propose surveillance systems that focus on smart campus Ambient Intelligence (AmI) user context. Specifically, in [69] the authors propose a surveillance system for Smart Campus, which focuses in smart labs and public spaces.…”

Section: ) Systems That Focus On Smart Campus Ambient Intelligence User Contextmentioning

confidence: 99%

Challenges and Solutions of Surveillance Systems in IoT-Enabled Smart Campus: A Survey

Anagnostopoulos¹,

Kostakos

Zaslavsky

et al. 2021

IEEE Access

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A Smart Campus is a miniature of a Smart City with a more demanding framework that enables learning, social interaction and creativity. To ensure a Smart Campus uninterruptible secure operation, a key requirement is that daily routines and activities are performed protected in an environment monitored unobtrusively by a robust surveillance system. The various components that compose such an environment, buildings, labs, public spaces, smart lighting, smart parking, or even smart traffic lights, require us to focus on surveillance systems, and recognize which detection activities to establish. In this paper, we perform a comparative assessment in the area of surveillance systems for Smart Campuses. A proposed taxonomy for IoT-enabled Smart Campus unfold five research dimensions: (1) physical infrastructure; (2) enabling technologies; (3) software analytics; (4) system security; and (5) research methodology. By applying this taxonomy and by adopting a weighted scoring model on the surveyed systems, we first present the state-ofthe-art, and then we make a comparative assessment and classify the systems. We extract valuable conclusions and inferences from this classification, providing insights and directions towards required services offered by surveillance systems for Smart Campus.

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“…9. Decreasing minutes beetwen two keep-alive messages in order to safe battery life intelligence services [16]. In particular, the SEPA instance contains the knowledge base of the SWAMP cloud and it acts as a publish-subscribe broker who notifies subscribers about changes in its graph database.…”

Section: Context Aware Tuning Of the Multi-sensor Nodementioning

confidence: 99%

Enabling Context Aware Tuning of Low Power Sensors for Smart Agriculture

Sindaco

Nanni²,

Aguzzi

et al. 2020

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)

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This paper describes an application for the context aware tuning of the data rate of a battery powered LoRaWAN multi-sensor node equipped with sensors measuring soil features like water content, temperature, conductivity, moisture and water table depth. The application aims at saving as much power as possible, granting at the same time the detection and accurate profiling of events localized in time and space (e.g., due to sudden heavy rain). The tuning rules are based on the interplay between the context heterogeneous actors (sensor data, forecasts, current season, irrigation requests) mediated by a Linked Data distribution platform interconnected to multiple private and public networks. An interoperable application is provided, whose components can be easily extended and reused.

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Ambient Intelligence Services in IoT Environments

Cited by 36 publications

References 0 publications

Predictors of Ambient Intelligence: An Empirical Study in Enterprises in Slovakia

Predictors of Ambient Intelligence: An Empirical Study in Enterprises in Slovakia

Challenges and Solutions of Surveillance Systems in IoT-Enabled Smart Campus: A Survey

Enabling Context Aware Tuning of Low Power Sensors for Smart Agriculture

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