The Internet of Things Supporting Context-Aware Computing: A Cultural Heritage Case Study

Piccialli, Francesco; Chianese, Angelo

doi:10.1007/s11036-017-0810-4

Cited by 37 publications

(21 citation statements)

References 21 publications

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“…The IoT phenomenon has been largely investigated on the technical side (Mighali et al 2015;Piccialli and Chianese 2017). Some approaches already try to facilitate the configuration of smart objects (Petrelli and Lechner 2014).…”

Section: Rationale and Backgroundmentioning

confidence: 99%

Analysing trade-offs in frameworks for the design of smart environments

Ardito

Desolda

Lanzilotti

et al. 2019

Behaviour & Information Technology

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Smart Interactive Experiences (SIEs) are usage situations enabled by the Internet of Things that empower users to interact with the surrounding environment. The goal of our research is to define methodologies and software environments to support the design of SIEs; more specifically, we focus on design paradigms suitable for experts of given domains, who however might not be experts in technology. In this context, this paper discusses some trade-offs that we identified between six different dimensions that characterize the quality of software environments for SIE design. The trade-offs emerged from the analysis of data collected in an experimental study that compared three different design paradigms to understand in which measure each paradigm supports the creative process for SIE design. After reporting on the study procedure and the data analyses, the paper illustrates how the resulting trade-offs led us to identify alternatives for SIE design paradigms, and to structure on their basis a modular architecture of a software platform where the strengths of the three paradigms can be exploited flexibly, i.e., depending on the constraints and the requirements characterizing specific design situations.

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Section: Rationale and Backgroundmentioning

confidence: 99%

Analysing trade-offs in frameworks for the design of smart environments

Ardito

Desolda

Lanzilotti

et al. 2019

Behaviour & Information Technology

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“…The potential of these connected objects is enormous. A 2016 study predicts that in 2020, more than half of the business tools and processes will make use of the Internet of Things …”

Section: The Role Of Wireless Sensor Network and Iot In E‐healthmentioning

confidence: 99%

“…A 2016 study predicts that in 2020, more than half of the business tools and processes will make use of the Internet of Things. [11][12][13][14][15] In the field of health, sensor networks and IoT would monitor the health of patients remotely, facilitate the life of the elderly, and allow them to remain independent.…”

Section: The Role Of Wireless Sensor Network and Iot In E-healthmentioning

confidence: 99%

Internet of Things ‐ integrated IR‐UWB technology for healthcare applications

Chehri

Mouftah

2019

Concurrency and Computation

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Summary Recent technology developments have produced small and smart biomedical sensors, which can be worn or implanted in the human body. These biosensors create closed wireless networks named Wireless Body Area Networks (WBAN). The WBAN will continuously observe the physiological state of patients for both diagnosis and prevention. Those include on‐body measurements such as the Electrocardiogram (ECG), Electroencephalogram (EEG), temperature, and blood pressure. Ultra‐Wide‐Band (UWB) is a technology that has received a lot of attention due to several unique features such as secure transmission, low noise, and low energy consumption. Given the fact that the patients' well‐being might be dependent on the accurate realization of such networks, a high level of design and implementation accuracy are maintained throughout the system. In this paper, we proposed an Impulse‐Radio Ultra‐Wideband system, which is composed of static biomedical nodes mounted on a patient's body to collect vital data and send it wirelessly to a central node or subsequent analysis by healthcare professionals. The performance of this network, such as the effect of node location, the number of transmitted symbols, multiuser interference, and intersymbol interference, is evaluated. We also study the physical layer and quality of service of this proposed architecture.

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“…It takes a lot of time to classify the data, and storing the data consumes a large amount of hardware resources. In order to solve this problem, an Internet of Things (IoT) sea clutter intelligent management system can be established to meet the different needs of sea clutter study by learning from other management architecture [39][40][41] in the future. The extracted data of the fixed range cells are normalized to improve training speed and prediction accuracy.…”

Section: The Proposed Sea Clutter Prediction System Based On Lstm Nnmentioning

confidence: 99%

Sea Clutter Amplitude Prediction Using a Long Short-Term Memory Neural Network

Zhang

et al. 2019

Remote Sensing

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In the marine environment, shore-based radars play an important role in military surveillance and sensing. Sea clutter is one of the main factors affecting the performance of shore-based radar. Affected by marine environmental factors and radar parameters, the fluctuation law of sea clutter amplitude is very complicated. In the process of training a sea clutter amplitude prediction model, the traditional method updates the model parameters according to the current input data and the parameters in the current model, and cannot utilize the historical information of sea clutter amplitude. It is only possible to learn the short-term variation characteristics of the sea clutter. In order to learn the long-term variation law of sea clutter, a sea clutter prediction system based on the long short-term memory neural network is proposed. Based on sea clutter data collected by IPIX radar, UHF-band radar and S-band radar, the experimental results show that the mean square error of this prediction system is smaller than the traditional prediction methods. The sea clutter suppression signal is extracted by comparing the predicted sea clutter data with the original sea clutter data. The results show that the proposed sea clutter prediction system has a good effect on sea clutter suppression.

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The Internet of Things Supporting Context-Aware Computing: A Cultural Heritage Case Study

Cited by 37 publications

References 21 publications

Analysing trade-offs in frameworks for the design of smart environments

Analysing trade-offs in frameworks for the design of smart environments

Internet of Things ‐ integrated IR‐UWB technology for healthcare applications

Sea Clutter Amplitude Prediction Using a Long Short-Term Memory Neural Network

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