A Living Lab and Testing Infrastructure for the Development of Innovative Smart Energy Solutions: the eLUX Laboratory of the University of Brescia

Flammini, A.; Pasetti, Marco; Rinaldi, Stefano; Bellagente, Paolo; Ciribini, Angelo Luigi Camillo; Tagliabue, Lavinia Chiara; Zavanella, Lucio; Zanoni, Simone; Oggioni, Giorgia; Pedrazzi, Giorgio

doi:10.23919/aeit.2018.8577329

Cited by 22 publications

(11 citation statements)

References 25 publications

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“…The datasets used in this work were obtained from a huge experiment currently ongoing at the eLUX laboratory at the University of Brescia, Italy. eLUX is a living laboratory for smart energy, smart grids, and smart environments for Industry 4.0 [ 58 ]. The laboratory concentrates and analyzes data coming from many distributed plants and from a huge quantity of sensors, meters, and actuators of different types, implementing an excellent example of the smart grid based on industrial-grade IoT systems.…”

Section: Case Study Definitionmentioning

confidence: 99%

An Evolving TinyML Compression Algorithm for IoT Environments Based on Data Eccentricity

Signoretti

Silva

Andrade

et al. 2021

Sensors

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Currently, the applications of the Internet of Things (IoT) generate a large amount of sensor data at a very high pace, making it a challenge to collect and store the data. This scenario brings about the need for effective data compression algorithms to make the data manageable among tiny and battery-powered devices and, more importantly, shareable across the network. Additionally, considering that, very often, wireless communications (e.g., low-power wide-area networks) are adopted to connect field devices, user payload compression can also provide benefits derived from better spectrum usage, which in turn can result in advantages for high-density application scenarios. As a result of this increase in the number of connected devices, a new concept has emerged, called TinyML. It enables the use of machine learning on tiny, computationally restrained devices. This allows intelligent devices to analyze and interpret data locally and in real time. Therefore, this work presents a new data compression solution (algorithm) for the IoT that leverages the TinyML perspective. The new approach is called the Tiny Anomaly Compressor (TAC) and is based on data eccentricity. TAC does not require previously established mathematical models or any assumptions about the underlying data distribution. In order to test the effectiveness of the proposed solution and validate it, a comparative analysis was performed on two real-world datasets with two other algorithms from the literature (namely Swing Door Trending (SDT) and the Discrete Cosine Transform (DCT)). It was found that the TAC algorithm showed promising results, achieving a maximum compression rate of 98.33%. Additionally, it also surpassed the two other models regarding the compression error and peak signal-to-noise ratio in all cases.

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Section: Case Study Definitionmentioning

confidence: 99%

An Evolving TinyML Compression Algorithm for IoT Environments Based on Data Eccentricity

Signoretti

Silva

Andrade

et al. 2021

Sensors

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“…Smart campus development is premised on the concept of cloud computing as well as other recent technologies (Benltoufa et al, 2018;Flammini et al, 2018). Isolated systems such as office management, finance management, education management and other related systems are integrated into one system through cloud computing (Nie, 2013).…”

Section: Implications Of Findings For the Development Of Smart Citiesmentioning

confidence: 99%

A Systems Thinking Model for Transitioning Smart Campuses to Cities

Omotayo

Awuzie

Ajayi

et al. 2021

Front. Built Environ.

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The contribution of smart campuses to smart cities’ development and vice versa has been elucidated in extant literature. The micro-transfer of smart technologies and probable procurement and contracting models remain critical for such contributions to take place. This study used a systems thinking approach to establish the interrelationships existing between smart campus technologies which can be scaled towards the development of smart cities and assess the critical failure factors negating the micro-transfer of these technologies to smart city development initiatives in developing countries. To achieve its objective, the study adopted a phenomenological research design wherein qualitative data was elicited from a purposively selected sample of seven interviewees in South African Universities. The next phase of the analysis involved a thematic approach for the production of a causal loop diagram (CLD) reflecting the interrelationships between the abovementioned facets and the associated impact on the transference of knowledge and technologies from smart campuses to smart cities. This CLD was subsequently validated by a cohort of five experts. Findings from the validation phase were incorporated in an improved CLD which provided different archetypes for engendering successful transference. This study holds salient implications for universities, cities and other stakeholders seeking to engage in a quadruple helix innovative arrangement for smart campus/city development. This study concluded that smart campuses could act as living labs for future smartness of cities globally. Government funding and willingness to produce smart cities from campuses is a fundamental feature of creating smart infrastructure in cities.

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“…The eLUX laboratory is a living lab for research, training, and services on smart-grids, smart-living and electric vehicles within the Engineering Campus of the University of Brescia. Further details on the assets and experiments of the laboratory can be found in [45].…”

Section: Application Examplementioning

confidence: 99%

On the Use of LoRaWAN for the Monitoring and Control of Distributed Energy Resources in a Smart Campus

et al. 2020

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The application of the most recent advances of the Internet-of-Things (IoT) technology to the automation of buildings is emerging as a promising solution to achieve greater efficiencies in energy consumption, and to allow the realization of sustainable models. The application of IoT has been demonstrated as effective in many fields, such as confirmed, for instance, by the Industry 4.0 concepts, which are revolutionizing modern production chains. By following this approach, the use of distributed control architectures and of IoT technologies (both wired and wireless) would result in effective solutions for the management of smart environments composed of groups of buildings, such as campuses. In this case, heterogeneous IoT solutions are typically adopted to satisfy the requirements of the very diverse possible scenarios (e.g., indoor versus outdoor coverage, mobile versus fixed nodes, just to mention a few), making their large-scale integration cumbersome. To cope with this issue, this paper presents an IoT architecture able to transparently manage different communication protocols in smart environments, and investigates its possible application for the monitoring and control of distributed energy resources in a smart campus. In particular, a use–case focused on the integration of the Long Range Wide Area Network (LoRaWAN) technology is considered to cope with heterogeneous indoor and outdoor communication scenarios. The feasibility analysis of the proposed solution is carried out by computing the scalability limits of the approach, based on the proposed smart campus data model. The results of the study showed that the proposed solution would be able to manage more than 10,000 nodes. An experimental validation of the LoRaWAN technology confirms its suitability in terms of coverage and latency, with a minimum LoRaWAN cell coverage range of 250 m, and a communication latency of about 400 ms. Finally, the advantages of the proposed solution in the supervision and management of a PV system are highlighted in a real-world scenario.

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A Living Lab and Testing Infrastructure for the Development of Innovative Smart Energy Solutions: the eLUX Laboratory of the University of Brescia

Cited by 22 publications

References 25 publications

An Evolving TinyML Compression Algorithm for IoT Environments Based on Data Eccentricity

An Evolving TinyML Compression Algorithm for IoT Environments Based on Data Eccentricity

A Systems Thinking Model for Transitioning Smart Campuses to Cities

On the Use of LoRaWAN for the Monitoring and Control of Distributed Energy Resources in a Smart Campus

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