Combining BIM, GIS, and IoT to Foster Energy Management and Simulation in Smart Cities

2021 ◽  
pp. 425-447
Author(s):  
Edoardo Patti ◽  
Francesco G. Brundu ◽  
Andrea Bellagarda ◽  
Lorenzo Bottaccioli ◽  
Niccolò Rapetti ◽  
...  
Keyword(s):  
Energy Management ◽  
Smart Cities ◽  
Control Strategies ◽  
Control Policy ◽  
Information Model ◽  
Environmental Data ◽  
Software Infrastructure ◽  
Heterogeneous Information ◽  
Building Information ◽  
Iot Devices

This chapter presents a novel distributed software infrastructure to enable energy management and simulation of novel control strategies in smart cities. In this context, the following heterogeneous information, describing the different entities in a city, needs to be taken into account to form a unified district information model: internet-of-things (IoT) devices, building information model, system information model, and georeferenced information system. IoT devices are crucial to monitor in (near-) real-time both building energy trends and environmental data. Thus, the proposed solution fulfills the integration and interoperability of such data sources providing also a correlation among them. Such correlation is the key feature to unlock management and simulation of novel energy policies aimed at optimizing the energy usage accounting also for its impact on building comfort. The platform has been deployed in a real-world district and a novel control policy for the heating distribution network has been developed and tested. Finally, experimental results are presented and discussed.

EEB Project System Integration and Technology Sperimentation Matrix

2021 ◽  
pp. 196-218
Author(s):  
Francesca Maria Ugliotti
Keyword(s):  
Decision Making ◽  
Energy Management ◽  
System Integration ◽  
Smart Cities ◽  
Three Dimensional ◽  
Semantic Data ◽  
Energy Efficient Buildings ◽  
Digital Twins ◽  
Urban Modelling ◽  
Iot Devices

Today an increasing number of cities are equipping themselves with three-dimensional urban modelling and simulation platforms for energy management to integrate both spatial and semantic data for enabling better decision-making. The work presented in this chapter is the result of the study carried out by Politecnico di Torino within the Energy Efficient Buildings (EEB) project. Collected data on urban and building scale are managed in specialized, independent, and heterogeneous domains such as GIS, BIM, and IoT devices for energy and electrical monitoring. Possible relationships among these datasets in the perspective of system integration have been carried out according to a rich matrix of experimentations. Specific tools, including innovative visualization technologies and web services, are put in place to allow final users to benefit from this data. The infrastructure is intended to establish a common interoperable ground among heterogeneous networks to achieve the goal of smart cities digital twins.

scholarly journals IoT Software Infrastructure for Energy Management and Simulation in Smart Cities

2017 ◽  
Vol 13 (2) ◽  
pp. 832-840 ◽  
Author(s):  
Francesco Gavino Brundu ◽  
Laura Rietto ◽  
Andrea Acquaviva ◽  
Edoardo Patti ◽  
Anna Osello ◽  
...  
Keyword(s):  
Energy Management ◽  
Smart Cities ◽  
Software Infrastructure

scholarly journals Virtualizing AI at the Distributed Edge Towards Intelligent IoT Applications

2021 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Claudia Campolo ◽  
Giacomo Genovese ◽  
Antonio Iera ◽  
Antonella Molinaro
Keyword(s):  
Resource Constraints ◽  
Smart Cities ◽  
Traditional Approach ◽  
Low Cost ◽  
Iot Applications ◽  
Software And Hardware ◽  
Iot Devices ◽  
Consumer Devices ◽  
Hardware Platforms ◽  
Smart Factories

Several Internet of Things (IoT) applications are booming which rely on advanced artificial intelligence (AI) and, in particular, machine learning (ML) algorithms to assist the users and make decisions on their behalf in a large variety of contexts, such as smart homes, smart cities, smart factories. Although the traditional approach is to deploy such compute-intensive algorithms into the centralized cloud, the recent proliferation of low-cost, AI-powered microcontrollers and consumer devices paves the way for having the intelligence pervasively spread along the cloud-to-things continuum. The take off of such a promising vision may be hurdled by the resource constraints of IoT devices and by the heterogeneity of (mostly proprietary) AI-embedded software and hardware platforms. In this paper, we propose a solution for the AI distributed deployment at the deep edge, which lays its foundation in the IoT virtualization concept. We design a virtualization layer hosted at the network edge that is in charge of the semantic description of AI-embedded IoT devices, and, hence, it can expose as well as augment their cognitive capabilities in order to feed intelligent IoT applications. The proposal has been mainly devised with the twofold aim of (i) relieving the pressure on constrained devices that are solicited by multiple parties interested in accessing their generated data and inference, and (ii) and targeting interoperability among AI-powered platforms. A Proof-of-Concept (PoC) is provided to showcase the viability and advantages of the proposed solution.

scholarly journals Improving Sustainability of Smart Cities through Visualization Techniques for Big Data from IoT Devices

2020 ◽  
Vol 12 (14) ◽  
pp. 5595 ◽  
Author(s):  
Ana Lavalle ◽  
Miguel A. Teruel ◽  
Alejandro Maté ◽  
Juan Trujillo
Keyword(s):  
Big Data ◽  
Smart City ◽  
Smart Cities ◽  
Fire Department ◽  
Heterogeneous Data ◽  
Fire Extinguishing ◽  
Heterogeneous Data Sources ◽  
Iot Devices ◽  
Visualization Techniques ◽  
The Impact

Fostering sustainability is paramount for Smart Cities development. Lately, Smart Cities are benefiting from the rising of Big Data coming from IoT devices, leading to improvements on monitoring and prevention. However, monitoring and prevention processes require visualization techniques as a key component. Indeed, in order to prevent possible hazards (such as fires, leaks, etc.) and optimize their resources, Smart Cities require adequate visualizations that provide insights to decision makers. Nevertheless, visualization of Big Data has always been a challenging issue, especially when such data are originated in real-time. This problem becomes even bigger in Smart City environments since we have to deal with many different groups of users and multiple heterogeneous data sources. Without a proper visualization methodology, complex dashboards including data from different nature are difficult to understand. In order to tackle this issue, we propose a methodology based on visualization techniques for Big Data, aimed at improving the evidence-gathering process by assisting users in the decision making in the context of Smart Cities. Moreover, in order to assess the impact of our proposal, a case study based on service calls for a fire department is presented. In this sense, our findings will be applied to data coming from citizen calls. Thus, the results of this work will contribute to the optimization of resources, namely fire extinguishing battalions, helping to improve their effectiveness and, as a result, the sustainability of a Smart City, operating better with less resources. Finally, in order to evaluate the impact of our proposal, we have performed an experiment, with non-expert users in data visualization.

scholarly journals A Self-Powered PMFC-Based Wireless Sensor Node for Smart City Applications

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Daniel Ayala-Ruiz ◽  
Alejandro Castillo Atoche ◽  
Erica Ruiz-Ibarra ◽  
Edith Osorio de la Rosa ◽  
Javier Vázquez Castillo
Keyword(s):  
Energy Harvesting ◽  
Low Power ◽  
Sensor Node ◽  
Smart Cities ◽  
Cloud Services ◽  
Environmental Data ◽  
Security And Privacy ◽  
Wireless Sensor ◽  
Ultra Low Power ◽  
Self Powered

Long power wide area networks (LPWAN) systems play an important role in monitoring environmental conditions for smart cities applications. With the development of Internet of Things (IoT), wireless sensor networks (WSN), and energy harvesting devices, ultra-low power sensor nodes (SNs) are able to collect and monitor the information for environmental protection, urban planning, and risk prevention. This paper presents a WSN of self-powered IoT SNs energetically autonomous using Plant Microbial Fuel Cells (PMFCs). An energy harvesting device has been adapted with the PMFC to enable a batteryless operation of the SN providing power supply to the sensor network. The low-power communication feature of the SN network is used to monitor the environmental data with a dynamic power management strategy successfully designed for the PMFC-based LoRa sensor node. Environmental data of ozone (O3) and carbon dioxide (CO2) are monitored in real time through a web application providing IoT cloud services with security and privacy protocols.

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