IoT platform for Smart Cities: Requirements and implementation case studies

Patti, Edoardo; Acquaviva, Andrea

doi:10.1109/rtsi.2016.7740618

Cited by 42 publications

(32 citation statements)

References 11 publications

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“…To demonstrate the model, we compare our framework with District Information Modeling and Management for Energy Reduction (DIMMER) [19].DIMMER is a distributed IoT software architecture to collect and correlate heterogeneous energy data into a distributed smart archive system for data analysis and management. As shown in table 1, both models aims to sharing the data among different stakeholders in Smart City scenarios.…”

Section: Resultsmentioning

confidence: 99%

An Interoperable Data Framework to Manipulate the Smart City Data using Semantic Technologies

Beseiso¹,

Al-Alwani²,

AlTameem³

2017

ijacsa

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Abstract-During the last decade, enormous volumes of urban data have been produced by the Government agencies, the NGOs and the citizens. In such a scenario, we are presented with a diverse set of data which holds valuable information. This information can be extracted and analyzed and have a number of usages for the well-being of citizens. The major impediment to achieve this goal is the data itself, the available data are redundant, scattered and come with various legacy formats. Data interoperability, scalability and integration are paramount issues which could not be resolved unless the scattered data silos are accessible with a standard representation. In this paper, we propose a framework that resolves the data interoperability and associated challenges in the smart city environment. The framework takes the raw smart city data from several resources and stores them in a NoSQL database. The framework transforms the scattered data into machine-processable data. Besides, the database is linked with an API and simple dashboard for further analysis, which can be utilized to build big data applications based on urban data so that government agencies can get a summarized overview of resource distribution.

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Section: Resultsmentioning

confidence: 99%

An Interoperable Data Framework to Manipulate the Smart City Data using Semantic Technologies

Beseiso¹,

Al-Alwani²,

AlTameem³

2017

ijacsa

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“…PHI-CIB adopts the open-source LinkSmart middleware [43] and extends its features to fulfill the requirements for a smart-city context. Indeed, an IoT middleware for a smart city needs (i) to be highly available, (ii) to scale up rapidly, and (iii) to provide a uniform interface to all deployed technologies [44,45].…”

Section: Data Collectionmentioning

confidence: 99%

Forecasting Heating Consumption in Buildings: A Scalable Full-Stack Distributed Engine

et al. 2019

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Predicting power demand of building heating systems is a challenging task due to the high variability of their energy profiles. Power demand is characterized by different heating cycles including sequences of various transient and steady-state phases. To effectively perform the predictive task by exploiting the huge amount of fine-grained energy-related data collected through Internet of Things (IoT) devices, innovative and scalable solutions should be devised. This paper presents PHi-CiB, a scalable full-stack distributed engine, addressing all tasks from energy-related data collection, to their integration, storage, analysis, and modeling. Heterogeneous data measurements (e.g., power consumption in buildings, meteorological conditions) are collected through multiple hardware (e.g., IoT devices) and software (e.g., web services) entities. Such data are integrated and analyzed to predict the average power demand of each building for different time horizons. First, the transient and steady-state phases characterizing the heating cycle of each building are automatically identified; then the power-level forecasting is performed for each phase. To this aim, PHi-CiB relies on a pipeline of three algorithms: the Exponentially Weighted Moving Average, the Multivariate Adaptive Regression Spline, and the Linear Regression with Stochastic Gradient Descent. PHi-CiB’s current implementation exploits Apache Spark and MongoDB and supports parallel and scalable processing and analytical tasks. Experimental results, performed on energy-related data collected in a real-world system show the effectiveness of PHi-CiB in predicting heating power consumption of buildings with a limited prediction error and an optimal horizontal scalability.

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“…• Awareness to end-users (IoT facilitates the interaction between users and things); • Exposure of web services and API via open and standard data formats (fostering the design and development of novel services) (Patti & Acquaviva, 2016). While IoT has already become a buzzword in the area of innovative urban design, there is still little understanding of the exact types of communication technologies expected to enable it.…”

Section: Iot-based Solutions For Smart City Gridsmentioning

confidence: 99%

The Practical Side of Iot Implementation in Smart Cities

Kravchenko¹

2019

Proceedings of the International Conferences ICT, Society, and Human Beings 2019; Connected Smart Cities 2019; And Web Based Co

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Smart cities are an innovative concept of sustainable urban design that gains popularity across the globe. Its core premise is the provision of smart educational, utility, traffic, waste and resource management, and health services with an advanced level of connectivity. The Internet of Things (IoT) is employed as the technology powering that connectivity and ensuring that the smart services operate based on intelligent data collection, sharing, and analysis. However, while IoT is actively explored today and exhibits numerous tech achievements, there are still pressing challenges for its practical implementation in the smart city architectures. This article explores the unresolved challenges such as impaired device interoperability, vulnerability of IoT grids to hacks, data security and privacy issues, as well as the slow transition of governments to smart services. The study also offers insights into the future directions of IoT research and development to empower smart city creation.

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IoT platform for Smart Cities: Requirements and implementation case studies

Cited by 42 publications

References 11 publications

An Interoperable Data Framework to Manipulate the Smart City Data using Semantic Technologies

An Interoperable Data Framework to Manipulate the Smart City Data using Semantic Technologies

Forecasting Heating Consumption in Buildings: A Scalable Full-Stack Distributed Engine

The Practical Side of Iot Implementation in Smart Cities

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