scispace - formally typeset
Open AccessJournal ArticleDOI

Deepint.net: A Rapid Deployment Platform for Smart Territories.

TLDR
In this paper, an efficient cyberphysical platform for the smart management of smart territories is presented, which facilitates the implementation of data acquisition and data management methods, as well as data representation and dashboard configuration.
Abstract
This paper presents an efficient cyberphysical platform for the smart management of smart territories. It is efficient because it facilitates the implementation of data acquisition and data management methods, as well as data representation and dashboard configuration. The platform allows for the use of any type of data source, ranging from the measurements of a multi-functional IoT sensing devices to relational and non-relational databases. It is also smart because it incorporates a complete artificial intelligence suit for data analysis; it includes techniques for data classification, clustering, forecasting, optimization, visualization, etc. It is also compatible with the edge computing concept, allowing for the distribution of intelligence and the use of intelligent sensors. The concept of smart cities is evolving and adapting to new applications; the trend to create intelligent neighbourhoods, districts or territories is becoming increasingly popular, as opposed to the previous approach of managing an entire megacity. In this paper, the platform is presented, and its architecture and functionalities are described. Moreover, its operation has been validated in a case study where the bike renting service of Paris—Velib’ Metropole has been managed. This platform could enable smart territories to develop adapted knowledge management systems, adapt them to new requirements and to use multiple types of data, and execute efficient computational and artificial intelligence algorithms. The platform optimizes the decisions taken by human experts through explainable artificial intelligence models that obtain data from IoT sensors, databases, the Internet, etc. The global intelligence of the platform could potentially coordinate its decision-making processes with intelligent nodes installed in the edge, which would use the most advanced data processing techniques.

read more

Citations
More filters
Journal ArticleDOI

Responsible Urban Innovation with Local Government Artificial Intelligence (AI): A Conceptual Framework and Research Agenda

TL;DR: In this article, the authors contribute to the existing responsible urban innovation discourse by focusing on local government artificial intelligence (AI) systems, providing a literature and practice overview, and a conceptual framework.
Journal ArticleDOI

Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures

TL;DR: This perspective paper concentrates on the “green AI” concept as an enabler of the smart city transformation, as it offers the opportunity to move away from purely technocentric efficiency solutions towards efficient, sustainable and equitable solutions capable of realizing the desired urban futures.
Journal ArticleDOI

The Evolution of City-as-a-Platform: Smart Urban Development Governance with Collective Knowledge-Based Platform Urbanism

TL;DR: The findings revealed the prospects and constraints for the realization of transformative and disruptive impacts on the government and society through the platform urbanism, along with disclosing the opportunities and challenges for smarter urban development governance with collective knowledge through platform urbanist.
Journal ArticleDOI

A Hybrid Model for COVID-19 Monitoring and Prediction

TL;DR: A model developed for the evolution of COVID in the city of Manizales, capital of the Department of Caldas, Colombia is presented, focusing on the methodology used to allow its application to other cases, as well as on the monitoring tools developed for this purpose.
Journal ArticleDOI

An efficient management platform for developing smart cities: Solution for real-time and future crowd detection

TL;DR: This article shows how deepint.net has been used to estimate pedestrian traffic on the streets of Melbourne (Australia) using the XGBoost algorithm, and presents a platform for capturing, integrating, analysing, and creating dashboards, alert systems, optimisation models, etc.
References
More filters
Journal ArticleDOI

Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities

TL;DR: The paper presents a brief overview of smart cities, followed by the features and characteristics, generic architecture, composition, and real-world implementations ofSmart cities, and some challenges and opportunities identified through extensive literature survey on smart cities.
Journal ArticleDOI

Cyber security challenges in Smart Cities: Safety, security and privacy.

TL;DR: This paper presents a model representing the interactions between person, servers and things in a Smart City, and examines two important and entangled challenges: security and privacy.
Journal ArticleDOI

Mobile Edge Computing Potential in Making Cities Smarter

TL;DR: The proposed scheme enforces an autonomic creation of MEC services to allow anywhere anytime data access with optimum QoE and reduced latency to ensure ultra-short latency through a smart MEC architecture capable of achieving the 1 ms latency dream for the upcoming 5G mobile systems.
Journal ArticleDOI

Intelligent Edge Computing for IoT-Based Energy Management in Smart Cities

TL;DR: This article presents an efficient energy scheduling scheme with deep reinforcement learning for the proposed framework of an IoT-based energy management system based on edge computing infrastructure withDeep reinforcement learning.
Journal ArticleDOI

Vehicular Fog Computing: Enabling Real-Time Traffic Management for Smart Cities

TL;DR: This article constructs a three-layer VFC model to enable distributed traffic management in order to minimize the response time of citywide events collected and reported by vehicles and formulated as an optimization problem by leveraging moving and parked vehicles as fog nodes.
Related Papers (5)