A model-driven middleware approach to reduce the semantic gap between application domains and the generic infrastructure of smart cities

2021 ◽  
Author(s):  
Paulo Cesar F. Melo
Keyword(s):  
Smart Cities ◽  
Semantic Gap ◽  
Model Driven

scholarly journals CrowDSL: Platform for Incidents Management in a Smart City Context

2021 ◽  
Vol 5 (3) ◽  
pp. 44
Author(s):  
Darío Rodríguez-García ◽  
Vicente García-Díaz ◽  
Cristian González García
Keyword(s):  
Smart City ◽  
Smart Cities ◽  
Expressive Power ◽  
Incident Reporting ◽  
Domain Specific Language ◽  
Crowd Sourcing ◽  
Specific Domain ◽  
Specific Language ◽  
Model Driven ◽  
Domain Specific

The final objective of smart cities is to optimize services and improve the quality of life of their citizens, who can play important roles due to the information they can provide. This information can be used in order to enhance many sectors involved in city activity such as transport, energy or health. Crowd-sourcing initiatives focus their efforts on making cities safer places that are adapted to the population size they host. In this way, citizens are able to report the issues they identify to the relevant body so that they can be fixed and, at the same time, they can provide useful information to other citizens. There are several projects aimed at reporting incidents in a smart city context. In this paper, we propose the use of model-driven engineering by designing a graphical domain-specific language to abstract and improve the incident-reporting process. With the use of a domain-specific language, we can obtain several benefits in our research for users and cities. For instance, we can shorten the time for reporting the events by users and, at the same time, we gain an expressive power compared to other methodologies for incident reporting. In addition, it can be reused and is centered in this specific domain after being studied. Furthermore, we have evaluated the DSL with different users, obtaining a high satisfaction percentage.

scholarly journals MIKADO: a smart city KPIs assessment modeling framework

2021 ◽  
Author(s):  
Martina De Sanctis ◽  
Ludovico Iovino ◽  
Maria Teresa Rossi ◽  
Manuel Wimmer
Keyword(s):  
Smart City ◽  
Smart Cities ◽  
Automatic Assessment ◽  
Modeling Framework ◽  
Domain Experts ◽  
Model Driven ◽  
Domain Specific ◽  
Modeling Process ◽  
Smart Governance ◽  
Definition Of

AbstractSmart decision making plays a central role for smart city governance. It exploits data analytics approaches applied to collected data, for supporting smart cities stakeholders in understanding and effectively managing a smart city. Smart governance is performed through the management of key performance indicators (KPIs), reflecting the degree of smartness and sustainability of smart cities. Even though KPIs are gaining relevance, e.g., at European level, the existing tools for their calculation are still limited. They mainly consist in dashboards and online spreadsheets that are rigid, thus making the KPIs evolution and customization a tedious and error-prone process. In this paper, we exploit model-driven engineering (MDE) techniques, through metamodel-based domain-specific languages (DSLs), to build a framework called MIKADO for the automatic assessment of KPIs over smart cities. In particular, the approach provides support for both: (i) domain experts, by the definition of a textual DSL for an intuitive KPIs modeling process and (ii) smart cities stakeholders, by the definition of graphical editors for smart cities modeling. Moreover, dynamic dashboards are generated to support an intuitive visualization and interpretation of the KPIs assessed by our KPIs evaluation engine. We provide evaluation results by showing a demonstration case as well as studying the scalability of the KPIs evaluation engine and the general usability of the approach with encouraging results. Moreover, the approach is open and extensible to further manage comparison among smart cities, simulations, and KPIs interrelations.

scholarly journals A Model-Driven Approach for Solving the Software Component Allocation Problem

Algorithms ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 354
Author(s):  
Issam Al-Azzoni ◽  
Julian Blank ◽  
Nenad Petrović
Keyword(s):  
Optimal Allocation ◽  
Smart Cities ◽  
Fog Computing ◽  
Linear Optimization ◽  
Software Component ◽  
Problem Definition ◽  
Allocation Problem ◽  
Multi Objective ◽  
Model Driven ◽  
Component Allocation

The underlying infrastructure paradigms behind the novel usage scenarios and services are becoming increasingly complex—from everyday life in smart cities to industrial environments. Both the number of devices involved and their heterogeneity make the allocation of software components quite challenging. Despite the enormous flexibility enabled by component-based software engineering, finding the optimal allocation of software artifacts to the pool of available devices and computation units could bring many benefits, such as improved quality of service (QoS), reduced energy consumption, reduction of costs, and many others. Therefore, in this paper, we introduce a model-based framework that aims to solve the software component allocation problem (CAP). We formulate it as an optimization problem with either single or multiple objective functions and cover both cases in the proposed framework. Additionally, our framework also provides visualization and comparison of the optimal solutions in the case of multi-objective component allocation. The main contributions introduced in this paper are: (1) a novel methodology for tackling CAP-alike problems based on the usage of model-driven engineering (MDE) for both problem definition and solution representation; (2) a set of Python tools that enable the workflow starting from the CAP model interpretation, after that the generation of optimal allocations and, finally, result visualization. The proposed framework is compared to other similar works using either linear optimization, genetic algorithm (GA), and ant colony optimization (ACO) algorithm within the experiments based on notable papers on this topic, covering various usage scenarios—from Cloud and Fog computing infrastructure management to embedded systems, robotics, and telecommunications. According to the achieved results, our framework performs much faster than GA and ACO-based solutions. Apart from various benefits of adopting a multi-objective approach in many cases, it also shows significant speedup compared to frameworks leveraging single-objective linear optimization, especially in the case of larger problem models.

scholarly journals Model-Driven Mobile CrowdSensing for Smart Cities

2018 ◽  
Author(s):  
Paulo César F. Melo ◽  
Fábio M. Costa
Keyword(s):  
Quality Of Life ◽  
Mobile Devices ◽  
Information And Communication Technologies ◽  
Smart Cities ◽  
Communication Technologies ◽  
Mobile Crowdsensing ◽  
Model Driven ◽  
Information And Communication ◽  
Models At Runtime

Making cities smarter can help improve city services, optimize resource and infrastructure utilization and increase quality of life. Smart Cities connect citizens in novel ways by leveraging the latest advances in information and communication technologies (ICT). The integration of rich sensing capabilities in today's mobile devices allows their users to actively participate in sensing the environment. In Mobile CrowdSensing (MCS) citizens of a Smart City collect, share and jointly use services based on sensed data. The main challenges for smart cities regarding MCS is the heterogeneity of devices and the dynamism of the environment. To overcome these challenges, this paper presents an architecture based on models at runtime (M@rt) to support dynamic MCS queries in Smart Cities. The architecture is proposed as an extension of the InterSCity platform, leveraging on its existing services and on its capability to integrate city infrastructure resources.

A Semantic Agile Approach for Reconfigurable Distributed Applications in Pervasive Environments

2020 ◽  
Vol 11 (2) ◽  
pp. 48-67 ◽  
Author(s):  
Abderrahim Lakehal ◽  
Adel Alti ◽  
Sébastien Laborie ◽  
Philippe Roose
Keyword(s):  
Life Cycle ◽  
Mobile Applications ◽  
Smart Cities ◽  
Situation Model ◽  
Distributed Applications ◽  
Use Cases ◽  
Context Aware ◽  
Distributed Application ◽  
Model Driven ◽  
Agile Approach

Nowadays, future mobile applications must have the ability to use distributed smart connected objects on various smart cities domains. Most existing mobile applications have mostly neglected to consider the user's current needs and their preferences that continuously quickly evolve. The authors have developed a novel framework to generate dynamically distributed application as service chains of components and optimize connected objects life cycle. The framework combines a generic context-aware ontology situation model, middleware and IoT for managing user's composite situations at the design and the run-time levels. The first level consists of modeling applications, profiles and usage contexts through a model-driven methodology considering the specified user's constraints. The second level consists of context monitoring mechanisms, situation reasoning and deploying adapted services using Kai-smart platform for meeting user's needs and its current contexts. The proposed framework is validated through several use cases in different smart domains.

Sign in / Sign up

Export Citation Format

Share Document