João Vitor Torres, Igor Drummond Alvarenga, Raouf Boutaba and Otto Carlos Muniz Bandeira Duarte. Evaluating CRoS-NDN: a comparative performance analysis of a controller-based routing scheme for named-data networking. , 2019.
The huge amount of content names available in Named-Data Networking (NDN) challenges both the required routing table size and the techniques for locating and forwarding information. Content copies and content mobility exacerbate the scalability challenge to reach content in the new locations. We present and analyze the performance of a proposed Controller-based Routing Scheme, named CRoS-NDN, which preserves NDN features using the same interest and data packets. CRoS-NDN supports content mobility and provides fast content recovery from copies that do not belong to the consumer-producer path because it splits identity from location without incurring FIB size explosion or supposing prefix aggregation. It provides features similar to Content Distribution Networks (CDN) in NDN, and improves the routing efficiency. We compare our proposal with similar routing protocols and derive analytical expressions for lower-bound efficiency and upper-bound latency. We also conduct extensive simulations to evaluate results in data delivery efficiency and delay. The results show the robust behavior of the proposed scheme achieving the best efficiency and delay performance for a wide range of scenarios. Furthermore, CRoS-NDN results in low use of processing time and memory for a growing number of prefixes.
Leonardo Leite, Carla Rocha, Fabio Kon, Dejan Milojicic and Paulo Meirelles. A Survey of DevOps Concepts and Challenges. ACM Computing Surveys (CSUR), Volume 52 Issue 6, 2019.
DevOps is a collaborative and multidisciplinary organizational effort to automate continuous delivery of new software updates while guaranteeing their correctness and reliability. The present survey investigates and discusses DevOps challenges from the perspective of engineers, managers, and researchers. We review the literature and develop a DevOps conceptual map, correlating the DevOps automation tools with these concepts. We then discuss their practical implications for engineers, managers, and researchers. Finally, we critically explore some of the most relevant DevOps challenges reported by the literature.
Christian Bongiorno, Daniele Santucci, Fabio Kon, Paolo Santi, and Carlo Ratti. Comparing bicycling and pedestrian mobility: Patterns of non-motorized human mobility in Greater Boston. Journal of Transport Geography, Volume 80, October, 2019.
During the past 100 years, many large cities around the world prioritized individual transportation in cars over more sustainable and healthier modes of transportation. As a result, traffic jams, air pollution, and fatal accidents are a daily reality in most metropolis, in both developed and developing countries. On the other hand, walking and bicycling are effective means of transportation for short to medium distances that offer advantages to both the city environment and the health of its citizens. While there is a large body of research in modeling and analysis of urban mobility based on motorized vehicles, there is much less research focusing on non-motorized vehicles, and almost no research on comparing pedestrian and cyclist behavior. In this paper, we present a detailed quantitative analysis of two datasets, for the same period and location, covering pedestrian and bike sharing mobility. We contrast the mobility patterns in the two modes and discuss their implications. We show how pedestrian and bike mobility are affected by temperature, precipitation and time of day. We also analyze the spatial distribution of non-motorized trips in Greater Boston and characterize the associated network of mobility flows with respect to multiple metrics. This work contributes to a better understanding of the characteristics of non-motorized urban mobility with respect to distance, duration, time of day, spatial distribution, as well as sensitivity to the weather.
Collaboration between MIT Senseable City Lab and USP
Matias Santos, Rodrigo Tinini, Gustavo Figueiredo and Daniel Batista. Data Analysis and Energy Consumption Prediction in a Cloud-Fog RAN Environment. IEEE Latin-American Conference on Communications (LATINCOM 2019), 2019.
The extraction of information from data collected in a myriad of environments provides unprecedented opportunities for a big range of actions such as decision making and better resource management. Benefits from its processes are relatively large for many network domains such as protocol design, hybrid architectures redesign, and resource management and optimization. Time series or historical data series can be used in several ways, like pattern analysis and prediction support, making it an important support tool for managers to develop goals and objectives focused on their business. The goal of this paper is to discuss the potential of data analysis in hybrid Cloud- Fog Radio Access Networks (CF-RAN) scenarios and present results of applications of the data in the process of prediction energy consumption. In particular, we analysed the knowledge data extraction of some metrics with a strong relationship with energy consumption and we perform a prediction by applying a deep learning algorithm using the previous four hour period to predict the next hour.
Rodrigo Tinini, Gustavo Figueiredo and Daniel Batista. A Batch Scheduling Algorithm for VPON Reconfiguration and BBU Migration in Hybrid Cloud-Fog RAN. 18th IEEE International Symposium on Network Computing and Applications (NCA 2019), 2019.
Hybrid Cloud-Fog Radio Access Network (CF-RAN) is a recent network architecture proposed to increase network coverage from CRAN while leveraging power consumption in future 5G networks. In CF-RAN, the processing of baseband signals from Remote Radio-Heads (RRHs) can be performed in virtualized BaseBand Units (vBBUs) located in the cloud or in fog nodes that are instantiated in function of the network demand. Through a Time-and-Wavelength Division Multiplexing Passive Optical Network (TWDM-PON), virtualized PONs (VPONs) can be dynamically created to support transmissions from RRHs to vBBUs. However, due to traffic fluctuations, the amount of necessary vBBUs and VPONs may change along a day. In this paper, we propose a batch scheduling algorithm based on Integer Linear Programming (ILP) to perform reconfiguration of VPONs and migration of vBBUs among processing nodes in function of fluctuation on traffic demands. Our results show that, in comparison to an incremental algorithm without reconfiguration of VPONs and vBBUs migration capacities, our algorithm reduces power consumption and bandwidth wastage by up to 28% and 57%, respectively, and also eliminates blocking probability.
Rodrigo Tinini, Daniel Batista, Gustavo Figueiredo, Massimo Tornatore and Biswanath Mukherjee. Energy-Efficient BaseBand Processing via vBBU Migration in Virtualized Cloud-Fog RAN. IEEE Global Communications Conference 2019 (IEEE GLOBECOM 2019), 2019.
Cloud-Fog Radio Access Networks (CF-RAN) were proposed as an alternative network architecture to alleviate the high fronthaul capacity requested in traditional Cloud RAN (CRAN) by moving some BaseBand Units (BBUs) from the cloud nodes to fog nodes closer to users. However, when BBU processing is moved into fog nodes, OPEX and CAPEX will increase, and the cost and energy savings introduced by CRAN will also reduce. Moreover, mobile traffic fluctuations may lead to an unbalanced resource utilization and energy-inefficient operation in fog nodes. To address this problem, processing functions in fog nodes could be activated and deactivated in function of network traffic and BBUs placed on fog nodes could be migrated to cloud nodes when network traffic is low. In this paper, we propose an Integer Linear Programming (ILP) formulation to address this dynamic resource allocation problem. By means of Network Functions Virtualization (NFV), virtualized BBUs (vBBUs) can be dynamically allocated and deallocated in fog nodes. Furthermore, considering the availability of cloud nodes and the optical fronthaul, vBBUs can be migrated from fog nodes to cloud nodes in order to balance processing loads and save energy. Compared to a baseline incremental algorithm without vBBU migration, our proposal reduces blocking probability in 89% and achieves power savings of 38%, while providing a very small rate of service interruption due to vBBUs migration.
Thales Bandiera Paiva, Javier Navaridas and Routo Terada. Robust Covert Channels Based on DRAM Power Consumption. International Conference on Information Security, 2019.
To improve the energy efficiency of computing systems, modern CPUs provide registers that give estimates on the power consumption. However, the ability to read the power consumption introduces one class of security concerns called covert channels, which are communication channels that enable one process to transmit a message to another one in a system where these processes were meant to be isolated. Our contribution consists in the first covert channel in which messages are transmitted by modulating the DRAM power consumption. The channel implementation outperforms similar proposals, achieving 1800 bps with 10% error, and 2400 bps with 15% error, when running on a notebook and on a desktop platforms, respectively, To test its robustness against application interference, we considered the channel’s performance when running concurrently with different benchmarks: MRBench, Terasort and LINPACK. When running on the notebook, the channel is fairly robust, achieving between 300 and 600 bps with around 10% error depending on the workload considered.
Thales Bandiera Paiva and Routo Terada. A timing attack on the HQC encryption scheme. Selected Areas in Cryptography, 2019.
The HQC encryption scheme is a promising code-based submission to NIST’s post-quantum cryptography standardization process. The scheme is based on the decisional decoding problem for random quasicyclic codes. One problem of the author’s submission to NIST is that the reference implementation is not constant-time. We use this to present the first timing attack against HQC. The attack is practical, requiring the attacker to record the decryption time of around 400 million ciphertexts for a set of HQC parameters corresponding to 128 bits of security. This makes the use of constant-time decoders mandatory for the scheme to be considered secure.
Luis Sant'Ana, Daniel Cordeiro and Raphael Y. de Camargo. PLB-HAC: Dynamic Load-Balancing for Heterogeneous Accelerator Clusters. 25th International Conference on Parallel and Distributed Computing (EuroPar 2019), Göttingen, Germany, August 26–30, 2019, 2019.
Efficient usage of Heterogeneous clusters containing combinations of CPUs and accelerators, such as GPUs and Xeon Phi boards requires balancing the computational load among them. Their relative processing speed for each target application is not available in advance and must be computed at runtime. Also, dynamic changes in the environment may cause these processing speeds to change during execution. We propose a Profile-based Load-Balancing algorithm for Heterogeneous Accelerator Clusters (PLB-HAC), which constructs a performance curve model for each resource at runtime and continuously adapt it to changing conditions. It dispatches execution blocks asynchronously, preventing synchronization overheads and other idleness periods due to imbalances. We evaluated the algorithm using data clustering, matrix multiplication, and bioinformatics applications and compared with existing load-balancing algorithms. PLB-HAC obtained the highest performance gains with more heterogeneous clusters and larger problems sizes, where a more refined load-distribution is required.
Gabriel Leopoldino and Ricardo da Rocha. An architecture for spontaneous and secure communication for mobile IoT in smart cities (in portuguese). XIII Workshop de Trabalhos de Iniciação Científica e de Graduação (WTICG) on XIX Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais (SBSeg), 2019.
Uma Cidade Inteligente compreende soluções que integram Internet das Coisas e serviços de software para permitir a construção de aplicações que explorem a disseminação de informações para melhorar a qualidade de vida da população. Neste cenário, arquiteturas de software devem permitir que dispositivos de IoT se conectem à infraestrutura de software que permeia uma Cidade Inteligente de maneira espontânea e mediada por dispositivos de maior poder computacional, como smartphones. Em contrapartida, tais arquiteturas devem prover serviços de segurança para impedir de agentes maliciosos explorem a infraestrutura e afetem o correto funcionamento das suas aplicações. Este trabalho apresenta uma arquitetura de comunicação segura para Internet das Coisas Móveis em cidades inteligentes baseada em uma infraestrutura de chaves públicas, que provê serviços de autenticação, confidencialidade e integridade das informações consumidas e publicadas por dispositivos e que são adequadas a diversos cenários de conectividade.
Ademar T. Akabane, Roger Immich, Edmundo R. M. Madeira and Leandro A. Villas. Applying the Vehicular Social Networks Paradigm to Improve the Urban Mobility Management (in Portuguese). XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, 2019.
The Advanced Traffic Management System (ATMS) has been increasingly used by urban mobility managers to improve vehicular traffic management. Many ATMSs employ centralized solutions because of the difficulty of selecting the most relevant vehicles, in highly dynamic networks, to detect congestion and suggest alternative routes. Furthermore, such solutions are not always scalable. On the other hand, the distributed solution needs to previously segment the entire scenario to select the vehicles. Moreover, such a solution suggests alternative routes, in a selfish fashion, which can lead to secondary congestions. Based on the found open issues, this work proposes a distributed urban mobility management system based on the vehicular social networks paradigm (VSNs) named MAESTRO. The VSNs paradigm emerged from the integration of intelligent wireless communication devices and social networks in the vehicular environment. Two different approaches can be explored in VSNs, i.e., the Social network analysis (SNA) and the Social Network Concepts (SNC). The proposed MAESTRO system adopts a combined use of both SNA and SNC approaches. Simulation results showed that the use of SNA and SNC, in a vehicular environment, has great potential in increasing the scalability of the system and also improving efficiency in the management of urban mobility.
Ademar Takeo Akabane, Roger Immich, Richard Wenner Pazzi, Edmundo Roberto Mauro Madeira and Leandro Aparecido Villas. Exploiting Vehicular Social Networks and Dynamic Clustering to Enhance Urban Mobility Management. Sensors 2019, 2019.
Transport authorities are employing advanced traffic management system (ATMS) to improve vehicular traffic management efficiency. ATMS currently uses intelligent traffic lights and sensors distributed along the roads to achieve its goals. Furthermore, there are other promising technologies that can be applied more efficiently in place of the abovementioned ones, such as vehicular networks and 5G. In ATMS, the centralized approach to detect congestion and calculate alternative routes is one of the most adopted because of the difficulty of selecting the most appropriate vehicles in highly dynamic networks. The advantage of this approach is that it takes into consideration the scenario to its full extent at every execution. On the other hand, the distributed solution needs to previously segment the entire scenario to select the vehicles. Additionally, such solutions suggest alternative routes in a selfish fashion, which can lead to secondary congestions. These open issues have inspired the proposal of a distributed system of urban mobility management based on a collaborative approach in vehicular social networks (VSNs), named SOPHIA. The VSN paradigm has emerged from the integration of mobile communication devices and their social relationships in the vehicular environment. Therefore, social network analysis (SNA) and social network concepts (SNC) are two approaches that can be explored in VSNs. Our proposed solution adopts both SNA and SNC approaches for alternative route-planning in a collaborative way. Additionally, we used dynamic clustering to select the most appropriate vehicles in a distributed manner. Simulation results confirmed that the combined use of SNA, SNC, and dynamic clustering, in the vehicular environment, have great potential in increasing system scalability as well as improving urban mobility management efficiency.
Roger Immich, Leandro Villas, Luiz Bittencourt and Edmundo Madeira. Multi-Tier Edge-to-Cloud Architecture for Adaptive Video Delivery. 7th International Conference on Future Internet of Things and Cloud (FiCloud 2019), 2019.
In the last few years, there has been a rapid proliferation of a wide range of real-time video services and applications. These technologies flood the wireless systems with video content on a daily basis. As a result of this sharp increase in video traffic, the prospect of errors due to network interference and congestion rises. Incidentally, the adoption of the 5th generation of wireless systems (5G) will allow this growth to be even greater due to its high bandwidth capacity and low latency. However, even with these improvements on the wireless capabilities, a reliable and high-quality video transmission still imposes several challenges, such as how to handle a large number of heterogeneous devices and how to better use the resource-richer Edge, Fog, and Cloud computing sources to meet the user’s requirements. To overcome these issues, this work proposes a multi-tier video delivery architecture relying upon several technologies such as Multi-access Edge computing (MEC), 5G slices, and microservice placement/chaining. Furthermore, to assess the proposed idea an experimental proof-of-concept testbed of the multi-tier architecture was designed, implemented, and evaluated using real-world tools and actual video sequences. The results obtained supported our claim that a multi-tier video delivery system is feasible and can greatly benefit the end-users.
Davi Morales, Antônio Chaves and Alvaro Fazenda. Parallel Clustering Search Applied to Capacitated Centered Clustering Problem. 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2019.
The Clustering Search (CS) is a hybrid method that tries to combine metaheuristics and local search heuristics so that the search is intensified only in promising regions of the solution space. In this paper we propose a new parallel method based on the CS, using the Genetic Algorithm as a solutions generator, to solve the Capacitated Centered Clustering Problem (CCCP). The CCCP is to partition a set of n points into p disjoint groups with limited capacity. Each point is associated with a demand value and the objective is to minimize the sum of the Euclidean distances between the points and their respective geometric centers. The parallel CS consists in a master-slave system implemented following a message passing approach in order to parallelize the local search component, which is the most computationally demanding procedure. The computational results show that the parallel CS is an effective strategy in terms of computational time and efficiency.
Andreiwid Correa and Flavio Silva. Laying the foundations for benchmarking open data automatically: a method for surveying data portals from the whole web. 20th Annual International Conference on Digital Government Research (dg.o 2019), 2019.
The widespread of open data portals in the global scenario makes automated methods for data gathering and assessment a landmark in benchmarking. Some practitioners have already introduced automated approaches focused on mainly quality assessment of selected lists of data portals, but little has been studied about how to ensure exercises happen on the widest range of initiatives across the world, even incipient ones. The purpose of this paper is to provide a method for surveying data portals from the whole web, aiming to produce a whitelist of URLs that point to healthy data portals on the internet. The method was tested on 3.3 billion web addresses from which we found 1,339 open data portals worldwide using the main software platforms in the market CKAN, Socrata, OpenDataSoft and ArcGIS Open Data. Findings showed the choice of the whole web approach increased the number of data portals found, besides offering a workaround for redundancy, discoverability and traceability issues of current sparse and manual-based repositories. This work contributes to development of a fully automated method towards building an independent, reliable and up to date repository as a single source of open data portals operated around the world as well as provide insights about dataset estimation and geographic localization, from which benchmarking exercises may benefit to happen on a larger scale, at higher frequency and with lower costs.
Shortlisted for the award
Fernanda Dallaqua, Alvaro Fazenda and Fabio Faria. ForestEyes Project: Can Citizen Scientists Help Rainforests?. 15th International Conference on eScience (eScience), 2019.
Scientific projects involving volunteers for analyzing, collecting data, and using their computational resources, known as Citizen Science (CS), have become popular due to advances in information and communication technology (ICT). Many CS projects have been proposed to involve citizens in different knowledge domain such as astronomy, chemistry, mathematics, and physics. This work presents a CS project called ForestEyes, which proposes to track deforestation in rainforests by asking volunteers to analyze and classify remote sensing images. These manually classified data are used as input for training a pattern classifier that will be used to label new remote sensing images. ForestEyes project was created on the Zooniverse.org CS platform, and to attest the quality of the volunteers’ answers, were performed early campaigns with remote sensing images from Brazilian Legal Amazon (BLA). The results were processed and compared to an oracle classification (PRODES – Amazon Deforestation Monitoring Project). Two and a half weeks after launch, more than 35,000 answers from 383 volunteers (117 anonymous and 266 registered users) were received, completing all 2050 tasks. The ForestEyes campaigns’ results have shown that volunteers achieved excellent effectiveness results in remote sensing image classification task. Furthermore, these results show that CS might be a powerful tool to quickly obtain a large amount of high-quality labeled data.
Julian J. Arango and Fernando Iazzetta. Silicon sounds (in Portuguese). Exposition catalog - Espaço das Artes, Universidade de São Paulo, April 1 to April 26, 2019., 2019.
L. S. OSHIRO and D. M. BATISTA. Preliminary Analysis of the Detection of Obfuscated Attacks with Low Cost Hardware in a Threat Detection System (in Portuguese). II Workshop de Trabalhos de Iniciação Científica e Graduação (WTG) do SBRC, 2019.
Tráfego de rede transmitido a altas taxas traz como consequência a necessidade de mecanismos de segurança mais eficientes, já que analisar pacote por pacote antes de tomar uma ação torna-se uma tarefa custosa em termos de processamento. Uma forma de resolver esse problema é com o desenvolvimento e implantação de sistemas de detecção de ameaças que utilizem mecanismos de aprendizado de máquina para antecipar os ataques. Este artigo apresenta os resultados preliminares obtidos na tentativa de melhorar um sistema como esse por meio da análise de ataques automatizados que empregam ofuscação e por meio da avaliação de desempenho de uma unidade Raspberry Pi que poderá ser usada como nó de processamento no sistema melhorado.
R. I. TININI, D. M. BATISTA, GUSTAVO BITTENCOURT FIGUEIREDO, M. TORNATORE, and B. MUKHERJEE. Low-Latency and Energy-Efficient BBU Placement and VPON Formation in Virtualized Cloud-Fog RAN. Journal of Optical Communications and Networking, 2019.
Cloud radio access networks (CRANs) make it possible to reduce power consumption in future 5G networks by decoupling baseband units (BBUs) from cell sites and centralizing the baseband processing from remote radio-heads (RRHs) in BBU pools in a cloud. Although this centralization can enable power savings, it imposes much higher traffic on the optical transport network used to connect RRHs to the BBU pool, i.e., the fronthaul. In this paper, we propose a hybrid cloud-fog RAN (CF-RAN) architecture that resorts to fog computing and to network function virtualization to replicate the processing capacity of a CRAN in local fog nodes closer to the RRHs that can be activated on demand to process surplus fronthaul/cloud traffic. We devise an integer linear programming (ILP) formulation and graph-based heuristics to decide when to activate fog nodes and how to dimension wavelengths on a time-and-wavelength division multiplexing passive optical network to support the fronthaul. Our results show that our architecture can consume up to 96% less energy than a traditional distributed RAN, providing a maximum transmission latency of about 20 μs between RRHs and BBUs even in large traffic scenarios. Moreover, we demonstrate that our graph-based heuristics can achieve the same optimal solutions of the ILP formulation but with a reduction of 99.86% in the execution time.
Diogo Goncalves and Luiz Bittencourt and Edmundo Madeira. An Analysis of User Mobility Prediction for Mobile Applications Migration On Fog Computing Environments(In Portuguese). Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC), 2019.
A Computação em Névoa provê a dispositivos IoT acesso com baixa latência a recursos computacionais presentes na borda da rede. Porém, neste ambiente a alta mobilidade de alguns desses dispositivos, como wearables ou dispositivos embarcados em veículos, traz grandes desafios para a alocação e gerência de recursos. Trabalhos recentes têm apresentado os benefícios do uso de predição de mobilidade dos usuários no processo de migração de aplicações neste ambiente. No entanto, uma má escolha do local de execução da aplicação devido a uma imprecisão na localização futura do usuário pode comprometer a qualidade da execução. Este trabalho apresenta uma análise do impacto de uma baixa acurácia na predição de mobilidade do usuário para melhorar o processo de migração de aplicações em ambientes de Névoa. Resultados de simulações indicaram que o uso de predição de mobilidade pode reduzir o número de migrações, mas um erro de cálculo da posição futura do usuário pode aumentar a latência média experimentada por ele em até 30%.
Allan Barbosa, Francisco Silva, Luciano Coutinho, Davi Santos and Ariel Teles. A Domain-Specific Modeling Language for Specification of Clinical Scores in Mobile Health. Proceedings of the 14th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE, 104-113, Heraklion, Crete, Greece, 2019.
Clinical scores are a widely discussed topic in health as part of modern clinical practice. In general, these tools predict clinical outcomes, perform risk stratification, aid in clinical decision making, assess disease severity or assist diagnosis. However, the problem is that clinical scores data are traditionally obtained manually, which can lead to incorrect data and result. In addition, by collecting biological/health data in real time from humans, the current mobile health (mHealth) solutions that computationally solve that problem are limited because those systems are developed considering the specificities of a single clinical score. This work is part of the MDD4ClinicalScores project that addresses the productivity in developing mHealth solutions for clinical scores through the use of Model Driven Development concepts. This paper focus in describing DSML4ClinicalScore, a high-level domain-specific modeling language that uses the Ecore metamodel to describe a clinical score sp ecification. To propose the DSML4ClinicalScore we analysed 89 clinical scores to define the artifacts of this proposed Metamodel. In the end, a practical case study using this DSML is provided to validate the DSML4ClinicalScore Metamodel, and to show how to use the proposal in a clinical situation scenario.
Adalberto Azevedo Jr., Fernando Benedito, Luciano Coutinho, Francisco Silva, Marcos Roriz Junior and Markus Endler. A Mobility Restriction Authoring Tool Approach based on a Domain Specific Modeling Language and Model Transformation. Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS, 525-534, Heraklion, Crete, Greece, 2019.
There are many situations in which there is a need to monitor the location and behavior of people and/or vehicles in order to detect possible irregularities and control where they are located and how they move, such as in companies, public transportation and public security. In this paper, we present MobCons-AT (Mobility Constraints Authoring Tool), an authoring tool that allows the specification of mobility restrictions rules that must be followed by mobile nodes. Rules are specified through a Domain-Specific Modeling Language (DSML) called MobCons-SL (Mobility Constraints Specification Language). Once specified in MobCons-SL, these rules are automatically transformed into software artifacts that performs the detection of the mobility restrictions violations performed by mobile nodes. This approach allows faster delivery time and lower the cost for the development of software systems aiming the detection of mobility restrictions. This paper also describes the use of MobCons-AT in two case studies, showing its applicability for diverse mobility scenarios.
Received the Best Paper Award in the Multiagent systems section.
Alexandre Heideker, Dener Silva, Ivan Zyrianoff, João Kleinschmidt and Carlos Kamienski. IMAIoT – Infrastructure Monitoring Agent for IoT (In Portuguese). Salão de Ferramentas do SBRC, 2019.
O conceito Internet da Coisas (IoT) é caracterizado pela presença de um número muito grande de dispositivos ligados à Internet, o que inclui ambientes urbanos, industriais e de agricultura. O gerenciamento e monitoramento destes dispositivos, virtuais ou físicos, em múltiplas plataformas de hardware e software, representa um grande desafio. Há soluções disponíveis no mercado, porém, específicas para certos domínios e plataformas e, em sua maioria, proprietárias e pouco personalizáveis. Apresentamos o IMAIoT, uma ferramenta para monitoramento de infraestruturas que utiliza protocolos e arquitetura altamente escalável de IoT para disponibilizar suas métricas. A versatilidade da ferramenta permite monitorar desde máquinas reais em um datacenter até dispositivos com como nós de computação em névoa.
Pedro Rezende, Somayeh Kianpisheh, Roch Glitho and Edmundo Madeira. An SDN-Based Framework for Routing Multi-Streams Transport Traffic Over Multipath Networks. 53rd IEEE International Conference on Communications, 2019.
Multi-stream transport protocols, such as SCTP and QUIC, tackle challenges such as Head of Line (HoL) faced by TCP. They are getting more and more deployed and currently carry more than 7% of the global Internet traffic. However, since these protocols have limited control over the actual routes the streams will take in the network, the streams are generally forwarded through a single and same path, even though there are generally multiple paths available in the network. Consequently, the traffic streams do not fully benefit from the available bandwidth and performance improvement remains limited. Software Defined Networks (SDN) separate control planes and data planes. It offers a complete view of the network to applications and enables network programmability through flexible rules. These rules may be used to ensure that the different streams generated by multi-stream transport protocols follow multiple paths in the network. In this paper, we propose an SDN-based framework for multi-stream transport protocols in multipath networks. The proposed framework provides an interface for applications to specify multi-stream rules. Based on these rules, the framework uses the services offered by the SDN Controller to ensure that the multiple streams go over multiple paths in the network. Experiments performed show that our proposal improves the QoS offered to the end users.
Mayurí Morais and Raphael de Camargo. A Framework for Scalable Data Analysis and Model Aggregation for Public Bus Systems. III Workshop de Computação Urbana (III CoUrb), 2019.
Urban mobility through quality public transportation is one of the major challenges for the consolidation of smart cities. Researchers developed different approaches for improving bus system reliability and information quality, including travel time prediction algorithms, network state evaluations, and bus bunching prevention strategies. The information provided by these approaches are complementary and could be aggregated for better predictions. In this work, we propose the architecture and a present a prototype implementation of a framework that enables the integration of several approaches, which we call models, into scalable and efficient composite models. For instance, travel time prediction models can use estimators of bus position, network state, and bus headways to deliver more accurate and reliable predictions. We evaluate the scalability of the framework, the CPU usage of the framework components, and the predictions of the travel time models. We show that real-time predictions using this framework can be feasible in large metropolitan areas, such as São Paulo city.
Received an honorable mention
Jucele França de Alencar Vasconcelos, Edson Norbeto Cáceres, Henrique Mongelli, Siang Wun Song, Frank Dehne and Jayme Luiz Szwarcfiter. New BSP/CGM algorithms for spanning trees. International Journal of High Performance Computing Applications. Volume 33, Issue 3, pp. 441-461, 2019.
Computing a spanning tree (ST) and a minimum spanning tree (MST) of a graph are fundamental problems in Graph Theory and arise as a subproblem in many applications. In this paper, we propose parallel algorithms to these problems. One of the steps of previous parallel MST algorithms relies on the heavy use of parallel list ranking which, though efficient in theory, is very time-consuming in practice. Using a different approach with a graph decomposition, we devised new parallel algorithms that do not make use of the list ranking procedure. We proved that our algorithms are correct, and for a graph G = (V;E), |V| = n and |E| = m, the algorithms can be executed on a BSP/CGM model using O(log p) communications rounds with O((n+m)/p) computation time for each round. To show that our algorithms have good performance on real parallel machines, we have implemented them on GPU (Graphics Processing Unit). The obtained speedups are competitive and showed that the BSP/CGM model is suitable for designing general purpose parallel algorithms.
Gustavo Covas, Eduardo F. Z. Santana and Fabio Kon. Evaluating Exclusive Lanes for Autonomous Vehicle Platoons. 33 rd ECMS International Conference on Modelling and Simulation (ECMS), 2019.
Digital Rails (DR) is a proposal for a system of exclusive lanes intended for autonomous vehicles. This paper presents the evaluation of this system using macroscopic traffic metrics, mainly average travel time. The DR system consists of a network of arterial roads with exclusive lanes where autonomous vehicles can travel in platoons. We
evaluated the impacts of this system on travel time using mesoscopic traffic simulation and real data from the city of São Paulo to create the simulation scenarios. The results show that the proposed system would bring reductions on the average travel time of the city commuters.
Arthur de M. Del Esposte, Eduardo F. Z. Santana, Lucas Kanashiro, Fabio M. Costa, Kelly R. Braghetto, Nelson Lago and Fabio Kon. Design and evaluation of a scalable smart city software platform with large-scale simulations. Future Generation Computer Systems, vol 93, 2019.
Smart Cities combine advances in Internet of Things, Big Data, Social Networks, and Cloud Computing technologies with the demand for cyber–physical applications in areas of public interest, such as Health, Public Safety, and Mobility. The end goal is to leverage the use of city resources to improve the quality of life of its citizens. Achieving this goal, however, requires advanced support for the development and operation of applications in a complex and dynamic environment. Middleware platforms can provide an integrated infrastructure that enables solutions for smart cities by combining heterogeneous city devices and providing unified, high-level facilities for the development of applications and services. Although several smart city platforms have been proposed in the literature, there are still open research and development challenges related to their scalability, maintainability, interoperability, and reuse in the context of different cities, to name a few. Moreover, available platforms lack extensive scientific validation, which hinders a comparative analysis of their applicability. Aiming to close this gap, we propose InterSCity, a microservices-based, open-source, smart city platform that enables the collaborative development of large-scale systems, applications, and services for the cities of the future, contributing to turn them into truly smart cyber–physical environments. In this paper, we present the architecture of the InterSCity platform, followed by a comprehensive set of experiments that evaluate its scalability. The experiments were conducted using a smart city simulator to generate realistic workloads used to assess the platform in extreme conditions. The experimental results demonstrate that the platform can scale horizontally to handle the highly dynamic demands of a large smart city while maintaining low response times. The experiments also show the effectiveness of the technique used to generate synthetic workloads.
Arthur Selle Jacobs, Ricardo José Pfitscher, Ronaldo Alves Ferreira, and Lisandro Zambenedetti Granville. Refining Network Intents for Self-Driving Networks. ACM SIGCOMM Computer Communication Review, Volume 48, Issue 5, October 2018., 2018.
Recent advances in artificial intelligence (AI) offer an opportunity for the adoption of self-driving networks. However, network operators or home-network users still do not have the right tools to exploit these new advancements in AI, since they have to rely on low-level languages to specify network policies. Intent-based networking (IBN) allows operators to specify high-level policies that dictate how the network should behave without worrying how they are translated into configuration commands in the network devices. However, the existing research proposals for IBN fail to exploit the knowledge and feedback from the network operator to validate or improve the translation of intents. In this paper, we introduce a novel intent-refinement process that uses machine learning and feedback from the operator to translate the operator’s utterances into network configurations. Our refinement process uses a sequence-to-sequence learning model to extract intents from natural language and the feedback from the operator to improve learning. The key insight of our process is an intermediate representation that resembles natural language that is suitable to collect feedback from the operator but is structured enough to facilitate precise translations. Our prototype interacts with a network operator using natural language and translates the operator input to the intermediate representation before translating to SDN rules. Our experimental results show that our process achieves a correlation coefficient squared (i.e., R-squared) of 0.99 for a dataset with 5000 entries and the operator feedback significantly improves the accuracy of our model.
Received the Best Paper Award of the ACM SIGCOMM Workshop on Self-Driving Networks 2018 in Budapest.
Fernanda de Camargo Magano and Kelly Rosa Braghetto. Abstracting Big Data Processing Tools for Smart Cities. 37th IEEE International Symposium on Reliable Distributed Systems, 2018.
Large volumes of data from various sources are generated continuously in cities. The processing and analysis of these data play a key role in the implementation of initiatives for smart cities. In order to process urban Big Data, it is essential to use high-performance tools to accelerate processing and provide quick answers. However, this use is not trivial because Big Data tools are not interoperable and require from their users knowledge of parallel and distributed computing and databases. In this work, we compare popular open-source Big Data processing frameworks and propose a software system to abstract and facilitate their use in smart city applications. The architecture of the system is composed by an interface to specify dataflow models as well as services to interpret these models and instantiate them in different Big Data tools. An implementation of the system on top of a smart city platform is also addressed.
Fernando Freire Scattone and Kelly Rosa Braghetto. A Microservices Architecture for Distributed Complex Event Processing in Smart Cities. 2018 IEEE 37th International Symposium on Reliable Distributed Systems Workshops (SRDSW), 2018.
A considerable volume of data is collected from sensors today and needs to be processed in real time. Complex Event Processing (CEP) is one of the most important techniques developed for this purpose. In CEP, each new sensor measurement is considered an event and new event types can be defined based on other events occurrence. There exists several open-source CEP implementations currently available, but all of them use orchestration to distribute event processing. This kind of architectural organization may harm system resilience, since it relies on a central core (i.e. the orchestrator). Any failures in the core might impact the whole system. Moreover, the core can become a bottleneck on system performance. In this work, a choreography-based microservices architecture is proposed for distributed CEP, in order to benefit from the low coupling and greater horizontal scalability this kind of architecture provides.
Alexandre Meslin, Noemi Rodriguez, and Markus Endler. A Scalable Multilayer Middleware for Distributed Monitoring and Complex Event Processing for Smart Cities. 2018 IEEE International Smart Cities Conference (ISC2), 2018.
In this paper, we present a hierarchical, distributed, cloud-based, context-aware architecture for collecting, processing and distributing data in a smart city. The architecture we are proposing has three hierarchical levels, and supports complex event processing (CEP) in several of them. At the lower layer, several mobile objects work as interfaces to sensors and actuators and provide processing capability for local filtering and detection. A second level, consisting of gateways and processing nodes, processes information from its locale and sends the data received from the mobile objects to the storage level using the existing network infrastructure. The highest level provides support for structured storage and queries of the information. Applications outside the platform can collect data through the external interface of the highest level. The system is under development using the InterSCity platform in the upper layer, the ContextNet middleware at the middle layer, and the Mobile-Hub application at the lower layer. It currently collects and processes data on buses running in the City of Rio de Janeiro.
Fernanda BJR Dallaqua, Fabio Augusto Faria, Alvaro L Fazenda. Active Learning Approaches for Deforested Area Classification. 31st Conference on Graphics, Patterns and Images (SIBGRAPI), 2018.
The conservation of tropical forests is a social and ecological relevant subject because of its important role in the global ecosystem. Forest monitoring is mostly done by extraction and analysis of remote sensing imagery (RSI) information. In the literature many works have been successful in remote sensing image classification through the use of machine learning techniques. Generally, traditional learning algorithms demand a representative and huge training set which can be an expensive procedure, especially in RSI, where the imagery spectrum varies along seasons and forest coverage. A semi-supervised learning paradigm known as active learning (AL) is proposed to solve this problem, as it builds efficient training sets through iterative improvement of the model performance. In the construction process of training sets, unlabeled samples are evaluated by a user-defined heuristic, ranked and then the most relevant samples are labeled by an expert user. In this work two different AL approaches (Confidence Heuristics and Committee) are presented to classify remote sensing imagery. In the experiments, our AL approaches achieve excellent effectiveness results compared with well-known approaches existing in the literature for two different datasets.
Thales Bandiera Paiva and Routo Terada. Improving the efficiency of a reaction attack on the QC-MDPC McEliece. IEICE Transactions on Fundamentals, Vol. E101-A, No. 10, pp1676-1686, October, 2018.
The QC-MDPC McEliece scheme was considered one of the most promising public key encryption schemes for efficient post-quantum secure encryption. As a variant of the McEliece scheme, it is based on the syndrome decoding problem, which is a hard problem from Coding Theory. Its key sizes are competitive with the ones of the widely used RSA cryptosystem, and it came with an apparently strong security reduction. For three years, the scheme has not suffered major threats, until the end of 2016, at the Asiacrypt, when Guo, Johansson, and Stankovski presented a reaction attack on the QC-MDPC that exploits one aspect that was not considered in the security reduction: the probability of a decoding failure to occur is lower when the secret key and the error used for encryption share certain properties. Recording the decoding failures, the attacker obtains information about the secret key and then use the information gathered to reconstruct the key. Guo et al. presented an algorithm for key reconstruction for which we can point two weaknesses. The first one is that it cannot deal with partial information about the secret key, resulting in the attacker having to send a large number of decoding challenges. The second one is that it does not scale well for higher security levels. To improve the attack, we propose a key reconstruction algorithm that runs faster than Guo’s et al. algorithm, even using around 20% less interactions with the secret key holder than used by their algorithm, considering parameters suggested for 80 bits of security. It also has a lower asymptotic complexity which makes it scale much better for higher security parameters. The algorithm can be parallelized straightforwardly, which is not the case for the one by Guo et al.
Danilo Carastan Santos, David C. Martins-Jr, Siang W. Song, Luiz C. S. Rozante and Raphael Y. de Camargo. A hybrid CPU-GPU-MIC algorithm for minimal hitting set enumeration. Concurrency and Computation: Practice and Experience. Accepted in 2018. Available online, 2018.
We present a hybrid exact algorithm for the Minimal Hitting Set (MHS) Enumeration Problem for highly heterogeneous CPU-GPU-MIC platforms. With several techniques that permit an efficient exploitation of each architecture, low communication cost and effective load balancing, we were able to enumerate MHSs for large instances in reasonable time, achieving good performance and scalability.We obtained speedups of up to 25.32 in comparison with using two six-core CPUs and we also enumerated MHSs for instances with tens of thousands of variables in less than 5 hours. We also evaluated our algorithm with a real-world driven dataset and, with a largeCPU-GPUcluster, we unprecedentedly enumerated in parallel large minimal hitting sets of this dataset in less than 8 hours. These results reinforce the statement that heterogeneous clusters of CPUs, GPUs and MICs can be used efficiently for high-performance computing.
Jucele França de Alencar Vasconcelos, Edson Norbeto Cáceres, Henrique Mongelli and Siang Wun Song. A new efficient parallel algorithm for minimum spanning tree. 30th International Symposisum on Computer Architecture and High Performance Computing (SBAC-PAD). Lyon, France, September 24 - 27, 2018.
In this paper, using the BSP/CGM model, we propose a parallel algorithm and implement it on a GPGPU to obtain a minimum spanning tree of a graph. Previous works for this problem are based on the solution of the list ranking problem which, though efficient in theory, did not produce good speedups in practice. In a later work, based on the idea of computing a structure called strut, we proposed a parallel algorithm under the BSP/CGP model to obtain a minimum spanning tree without using list ranking. It is based on the construction of an auxiliary bipartite graph and uses integer sorting. In this paper, we improve that work in some aspects. The proposed algorithm does not require the computation of the bipartite graph, and the strut construction does not require the sorting algorithm. The efficiency and scalability of the proposed algorithm are verified through experimental results obtained by an implementation on GPGPU.
Ademar T. Akabane, Roger Immich, Richard W. Pazzi, Edmundo R. M. Madeira and Leandro A. Villas. Distributed Egocentric Betweenness Measure as a Vehicle Selection Mechanism in VANETs: A Performance Evaluation Study. Sensors 2018, 18(8), 2731, August, 2018.
In the traditional approach for centrality measures, also known as sociocentric, a network node usually requires global knowledge of the network topology in order to evaluate its importance. Therefore, it becomes difficult to deploy such an approach in large-scale or highly dynamic networks. For this reason, another concept known as egocentric has been introduced, which analyses the social environment surrounding individuals (through the ego-network). In other words, this type of network has the benefit of using only locally available knowledge of the topology to evaluate the importance of a node. It is worth emphasizing that in this approach, each network node will have a sub-optimal accuracy. However, such accuracy may be enough for a given purpose, for instance, the vehicle selection mechanism (VSM) that is applied to find, in a distributed fashion, the best-ranked vehicles in the network after each topology change. In order to confirm that egocentric measures can be a viable alternative for implementing a VSM, in particular, a case study was carried out to validate the effectiveness and viability of that mechanism for a distributed information management system. To this end, we used the egocentric betweenness measure as a selection mechanism of the most appropriate vehicle to carry out the tasks of information aggregation and knowledge generation. Based on the analysis of the performance results, it was confirmed that a VSM is extremely useful for VANET applications, and two major contributions of this mechanism can be highlighted: (i) reduction of bandwidth consumption; and (ii) overcoming the issue of highly dynamic topologies. Another contribution of this work is a thorough study by implementing and evaluating how well egocentric betweenness performs in comparison to the sociocentric measure in VANETs. Evaluation results show that the use of the egocentric betweenness measure in highly dynamic topologies has demonstrated a high degree of similarity compared to the sociocentric approach.
Karima Velasquez, David Perez Abreu, Marcio R. M. Assis, Carlos Senna, Diego F. Aranha, Luiz F. Bittencourt, Nuno Laranjeiro, Marilia Curado, Marco Vieira, Edmundo Monteiro and Edmundo Madeira. Fog orchestration for the Internet of Everything: state-of-the-art and research challenges. Journal of Internet Services and Applications, 9 (14) July, 2018.
Recent developments in telecommunications have allowed drawing new paradigms, including the Internet of Everything, to provide services by the interconnection of different physical devices enabling the exchange of data to enrich and automate people’s daily activities; and Fog computing, which is an extension of the well-known Cloud computing, bringing tasks to the edge of the network exploiting characteristics such as lower latency, mobility support, and location awareness. Combining these paradigms opens a new set of possibilities for innovative services and applications; however, it also brings a new complex scenario that must be efficiently managed to properly fulfill the needs of the users. In this scenario, the Fog Orchestrator component is the key to coordinate the services in the middle of Cloud computing and Internet of Everything. In this paper, key challenges in the development of the Fog Orchestrator to support the Internet of Everything are identified, including how they affect the tasks that a Fog service Orchestrator should perform. Furthermore, different service Orchestrator architectures for the Fog are explored and analyzed in order to identify how the previously listed challenges are being tackled. Finally, a discussion about the open challenges, technological directions, and future of the research on this subject is presented.
Paulo César Ferreira Melo and Fábio Moreira Costa. Model-Driven Mobile CrowdSensing for Smart Cities. WBCI 2018: 1st Brazilian Workshop on Smart Cities, 2018.
Making cities smarter can help improve city services, optimize resource and infrastructure utilization and increase citizens’ quality of life. The Smart Cities connects citizens in novel ways by leveraging the latest advances in information and communication technologies (ICT). The integration of rich sensing capabilities (e.g. camera, microphone, accelerometer, GPS) in today’s mobile devices allows their users to sense their environment. In Mobile CrowdSensing (MCS) the citizens of the Smart City collect, share and jointly use services based on the sensed data. The main challenges for smart city 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 MCS queries in Smart Cities. This new architecture is an extension of the InterSCity platform to leverage all existing infrastructure.
Diogo Gonçalves, Karima Velasquez, Marilia Curado, Luiz Bittencourt and Edmundo Madeira. Proactive Virtual Machine Migration in Fog Environments. IEEE Symposium on Computers and Communications (ISCC 2018), 2018.
Fog computing provides a low latency access to resources at the edge of the network for resource-constrained devices. The high mobility of some of these devices, such as vehicles, brings great challenges related to resource allocation and management. In order to improve the management of computing resources utilized by mobile users connected to the Fog infrastructure, this paper proposes a virtual machine placement and migration decision model based on mobility prediction. Simulations have shown that moving the virtual machine to a Fog node ahead of the user’s route using the proposed approach can decrease by almost 50% the number of migrations needed by the user. The Fog architecture provides an average latency of about 15 milliseconds for the users’ applications and the proposed approach presents a lower latency compared to a greedy approach for the VM placement problem.
RODRIGO IZIDORO TININI, DANIEL MACEDO BATISTA, and GUSTAVO BITTENCOURT FIGUEIREDO. Energy-Efficient VPON Formation and Wavelength Dimensioning in Cloud-Fog RAN over TWDM-PON. IEEE Symposium on Computers and Communications (ISCC), 2018.
Optical networks are commonly adopted to implement the fronthaul of Cloud-RAN (CRAN) and Hybrid-CRAN (H-CRAN) architectures to support the 5G traffic. The Cloud-Fog RAN (CF-RAN) architecture promotes cloud and local fog processing of baseband signals with the novelty of activating on demand processing capabilities under the Fog Computing and Network Functions Virtualization (NFV) paradigms. Timeand-Wavelength-Division-Multiplexed Passive Optical Networks (TWDM-PON) are used to implement the fronthaul due to the capacity of Virtual-PON (VPON) formation, which can reduce power consumption by allocating as many Remote Radio Heads (RRH) as possible into a single optical channel. However, the TWDM-PON wavelengths must be assigned to the VPONs in an exclusive manner so different VPONs do not share a common wavelength and thus VPONs do not collide on the optical links. In this paper we propose an Energy-Efficient VPON Formation and Wavelength Dimensioning formulation through Integer Linear Programming (ILP) to efficiently form VPONs and dimension the wavelengths so collisions never occurs. Results from the experiments show that the ILP formulation was able to form VPONs and dimension the wavelengths to the network nodes on the most energy-efficient way, achieving gains in the order of 80.27% in comparison to a random VPON formation approach.
L. G. A. RODRIGUEZ, J. S. TRAZZI, V. FOSSALUZA, R. CAMPIOLO, and D. M. BATISTA. Analysis of Vulnerability Disclosure Delays from the National Vulnerability Database. I Workshop de Segurança Cibernética em Dispositivos Conectados (WSCDC) do SBRC, 2018.
The Internet contains vast amounts of data; consequently, hindering information retrieval. Resources, such as the National Vulnerability Database (NVD), have emerged to remedy this situation. Organizations largely depend on the NVD in order to disclose vulnerabilities and collaborate towards a solution. However, there has been evidence that other sources are disclosing vulnerabilities more efficiently and rapidly. The objective of this paper is to evaluate vulnerability disclosure delays from the NVD in order to state its efficiency. Among several findings, we observed that the majority of vulnerabilities are delayed within 1-7 days. Based on these results, we provide recommendations for those who currently rely only on NVD, such as IoT manufacturers and developers.
M. RIVA, H. RODRIGUES, F. ALMEIDA, G. B. FIGUEIREDO, R. I. TININI, R. M. CESAR JUNIOR, and D. M. BATISTA. An Elastic Optical Network-based Architecture for the 5G Fronthaul. Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC), 2018.
In 5G networks, the traffic demands are expected to increase significantly. To deal with this problem, many research efforts focus on TWDM-PON for the fronthaul. However, TWDM-PON suffers from the issue of wasted bandwidth when a demand is smaller than the channel. In order to avoid this inefficiency, we propose an OFDM-PON supported architecture for the CloudRadio Access Network (C-RAN) topology to implement the technology of elastic optical networks into C-RAN. Experiments show that OFDM-PON allows for improved usage of the bandwidth (between 145% and 218% improvement), similar average wait time for requests (average difference below 7%), and similar request loss (average difference below 2%) in comparison to TWDM.
Antonio Deusany de Carvalho Junior and Victor Seiji Hariki and Alfredo Goldman. Sensing Trees in Smart Cities with Open-Design Hardware. IEEE 17th International Symposium on Network Computing and Applications (NCA), 2018.
Tree fall is a major issue in large cities as it may obstruct roads and lead to traffic jams, and even injure people. Following these kind of incidents, monitoring the environment through sensors and Internet of Things technologies emerges as an important preventive approach. Another point is that the net primary productivity has a sensitive relation with trees characteristics including plant respiration and net photosynthesis which are highly sensitive to temperature. The health of the tree can then be monitored through leaf transpiration, sap flow measurement, environment evaluation as these methods offer indicators on any chance of a fall. The solution proposed in this paper includes an open-design hardware setup for monitoring trees in Smart Cities with Internet of Things. The setup is connected to a Smart City platform aiming to facilitate the use by other researchers and botanics, specially. Therefore, we noticed that the daily sensing data results in a huge amount of information that requires a balance between Big Data and Edge Computing approaches to process the whole collected data. The evaluation of the sensing data is presented together with the proposed architecture.
Markus Endler and Francisco Silva e Silva. Past, Present and Future of the ContextNet IoMT Middleware. International Workshop on Very Large Internet of Things - VLIoT, 2018.
The Internet of Things with support to mobility is already transforming many application domains, such as smart cities and homes, environmental monitoring, health care, manufacturing, logistics, public security etc. in that it allows to collect and analyze data from the environment, people and machines, and to implement some form of digital control or steering on these elements of the physical world. But in order to speed the development of applications for the Internet of Mobile Things (IoMT), some middleware is required. This paper summarizes seven years of research and development on the ContextNet middleware aimed at IoMT, discusses what we achieved and what we have learned so far. We also share our vision of possible future challenges and developments in the Internet of Mobile Things.
Diego Vieira Neves, Felipe Cordeiro Alves Dias and Daniel Cordeiro. Using Supervised learning to analyze reliability of crowdsourced bus location data (in Portuguese). WBCI 2018: Primeiro Workshop Brasileiro de Cidades Inteligentes, 2018.
Intelligent Transportation Systems allows sensors and GPS devices to monitor public transport systems in Smart Cities. Capturing and processing this data should, in theory, allow systems to make the public transport more reliable and predictable for the citizens, which would improve the quality of life of the urban population and the environment. Insufficient or low-quality data, nevertheless, may prevent its use on such real-time systems. This work studies the use of data obtained from crowdsourcing as an alternative to augment this data. In order to mitigate the uncertainties introduced by the crowdsourced data, this work proposes a reliability model for crowdsourced data conceived for the São Paulo bus-based public transport system.
Original title: Uso de aprendizado supervisionado para análise de confiabilidade de dados de crowdsourcing sobre posicionamento de ônibus
Diogo M. Gonçalves, Luiz F. Bittencourt and Edmundo M. R. Madeira. Proactive migration of virtual machines for mobile applications in fog computing (in Portuguese). XXXVI Simpósio Brasileiro de Redes de Computadores (SBRC), 2018.
Applications often utilize the cloud as support for processing and storage. The variety of mobile applications also brings a diversity of quality of service requirements, as for example strict delay and availability requirements. Fog computing includes computing services at the edge of the network so as response times can be reduced. In this paper, we present one policy for proactive virtual machine migration in fog computing in order to improve management of computing resources utilized by vehicles connected to this infrastructure. Simulations suggest that using knowledge about the future user’s path can improve the resource management of fog ecosystem, maintaining users’ virtual machine in fog devices as close as possible to the vehicle path. Simulations suggest that the presented policy reduce the total of migration along the user’s path without affecting the quality response time of virtual machines allocated to the fog.
Original title: Migração proativa de máquinas virtuais para aplicações móveis na computação em névoa.
Pedro H. A. Rezende and Edmundo R. M. Madeira. A network slicing component for multi-tenant support in LTE RANs (in Portuguese). XXXVI Simpósio Brasileiro de Redes de Computadores (SBRC), 2018.
5G networks intend to integrate network slicing into their architecture aiming to satisfy the different service levels of an abundant amount of devices. Network Slicing relies on softwarization technologies, such as SDN and NFV, to instantiate slices (virtual networks) on top of the same physical substrate. This work introduces the “Otimizador de Slices”, a component developed as an extension of LTE’s evolved NodeB, responsible to perform network slicing for LTE downlink transmission. This component receives slice’s information from multiple Service Providers and, based on the analysis of these information and on the network state, the proposed component selects the best slice to be scheduled at the moment. Simulations were performed to validate our proposal and expose the benefits that can be obtained by it, such as an enhancement of end user’s QoS experience.
Original title: Um componente de network slicing para o suporte de multi-inquilinos nas RANs do LTE
Melissa Wen, Thatiane de O. Rosa, Mariana C. Souza, Robson P. Aleixo, Camilla Alves, Lucas Sá, Eduardo Felipe Zambom Santana, Fabio Kon. Creation of a Model for Bus Movement Simulation Based on Real Data (in Portuguese). Primeiro Workshop Brasileiro de Cidades Inteligentes, 2018.
The socio-spatial dynamics of a city undergoes constant changes over time. Consequently,the road network and the public transport system need continuous optimization to meet citizen demands. An alternative to reduce costs and impacts on evaluation of solutions is the use of simulators and models consistent with reality. Considering that, we processed vehicle tracking data and bus system planning information of São Paulo to improve the bus movement model used by InterSCSimulator, a highly scalable simulator for smart cities. In this paper, we present a mobility model based on real data from the São Paulo bus service to make the simulator more effective when recreating urban mobility scenarios.
Original title: Criação de Modelo para Simulação de Movimentação de Ônibus a Partir de Dados Reais
Eduardo Felipe Zambom Santana, Lucas Kanashiro, Fabio Kon. Mobility Traces Generation for Vehicular Network Experiments (in Portuguese). Segundo Workshop de Computação Urbana (CoUrb 2018), 2018.
Information and Communication Technologies can improve the trafﬁc in big cities. The deployment of vehicular networks, in which cars can communicate to each other and with the road infrastructure, is an area that is receiving a lot of attention in the last years. However, make tests and experiments in real environments are yet a challenge. This paper presents the development of a mobility trace to the city of São Paulo using InterSCSimulator, a large-scale, open-source Smart City simulator. The trace covers an area of 25 km2 and simulates more than 4 million travels (cars and buses) during a day in the city. The generated trace was tested as input in the NS-3 network simulator.
Original title: Geração de Rastros de Mobilidade para Experimentos em Redes Veiculares
Pedro H. A. Rezende and Edmundo R. M. Madeira. An adaptive network slicing for LTE Radio Access Networks. 10th Wireless Days Conference - WD'18, 2018.
5G mobile systems are envisioned to satisfy the service requirements from a diversity of vertical industries. Network Slicing, which is a promising technology to be integrated into 5G systems, enables multiple virtual networks to be created on top of a physical substrate. These multiple virtual networks (or network slices) are tailored according to the users’ needs. The consolidation of multiple technologies, such as SDN and NFV, provides all the elasticity, programmability and modularity necessary to manage network slices. In this paper, we present a Slice Optimizer component as an extension to LTE’s evolved NodeB to realize the concept of network slicing on LTE Radio Access Networks. This proposed component communicates with an SDN Controller to receive information regarding the network slices and adapts the slices according to the network state. Simulations were performed to validate the Slice Optimizer and highlight the benefits that can be achieved with our proposal, such as the improvement of user’s QoS experience due to a more efficient use of network resources.
Antonio Gonzalez Pastana Lobato, Martin Andreoni Lopez, Igor Jochem Sanz, Alvaro A. Cardenas, Otto Carlos Muniz Bandeira Duarte, Guy Pujolle. An Adaptive Real-Time Architecture for Zero-Day Threat Detection. International Conference on Communications - ICC 2018, 2018.
Attackers create new threats and constantly change their behavior to mislead security systems. In this paper, we propose an adaptive threat detection architecture that trains its detection models in real time. The major contributions of the proposed architecture are: i) gather data about zero-day attacks and attacker behavior using honeypots in the network; ii) process data in real time and achieve high processing throughput through detection schemes implemented with stream processing technology; iii) use of two real datasets to evaluate our detection schemes, the first from a major network operator in Brazil and the other created in our lab; iv) design and development of adaptive detection schemes including both online trained supervised classification schemes that update their parameters in real time and learn zero-day threats from the honeypots, and online trained unsupervised anomaly detection schemes that model legitimate user behavior and adapt to changes. The performance evaluation results show that proposed architecture maintains an excellent trade-off between threat detection and false positive rates and achieves high classification accuracy of more than 90%, even with legitimate behavior changes and zero-day threats.
Eduardo Felipe Zambom Santana, Lucas Kanashiro, Diego Bogado Tomasiello, Fabio Kon and Mariana Gianotti. Analyzing Urban Mobility Carbon Footprint with Large-scale, Agent-based Simulation. 7th International Conference on Smart Cities and Green ICT Systems, 2018.
The growth of cities around the world bring new challenges to urban management and planning. Tools, such as simulators, can help the decision-making process by enabling the understanding of the current situation of the city and comparison of multiple scenarios with regard to changes in the urban infrastructure and in public policy. This paper presents an analysis of mobility parameters, such as distance, cost, travel time, and carbon footprint, for different simulated scenarios in a large metropolis in a developing country. We simulated the scenarios using an open source, large-scale, agent-based Smart City simulator that we developed.
Antonio D. de Carvalho Jr., Alfredo Goldman, Fabio Kon and Marcos Buckeridge. IoTrees: Sensing the city through its trees (in Portuguese). Revista Computação Brasil n. 37, 2018.
A presença de árvores saudáveis no espaço urbano é algo fundamental para a qualidade de vida na cidade. Um projeto de design aberto para monitorar o ambiente por meio das árvores pode dar bons frutos? Esse é o desafio do projeto “Internet of Trees”.
Hugo Resende, Alvaro Luiz Fazenda and Marcos Gonçalves Quiles. Parallel Algorithm for Dynamic Community Detection. 8th Workshop on Applications for Multi-Core Architectures, 2017.
Many real systems can be naturally modeled by complex networks. A complex network represents an abstraction of the system regarding its components and their respective interactions. Thus, by scrutinizing the network, interesting properties of the system can be revealed. Among them, the presence of communities, which consists of groups of densely connected nodes, is a significant one. For instance, a community might reveal patterns, such as the functional units of the system, or even groups correlated people in social networks. Albeit important, the community detection process is not a simple computational task, in special when the network is dynamic. Thus, several researchers have addressed this problem providing distinct methods, especially to deal with static networks. Recently, a new algorithm was introduced to solve this problem. The approach consists of modeling the network as a set of particles inspired by a N-body problem. Besides delivering similar results to state-of-the-art community detection algorithm, the proposed model is dynamic in nature; thus, it can be straightforwardly applied to time-varying complex networks. However, the Particle Model still has a major drawback. Its computational cost is quadratic per cycle, which restricts its application to mid-scale networks. To overcome this limitation, here, we present a novel parallel algorithm using many-core high-performance resources. Through the implementation of a new data structure, named distance matrix, was allowed a massive parallelization of the particles interactions. Simulation results show that our parallel approach, running both traditional CPUs and hardware accelerators based on multicore CPUs and GPUs, can speed up the method permitting its application to large-scale networks.
Danilo Carastan-Santos, David Martins Jr., Luiz Rozante, Siang Song and Raphael de Camargo. A hybrid CPU-GPU-MIC algorithm for hitting set problem. XVIII Simpósio em Sistemas Computacionais de Alto Desempenho - WSCAD 2017. Campinhas, 17 a 20 de outubro de 2017, pp. 196-207, 2017.
We present a hybrid exact algorithm for the Hitting Set Problem (HSP) for highly heterogeneous CPU-GPU-MIC platforms. With several techniques that permit an efficient exploitation of each architecture, low communication cost and effective load balancing, we were able to solve large HSP instances in reasonable time, achieving good performance and scalability. We obtained speedups of up to 25.32 in comparison with using two six-core CPUs and exact HSP solutions for instances with tens of thousands of variables in less than 5 hours. These results reinforce the statement that heterogeneous clusters of CPUs, GPUs and MICs can be used efficiently for high-performance computing.
Rodrigo Izidoro Tinini, Larissa C. M. Reis, Daniel Macêdo Batista, Gustavo Bittencourt Figueiredo, Massimo Tornatore, and Biswanath Mukherjee. Optimal Placement of Virtualized BBU Processing in Hybrid Cloud-Fog RAN over TWDM-PON. Accepted for publication in IEEE Global Communications Conference (GLOBECOM), 2017.
In the context of future Cloud Radio Access Net- works (CRAN), optical networks will play an important role to provide the required transport capacity between cell-sites and processing pools, especially for future 5G scenarios. For instance, using CPRI fronthaul technologies a single antenna element can generate data up to 24.3Gbps even with current configurations of radio transmissions, and it is expected to generate up to Tbps with the advance of technology. So, the transport segment of a 5G network needs to be accurately planned to accommodate all the generated traffic. In this work, we propose the use of a Passive Optical Network (PON) jointly with the emergent paradigms of Fog Computing and Network Function Virtualization (NFV) to energy-efficiently support the high traffic transported in emergent mobile networks in an hybrid architecture called Cloud/Fog RAN (CF-RAN) that allows local and remote baseband processing. We introduce an Integer Linear Programming (ILP) model to schedule the processing of CPRI demands among the processing nodes of the network and turn on or off processing functions on demand. Our approach is able to accommodate demands on the nodes of the network in the most energy efficient way. We compare our results with CRAN and distributed architectures (DRAN) and show that an energy efficient planning can achieve considerable gains in power consumption.
Accepted for publication in IEEE Global Communications Conference (GLOBECOM) 2017
Eduardo Felipe Zambom Santana, Ana Paula Chaves, Marco Aurelio Gerosa, Fabio Kon and Dejan Milojicic. Software Platforms for Smart Cities: Concepts, Requirements, Challenges, and a Unified Reference Architecture. ACM Computing Surveys, 50 (6), January, 2017.
Making cities smarter help improve city services and increase citizens’ quality of life. Information and communication technologies (ICT) are fundamental for progressing towards smarter city environments. Smart City software platforms potentially support the development and integration of Smart City applications. However, the ICT community must overcome current significant technological and scientific challenges before these platforms can be widely used. This paper surveys the state-of-the-art in software platforms for Smart Cities. We analyzed 23 projects with respect to the most used enabling technologies, as well as functional and non-functional requirements, classifying them into four categories: Cyber-Physical Systems, Internet of Things, Big Data, and Cloud Computing. Based on these results, we derived a reference architecture to guide the development of next-generation software platforms for Smart Cities. Finally, we enumerated the most frequently cited open research challenges, and discussed future opportunities. This survey gives important references for helping application developers, city managers, system operators, end-users, and Smart City researchers to make project, investment, and research decisions.
F. M. Costa, K. A. Morris, F. Kon and P. J. Clarke. Model-Driven Domain-Specific Middleware. 37th IEEE International Conference on Distributed Computing Systems (ICDCS), 2017.
Middleware was introduced to facilitate the development of sophisticated applications based on a uniform methodology and industry standards. However, early research and practice suggested that no one-size-fits-all approach was suitable for all application domains and scenarios. This gave rise to industry initiatives to standardize domain-specific middleware services and profiles, as well as research efforts on configurable, reflective, and adaptive middleware. The industry’s approach led to easy deployment, although with a level of flexibility limited by the extent of existing profiles. The approach of the research community, on the other hand, enabled high flexibility, allowing any middleware configuration to be defined. Nevertheless, creating sound configurations using this approach is a challenging task, limiting the target audience to expert engineers. As a consequence, both initiatives do not scale with the current proliferation of specialized application domains. In this paper, we target this problem with an approach that leverages model-driven engineering for the construction of domain-specific middleware platforms. A set of high-level, yet expressive, building blocks is defined in the form of a metamodel, which is used to create models that specify the desired middleware configuration. We argue that this approach enables the rapid development of middleware platforms to match the proliferation of application domains, at the same time as it does not require per-application middleware construction or even highly skilled middleware engineers. We present the current state of our research and discuss research directions to fully realize the approach.
João Eduardo Ferreira, José Antônio Visintin, Jun Okamoto Jr. and Calton Pu. Smart Services: A Case Study on Smarter Public Safety by a Mobile App for University of São Paulo. The 2017 IEEE Conference on Smart City Innovations (IEEE SCI), 2017.
The University of São Paulo has faced public safety issues a long the years. Due to its size preventive surveillance by the campus security guard cannot be effective all the times. In order to bring a safer environment to its public of more than 60,000 daily users, a smart public safety system is being developed. This is a complex system, spread throughout all the University’s campuses. It is composed of a smart surveillance cameras system, a back office system with a workflow engine and a mobile application within a collaborative concept. The smart cameras system is being deployed and the mobile application together with the back office system is being used this past year with satisfactory results. The mobile application is the user entry point to report several security and campus maintenance related issues that are automatically directed to the responder team for immediate action in the case of security or enters an automated workflow engine in the case of campus maintenance. This paper presents the structure created towards achieving a smarter public safety environment, details of the implementation, presents statistical data collected by the system showing its effectiveness and concludes showing the improvements introduced in the university community safety and welfare.
Danilo Carastan Santos, Raphael Y. de Camargo, David C. Martins-Jr, Siang W. Song and Luiz C. S. Rozante. Finding exact hitting set solutions for systems biology applications using heterogeneous GPU clusters. Future Generation Computer Systems. Elsevier. Vol. 67, pp. 418-429., 2017.
The Systems Biology field presents several complex combinatorial problems that can be in part reduced to an instance of the Hitting Set Problem (HSP), which is NP-Hard. These reduced problems often come with a large amount of data that needs to be processed, such as gene expression profiles, resulting in prohibitive computational costs for finding the exact solutions. There are some proposals to obtain exact solutions for HSP, including an approach which uses GPUs. However, such an approach is not scalable for real input sizes (thousands of variables). We propose a novel algorithm for solving HSP instances with thousands of variables by using: (i) clause sorting, which enables the efficient discarding of non-solution candidates, (ii) parallel generation and evaluation of candidate solutions through the use of GPUs, and (iii) support for multiple GPUs. To permit the execution on heterogeneous clusters, we determine the minimum kernel size that does not incur extra overhead and distribute tasks among available GPUs on demand. Our experimental results show that the combination of these techniques results in a speedup of 118.5, when using eight NVIDIA Tesla K20c in comparison with a ten-core Intel Xeon E5-2690 processor. Consequently, our algorithm can enable the usage of exact algorithms for solving the Hitting Set problem and applying it to real world problems.
Leissi Margarita Castañeda Leon and Paulo André Vechiatto De Miranda. Multi-Object Segmentation by Hierarchical Layered Oriented Image Foresting Transform. 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), 2017.
This paper introduces a new method for multi-object segmentation in images, named as Hierarchical Layered Oriented Image Foresting Transform (HLOIFT). As input, we have an image, a tree of relations between image objects, with the individual high-level priors of each object coded in its nodes, and the objects’ seeds. Each node of the tree defines a weighted digraph, named as layer. The layers are then integrated by the geometric interactions, such as inclusion and exclusion relations, extracted from the given tree into a unique weighted digraph, named as hierarchical layered digraph. A single energy optimization is performed in the hierarchical layered weighted digraph by Oriented Image Foresting Transform (OIFT) leading to globally optimal results satisfying all the high-level priors. We evaluate our framework in the multi-object segmentation of medical and synthetic images, obtaining results comparable to the state-of-the-art methods, but with low computational complexity. Compared to multi-object segmentation by min-cut/max-flow algorithm, our approach is less restrictive, leading to globally optimal results in more general scenarios.
Jucele Vasconcelos, Edson Caceres, Henrique Mongelli and Siang Wun Song. A parallel algorithm for minimum spanning tree on GPU. International Symposium on Computer Architecture and High Performance Computing Workshops (WAMCA 2017), 2017.
Computing a minimum spanning tree (MST) of a graph is a fundamental problem in Graph Theory and arises as a subproblem in many applications. In this paper, we propose a parallel MST algorithm and implement it on a GPU (Graphics Processing Unit). One of the steps of previous parallel MST algorithms is a heavy use of parallel list ranking. Besides the fact that list ranking is present in several parallel libraries, it is very time-consuming. Using a different graph decomposition, called strut, we devised a new parallel MST algorithm that does not make use of the list ranking procedure. Based on the BSP/CGM model we proved that our algorithm is correct and it finds the MST after O(log p) iterations (communication and computation rounds). To show that our algorithm has a good performance onreal parallel machines, we have implemented it on GPU. The way that we have designed the parallel algorithm allowed us to exploit the computing power of the GPU. The efficiency of the algorithm was confirmed by our experimental results. The tests performed show that, for randomly constructed graphs, with vertex numbers varying from 10,000 to 30,000 and density between 0.02 and 0.2, the algorithm constructs an MST in a maximum of six iterations. When the graph is not very sparse, our implementation achieved a speedup of more than 50, for some instances as high 296, over a minimum spanning tree sequential algorithm previously proposed in the literature.
Received the best paper award.
Arthur de M. Del Esposte, Fabio Kon, Fabio M. Costa and Nelson Lago. InterSCity: A Scalable Microservice-based Open Source Platform for Smart Cities. 6th International Conference on Smart Cities and Green ICT Systems, 2017.
Smart City technologies emerge as a potential solution to tackle common problems in large urban centers by using city resources efficiently and providing quality services for citizens. Despite the various advances in middleware technologies to support future smart cities, there are no universally accepted platforms yet. Most of the existing solutions do not provide the required flexibility to be shared across cities. Moreover, the extensive use and development of non-open-source software leads to interoperability issues and limits the collaboration among R&D groups. In this paper, we explore the use of a microservices architecture to address key practical challenges in smart city platforms. We present InterSCity, a microservice-based open source smart city platform that aims at supporting collaborative, novel smart city research, development, and deployment initiatives. We discuss how the microservice approach enables a flexible, extensible, and loosely coupled architecture and present experimental results demonstrating the scalability of the proposed platform.
Received the Best Student Paper Award
Eduardo F. Z. Santana, Nelson Lago, Fabio Kon and Dejan S. Milojicic. InterSCSimulator: Large-Scale Traffic Simulation in Smart Cities using Erlang. 18th Workshop on Multi-agent-based Simulation, 2017.
Large cities around the world face numerous challenges to guarantee the quality of life of its citizens. A promising approach to cope with these problems is the concept of Smart Cities, of which the main idea is the use of Information and Communication Technologies to improve city services. Being able to simulate the execution of Smart Cities scenarios would be extremely beneficial for the advancement of the field. Such a simulator, like many others, would need to represent a large number of various agents (e.g. cars, hospitals, and gas pipelines). One possible approach for doing this in a computer system is to use the actor model as a programming paradigm so that each agent corresponds to an actor. The Erlang programming language is based on the actor model and is the most commonly used implementation of it. In this paper, we present the first version of InterSCSimulator, an open-source, extensible, large-scale Traffic Simulator for Smart Cities developed in Erlang, capable of simulating millions of agents using a real map of a large city. Future versions will be extended to address other Smart City domains.
Daniel Macêdo Batista, Alfredo Goldman, Roberto Hirata Jr., Fabio Kon, Fabio M. Costa and Markus Endler. InterSCity: Addressing Future Internet Research Challenges for Smart Cities. 7th IEEE International Conference on Network of the Future, 2016.
The Future Internet will integrate large-scale systems constructed from the composition of thousands of distributed services, while interacting directly with the physical world via sensors and actuators, which compose the Internet of Things. This Future Internet will enable the realization of the Smart Cities vision, in which the urban infrastructure will be used to its fullest extent to offer a better quality of life for its citizens. Key to the efficient and effective realization of Smart Cities is the scientific and technological research covering the multiple layers that make up the Internet. This paper discusses the research challenges and initiatives related to Future Internet and Smart Cities in the scope of the InterSCity project. The challenges and initiatives are organized in three fronts: (1) Networking and High-Performance Distributed Computing; (2) Software Engineering for the Future Internet; and (3) Analysis and Mathematical Modeling for the Future Internet and Smart Cities. InterSCity aims at developing an integrated open-source platform containing all the major building blocks for the development of robust, integrated, sophisticated applications for the smart cities of the future.
Fabio Kon, Nelson Lago and Roberto Speicys Cardoso. Smart Cities for better quality of life (in Portuguese). Artigo opinativo publicado no Jornal O Estado de São Paulo (edição de 14 de outubro), 2016.
A preocupação atual com os recursos tecnológicos na gestão urbana é a chave para a melhoria da vida nas cidades e para a cidadania nas décadas vindouras, com foco em:
Original title: Cidades Inteligentes por mais Qualidade de Vida
Fabio Kon and Eduardo Felipe Zambom Santana. Smart Cities: Concepts, platforms, and challenges (in Portuguese). Jornadas de Atualização em Informática (JAI), 2016.
With the growth of the urban population, the infrastructural problems and limited resources of thousands of cities around the world affect negatively the lives of billions of people. Making cities smarter can help improving city services and increasing the quality of life of their citizens. Information and communication technologies (ICT) are a fundamental means to move towards smarter city environments. Using a software platform on top of which Smart City applications can be deployed facilitates the development and integration of such applications. However, there are, currently, significant technological and scientific challenges that must be faced by the ICT community before these platforms can be widely used. This chapter presents the state-of-the-art and the state-of-the-practice in Smart Cities environments. We analyze eleven smart city platforms and eleven smart city initiatives with respect to the most used enabling technologies as well as functional and non-functional requirements. Finally, we enumerate open research challenges and comment on our vision for the area in the future.
Original title: Cidades Inteligentes: Conceitos, plataformas e desafios