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 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.
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, 2018.
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.
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.
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.
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.
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.
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