GrAnt: Inferring Best Forwarders from Complex Networks' Dynamics through a Greedy Ant Colony Optimization

This paper presents a new prediction-based forwarding protocol for the complex and dynamic Delay Tolerant Networks (DTN). The proposed protocol is called GrAnt (Greedy Ant) as it uses a greedy transition rule for the Ant Colony Optimization (ACO) metaheuristic to select the most promising forwarder nodes or to provide the exploitation of good paths previously found. The main motivation for the use of ACO is to take advantage of its population-based search and of the rapid adaptation of its learning framework. Considering data from heuristic functions and pheromone concentration, the GrAnt protocol includes three modules: routing, scheduling, and buffer management. To the best of our knowledge, this is the first unicast protocol that employs a greedy ACO which: (1) infers best promising forwarders from nodes' social connectivity, (2) determines the best paths to be followed to a message reach its destination, while limiting the message replications and droppings, (3) performs message transmission scheduling and buffer space management. GrAnt is compared to Epidemic and PROPHET protocols in two different scenarios: a working day and a community mobility model. Simulation results obtained by ONE simulator show that in both environments, GrAnt achieves higher delivery ratio, lower messages redundancy, and fewer dropped messages than Epidemic and PROPHET.Cet article porte sur la proposition d'un protocole d'acheminement pour les réseaux complexes et dynamiques du type tolérants aux délais (DTN), qui est basé sur l'estimation de possibilités futures de contact. Le protocole proposé est appelé GrAnt (Greedy Ant) car il utilise une règle de transition greedy pour la méta-heuristique d'optimisation par colonies de fourmis (ACO). Cette méta-heuristique donne à GrAnt la possibilité de sélectionner les relais les plus prometteuses ou d'exploiter les bons chemins préalablement trouvé. La motivation principale pour l'utilisation de l'ACO est de profiter de son mécanisme de recherche basée sur population et de son apprentissage et adaptation rapide. En utilisant des simulations basées sur des modèles synthétiques de mobilité, nous montrons que GrAnt est en mesure d'adapter conformément son acheminement dans des différents scénarios et possède une meilleure performance comparée à des protocoles comme Epidemic et PROPHET, en plus de la génération de faible surcharge

A Social-aware Routing Protocol for Opportunistic Networks

International audienceThis work proposes the Cultural Greedy Ant (CGrAnt) protocol to solve the problem of data delivery in opportunistic and intermittently connected networks referred to as Delay Tolerant Networks (DTNs). CGrAnt is a hybrid Swarm Intelligence-based forwarding protocol designed to address the dynamic and complex environment of DTNs. CGrAnt is based on: (1) Cultural Algorithms (CA) and Ant Colony Optimization (ACO) and (2) operationalmetrics that characterize the opportunistic social connectivity between wireless users. The most promising message forwarders are selected via a greedy transition rule based on local and global information captured from the DTN environment. Using simulations, we rst analyze the inuence of the ACO operators and CA knowledge on the CGrAnt performance. We then compare the performance of CGrAnt with the PROPHET and Epidemic protocols under varying networking parameters. The results show that CGrAnt achieves the highest delivery ratio (gains of 99.12% compared with PROPHET and 40.21% compared with Epidemic) and the lowest message replication (63.60% lower than PROPHET and 60.84% lower than Epidemic)

Cultural GrAnt: um protocolo de roteamento baseado em inteligência coletiva para redes tolerantes a atrasos

This work presents a new routing protocol for complex and dynamic Delay Tolerant Networks (DTN). The proposed protocol is called Cultural GrAnt (Greedy Ant), as it uses a hybrid system composed of a Cultural Algorithm and a greedy version of the Ant Colony Optimization (ACO) metaheuristic. In Cultural GrAnt, ACO represents the population space of the cultural algorithm and uses a greedy transition rule to either exploit previously found good paths or explore new paths by selecting, among a set of candidates, the most promising message forwarders. The main motivation for using ACO is to take advantage of its population-based search and adaptive learning framework. Conversely, CA gathers information during the evolutionary process and uses it to guide the population and thus accelerate learning while providing more efficient solutions. Considering information from heuristic functions, pheromone concentration, and knowledge stored in the CA belief space, the Cultural GrAnt protocol includes three modules: routing, scheduling, and buffer management. To the best of our knowledge, this is the first routing protocol that employs both ACO and CA to infer the best message forwarders using opportunistic information about social connectivity between nodes, determine the best paths a message must follow to eventually reach its destination while limiting message replications and droppings, and perform message transmission scheduling and buffer space management. Cultural GrAnt is compared to the Epidemic and PROPHET protocols in two different mobility scenarios: an activity-based movement model, which simulates the daily lives of people in their work, leisure and rest activities; and a community-based movement model. Simulation results obtained by the ONE simulator show that, in both scenarios, Cultural GrAnt achieves a higher delivery ratio, lower message replication, and fewer dropped messages than Epidemic and PROPHET.Esta tese apresenta um novo protocolo de roteamento voltado para as Redes Tolerantes a Atrasos que exibem comportamentos complexos e dinâmicos. O protocolo proposto chama-se Cultural GrAnt (do inglês Cultural Greedy Ant) uma vez que este utiliza um sistema híbrido composto por um Algoritmo Cultural (AC) e uma versão gulosa da meta-heurística de Otimização por Colônia de Formigas (ACO). No Cultural GrAnt, o ACO representa o espaço populacional de um AC e utiliza uma regra de transição gulosa de modo a intensificar bons caminhos já encontrados ou explorar novos caminhos através da seleção, dentre um conjunto de candidatos, dos nós encaminhadores de mensagens mais promissores. A principal motivação para o uso do ACO é tirar proveito da sua busca baseada em população de indivíduos e da adaptação da sua estrutura de aprendizado. O AC obtém informações durante o processo evolucionário e as utiliza para guiar a população e, então, acelerar o aprendizado enquanto provê soluções mais eficientes. Considerando informações de funções heurísticas, concentração de feromônio e conhecimentos armazenados no espaço de crenças do AC, o protocolo Cultural GrAnt inclui três módulos: roteamento; escalonamento; e gerenciamento de buffer. Esse é o primeiro protocolo de roteamento que emprega ACO e AC de modo a: inferir os melhores encaminhadores de mensagens através de informações oportunistas sobre a conectividade social entre os nós; determinar os melhores caminhos que uma mensagem deve seguir para eventualmente alcançar o seu destino final, enquanto limita o número de replicações e descartes de mensagens na rede; determinar a ordem de escalonamento das mensagens; e gerenciar o espaço de armazenamento do buffer dos nós. O protocolo Cultural GrAnt é comparado com os protocolos Epidêmico e PROPHET em dois cenários de mobilidade distintos: um modelo de movimento baseado em atividades, onde simula-se o dia-a-dia de pessoas em suas atividades de trabalho, lazer e descanso; e um modelo de movimento baseado em comunidades de pessoas. Os resultados de simulações obtidos através do simulador ONE mostram que em ambos os cenários, o protocolo Cultural GrAnt alcança uma taxa mais alta de entrega de mensagens, uma replicação menor de mensagens e um número menor de mensagens descartadas se comparado com os protocolos Epidêmico e PROPHET

Uso da Computação em Névoa para Coleta e Análise de Dados em Cidades Inteligentes

This work proposes a two-layer communication architecture based on fog computing for smart cities. The architecture aims to store, process, and aggregate sensor data. To choose the most suitable communication protocol for the proposed architecture, we analyze the performance of the two most popular protocols for Internet of Things (IoT), the Message Queuing Telemetry Transport (MQTT) and the Constrained Application Protocol (CoAP). We considered latency, bandwidth consumption, efficiency in case of data loss, and energy efficiency as performance metrics. After the protocol is chosen, we employ a topic-based publish/subscribe system for sending and receiving messages, as well as issuing alerts based on received information. Two case studies on monitoring are shown: water quality in reservoirs and city air conditions. Results show that the application is able to receive data and issue alerts for both case studies

Inteligência Artificial - Técnica de Busca Local para Melhorar a Meta-heurística de Otimização por Colônia de Formigas no Agrupamento de Instâncias em Bases de Dados

Este artigo propõe um algoritmo de agrupamento baseado na metaheurística de otimização por colônia de formigas. Com base nas soluções construídas pelas formigas artificiais, nas distâncias entre os centros dos clusters e suas instâncias, e na busca estocástica do algoritmo, a população de formigas é guiada no agrupamento de instâncias de forma mais eficiente. Uma técnica de busca local é aplicada no final de cada solução obtida por uma formiga na tentativa de melhorar a qualidade desta e reduzir o tempo de convergência da busca. Experimentos em bases de dados mostram a eficiência da meta-heurística pura e com o uso da técnica de busca local em termos da qualidade da solução e do número médio de avaliações da função objetivo

Notification Oriented Paradigm for Distributed Systems

Notification Oriented Paradigm (NOP) has been proposed as a newway to design software that is more efficient, decoupled, and with better performance than other paradigms. NOP is built based on a well-defined set of entities that interact by means of notifications. The way those entities are designedenables a declarative and rule-based programming model that is suitable fordistributed systems. This paper introduces a method to write distributed NOPprograms that maintains the same characteristics of performance and cohesionthat its local counterpart has. The method is presented with two case studiesthat have their design and performance compared to equivalent programs writtenwith traditional models and paradigms. The results show that distributedNOP programs behave correctly and, beyond the distribution, present similarbenefits as their single instance counterparts

Análise de desempenho de protocolos para computação em névoa

ABSTRACTThe rise in popularity of mobile devices has been made possibledue to advances in processing power and the miniaturization ofmodern processors. Mobile devices are often embedded systemswith limited resources such as battery, processing power, and memory,which impact the user experience. These devices located atthe edge of the network can also generate large amounts of data,which makes it difficult to centralize the data in the cloud. To addressthe scalability issues, fog computing is used to intermediatestorage and communication services between cloud computing andend devices, allowing for decentralized and scalable data. In fogcomputing, a message server may be used to distribute informationthrough one or more communication channels, reducing computationalresources. To transmit messages, it is necessary to choosea communication protocol, and it is important to consider the limitationsof mobile devices when analyzing the behavior of fogcommunication protocols. This paper evaluates the performanceof three fog computing protocols: MQTT, AMQP, and STOMP. Resultsindicate that MQTT achieves the best performance in termsof power and processing consumption, AMQP has lower memoryusage, and STOMP has a shorter round trip time for each message

Application of Bio-inspired Metaheuristics in the Data Clustering Problem

Clustering analysis includes a number of different algorithms and methods for grouping objects by their similar characteristics into categories. In recent years, considerable effort has been made to improve such algorithms performance. In this sense, this paper explores three different bio-inspired metaheuristics in the clustering problem: Genetic Algorithms (GAs), Ant Colony Optimization (ACO), and Artificial Immune Systems (AIS). This paper proposes some refinements to be applied to these metaheuristics in order to improve their performance in the data clustering problem. The performance of the proposed algorithms is compared on five different numeric UCI databases. The results show that GA, ACO and AIS based algorithms are able to efficiently and automatically forming natural groups from a pre-defined number of clusters