Current and emerging large-scale networks, for example the current Internet and the future Internet of Things, target supporting billions of networked entities to provide a wide variety of services and resources. Such complexity results in network-data from different sources with special characteristics, such as widely diverse users and services, multiple media (e.g., text, audio, video, etc.), high-dimensionality (i.e., large sets of attributes) and various dynamic concerns (e.g., time-sensitive data). With huge amounts of network data with such characteristics, there are significant challenges to a) recognize emergent and anomalous behavior in network traffic and b) make intelligent decisions for efficient and effective network operations. Fortunately, numerous analyses of Internet traffic have demonstrated that network traffic data exhibit multi-dimensional patterns that can be learned in order to enable discovery of data semantics. We claim that extracting and managing network semantics from traffic patterns and building conceptual models to be accessed on-demand would help in mitigating the aforementioned challenges. The current Internet, contemporary networking architectures and current tools for managing large network-data largely lack capabilities to 1) represent, manage and utilize the wealth of multi-dimensional traffic data patterns; 2) extract network semantics to support Internet intelligence through efficiently building conceptual models of Internet entities at different levels of granularity; and 3) predict future events (e.g., attacks) and behaviors (e.g., QoS of unfamiliar services) based on learned semantics. We depict the limited utilization of traffic semantics in networking operations as the “Internet Semantics Gap (ISG)”. We hypothesize that endowing the Internet and next generation networks with a “memory” system that provides data and semantics management would help resolve the ISG and enable “Internet Intelligence”. We seek to enable networked entities, at runtime and on-demand, to systematically: 1) learn and retrieve network semantics at different levels of granularity related to various Internet elements (e.g., services, protocols, resources, etc.); and 2) utilize extracted semantics to improve network operations and services in various aspects ranging from performance, to quality of service, to security and resilience. In this dissertation, we propose a distributed network memory management system, termed NetMem, for Internet intelligence. NetMem design is inspired by the functionalities of human memory to efficiently store Internet data and extract and utilize traffic data semantics in matching and prediction processes, and building dynamic network-concept ontology (DNCO) at different levels of granularity. The DNCO provides dynamic behavior models for various Internet elements. Analogous to human memory functionalities, NetMem has a memory system structure comprising short-term memory (StM) and long-term memory (LtM). StM maintains highly dynamic network data or data semantics with lower levels of abstraction for short time, while LtM keeps for long time slower varying semantics with higher levels of abstraction. Maintained data in NetMem can be accessed and learned at runtime and on-demand. From a system’s perspective, NetMem can be viewed as an overlay network of distributed “memory” agents, called NMemAgents, located at multiple levels targeting different levels of data abstraction and scalable operation. Our main contributions are as follows: • Biologically-inspired customizable application-agnostic distributed network memory management system with efficient processes for extracting and classifying high-level features and reasoning about rich semantics in order to resolve the ISG and target Internet intelligence. • Systematic methodology using monolithic and hybrid intelligence techniques for efficiently managing data semantics and building runtime-accessible dynamic ontology of correlated concept classes related to various Internet elements and at different levels of abstraction and granularity that would facilitate: ▪ Predicting future events and learning about new services; ▪ Recognizing and detecting of normal/abnormal and dynamic/emergent behavior of various Internet elements; ▪ Satisfying QoS requirements with better utilization of resources. We have evaluated the NetMem’s efficiency and effectiveness employing different semantics reasoning algorithms. We have evaluated NetMem operations over real Internet traffic data with and without using data dimensionality reduction techniques. We have demonstrated the scalability and efficiency of NetMem as a distributed multi-agent system using an analytical model. The effectiveness of NetMem has been evaluated through simulation using real offline data sets and also via the implementation of a small practical test-bed. Our results show the success of NetMem in learning and using data semantics for anomaly detection and enhancement of QoS satisfaction of running services. / Ph. D.

Surveillance systems are usually employed to monitor wide areas in which their usersaim to detect and/or observe events or phenomena of their interest. The use ofwireless sensor networks in such systems is of particular interest as these networks can provide a relative low cost and robust solution to cover large areas. Emerging applications in this context are proposing the use of wireless sensor networks composed of both static and mobile sensor nodes. Motivation for this trend is toreduce deployment and operating costs, besides providing enhanced functionalities.The usage of both static and mobile sensor nodes can reduce the overall systemcosts, by making low-cost simple static sensors cooperate with more expensive andpowerful mobile ones. Mobile wireless sensor networks are also desired in somespecific scenarios in which mobility of sensor nodes is required, or there is a specificrestriction to the usage of static sensors, such as secrecy. Despite the motivation,systems that use different combinations of static and mobile sensor nodes are appearing and with them, challenges in their interoperation. This is specially the case for surveillance systems.This work focuses on the proposal of solutions for wireless sensor networks including static and mobile sensor nodes specifically regarding cooperative andcontext aware mission setup and performance. Orthogonally to the setup and performance problems and related cooperative and context aware solutions, the goalof this work is to keep the communication costs as low as possible in the executionof the proposed solutions. This concern comes from the fact that communication increases energy consumption, which is a particular issue for energy constrained sensor nodes often used in wireless sensor networks, especially if battery supplied. Inthe case of the mobile nodes, this energy constraint may not be valid, since their motion might need much more energy. For this type of node the problem incommunicating is related to the links’ instabilities and short time windows availableto receive and transmit data. Therefore, it is better to communicate as little as possible. For the interaction among static and mobile sensor nodes, all thesecommunication constraints have to be considered.For the interaction among static sensor nodes, the problems of dissemination and allocation of sensing missions are studied and a solution that explores local information is proposed and evaluated. This solution uses mobile software agentsthat have capabilities to take autonomous decisions about the mission dissemination and allocation using local context information so that the mission’s requirementscan be fulfilled. For mobile wireless sensor networks, the problem studied is how to perform the handover of missions among the nodes according to their movements.This problem assumes that each mission has to be done in a given area of interest. In addition, the nodes are assumed to move according to different movement patterns,passing through these areas. It is also assumed that they have no commitment in staying or moving to a specific area due to the mission that they are carrying. To handle this problem, a mobile agent approach is proposed in which the agents implement the sensing missions’ migration from node to node using geographical context information to decide about their migrations. For the networks combining static and mobile sensor nodes, the cooperation among them is approached by abiologically-inspired mechanism to deliver data from the static to the mobile nodes.The mechanism explores an analogy based on the behaviour of ants building and following trails to provide data delivery, inspired by the ant colony algorithm. It is used to request the displacement of mobile sensors to a given location according tothe need of more sophisticated sensing equipment/devices that they can provide, so that a mission can be accomplished.The proposed solutions are flexible, being able to be applied to different application domains, and less complex than many existing approaches. The simplicity of the solutions neither demands great computational efforts nor large amounts of memory space for data storage. Obtained experimental results provide evidence of the scalability of these proposed solutions, for example by evaluatingtheir cost in terms of communication, among other metrics of interest for eachsolution. These results are compared to those achieved by reference solutions (optimum and flooding-based), providing indications of the proposed solutions’ efficiency. These results are considered close to the optimum one and significantly better than the ones achieved by flooding-based solutions.

Sistemas de vigilância são geralmente empregados no monitoramento de áreas de grandes dimensões nas quais seus usuários visam detectar ou observar fenômenos de seu interesse. O uso de redes de sensores sem fio nesses sistemas apresenta especial interesse, uma vez que essas redes podem apresentar soluções de baixo custo e robustas para cobrir áreas extensas. Neste contexto, novas aplicações têm surgido propondo o uso de redes de sensores sem fio compostas por nós sensores estáticos e móveis. Uma das motivações para esta tendência é a redução do custo de implantação e operação do sistema, além da possibilidade de proporcionar incremento em suas funcionalidades. O foco desta tese se concentra na proposta de soluções para redes de sensores sem fio com uso cooperativo de sensores estáticos e móveis, com particular atenção a sensibilidade ao contexto na configuração e execução de missões de sensoriamento. O objetivo é manter um baixo custo de comunicação associado às soluções propostas. Esta preocupação se dá pelo fato da comunicação aumentar o consumo de energia em redes de sensores, o que é um problema importante para nós sensores com limitada fonte de energia, i.e. baterias. No caso de nós sensores móveis, esta limitação pode não ser relevante, uma vez que seu movimento deve consumir uma quantidade muito mais expressiva de energia do que a comunicação. Neste caso, o problema se relaciona à estabilidade dos enlaces, bem como ao curto intervalo de tempo disponível para transmitir e receber dados. Logo, o melhor é comunicar o menos possível. Com relação à interação entre nós sensores estáticos, os problemas de disseminação e alocação de missões de sensoriamento são estudados e uma solução que explora o uso de informações locais é proposta e avaliada. Esta solução emprega agentes de software móveis que têm a capacidade de tomar decisões autônomas através do uso de informações de contexto local. Para redes de sensores móveis, o problema estudado se refere a como transferir missões entre os nós sensores de acordo com seu movimento e localização em relação aos locais onde as missões devem ser executadas. Para tratar este problema, uma abordagem baseada em agentes móveis é proposta, na qual os agentes implementam a migração das missões de sensoriamento usando informações de contexto geográfico para decidir a respeito de suas migrações. Para redes de sensores com sensores estáticos e móveis, a cooperação entre eles é abordada através de um mecanismo com inspiração biológica para realizar a realizar a entrega de dados emitidos pelos sensores estáticos aos sensores móveis. Para isto, explora-se uma analogia baseada no comportamento de formigas na construção e seguimento de trilhas. As soluções propostas são flexíveis, sendo aplicáveis a diferentes domínios de aplicação. Resultados experimentais evidenciam sua escalabilidade, avaliando, por exemplo, seu custo em termos de comunicação, além de outras métricas de interesse para cada uma das soluções. Estes resultados são comparados aos atingidos por soluções de referência (solução ótima teórica e baseada em inundação), indicando sua eficiência. Estes resultados são próximos do ótimo teórico e significativamente melhores que aqueles atingidos por soluções baseadas em técnicas de inundação. / Surveillance systems are usually employed to monitor wide areas in which their users are interested in detecting and/or observing events or phenomena of their interest. The use of wireless sensor networks in such systems is of particular interest as these networks can provide a relative low cost and robust solution to cover large areas. Emerging applications in this context are proposing the use of wireless sensor networks composed of both static and mobile sensor nodes. Motivation for this trend is to reduce deployment and operating costs, besides providing enhanced functionalities. This work focuses on the proposal of solutions for wireless sensor networks including static and mobile sensor nodes specifically regarding cooperative and context aware mission setup and performance. The goal is to keep the communication costs as low as possible in the execution of the proposed solutions. This concern comes from the fact that communication increases energy consumption, which is a particular issue for energy constrained sensor nodes often used in wireless sensor networks, especially if battery supplied. In the case of the mobile nodes, this energy constraint may not be valid, since their motion might need much more energy, but links instabilities and short time windows available to receive and transmit data. Therefore, it is better to communicate as little as possible. For the interaction among static sensor nodes, the problems of dissemination and allocation of sensing missions are studied and a solution that explores local information is proposed and evaluated. This solution uses mobile software agents that have capabilities to take autonomous decisions about the mission dissemination and allocation using local context information. For mobile wireless sensor networks, the problem studied is how to perform handover of missions among the nodes according to their movements and locations in relation to the place where the missions have to be performed. To handle this problem, a mobile agent approach is proposed in which the agents implement the sensing missions’ migration from node to node using geographical context information to decide about their migrations. For the networks combining static and mobile sensor nodes, the cooperation among them is approached by a biologically-inspired mechanism to deliver data from the static to the mobile nodes. The data delivery mechanism explores an analogy based on the behaviour of ants building and following trails, inspired by the ant colony algorithm. The proposed solutions are flexible, being able to be applied to different application domains. Obtained experimental results provide evidence of the scalability of these proposed solutions, for example by evaluating their cost in terms of communication, among other metrics of interest for each solution. These results are compared to those achieved by reference solutions (theoretical optimum and floodingbased), providing indications of the proposed solutions’ efficiency. These results are considered close to the theoretical optimum one and significantly better than the ones achieved by flooding-based solutions.

Sistemas de vigilância são geralmente empregados no monitoramento de áreas de grandes dimensões nas quais seus usuários visam detectar ou observar fenômenos de seu interesse. O uso de redes de sensores sem fio nesses sistemas apresenta especial interesse, uma vez que essas redes podem apresentar soluções de baixo custo e robustas para cobrir áreas extensas. Neste contexto, novas aplicações têm surgido propondo o uso de redes de sensores sem fio compostas por nós sensores estáticos e móveis. Uma das motivações para esta tendência é a redução do custo de implantação e operação do sistema, além da possibilidade de proporcionar incremento em suas funcionalidades. O foco desta tese se concentra na proposta de soluções para redes de sensores sem fio com uso cooperativo de sensores estáticos e móveis, com particular atenção a sensibilidade ao contexto na configuração e execução de missões de sensoriamento. O objetivo é manter um baixo custo de comunicação associado às soluções propostas. Esta preocupação se dá pelo fato da comunicação aumentar o consumo de energia em redes de sensores, o que é um problema importante para nós sensores com limitada fonte de energia, i.e. baterias. No caso de nós sensores móveis, esta limitação pode não ser relevante, uma vez que seu movimento deve consumir uma quantidade muito mais expressiva de energia do que a comunicação. Neste caso, o problema se relaciona à estabilidade dos enlaces, bem como ao curto intervalo de tempo disponível para transmitir e receber dados. Logo, o melhor é comunicar o menos possível. Com relação à interação entre nós sensores estáticos, os problemas de disseminação e alocação de missões de sensoriamento são estudados e uma solução que explora o uso de informações locais é proposta e avaliada. Esta solução emprega agentes de software móveis que têm a capacidade de tomar decisões autônomas através do uso de informações de contexto local. Para redes de sensores móveis, o problema estudado se refere a como transferir missões entre os nós sensores de acordo com seu movimento e localização em relação aos locais onde as missões devem ser executadas. Para tratar este problema, uma abordagem baseada em agentes móveis é proposta, na qual os agentes implementam a migração das missões de sensoriamento usando informações de contexto geográfico para decidir a respeito de suas migrações. Para redes de sensores com sensores estáticos e móveis, a cooperação entre eles é abordada através de um mecanismo com inspiração biológica para realizar a realizar a entrega de dados emitidos pelos sensores estáticos aos sensores móveis. Para isto, explora-se uma analogia baseada no comportamento de formigas na construção e seguimento de trilhas. As soluções propostas são flexíveis, sendo aplicáveis a diferentes domínios de aplicação. Resultados experimentais evidenciam sua escalabilidade, avaliando, por exemplo, seu custo em termos de comunicação, além de outras métricas de interesse para cada uma das soluções. Estes resultados são comparados aos atingidos por soluções de referência (solução ótima teórica e baseada em inundação), indicando sua eficiência. Estes resultados são próximos do ótimo teórico e significativamente melhores que aqueles atingidos por soluções baseadas em técnicas de inundação. / Surveillance systems are usually employed to monitor wide areas in which their users are interested in detecting and/or observing events or phenomena of their interest. The use of wireless sensor networks in such systems is of particular interest as these networks can provide a relative low cost and robust solution to cover large areas. Emerging applications in this context are proposing the use of wireless sensor networks composed of both static and mobile sensor nodes. Motivation for this trend is to reduce deployment and operating costs, besides providing enhanced functionalities. This work focuses on the proposal of solutions for wireless sensor networks including static and mobile sensor nodes specifically regarding cooperative and context aware mission setup and performance. The goal is to keep the communication costs as low as possible in the execution of the proposed solutions. This concern comes from the fact that communication increases energy consumption, which is a particular issue for energy constrained sensor nodes often used in wireless sensor networks, especially if battery supplied. In the case of the mobile nodes, this energy constraint may not be valid, since their motion might need much more energy, but links instabilities and short time windows available to receive and transmit data. Therefore, it is better to communicate as little as possible. For the interaction among static sensor nodes, the problems of dissemination and allocation of sensing missions are studied and a solution that explores local information is proposed and evaluated. This solution uses mobile software agents that have capabilities to take autonomous decisions about the mission dissemination and allocation using local context information. For mobile wireless sensor networks, the problem studied is how to perform handover of missions among the nodes according to their movements and locations in relation to the place where the missions have to be performed. To handle this problem, a mobile agent approach is proposed in which the agents implement the sensing missions’ migration from node to node using geographical context information to decide about their migrations. For the networks combining static and mobile sensor nodes, the cooperation among them is approached by a biologically-inspired mechanism to deliver data from the static to the mobile nodes. The data delivery mechanism explores an analogy based on the behaviour of ants building and following trails, inspired by the ant colony algorithm. The proposed solutions are flexible, being able to be applied to different application domains. Obtained experimental results provide evidence of the scalability of these proposed solutions, for example by evaluating their cost in terms of communication, among other metrics of interest for each solution. These results are compared to those achieved by reference solutions (theoretical optimum and floodingbased), providing indications of the proposed solutions’ efficiency. These results are considered close to the theoretical optimum one and significantly better than the ones achieved by flooding-based solutions.

Sistemas de vigilância são geralmente empregados no monitoramento de áreas de grandes dimensões nas quais seus usuários visam detectar ou observar fenômenos de seu interesse. O uso de redes de sensores sem fio nesses sistemas apresenta especial interesse, uma vez que essas redes podem apresentar soluções de baixo custo e robustas para cobrir áreas extensas. Neste contexto, novas aplicações têm surgido propondo o uso de redes de sensores sem fio compostas por nós sensores estáticos e móveis. Uma das motivações para esta tendência é a redução do custo de implantação e operação do sistema, além da possibilidade de proporcionar incremento em suas funcionalidades. O foco desta tese se concentra na proposta de soluções para redes de sensores sem fio com uso cooperativo de sensores estáticos e móveis, com particular atenção a sensibilidade ao contexto na configuração e execução de missões de sensoriamento. O objetivo é manter um baixo custo de comunicação associado às soluções propostas. Esta preocupação se dá pelo fato da comunicação aumentar o consumo de energia em redes de sensores, o que é um problema importante para nós sensores com limitada fonte de energia, i.e. baterias. No caso de nós sensores móveis, esta limitação pode não ser relevante, uma vez que seu movimento deve consumir uma quantidade muito mais expressiva de energia do que a comunicação. Neste caso, o problema se relaciona à estabilidade dos enlaces, bem como ao curto intervalo de tempo disponível para transmitir e receber dados. Logo, o melhor é comunicar o menos possível. Com relação à interação entre nós sensores estáticos, os problemas de disseminação e alocação de missões de sensoriamento são estudados e uma solução que explora o uso de informações locais é proposta e avaliada. Esta solução emprega agentes de software móveis que têm a capacidade de tomar decisões autônomas através do uso de informações de contexto local. Para redes de sensores móveis, o problema estudado se refere a como transferir missões entre os nós sensores de acordo com seu movimento e localização em relação aos locais onde as missões devem ser executadas. Para tratar este problema, uma abordagem baseada em agentes móveis é proposta, na qual os agentes implementam a migração das missões de sensoriamento usando informações de contexto geográfico para decidir a respeito de suas migrações. Para redes de sensores com sensores estáticos e móveis, a cooperação entre eles é abordada através de um mecanismo com inspiração biológica para realizar a realizar a entrega de dados emitidos pelos sensores estáticos aos sensores móveis. Para isto, explora-se uma analogia baseada no comportamento de formigas na construção e seguimento de trilhas. As soluções propostas são flexíveis, sendo aplicáveis a diferentes domínios de aplicação. Resultados experimentais evidenciam sua escalabilidade, avaliando, por exemplo, seu custo em termos de comunicação, além de outras métricas de interesse para cada uma das soluções. Estes resultados são comparados aos atingidos por soluções de referência (solução ótima teórica e baseada em inundação), indicando sua eficiência. Estes resultados são próximos do ótimo teórico e significativamente melhores que aqueles atingidos por soluções baseadas em técnicas de inundação. / Surveillance systems are usually employed to monitor wide areas in which their users are interested in detecting and/or observing events or phenomena of their interest. The use of wireless sensor networks in such systems is of particular interest as these networks can provide a relative low cost and robust solution to cover large areas. Emerging applications in this context are proposing the use of wireless sensor networks composed of both static and mobile sensor nodes. Motivation for this trend is to reduce deployment and operating costs, besides providing enhanced functionalities. This work focuses on the proposal of solutions for wireless sensor networks including static and mobile sensor nodes specifically regarding cooperative and context aware mission setup and performance. The goal is to keep the communication costs as low as possible in the execution of the proposed solutions. This concern comes from the fact that communication increases energy consumption, which is a particular issue for energy constrained sensor nodes often used in wireless sensor networks, especially if battery supplied. In the case of the mobile nodes, this energy constraint may not be valid, since their motion might need much more energy, but links instabilities and short time windows available to receive and transmit data. Therefore, it is better to communicate as little as possible. For the interaction among static sensor nodes, the problems of dissemination and allocation of sensing missions are studied and a solution that explores local information is proposed and evaluated. This solution uses mobile software agents that have capabilities to take autonomous decisions about the mission dissemination and allocation using local context information. For mobile wireless sensor networks, the problem studied is how to perform handover of missions among the nodes according to their movements and locations in relation to the place where the missions have to be performed. To handle this problem, a mobile agent approach is proposed in which the agents implement the sensing missions’ migration from node to node using geographical context information to decide about their migrations. For the networks combining static and mobile sensor nodes, the cooperation among them is approached by a biologically-inspired mechanism to deliver data from the static to the mobile nodes. The data delivery mechanism explores an analogy based on the behaviour of ants building and following trails, inspired by the ant colony algorithm. The proposed solutions are flexible, being able to be applied to different application domains. Obtained experimental results provide evidence of the scalability of these proposed solutions, for example by evaluating their cost in terms of communication, among other metrics of interest for each solution. These results are compared to those achieved by reference solutions (theoretical optimum and floodingbased), providing indications of the proposed solutions’ efficiency. These results are considered close to the theoretical optimum one and significantly better than the ones achieved by flooding-based solutions.