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.

Stigmergy refers to indirect communication that was originally found in biological systems. It is used for self-organization by ants, bees, and flocks of birds, by allowing individuals to focus on local information. Through local communication among individuals, larger patterns are formed without centralized communication. This self-organization is just one type of system studied within complex systems. Systems of ants, bees, and flocks of birds are considered complex because they exhibit emergent behavior: the outcome is more than the sum of the individual parts. Emergent behavior can be found in many other systems as well. One example is the Internet, which is a series of computers organized in a self-organized fashion. Complexity can also be defined through properties other than emergent behavior, such as existing on multiple scales. Many biological systems are multi-scale. For instance, cancer exists on many scales, including the sub-cellular and cellular levels. Many computing systems are also multi-scale, as there may be both individual and system-wide controls interacting together to determine the output. Many multi-agent systems would fall into this category, as would many large software systems. In this dissertation I examine complex systems in artificial intelligence and biology: the growth of cancer, population dynamics, emotions, multi-agent fault tolerance, and real-time strategic AI for games. My goal is twofold: a) to develop novel computational models of complex biological systems, and b) to tackle key AI research questions by proposing new algorithms and techniques that are inspired by those complex biological systems. In all of these cases I design variations on stigmergic communication to accomplish the task at hand. My contributions are a new agent-based cancer growth model, a proposed use of location communication for removing cancer, improved multi-agent fault tolerance through localized messaging, a new approach to modeling predator-prey dynamics using computational emotions, and improved strategic game AI through computational emotions.

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This thesis presents the first investigation of the impact of GPU computing on cognitive robotics by providing a series of novel experiments in the area of action and language acquisition in humanoid robots and computer vision. Cognitive robotics is concerned with endowing robots with high-level cognitive capabilities to enable the achievement of complex goals in complex environments. Reaching the ultimate goal of developing cognitive robots will require tremendous amounts of computational power, which was until recently provided mostly by standard CPU processors. CPU cores are optimised for serial code execution at the expense of parallel execution, which renders them relatively inefficient when it comes to high-performance computing applications. The ever-increasing market demand for high-performance, real-time 3D graphics has evolved the GPU into a highly parallel, multithreaded, many-core processor extraordinary computational power and very high memory bandwidth. These vast computational resources of modern GPUs can now be used by the most of the cognitive robotics models as they tend to be inherently parallel. Various interesting and insightful cognitive models were developed and addressed important scientific questions concerning action-language acquisition and computer vision. While they have provided us with important scientific insights, their complexity and application has not improved much over the last years. The experimental tasks as well as the scale of these models are often minimised to avoid excessive training times that grow exponentially with the number of neurons and the training data. This impedes further progress and development of complex neurocontrollers that would be able to take the cognitive robotics research a step closer to reaching the ultimate goal of creating intelligent machines. This thesis presents several cases where the application of the GPU computing on cognitive robotics algorithms resulted in the development of large-scale neurocontrollers of previously unseen complexity enabling the conducting of the novel experiments described herein.

Rapid prototyping tools exist in many fields of science and engineering, but are rare in biology especially not general tools that can handle the diversity and complexity of the many spatial and temporal scales in nature. In this thesis a general use, cell-based, middle-out biology emulation programming framework (outlining a programming paradigm) is presented, that enables biologists to emulate and use virtual biological systems of previously unimaginable complexity and potentially get results accurate enough to be used in research and ultimately, in clinical practice, such as diagnosis or operations. With this technology, virtual organisms can be created that are viable, fit and can be optimised for any task that arises. The tool, realised with a programming framework created for the C++ language is detailed and demonstrated through several examples of increasing complexity, namely several example organisms and a cancer emulation, showing both viable virtual organisms and usable experimental results.

The cellular nature of many biological materials, providing them with low density, high strength and high toughness, have fascinated many researchers in the field of botany and structural biology since at least one century. Bamboo, sponges, trabecular bone, tooth and honeybee combs are only few examples of natural materials with cellular architecture. It has been widely recognised that the geometric and mechanical characteristics of the microscopic building blocks play a fundamental role on the behavior observed at the macroscale. Up to date, many efforts have been devoted to the analysis of cellular materials with empty cells to predict the structure-property relations that link the macroscopic properties to the mechanics of their underlying microstructure. Surprisingly, notwithstanding the great advantages of the composite solutions in nature, in the literature a limited number of investigations concern cellular structures having the internal volumes of the cells filled with fluids, fibers or other bulk materials as commonly happens in biology. In particular, a continuum model has not been derived and explicit formulas for the effective elastic constants and constitutive relations are currently not available. To provide a contribution in this limitedly explored research area, this thesis describes the mathematical formulation and modelling technique leading to explicit expressions for the macroscopic elastic constants and stress-strain relations of biologically inspired composite cellular materials. Two examples are included. The first deals with a regular hexagonal architecture inspired by the biological parenchyma tissue. The second concerns a mutable cellular structure, composed by mutable elongated hexagonal cells, inspired by the hygroscopic keel tissue of the ice plant Delosperma nakurense. In both cases, the predicted results are found to be in very good agreement with the available data in the literature. Then, by taking into account the benefits offered by the complex hierarchical organisation of many natural systems, the attention is focused on the potential value of adding structural hierarchy into two-dimensional composite cellular materials having a self-similar hierarchical architecture, in the first case, and different levels with different cell topologies, in the second. In contrast to the traditional cellular materials with empty cells, the analysis reveals that, in the cell-filled configuration, introducing levels of hierarchy leads to an improvement in the specific stiffness. Finally, to offer concrete and relevant tools to engineers for developing future generations of materials with enhanced performance and unusual functionalities, a novel strategy to obtain a honeycomb with mutable cells is proposed. The technique, based on the ancient Japanese art of kirigami, consists in creating a pattern of cuts into a flat sheet of starting material, which is then stretched to give a honeycomb architecture. It emerges a vast range of effective constants that the so-called kirigami honeycomb structures can be designed with, just by changing the value of the applied stretch.

Brains display very high-level parallel computation, fault-tolerance, and adaptability, all of which are what we struggle to recreate in engineered systems. The neurocomputer (an organic computer built from living neurons) seems possible and may lead to a new generation of computing device that can operate in a brain-like manner. Cultured neuronal networks on multi-electrode arrays (MEAs) are one of the best candidates for the neurocomputer for their controllability, accessibility, flexibility, and the ability to self-organize. I explored the possibility of the neurocomputer by studying whether we can show goal-directed learning, one of the most fascinating behavior of brains, in cultured networks. Inspired by the brain, which needs to be embodied in some way and interact with its surroundings in order to give a purpose to its activities, we have developed tools for closing the sensory-motor loop between a cultured network and a robot or an artificial animal (an animat), termed a ¡§hybrot¡¨. In order to efficiently find an effective closed-loop design among infinite potential options, I constructed a biologically-inspired simulated network. By using this simulated network, I designed: (1) a statistic that can effectively and efficiently decode network functional plasticity, and (2) feedback stimulations and an adaptive training algorithm to encode sensory information and to direct network plasticity. By closing the sensory-motor loop with these decoding and encoding designs, we successfully demonstrated a simple adaptive goal-directed behavior: learning to move in a user-defined direction, and further showed that multiple tasks could be learned simultaneously. These results suggest that even though a cultured network lacks the 3-D structure of the brain, it still can be functionally shaped and show meaningful behavior. To our knowledge, this is the first demonstration of goal-directed learning in embodied cultured networks. Extending from these findings, I further proposed a research plan to optimize closed-loop designs for evaluating the maximal learning capacity (or even true intelligence) of the cultured network. Knowledge gained from effective closed-loop designs provides insights about learning and memory in the nervous system, which could influence the design of neurocomputers, future artificial neural networks, and more effective neuroprosthetics.

Many biological systems are known to accomplish complex tasks in a decentralized, robust, and scalable manner - characteristics that are desirable to the coordination of engineered systems as well. Inspired by nature, we produce coordination strategies for a network of heterogenous agents and in particular, we focus on intelligent collective systems. Bottlenose dolphins and African lions are examples of intelligent collective systems since they exhibit sophisticated social behaviors and effortlessly transition between functionalities. Through preferred associations, specialized roles, and self-organization, these systems forage prey, form alliances, and maintain sustainable group sizes. In this thesis, we take a three-phased approach to bioinspiration: in the first phase, we produce agent-based models of specific social behaviors observed in nature. The goal of these models is to capture the underlying biological phenomenon, yet remain simple so that the models are amenable to analysis. In the second phase, we produce bio-inspired algorithms that are based on the simple biological models produced in the first phase. Moreover, these algorithms are developed in the context of specific coordination tasks, e.g., the multi-agent foraging task. In the final phase of this work, we tailor these algorithms to produce coordination strategies that are ready to be deployed in target applications.

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.

ITC/USA 2013 Conference Proceedings / The Forty-Ninth Annual International Telemetering Conference and Technical Exhibition / October 21-24, 2013 / Bally's Hotel & Convention Center, Las Vegas, NV / In this paper, a new technique utilizing a scatterer of high dielectric constant in between electrically small antennas to achieve good Direction of arrival (DOA) estimation performance is demonstrated. The phase information of the received signal at the antennas is utilized for direction estimation. The impact of the property of the scatterer on the directional sensitivity and the output signal to noise ratio (SNR) level are studied. Finally the DOA estimation accuracy is analyzed with the proposed technique under the consumption of white Gaussian noise environment.