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For complex computer networks with many tunable parameters and network performance objectives, the task of selecting the ideal network operating state is difficult. To improve the performance of these kinds of networks, this research proposes the idea of the cognitive network. A cognitive network is a network composed of elements that, through learning and reasoning, dynamically adapt to varying network conditions in order to optimize end-to-end performance. In a cognitive network, decisions are made to meet the requirements of the network as a whole, rather than the individual network components. We examine the cognitive network concept by first providing a definition and then outlining the difference between it and other cognitive and cross-layer technologies. From this definition, we develop a general, three-layer cognitive network framework, based loosely on the framework used for cognitive radio. In this framework, we consider the possibility of a cognitive process consisting of one or more cognitive elements, software agents that operate somewhere between autonomy and cooperation. To understand how to design a cognitive network within this framework we identify three critical design decisions that affect the performance of the cognitive network: the selfishness of the cognitive elements, their degree of ignorance, and the amount of control they have over the network. To evaluate the impact of these decisions, we created a metric called the price of a feature, defined as the ratio of the network performance with a certain design decision to the performance without the feature. To further aid in the design of cognitive networks, we identify classes of cognitive networks that are structurally similar to one another. We examined two of these classes: the potential class and the quasi-concave class. Both classes of networks will converge to Nash Equilibrium under selfish behavior and in the quasi-concave class this equilibrium is both Pareto and globally optimal. Furthermore, we found the quasi-concave class has other desirable properties, reacting well to the absence of certain kinds of information and degrading gracefully under reduced network control. In addition to these analytical, high level contributions, we develop cognitive networks for two open problems in resource management for self-organizing networks, validating and illustrating the cognitive network approach. For the first problem, a cognitive network is shown to increase the lifetime of a wireless multicast route by up to 125\%. For this problem, we show that the price of selfishness and control are more significant than the price of ignorance. For the second problem, a cognitive network minimizes the transmission power and spectral impact of a wireless network topology under static and dynamic conditions. The cognitive network, utilizing a distributed, selfish approach, minimizes the maximum power in the topology and reduces (on average) the channel usage to within 12\% of the minimum channel assignment. For this problem, we investigate the price of ignorance under dynamic networks and the cost of maintaining knowledge in the network. Today's computer networking technology will not be able to solve the complex problems that arise from increasingly bandwidth-intensive applications competing for scarce resources. Cognitive networks have the potential to change this trend by adding intelligence to the network. This work introduces the concept and provides a foundation for future investigation and implementation. / Ph. D.

Ad hoc networks are emerging as a cost-effective, yet, powerful tool for communication. These systems, where networks can emerge and converge on-the-fly, are guided by the forward-looking goals of providing ubiquitous connectivity and constant access to information. Due to power and bandwidth constraints, the vulnerability of the wireless medium, and the multi-hop nature of ad hoc networks, these networks are becoming increasingly complex dynamic systems. Besides, modern radios are empowered to be reconfigurable, which harbors the temptation to exploit the system. To understand the implications of these issues, some of which pose significant challenges to efficient network design, we study topology control using game theory. We develop a game-theoretic framework of topology control that broadly captures the radio parameters, one or more of which can be tuned under the purview of topology control. In this dissertation, we consider two parameters, viz. transmit power and channel, and study the impact of controlling these on the emergent topologies. We first examine the impact of node selfishness on the network connectivity and energy efficiency under two levels of selfishness: (a) nodes cooperate and forward packets for one another, but selfishly minimize transmit power levels and; (b) nodes selectively forward packets and selfishly control transmit powers. In the former case, we characterize all the Nash Equilibria of the game and evaluate the energy efficiency of the induced topologies. We develop a better-response-based dynamic that guarantees convergence to the minimal maximum power topology. We extend our analysis to dynamic networks where nodes have limited knowledge about network connectivity, and examine the tradeoff between network performance and the cost of obtaining knowledge. Due to the high cost of maintaining knowledge in networks that are dynamic, mobility actually helps in information-constrained networks. In the latter case, nodes selfishly adapt their transmit powers to minimize their energy consumption, taking into account partial packet forwarding in the network. This work quantifies the energy efficiency gains obtained by cooperation and corroborates the need for incentivizing nodes to forward packets in decentralized, energy-limited networks. We then examine the impact of selfish behavior on spectral efficiency and interference minimization in multi-channel systems. We develop a distributed channel assignment algorithm to minimize the spectral footprint of a network while establishing an interference-free connected network. In spite of selfish channel selections, the network spectrum utilization is shown to be within 12% of the minimum on average. We then extend the analysis to dynamic networks where nodes have incomplete network state knowledge, and quantify the price of ignorance. Under the limitations on the number of available channels and radio interfaces, we analyze the channel assignment game with respect to interference minimization and network connectivity goals. By quantifying the interference in multi-channel networks, we illuminate the interference reduction that can be achieved by utilizing orthogonal channels and by distributing interference over multiple channels. In spite of the non-cooperative behavior of nodes, we observe that the selfish channel selection algorithm achieves load balancing. Distributing the network control to autonomous agents leaves open the possibility that nodes can act selfishly and the overall system is compromised. We advance the need for considering selfish behavior from the outset, during protocol design. To overcome the effects of selfishness, we show that the performance of a non-cooperative network can be enhanced by appropriately incentivizing selfish nodes. / Ph. D.

Abstract As mobile Internet access traffic continues to grow at an explosive rate and wireless networks continue to diverge into multiple access technologies with partly overlapping sets of features, new solutions for efficient use of these networks are vital. Cognitive network management provides tools to tackle this challenge by automatically learning from past experience the characteristics and usage patterns of the connected devices, thus enabling autonomic optimization of those connections. Cognitive network management requires a vast amount of information in order to function effectively, making collaboration of the networked devices essential as the best sources of information are scattered throughout the network. This makes efficient information dissemination one of the key enablers for autonomic networking. This dissertation studies managing control information related to autonomic selection of access networks and adapting services in a heterogeneous wireless network environment. It presents a solution to simple and efficient information dissemination in the form of Distributed Decision Engine— a CEP-like system enabling the building of a highly scalable and dynamic messaging system enabling dissemination, analysis and control of the complex series of interrelated events in the network. The dissertation also presents results showing a clear benefit in using cross-layer and cross-domain information in a modern wireless environment and validates the final prototype implementation of DDE with laboratory measurements. Effective use of disseminated cross-layer information is another key element in autonomic wireless networking. This dissertation also focuses on intelligent decision-making based on cross-layer information by presenting test results which attest that the performance of an autonomic wireless networking system can be improved by using cognitive techniques in its management algorithms, and that hierarchy and coordination can be utilized to minimize the effect of conflicting decisions of the system. / Tiivistelmä Mobiilin Internet-liikenteen räjähdysmäinen kasvu ja langattomien verkkojen jatkuva jakautuminen useisiin tekniikoihin vaativat uusia ratkaisuja näiden verkkojen tehokkaaseen käyttöön. Kognitiivinen verkon hallinta mahdollistaa oppimisen, minkä avulla laitteiden yhteyksiä voidaan optimoida autonomisesti aiemman kokemuksen perusteella. Tällainen optimointi vaatii kuitenkin valtavan määrän verkosta ja laitteista kerättyä tietoa, mikä tekee tehokkaasta tiedonjakelusta keskeisen elementin autonomisessa verkon hallinnassa. Tässä väitöskirjassa tutkitaan verkon valintaan ja palveluiden sopeuttamiseen vaadittavan tiedon välittämistä ja hallintaa autonomisissa langattomissa verkoissa. Ratkaisuna yksinkertaiseen ja tehokkaaseen tiedonvälitykseen esitellään hajautettu Distributed Decision Engine -komponentti, joka mahdollistaa skaalautuvan tiedon jakelu-, analysointi- ja hallintajärjestelmän rakentamisen. Lisäksi väitöskirjassa kuvataan myös tuloksia, jotka osoittavat, että verkkokerrosten välisen tiedon käyttämisellä voidaan saavuttaa selvää etua, sekä tuloksia, jotka vahvistavat DDE-prototyyppitoteutuksen toimivuuden laboratoriomittauksin. Verkkokerrosten välisen tiedon tehokas hyödyntäminen on toinen keskeinen tekijä autonomisessa langattomien verkkojen hallinnassa. Väitöskirjassa käsitellään myös älykästä päätöksentekoa kyseisen informaation pohjalta sekä esitellään tuloksia, jotka osoittavat, että päätöksentekoa autonomisessa langattomien verkkojen hallinnassa voidaan parantaa kognitiivisilla tekniikoilla. Lisäksi esitetyt tulokset osoittavat, että hierarkialla sekä koordinoinnilla voidaan minimoida ristiriitaisten päätösten vaikutukset järjestelmään.