Channel assignment in multi-channel multiradio wireless networks poses a significant challenge due to scarcity of number of channels available in the wireless spectrum. Further, additional care has to be taken to consider the interference characteristics of the nodes in the network especially when nodes are in different collision domains. This work views the problem of channel assignment in multi-channel multi-radio networks with multiple collision domains as a non-cooperative game where the objective of the players is to maximize their individual utility by minimizing its interference. Necessary and sufficient conditions are derived for the channel assignment to be a Nash Equilibrium (NE) and efficiency of the NE is analyzed by deriving the lower bound of the price of anarchy of this game. A new fairness measure in multiple collision domain context is proposed and necessary and sufficient conditions for NE outcomes to be fair are derived. The equilibrium conditions are then applied to solve the channel assignment problem by proposing three algorithms, based on perfect/imperfect information, which rely on explicit communication between the players for arriving at an NE. A no-regret learning algorithm known as Freund and Schapire Informed algorithm, which has an additional advantage of low overhead in terms of information exchange, is proposed and its convergence to the stabilizing outcomes is studied. New performance metrics are proposed and extensive simulations are done using Matlab to obtain a thorough understanding of the performance of these algorithms on various topologies with respect to these metrics. It was observed that the algorithms proposed were able to achieve good convergence to NE resulting in efficient channel assignment strategies.
Given a large population, it is an intensive task to gather individual preferences over a set of alternatives and arrive at an aggregate or collective preference of the population. We show that social network underlying the population can be harnessed to accomplish this task effectively, by sampling preferences of a small subset of representative nodes. We first develop a Facebook app to create a dataset consisting of preferences of nodes and the underlying social network, using which, we develop models that capture how preferences are distributed among nodes in a typical social network. We hence propose an appropriate objective function for the problem of selecting best representative nodes. We devise two algorithms, namely, Greedy-min which provides a performance guarantee for a wide class of popular voting rules, and Greedy-sum which exhibits excellent performance in practice. We compare the performance of these proposed algorithms against randompolling and popular centrality measures, and provide a detailed analysis of the obtained results. Our analysis suggests that selecting representatives using social network information is advantageous for aggregating preferences related to personal topics (e.g., lifestyle), while random polling with a reasonable sample size is good enough for aggregating preferences related to social topics (e.g., government policies).
There are numerous types of networks in the realworld which involve strategic actors: supply chain networks, logistics networks, company networks, and social networks. In this investigation, we explore the topologies of decentralized networks that will be formed by strategic actors who interact with one another. In particular, we analyze a network formation game in a strategic setting where payoffs of individuals depend only on their immediate neighbourhood. These localized payoffs incorporate the social capital emanating from bridging positions that nodes hold in the network. Using this novel and appealing model of network formation, our study explores the structure of networks that form, satisfying pairwise stability or efficiency or both. We derive sufficient conditions for the pairwise stability of several interesting network structures. We characterize topologies of efficient networks by applying classical results from extremal graph theory and discover that the Turán graph (or the complete equi-bipartite network) emerges as the unique efficient network under many configurations of parameters. We examine the tradeoffs between topologies of pairwise stable networks and efficient networks using the notion of price of stability. We identify several parameter configurations where the price of stability is 1 (or at least lower bounded by 0.5) in the proposed model. This leads to another key insight of this paper: under mild conditions, efficient networks will form when strategic individuals choose to add or delete links based on only localized payoffs. We study the dynamics of the proposed model by designing a simple myopic best response updating rule and implementing it on a customized network formation test-bed.
In this investigation, we analyze a network formation game in a strategic setting where pay-offs of individuals depend only on their immediate neighbourhood. These localized pay-offs incorporate the social capital emanating from bridging positions that nodes hold in the network. Using this simple and novel model of network formation, our study explores the structure of networks that form, satisfying pairwise stability or efficiency or both. We derive sufficient conditions for the pairwise stability of several interesting network structures. We characterize topologies of efficient networks by drawing upon classical results from extremal graph theory and discover that the Turan graph (or the complete equi-bipartite network) emerges as the unique efficient network under many configurations of parameters. We examine the trade-offs between topologies of pairwise stable networks and efficient networks using the notion of price of stability. Interestingly, we find that price of stability is equal to 1 for almost all configurations of parameters in the proposed model; and for the rest of the configurations, we obtain a lower bound of 0.5. This leads to another key insight of this article: under mild conditions, efficient networks will form when strategic individuals choose to add or delete links based on only localized pay-offs. We study the dynamics of the proposed model by designing a simple myopic best response updating rule and implementing it on a customized network formation testbed.
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