Eleni Stai

Exploiting socio-physical network interactions via a utility-based framework for resource management in mobile social networks

Mobile social networks have the lions share in modern mobile telecommunications, and their interaction with the underlying infrastructure networks has attracted significant attention due to its impact on resource management. In this article, we present and demonstrate a framework for addressing such interplay between online social networks and wireless communications by exploiting principles from the theory of utility-based engineering and elements from social network analysis. We aim at a holistic design framework that allows the joint development of improved resource management mechanisms for future mobile wireless infrastructures and their social counterparts. We demonstrate the proposed methodology and reveal the key aspects of designing and exploiting convenient utility functions within the framework of network science in order to better manage the available resources, improve infrastructures, and eventually obtain from them the maximum possible benefit. We establish the above principles and emerging design potentials in future complex networks by presenting two tangible examples where personalized advertising and topology control in MSNs are used to exploit and validate different network and individual socio-utility maximization features, respectively.

A hyperbolic space analytics framework for big network data and their applications

Big data analytics have generated a paradigm shift in modern data analysis and decision making in almost every aspect of human society. Nowadays, massive amounts of generated network and correlated (networked) data pose critical computational and storage challenges, requiring the development of radical techniques to manage, process, and analyze them more efficiently. We propose embedding such data and their correlations in hyperbolic metric spaces as one approach aspiring to radically change current practices. In this article, we explore the potential that such data embedding and the corresponding hyperbolic space based data analytics can offer to networks, their applications, and their services. We demonstrate how this approach may lead to more efficient and scalable problem solving within diverse application domains, such as network design/analysis, network resource allocation optimization, and network economics/marketing, paving the way for more diverse and effective solutions in the future.

Topology Enhancements in Wireless Multihop Networks: A Top-Down Approach

Contemporary traffic demands call for efficient infrastructures capable of sustaining increasing volumes of social communications. In this work, we focus on improving the properties of wireless multihop networks with social features through network evolution. Specifically, we introduce a framework, based on inverse Topology Control (iTC), for distributively modifying the transmission radius of selected nodes, according to social paradigms. Distributed iTC mechanisms are proposed for exploiting evolutionary network churn in the form of edge/node modifications, without significantly impacting available resources. We employ continuum theory for analytically describing the proposed top-down approach of infusing social features in physical topologies. Through simulations, we demonstrate how these mechanisms achieve their goal of reducing the average path length, so as to make a wireless multihop network scale like a social one, while retaining its original multihop character. We study the impact of the proposed topology modifications on the operation and performance of the network with respect to the average throughput, delay, and energy consumption of the induced network.

Evolutionary Dynamics of Complex Communications Networks

Until recently, most network design techniques employed a bottom-up approach with lower protocol layer mechanisms affecting the development of higher ones. This approach, however, has not yielded fascinating results in the case of wireless distributed networks. Addressing the emerging aspects of modern network analysis and design, Evolutionary Dynamics of Complex Communications Networks introduces and develops a top-bottom approach where elements of the higher layer can be exploited in modifying the lowest physical topologyclosing the network design loop in an evolutionary fashion similar to that observed in natural processes.This book provides a complete overview of contemporary design approaches from the viewpoint of network science and complex/social network analysis. A significant part of the text focuses on the classification and analysis of various network modification mechanisms for wireless decentralized networks that exploit social features from relevant online social networks. Each chapter begins with learning objectives and introductory material and slowly builds to more detailed analysis and advanced concepts. Each chapter also identifies open issues, while by the end of the book, potential research directions are summarized for the more interested researcher or graduate student.The approach outlined in the book will help network designers and administrators increase the value of their infrastructure without requiring any significant additional investment. Topics covered include: basic network graph models and properties, cognitive methods and evolutionary computing, complex and social network analysis metrics and features, and analysis and development of the distinctive structure and features of complex networks. Considering all aspects of modern network analysis and design, the text covers the necessary material and background to make it a suitable source of reference for graduate students, postdoctoral researchers, and scientists

Socially-Inspired Topology Improvements in Wireless Multi-Hop Networks

Several graph structures have emerged for describing different network types and the interactions among their entities. Various properties have been identified in each of these network graphs, with no single structure bearing all the desired features that would constitute it an optimal network graph. This work aims for the first time at infusing the desired properties of a small-world network into the core structure of a wireless multi-hop network, thus embedding social structure on an artificial network that emerges in the development of wireless communication services. In this paper, topology control based approaches are proposed, serving the purpose of adding communication links in a multi-hop network in an intelligent and effective manner. Through analysis and simulation, we demonstrate how the proposed methods decrease the average path length between randomly selected node pairs and properly scale the clustering coefficient of a multi-hop network, by exploiting social structure characteristics of small-world networks, thus allowing the development of more demanding services on top of wireless multi- hop networks.

Performance-aware cross-layer design in wireless multihop networks via a weighted backpressure approach

In this paper, we study, analyze, and evaluate a performance-aware cross-layer design approach for wireless multihop networks. Through network utility maximization (NUM) and weighted network graph modeling, a cross-layer algorithm for performing jointly routing, scheduling, and congestion control is introduced. The performance awareness is achieved by both the appropriate definition of the link weights for the corresponding applications requirements and the introduction of a weighted backpressure (BP) routing/scheduling. Contrary to the conventional BP, the proposed algorithm scales the congestion gradients with the appropriately defined per-pair (link, destination) weights. We analytically prove the queue stability achieved by the proposed cross-layer scheme, while its convergence to a close neighborhood of the optimal source rates values is proven via an ε-subgradient approach. The issue of the weights assignment based on various quality-of-service (QoS) metrics is also investigated. Through modeling and simulation, we demonstrate the performance improvements that can be achieved by the proposed approach-when compared against existing methodologies in the literature-for two different examples with diverse application requirements, emphasizing respectively on delay and trustworthiness.

User Optimal Throughput-Delay Trade-off in Multihop Networks Under NUM Framework

In this letter we address the problem of users desired trade-off between optimal throughput and delay in wireless multihop networks under the framework of Network Utility Maximization (NUM). Via dynamic utility design and proper adaptation of the constraint set of the NUM problem, the impact of users delay sensitivity on the resulting optimal throughput is considered. A cross-layer policy is proposed-stemming from Lyapunov drift techniques-that performs routing, scheduling and congestion control optimizing the above trade-off. Via modeling, analysis and simulations the performance of the proposed problem formulation and policy is evaluated.

A class of backpressure algorithms for networks embedded in hyperbolic space with controllable delay-throughput trade-off

Future communications consist of an increasing number of wireless parts, while simultaneously need to support the widespread multimedia applications imposed by social networks. These human-machine systems, driven by both real time social interactions and the challenges of the wireless networks design, call for efficient and easy to implement, distributed cross-layer algorithms for their operation. Performance metrics such as throughput, delay, trust, energy consumption, need to be improved and optimized aiming at high quality communications. We investigate the coveted throughput-delay trade-off in static wireless multihop networks based on a computer-aided design of the backpressure scheduling/routing algorithm for networks embedded in hyperbolic space. Both routing and scheduling exploit the hyperbolic distances to orient the packets to the destination and prioritize the transmissions correspondingly. The proposed design provides us with the freedom of controlling its theoretical throughput optimality and of counterbalancing its practical performance through simulations, leading to significant improvements of the throughput-delay trade-off.

Social networks over wireless networks

We consider the formation, operation and maintenance of dynamic social networks (among human users) supported by technological communication networks such as wireless networks, or hybrid wireline-wireless networks. The technological (physical) networks of interest display dynamic behavior in several dimensions, including variable connectivity, variable congestion, variable link characteristics. As broad-band wireless devices and networks are becoming ubiquitous these human-machine systems, that combine the social aspects and behavioral activities of humans with the technological characteristics of the underlying physical networks, provide several important challenges in efforts to model them, evaluate their performance and dynamically control them so certain performance requirements are met. These include combinations of performance, trust, privacy, energy efficiency. In this paper we develop novel models for these complex human-machine systems that incorporate social network behavioral models and wireless network models that are inspired from statistical physics (hyperbolic graphs). We investigate the performance of wireless network protocols that support and respond to the constraints implied by the social network they support.

A coalitional game based approach for multi-metric optimal routing in wireless networks

Achieving high Quality of Service (QoS) over wireless multihop networks calls for enhanced routing/scheduling algorithms. Towards this direction it has been shown in the literature that the Greedy Backpressure algorithm which combines routing based on greedy hyperbolic embedding with backpressure scheduling, achieves to improve delay while remains throughput optimal. However, the performance of such an approach is significantly affected by the selection of the corresponding spanning tree used for greedily embedding the network into the hyperbolic space. Our work aims exactly at addressing this issue, that is the construction of an appropriate spanning tree that improves the cost of the paths used by the Greedy Backpressure approach, when considering a more generic weighted network graph modeling. The latter allows us to take into consideration the link costs in the routing process, which in turn may result in the simultaneous improvement of multiple performance metrics. To address the problem under consideration, we propose a coalition formation game framework among the network nodes, so that they can decide cooperatively for the spanning tree, via trading their value functions designed to depend on the link weights. We prove that the stable outcome of the coalitional game is a spanning tree of the network, and study through simulations the induced improvement in the network performance. Furthermore, we extend the framework for a scenario with multiple costs on each link through multi-tree hyperbolic embedding.