Maximum Coverage and Maximum Connected Covering in Social Networks with Partial Topology Information

Viral marketing campaigns seek to recruit the most influential individuals to cover the largest target audience. This can be modeled as the well-studied maximum coverage problem. There is a related problem when the recruited nodes are connected. It is called the maximum connected cover problem. This problem ensures a strong coordination between the influential nodes which are the backbone of the marketing campaign. In this work, we are interested on both of these problems. Most of the related literature assumes knowledge about the topology of the network. Even in that case, the problem is known to be NP-hard. In this work, we propose heuristics to the maximum connected cover problem and the maximum coverage problem with different knowledge levels about the topology of the network. We quantify the difference between these heuristics and the local and global greedy algorithms

Green Base Station Placement for Microwave Backhaul Links

Wireless mobile backhaul networks have been proposed as a substitute in cases in which wired alternatives are not available due to economical or geographical reasons. In this work, we study the location problem of base stations in a given region where mobile terminals are distributed according to a certain probability density function and the base stations communicate through microwave backhaul links. Using results of optimal transport theory, we provide the optimal asymptotic distribution of base stations in the considered setting by minimizing the total power over the whole network.Comment: Proceedings of the International Symposium on Ubiquitous Networking (UNet'17), May 2017, Casablanca, Morocc

Information Spreading on Almost Torus Networks

Epidemic modeling has been extensively used in the last years in the field of telecommunications and computer networks. We consider the popular Susceptible-Infected-Susceptible spreading model as the metric for information spreading. In this work, we analyze information spreading on a particular class of networks denoted almost torus networks and over the lattice which can be considered as the limit when the torus length goes to infinity. Almost torus networks consist on the torus network topology where some nodes or edges have been removed. We find explicit expressions for the characteristic polynomial of these graphs and tight lower bounds for its computation. These expressions allow us to estimate their spectral radius and thus how the information spreads on these networks

Defensive Resource Allocation in Social Networks

In this work, we are interested on the analysis of competing marketing campaigns between an incumbent who dominates the market and a challenger who wants to enter the market. We are interested in (a) the simultaneous decision of how many resources to allocate to their potential customers to advertise their products for both marketing campaigns, and (b) the optimal allocation on the situation in which the incumbent knows the entrance of the challenger and thus can predict its response. Applying results from game theory, we characterize these optimal strategic resource allocations for the voter model of social networks.Comment: arXiv admin note: text overlap with arXiv:1402.538

Evolution of Social Power for Opinion Dynamics Networks

This article studies the evolution of opinions and interpersonal influence structures in a group of agents as they discuss a sequence of issues, each of which follows an opinion dynamics model. In this work, we propose a general opinion dynamics model and an evolution of interpersonal influence structures based on the model of reflected appraisals proposed by Friedkin. Our contributions can be summarized as follows: (i) we introduce a model of opinion dynamics and evolution of interpersonal influence structures between issues viewed as a best response cost minimization to the neighbor's actions, (ii) we show that DeGroot's and Friedkin-Johnsen's models of opinion dynamics and their evolution of interpersonal influence structures are particular cases of our proposed model, and (iii) we prove the existence of an equilibrium. This work is a step towards providing a solid formulation of the evolution of opinions and interpersonal influence structures over a sequence of issues

Optimal Base Station Placement: A Stochastic Method Using Interference Gradient In Downlink Case

In this paper, we study the optimal placement and optimal number of base stations added to an existing wireless data network through the interference gradient method. This proposed method considers a sub-region of the existing wireless data network, hereafter called region of interest. In this region, the provider wants to increase the network coverage and the users throughput. In this aim, the provider needs to determine the optimal number of base stations to be added and their optimal placement. The proposed approach is based on the Delaunay triangulation of the region of interest and the gradient descent method in each triangle to compute the minimum interference locations. We quantify the increase of coverage and throughput.Comment: This work has been presented in the 5th International ICST Conference on Performance Evaluation Methodologies and Tools (Valuetools 2011

Magnetworks: how mobility impacts the design of Mobile Networks

In this paper we study the optimal placement and optimal number of active relay nodes through the traffic density in mobile sensor ad-hoc networks. We consider a setting in which a set of mobile sensor sources is creating data and a set of mobile sensor destinations receiving that data. We make the assumption that the network is massively dense, i.e., there are so many sources, destinations, and relay nodes, that it is best to describe the network in terms of macroscopic parameters, such as their spatial density, rather than in terms of microscopic parameters, such as their individual placements. We focus on a particular physical layer model that is characterized by the following assumptions: i) the nodes must only transport the data from the sources to the destinations, and do not need to sense the data at the sources, or deliver them at the destinations once the data arrive at their physical locations, and ii) the nodes have limited bandwidth available to them, but they use it optimally to locally achieve the network capacity. In this setting, the optimal distribution of nodes induces a traffic density that resembles the electric displacement that will be created if we substitute the sources and destinations with positive and negative charges respectively. The analogy between the two settings is very tight and have a direct interpretation in wireless sensor networks

Continuum Equilibria and Global Optimization for Routing in Dense Static Ad Hoc Networks

We consider massively dense ad hoc networks and study their continuum limits as the node density increases and as the graph providing the available routes becomes a continuous area with location and congestion dependent costs. We study both the global optimal solution as well as the non-cooperative routing problem among a large population of users where each user seeks a path from its origin to its destination so as to minimize its individual cost. Finally, we seek for a (continuum version of the) Wardrop equilibrium. We first show how to derive meaningful cost models as a function of the scaling properties of the capacity of the network and of the density of nodes. We present various solution methodologies for the problem: (1) the viscosity solution of the Hamilton-Jacobi-Bellman equation, for the global optimization problem, (2) a method based on Green's Theorem for the least cost problem of an individual, and (3) a solution of the Wardrop equilibrium problem using a transformation into an equivalent global optimization problem

On the Throughput-Delay Trade-off in Georouting Networks

We study the scaling properties of a georouting scheme in a wireless multi-hop network of $n$ mobile nodes. Our aim is to increase the network capacity quasi linearly with $n$ while keeping the average delay bounded. In our model, mobile nodes move according to an i.i.d. random walk with velocity $v$ and transmit packets to randomly chosen destinations. The average packet delivery delay of our scheme is of order $1/v$ and it achieves the network capacity of order $\frac{n}{\log n\log\log n}$. This shows a practical throughput-delay trade-off, in particular when compared with the seminal result of Gupta and Kumar which shows network capacity of order $\sqrt{n/\log n}$ and negligible delay and the groundbreaking result of Grossglausser and Tse which achieves network capacity of order $n$ but with an average delay of order $\sqrt{n}/v$. We confirm the generality of our analytical results using simulations under various interference models.Comment: This work has been submitted to IEEE INFOCOM 201