Control of Diffusion Processes in Multi-agent Networks

Diffusion processes are instrumental in describing the movement of a continuous quantity in a generic network of interacting agents. Here, we present a probabilistic framework for diffusion in networks and propose to classify agent interactions according to two protocols where the total network quantity is conserved or variable. For both protocols, our focus is on asymmetric interactions between agents involving directed graphs. Specifically, we define how the dynamics of conservative and non-conservative networks relate to the weighted in-degree Laplacian and the weighted out-degree Laplacian. We show how network diffusion can be externally manipulated by applying time-varying input functions at individual nodes. The network control and design schemes enable flow modifications that allow the alteration of the dynamic and stationary behavior of the network in conservative and non-conservative networks. The proposed framework is relevant in the context of group coordination, herding behavior, distributed algorithms, and network control

Characterization and Control of Conservative and Nonconservative Network Dynamics

Diffusion processes are instrumental to describe the movement of a continuous quantity in a generic network of interacting agents. Here, we present a probabilistic framework for diffusion in networks and study in particular two classes of agent interactions depending on whether the total network quantity follows a conservation law. Focusing on asymmetric interactions between agents, we define how the dynamics of conservative and non-conservative networks relate to the weighted in-degree and out-degree Laplacians. For uncontrolled networks, we define the convergence behavior of our framework, including the case of variable network topologies, as a function of the eigenvalues and eigenvectors of the weighted graph Laplacian. In addition, we study the control of the network dynamics by means of external controls and alterations in the network topology. For networks with exogenous controls, we analyze convergence and provide a method to measure the difference between conservative and non-conservative network dynamics based on the comparison of their respective attainability domains. In order to construct a network topology tailored for a desired behavior, we propose a Markov decision process (MDP) that learns specific network adjustments through a reinforcement learning algorithm. The presented network control and design schemes enable the alteration of the dynamic and stationary network behavior in conservative and non-conservative networks

Packet Reception Probabilities in Vehicular Communications Close to Intersections

Vehicular networks allow vehicles to share information and are expected to be an integral part of future intelligent transportation systems (ITS). To guide and validate the design process, analytical expressions of key performance metrics such as packet reception probabilities and throughput are necessary, in particular for accident-prone scenarios such as intersections. In this paper, we present a procedure to analytically determine the packet reception probability and throughput of a selected link, taking into account the relative increase in the number of vehicles (i.e., possible interferers) close to an intersection. We consider both slotted Aloha and CSMA/CA MAC protocols, and show how the procedure can be used to model different propagation environments of practical relevance. The procedure is validated for a selected set of case studies at low traffic densities

Resource Allocation for Cognitive Small Cell Networks: A Cooperative Bargaining Game Theoretic Approach

Cognitive small cell networks have been envisioned as a promising technique for meeting the exponentially increasing mobile traffic demand. Recently, many technological issues pertaining to cognitive small cell networks have been studied, including resource allocation and interference mitigation, but most studies assume non-cooperative schemes or perfect channel state information (CSI). Different from the existing works, we investigate the joint uplink subchannel and power allocation problem in cognitive small cells using cooperative Nash bargaining game theory, where the cross-tier interference mitigation, minimum outage probability requirement, imperfect CSI and fairness in terms of minimum rate requirement are considered. A unified analytical framework is proposed for the optimization problem, where the near optimal cooperative bargaining resource allocation strategy is derived based on Lagrangian dual decomposition by introducing time-sharing variables and recalling the Lambert-W function. The existence, uniqueness, and fairness of the solution to this game model are proved. A cooperative Nash bargaining resource allocation algorithm is developed, and is shown to converge to a Pareto-optimal equilibrium for the cooperative game. Simulation results are provided to verify the effectiveness of the proposed cooperative game algorithm for efficient and fair resource allocation in cognitive small cell networks

An objective based classification of aggregation techniques for wireless sensor networks

Wireless Sensor Networks have gained immense popularity in recent years due to their ever increasing capabilities and wide range of critical applications. A huge body of research efforts has been dedicated to find ways to utilize limited resources of these sensor nodes in an efficient manner. One of the common ways to minimize energy consumption has been aggregation of input data. We note that every aggregation technique has an improvement objective to achieve with respect to the output it produces. Each technique is designed to achieve some target e.g. reduce data size, minimize transmission energy, enhance accuracy etc. This paper presents a comprehensive survey of aggregation techniques that can be used in distributed manner to improve lifetime and energy conservation of wireless sensor networks. Main contribution of this work is proposal of a novel classification of such techniques based on the type of improvement they offer when applied to WSNs. Due to the existence of a myriad of definitions of aggregation, we first review the meaning of term aggregation that can be applied to WSN. The concept is then associated with the proposed classes. Each class of techniques is divided into a number of subclasses and a brief literature review of related work in WSN for each of these is also presented

Interference Modeling and Spectrum Allocation in Two-Tier Networks

ISBN: 978110702309

Diffusion control in multi-agent networks

Diffusion processes are a fundamental way to describe the transfer of a continuous quantity in a generic network of interacting agents. In this work, we establish a probabilistic framework for diffusion in networks. In addition, we classify agent interactions according to two protocols where the total network quantity is conserved or variable. For both protocols, we use directed graphs to model asymmetric interactions between agents. Specifically, we define how the dynamics of conservative and non-conservative networks relate to the weighted in-degree and out-degree Laplacians respectively. Our framework enables the addition and subtraction of the considered quantity to and from a set of agents. This allows the framework to accommodate external network control and targeted network design. We show how network diffusion can be externally manipulated by injecting time-varying input functions at individual nodes. Desirable network structures can also be constructed by modifying the dominant diffusion modes. To this purpose, we propose a Markov decision process that learns these network adjustments through a reinforcement learning algorithm, suitable for large networks. The proposed network control and design schemes enable flow modifications that promote the alteration of the dynamic and stationary behavior of the network in conservative and non-conservative networks

Energy Efficiency Analysis of Two-Tier Heterogeneous Networks

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