Traffic Offloading/Onloading in Multi-RAT Cellular Networks

We analyze next generation cellular networks, offering connectivity to mobile users through multiple radio access technologies (RATs), namely LTE and WiFi. We develop a framework based on the Markovian agent formalism, which can model several aspects of the system, including user traffic dynamics and radio resource allocation. In particular, through a mean-field solution, we show the ability of our framework to capture the system behavior in flash-crowd scenarios, i.e., when a burst of traffic requests takes place in some parts of the network service area. We consider a distributed strategy for the user RAT selection, which aims at ensuring high user throughput, and investigate its performance under different resource allocation scheme

A Formal Framework for Modeling Trust and Reputation in Collective Adaptive Systems

Trust and reputation models for distributed, collaborative systems have been studied and applied in several domains, in order to stimulate cooperation while preventing selfish and malicious behaviors. Nonetheless, such models have received less attention in the process of specifying and analyzing formally the functionalities of the systems mentioned above. The objective of this paper is to define a process algebraic framework for the modeling of systems that use (i) trust and reputation to govern the interactions among nodes, and (ii) communication models characterized by a high level of adaptiveness and flexibility. Hence, we propose a formalism for verifying, through model checking techniques, the robustness of these systems with respect to the typical attacks conducted against webs of trust.Comment: In Proceedings FORECAST 2016, arXiv:1607.0200

Patch-based Hybrid Modelling of Spatially Distributed Systems by Using Stochastic HYPE - ZebraNet as an Example

Individual-based hybrid modelling of spatially distributed systems is usually expensive. Here, we consider a hybrid system in which mobile agents spread over the space and interact with each other when in close proximity. An individual-based model for this system needs to capture the spatial attributes of every agent and monitor the interaction between each pair of them. As a result, the cost of simulating this model grows exponentially as the number of agents increases. For this reason, a patch-based model with more abstraction but better scalability is advantageous. In a patch-based model, instead of representing each agent separately, we model the agents in a patch as an aggregation. This property significantly enhances the scalability of the model. In this paper, we convert an individual-based model for a spatially distributed network system for wild-life monitoring, ZebraNet, to a patch-based stochastic HYPE model with accurate performance evaluation. We show the ease and expressiveness of stochastic HYPE for patch-based modelling of hybrid systems. Moreover, a mean-field analytical model is proposed as the fluid flow approximation of the stochastic HYPE model, which can be used to investigate the average behaviour of the modelled system over an infinite number of simulation runs of the stochastic HYPE model.Comment: In Proceedings QAPL 2014, arXiv:1406.156

Dynamics of Social Networks: Multi-agent Information Fusion, Anticipatory Decision Making and Polling

This paper surveys mathematical models, structural results and algorithms in controlled sensing with social learning in social networks. Part 1, namely Bayesian Social Learning with Controlled Sensing addresses the following questions: How does risk averse behavior in social learning affect quickest change detection? How can information fusion be priced? How is the convergence rate of state estimation affected by social learning? The aim is to develop and extend structural results in stochastic control and Bayesian estimation to answer these questions. Such structural results yield fundamental bounds on the optimal performance, give insight into what parameters affect the optimal policies, and yield computationally efficient algorithms. Part 2, namely, Multi-agent Information Fusion with Behavioral Economics Constraints generalizes Part 1. The agents exhibit sophisticated decision making in a behavioral economics sense; namely the agents make anticipatory decisions (thus the decision strategies are time inconsistent and interpreted as subgame Bayesian Nash equilibria). Part 3, namely {\em Interactive Sensing in Large Networks}, addresses the following questions: How to track the degree distribution of an infinite random graph with dynamics (via a stochastic approximation on a Hilbert space)? How can the infected degree distribution of a Markov modulated power law network and its mean field dynamics be tracked via Bayesian filtering given incomplete information obtained by sampling the network? We also briefly discuss how the glass ceiling effect emerges in social networks. Part 4, namely \emph{Efficient Network Polling} deals with polling in large scale social networks. In such networks, only a fraction of nodes can be polled to determine their decisions. Which nodes should be polled to achieve a statistically accurate estimates

Reinforcement Learning for Mobile Robot Collision Avoidance in Navigation Tasks

Collision avoidance is fundamental for mobile robot navigation. In general, its solutions include: {\it map-based} and {\it mapless approaches.} In the map-based approach, robots pre-plan collision-free paths based on an environment map and follow their paths during navigation. On the other hand, the mapless approach requires robots to avoid collisions without referencing to an environment map. This thesis first studies the map-based approach for multiple robots to collectively build environment maps. In this study, a robot following a pre-planned path may encounter unexpected obstacles, such as other moving robots and obstacles inaccurately presented on an environment map. This motivates us to study mapless collision avoidance in the second part of the thesis. Mapless collision avoidance requires a robot to infer an optimal action based on sensor data and operate in real time. Inferring an optimal action in a timely manner is computationally expensive, particularly when a robot has limited on-board computing resources. To avoid the expensive online action inferring, this thesis presents a reinforcement learning approach which learns policies for mapless collision avoidance under real-world settings. We first propose a Real-Time Actor-Critic Architecture (RTAC) to support asynchronous reinforcement learning under real-time constraint. Based on RTAC, we propose asynchronous reinforcement learning methods for mapless collision avoidance of various numbers of robots under different environment configurations. Through extensive experiments, we demonstrate that RTAC serves as a solid foundation to support multi-task and multi-agent learning for mapless collision avoidance under asynchronous settings