MAC design for WiFi infrastructure networks: a game-theoretic approach

In WiFi networks, mobile nodes compete for accessing a shared channel by means of a random access protocol called Distributed Coordination Function (DCF). Although this protocol is in principle fair, since all the stations have the same probability to transmit on the channel, it has been shown that unfair behaviors may emerge in actual networking scenarios because of non-standard configurations of the nodes. Due to the proliferation of open source drivers and programmable cards, enabling an easy customization of the channel access policies, we propose a game-theoretic analysis of random access schemes. Assuming that each node is rational and implements a best response strategy, we show that efficient equilibria conditions can be reached when stations are interested in both uploading and downloading traffic. More interesting, these equilibria are reached when all the stations play the same strategy, thus guaranteeing a fair resource sharing. When stations are interested in upload traffic only, we also propose a mechanism design, based on an artificial dropping of layer-2 acknowledgments, to force desired equilibria. Finally, we propose and evaluate some simple DCF extensions for practically implementing our theoretical findings.Comment: under review on IEEE Transaction on wireless communication

Non-linear protocols for optimal distributed consensus in networks of dynamic agents.

We consider stationary consensus protocols for networks of dynamic agents with fixed topologies. At each time instant, each agent knows only its and its neighbors’ state, but must reach consensus on a group decision value that is function of all the agents’ initial state. We show that the agents can reach consensus if the value of such a function is time-invariant when computed over the agents’ state trajectories. We use this basic result to introduce a non-linear protocol design rule allowing consensus on a quite general set of values. Such a set includes, e.g., any generalized mean of order p of the agents’ initial states. As a second contribution we show that our protocol design is the solution of individual optimizations performed by the agents. This notion suggests a game theoretic interpretation of consensus problems as mechanism design problems. Under this perspective a supervisor entails the agents to reach a consensus by imposing individual objectives. We prove that such objectives can be chosen so that rational agents have a unique optimal protocol, and asymptotically reach consensus on a desired group decision value. We use a Lyapunov approach to prove that the asymptotical consensus can be reached when the communication links between nearby agents define a time-invariant undirected network. Finally we perform a simulation study concerning the vertical alignment maneuver of a team of unmanned air vehicles

Neuro-dynamic programming for cooperative inventory control

In Multi-Retailer Inventory Control the possibility of sharing set up costs motivates communication and coordination among the retailers. We solve the problem of finding suboptimal distributed reordering policies which minimize set up, ordering, storage and shortage costs, incurred by the retailers over a finite horizon. Neuro-Dynamic Programming (NDP) reduces the computational complexity of the solution algorithm from exponential to polynomial on the number of retailers

Multiple UAV cooperative path planning via neuro-dynamic programming

In this paper, a team of n Unmanned Air-Vehicles (UAVs) in cooperative path planning is given the task of reaching the assigned target while i) avoiding threat zones ii) synchronizing minimum time arrivals on the target, and iii) ensuring arrivals coming from different directions. We highlight three main contributions. First we develop a novel hybrid model and suit it to the problem at hand. Second, we design consensus protocols for the management of information. Third, we synthesize local predictive controllers through a distributed, scalable and suboptimal neuro-dynamic programming (NDP) algorithm

Dissensus, death and division

The modeling of switching systems describing networks where death and duplication processes occur is described. A dissensus protocol, complementary to consensus protocol, is introduced and the convergence or divergence of the agents' state evolution is studied. We discuss some properties of the topology reached by the network when different rules of duplication and inheritance are implemented. © 2009 AACC

Learning Research Methods and Processes via Sharing Experience in a BLOG

Abstract — The goal is to increase knowledge about different research methods that have been employed in the information technology field by supporting the information exchange, collaboration, and cooperation between researchers. We stress the importance of sharing knowledge through storytelling. Welldesigned, well-told stories can help others learn from past situations to respond more effectively in future situation. A blog is presented where PhD students and researchers are invited to collaborate by providing their stories, reading and commenting existing stories. This infrastructure allows researchers and PhD students to write the contents posing questions and finding answers on the relationship between research process and research results

Noncooperative dynamic games for inventory applications: A consensus approach

We focus on a finite horizon noncooperative dynamic game where the stage cost of a single player associated to a decision is a monotonically nonincreasing function of the total number of players making the same decision. For the singlestage version of the game, we characterize Nash equilibria and derive a consensus protocol that makes the players converge to the unique Pareto optimal Nash equilibrium. Such an equilibrium guarantees the interests of the players and is also social optimal in the set of Nash equilibria. For the multi-stage version of the game, we present an algorithm that converges to Nash equilibria, unfortunately not necessarily Pareto optimal. The algorithm returns a sequence of joint decisions, each one obtained from the previous one by an unilateral improvement on the part of a single player. The sequence with which the players act is chosen a priori and may influence the Nash equilibrium to which the path converges. We also specialize the game to a multi-retailer inventory system, where competing retailers aim at coordinating their supply strategies in order to minimize their local costs