A Spectrum of TCP-friendly Window-Based Congestion Control Algorithms

The increasing diversity of Internet application requirements has spurred recent interest in transport protocols with flexible transmission controls. In window-based congestion control schemes, increase rules determine how to probe available bandwidth, whereas decrease rules determine how to back off when losses due to congestion are detected. The control rules are parameterized so as to ensure that the resulting protocol is TCP-friendly in terms of the relationship between throughput and loss rate. This paper presents a comprehensive study of a new spectrum of window-based congestion controls, which are TCP-friendly as well as TCP-compatible under RED. Our controls utilize history information in their control rules. By doing so, they improve the transient behavior, compared to recently proposed slowly-responsive congestion controls such as general AIMD and binomial controls. Our controls can achieve better tradeoffs among smoothness, aggressiveness, and responsiveness, and they can achieve faster convergence. We demonstrate analytically and through extensive ns simulations the steady-state and transient behavior of several instances of this new spectrum.

CSR: Constrained Selfish Routing in Ad-hoc Networks

Routing protocols for ad-hoc networks assume that the nodes forming the network are either under a single authority, or else that they would be altruistically forwarding data for other nodes with no expectation of a return. These assumptions are unrealistic since in ad-hoc networks, nodes are likely to be autonomous and rational (selfish), and thus unwilling to help unless they have an incentive to do so. Providing such incentives is an important aspect that should be considered when designing ad-hoc routing protocols. In this paper, we propose a dynamic, decentralized routing protocol for ad-hoc networks that provides incentives in the form of payments to intermediate nodes used to forward data for others. In our Constrained Selfish Routing (CSR) protocol, game-theoretic approaches are used to calculate payments (incentives) that ensure both the truthfulness of participating nodes and the fairness of the CSR protocol. We show through simulations that CSR is an energy efficient protocol and that it provides lower communication overhead in the best and average cases compared to existing approaches.

An Integrated Neural Network-Event-Related Potentials Model of Temporal and Probability Context Effects on Event Categorization

We present a neural network that adapts and integrates several preexisting or new modules to categorize events in short term memory (STM), encode temporal order in working memory, evaluate timing and probability context in medium and long term memory. The model shows how processed contextual information modulates event recognition and categorization, focal attention and incentive motivation. The model is based on a compendium of Event Related Potentials (ERPs) and behavioral results either collected by the authors or compiled from the classical ERP literature. Its hallmark is, at the functional level, the interplay of memory registers endowed with widely different dynamical ranges, and at the structural level, the attempt to relate the different modules to known anatomical structures.