Mobility models have traditionally been tailored to specific application domains such as human, military, or ad hoc transportation scenarios. This tailored approach often renders a mobility model useless when the application domain changes, and leads to wrong conclusions about the performance of protocols and applications running atop of different domains. In this work we propose and implement a mobility modeling framework (UMMF) based on the observation that the mobility characteristics of most mobility-based applications can be captured in terms of a few fundamental factors: (1) Targets; (2) Obstacles; (3) Dynamic events; (4) Navigation; (5) Steering behaviors; and (6) Dynamic behaviors. We demonstrate the mapping of application-domain-specifics to UMMF elements, showing the power and flexibility of our approach.

We present a transport protocol whose goal is to reduce power consumption without compromising delivery requirements of applications. To meet its goal of energy efficiency, our transport protocol (1) contains mechanisms to balance end-to-end vs. local retransmissions; (2) minimizes acknowledgment traffic using receiver regulated rate-based flow control combined with selected acknowledgements and in-network caching of packets; and (3) aggressively seeks to avoid any congestion-based packet loss. Within a recently developed ultra low-power multi-hop wireless network system, extensive simulations and experimental results demonstrate that our transport protocol meets its goal of preserving the energy efficiency of the underlying network.

In this paper we explore the evolution of both the Internet's most heavily used transport protocol, TCP, and the current network environment with respect to how the network's evolution ultimately impacts end-to-end protocols. The traditional end-to-end assumptions about the Internet are increasingly challenged by the introduction of intermediary network elements (middleboxes) that intentionally or unintentionally prevent or alter the behavior of end-to-end communications. This paper provides measurement results showing the impact of the current network environment on a number of traditional and proposed protocol mechanisms (e.g., Path MTU Discovery, Explicit Congestion Notification, etc.). In addition, we investigate the prevalence and correctness of implementations using proposed TCP algorithmic and protocol changes (e.g., selective acknowledgment-based loss recovery, congestion window growth based on byte counting, etc.). We present results of measurements taken using an active measurement framework to study web servers and a passive measurement survey of clients accessing information from our web server. We analyze our results to gain further understanding of the differences between the behavior of the Internet in theory versus the behavior we observed through measurements. In addition, these measurements can be used to guide the definition of more realistic Internet modeling scenarios.

Very few techniques have been proposed for estimating traffic matrices in the context of Internet traffic. Our work on POP-to-POP traffic matrices (TM) makes two contributions. The primary contribution is the outcome of a detailed comparative evaluation of the three existing techniques. We evaluate these methods with respect to the estimation errors yielded, sensitivity to prior information required and sensitivity to the statistical assumptions they make. We study the impact of characteristics such as path length and the amount of link sharing on the estimation errors. Using actual data from a Tier-1 backbone, we assess the validity of the typical assumptions needed by the TM estimation techniques. The secondary contribution of our work is the proposal of a new direction for TM estimation based on using choice models to model POP fanouts. These models allow us to overcome some of the problems of existing methods because they can incorporate additional data and information about POPs and they enable us to make a fundamentally different kind of modeling assumption. We validate this approach by illustrating that our modeling assumption matches actual Internet data well. Using two initial simple models we provide a proof of concept showing that the incorporation of knowledge of POP features (such as total incoming bytes, number of customers, etc.) can reduce estimation errors. Our proposed approach can be used in conjunction with existing or future methods in that it can be used to generate good priors that serve as inputs to statistical inference techniques.