Showing papers by "Sonia Fahmy published in 2010"

Constructing maximum-lifetime data gathering forests in sensor networks

Abstract: Energy efficiency is critical for wireless sensor networks. The data-gathering process must be carefully designed to conserve energy and extend network lifetime. For applications where each sensor continuously monitors the environment and periodically reports to a base station, a tree-based topology is often used to collect data from sensor nodes. In this work, we first study the construction of a data-gathering tree when there is a single base station in the network. The objective is to maximize the network lifetime, which is defined as the time until the first node depletes its energy. The problem is shown to be NP-complete. We design an algorithm that starts from an arbitrary tree and iteratively reduces the load on bottleneck nodes (nodes likely to soon deplete their energy due to high degree or low remaining energy). We then extend our work to the case when there are multiple base stations and study the construction of a maximum-lifetime data-gathering forest. We show that both the tree and forest construction algorithms terminate in polynomial time and are provably near optimal. We then verify the efficacy of our algorithms via numerical comparisons.

Sleep/wake scheduling for multi-hop sensor networks: Non-convexity and approximation algorithm

Abstract: We investigate the problem of sleep/wake scheduling for low duty cycle sensor networks. Our work differs from prior work in that we explicitly consider the effect of synchronization error in the design of the sleep/wake scheduling algorithm. In our previous work, we studied sleep/wake scheduling for single hop communication, e.g., intra-cluster communication between a cluster head and cluster members. We showed that there is an inherent trade-off between energy consumption and message delivery performance (defined as the message capture probability). We proposed an optimal sleep/wake scheduling algorithm, which satisfies a given message capture probability threshold with minimum energy consumption. In this work, we consider multi-hop communication. We remove the previous assumption that the capture probability threshold is already given, and study how to decide the per-hop capture probability thresholds to meet the Quality of Services (QoS) requirements of the application. In many sensor network applications, the QoS is decided by the amount of data delivered to the base station(s), i.e., the multi-hop delivery performance. We formulate an optimization problem to set the capture probability threshold at each hop such that the network lifetime is maximized, while the multi-hop delivery performance is guaranteed. The problem turns out to be non-convex and hence cannot be efficiently solved using standard methods. By investigating the unique structure of the problem and using approximation techniques, we obtain a solution that provably achieves at least 0.73 of the optimal performance. Our solution is extremely simple to implement.

Synergy: An overlay internetworking architecture and implementation

Abstract: A multitude of overlay network designs for resilient routing, multicasting, quality of service, content distribution, storage, and object location have been proposed. Overlay networks offer several attractive features, including ease of deployment, flexibility, adaptivity, and an infrastructure for collaboration among hosts. In this paper, we explore cooperation among co-existing, possibly heterogeneous, overlay networks. We discuss a spectrum of cooperative forwarding and information sharing services, and investigate the associated scalability, heterogeneity, and security problems. Motivated by these services, we design Synergy, a utility-based overlay internetworking architecture that fosters overlay cooperation. Our architecture promotes fair peering relationships to achieve synergism. Results from Internet experiments with cooperative forwarding overlays indicate that our Synergy prototype improves delay, throughput, and loss performance, while maintaining the autonomy and heterogeneity of individual overlay networks.

Predicting Prefix Availability in the Internet

Abstract: The Border Gateway Protocol (BGP) maintains inter-domain routing information by announcing and withdrawing IP prefixes, possibly resulting in temporary prefix unreachability. Prefix availability observed from different vantage points in the Internet can be lower than standards promised by Service Level Agreements (SLAs). In this paper, we develop a framework for predicting long-term prefix availability, given short-duration prefix information from publicly available BGP routing databases. We compare three prediction models, and find that bagged decision trees perform the best when predicting for long future durations, whereas a simple model works well for short prediction durations. We show that mean time to failure and to recovery outperform past availability in terms of their importance for predicting availability for long durations. We also find that predictability is higher in the year 2009, compared to four years earlier. Our models allow ISPs to adjust BGP routing policies if predicted availability is low, and the models are useful for cloud computing systems, P2P, and VoIP applications.

Distributed Partial Inference under Churn

Abstract: -Effective network measurement can significantly improve application performance. One of the main challenges in obtaining network measurements is to achieve high accuracy while consuming few network resources. To address this problem, several inference mechanisms have been proposed. These mechanisms can provide the O(n2 ) end-to-end measurements among n nodes using O(n) measurements, with some loss in accuracy. We construct a measurement request graph where a measurement request issued by an application (e.g., for delay between two nodes) is represented by an edge between the nodes. When the measurement request graph is sparse, an inference mechanism operating over all the nodes (complete inference) incurs unnecessary cost. Given a measurement request graph, our goal is to optimize the number of measurements as well as improve overall accuracy by applying inference only to dense sub-graphs, while taking the other measurements directly. We call this technique partial inference. Previous work only considered static measurement request graphs. However, the measurement request graph can be dynamic when nodes frequently join and leave the network. This paper designs and evaluates a distributed algorithm where each node decides if it should participate in an inference mechanism based on limited information.