Decoy routing is a powerful circumvention mechanism intended to provide secure communications that cannot be monitored, detected, or disrupted by a third party who controls the user's network infrastructure. Current decoy routing protocols have weaknesses, however: they either make the unrealistic assumption that routes through the network are symmetric (i.e., the router implementing the decoy routing protocol must see all of the traffic, in both directions, from each connection it modifies), or their protocol requires modifying the route taken by packets in connections that use the protocol, and these route changes are detectable by a third party. We present Rebound, a decoy routing protocol that tolerates asymmetric routes without modifying the route taken by any packet that passes through the decoy router, making it more difficult to detect or disrupt than previous decoy routing protocols.
Structural change and uncertainty are fundamental properties of an ad hoc network, making it difficult to develop communication strategies, i.e., network-level approaches to transport data from sender to receiver. At a basic level, change and uncertainty affect how long any state maintained by a communication strategy remains useful, and so influence the trade-offs made to collect that state. In this paper, we introduce a framework for organizing the decision space for deciding when a communication strategy should maintain state, and what type of state should be maintained, in an ad hoc network. The framework is based on our observation that three network properties (connectivity, unpredictability, and resource contention) determine when state is useful. Using the framework, we make three contributions. First, we illustrate the framework by showing an instantiation in terms of specific measures that can be used to describe a network setting. Second, we validate the framework by showing it correctly and consistently organizes the decision space for different communication strategies. Finally, we demonstrate the analytic power of the framework by using it to (1) uncover surprising aspects of well-known traces, and (2) identify the need for, and value of, a new strategy for network communication.
Abstract-This paper investigates learning hierarchical statistical activity models in indoor environments. The Abstract Hidden Markov Model (AHMM) is used to represent behaviors in stochastic environments. We train the model using both labeled and unlabeled data and estimate the parameters using Expectation Maximization (EM). Results are shown on three datasets: data collected in a lab environment, data collected in a home environment and simulated data. The results show that hierarchical models outperform flat models.
Abstract-We consider the problem of configuring sensors in an adaptive sensor network being used to monitor meteorological features. One way to decide future sensor configurations is to base them on information currently being collected. For instance, if a meteorological sensor network is being used to monitor storms in Oklahoma, then the sensors could be dynamically configured based on the predicted storm locations. While Kalman filters and their extensions are commonly used for prediction and tracking, they have been primarily applied to objects with known or fixed dynamics such as missiles or people. We explore the advantages and limitations of using Kalman filters to track objects with nonstationary dynamics (e.g., a storm can grow in size). In particular, we focus on tracking meteorological features over time with the objective of using this information to determine where radars should focus their sensing. We present results for tracking storm cells comparing least-squares regression with Kalman filter and switching Kalman filter methods. Our results show that on average the Kalman filter methods better predict the future location of a storm centroid than does a least-squares regression algorithm currently in use for meteorological storm tracking.
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