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Mainland, Geoff

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Mainland

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Geoff

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Mainland, Geoff

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Author's Publications: search.filters.isAuthorOfPublication.e0e38748-3a3e-489a-be9a-a22a2392c22e

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    LiveNet: Using Passive Monitoring to Reconstruct Sensor Network Dynamics
    (2007) Chen, Bor-rong; Peterson, Geoffrey; Mainland, Geoff; Welsh, Matt
    Understanding the behavior of deployed sensor networks is difficult as they become more sophisticated and larger in scale. Much of the difficulty comes from the lack of tools to provide a global view on the network dynamics. This paper describes LiveNet, a set of tools and techniques for reconstructing complex dynamics of live sensor network deployments. LiveNet is based on the use of passive sniffers co-deployed with the network. We address several challenges: merging multiple sniffer traces, determining coverage of sniffers, inference of missing information for path reconstruction and high-level analyses with application-specific knowledge. To validate LiveNet’s accuracy, we conduct controlled experiments on an indoor testbed. Finally, we present data from a real deployment using LiveNet. The results show that LiveNet is able to to reconstruct network topology, bandwidth usage, routing paths, identify hot-spot nodes, and disambiguate failures observed at application level without instrumenting application code.
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    Using Virtual Markets to Program Global Behavior in Sensor Networks
    (Association for Computing Machinery, 2004) Mainland, Geoff; Kang, Laura; Lahaie, Sébastien; Parkes, David; Welsh, Matt
    This paper presents market-based macroprogramming (MBM), a new paradigm for achieving globally efficient behavior in sensor networks. Rather than programming the individual, low-level behaviors of sensor nodes, MBM defines a virtual market where nodes sell "actions" (such as taking a sensor reading or aggregating data) in response to global price information. Nodes take actions to maximize their own utility, subject to energy budget constraints. The behavior of the network is determined by adjusting the price vectors for each action, rather than by directly specifying local node actions, resulting in a globally efficient allocation of network resources. We present the market-based macro-programming paradigm, as well as several experiments demonstrating its value for a sensor network vehicle tracking application.
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    Decentralized, Adaptive Resource Allocation for Sensor Networks
    (USENIX, 2005) Mainland, Geoff; Parkes, David; Welsh, Matt
    This paper addresses the problem of resource allocation in sensor networks. We are concerned with how to allocate limited energy, radio bandwidth, and other resources to maximize the value of each node's contribution to the network. Sensor networks present a novel resource allocation challenge: given extremely limited resources, varying node capabilities, and changing network conditions, how can one achieve efficient global behavior? Currently, this is accomplished by carefully tuning the behavior of the low-level sensor program to accomplish some global task, such as distributed event detection or in-network data aggregation. This manual tuning is difficult, error-prone, and typically does not consider network dynamics such as energy depletion caused by bursty communication patterns. We present Self-Organizing Resource Allocation (SORA), a new approach for achieving efficient resource allocation in sensor networks. Rather than manually tuning sensor resource usage, SORA defines a virtual market in which nodes sell goods (such as sensor readings or data aggregates) in response to prices that are established by the programmer. Nodes take actions to maximize their profit, subject to energy budget constraints. Nodes individually adapt their operation over time in response to feedback from payments, using reinforcement learning. The behavior of the network is determined by the price for each good, rather than by directly specifying local node programs. SORA provides a useful set of primitives for controlling the aggregate behavior of sensor networks despite variance of individual nodes. We present the SORA paradigm and a sensor network vehicle tracking application based on this design, as well as an extensive evaluation demonstrating that SORA realizes an efficient allocation of network resources that adapts to changing network conditions.