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Abstract-This paper addresses the problem of managing a wireless sensor network with mobile managers. The mobile managers should be able to create their connectivity to the nodes they manage, and advertise their interests in the management data to be collected. Also, the network nodes should self-manage their connectivity to the managers in order to forward the management data. To meet these requirements, we propose (1) an algorithm for creating and maintaining encounter-associated management connectivity and (2) both management data exchange protocols and programming abstractions for network management applications. Both our simulation-based evaluation and experimentation of the proposed algorithm and architectural elements on real sensors demonstrated their effectiveness in meeting the requirements.

Abstract-New energy-efficient linear forecasting methods are proposed for various sensor network applications, including innetwork data aggregation and mining. The proposed methods are designed to minimize the number of trend changes for a given application-specified forecast quality metric. They also self-adjust the model parameters, the slope and the intercept, based on the forecast errors observed via measurements. As a result, they incur O(1) space and time overheads, a critical advantage for resource-limited wireless sensors. An extensive simulation study based on real-world and synthetic time-series data shows that the proposed methods reduce the number of trend changes by 20%∼50% over the existing well-known methods for a given forecast quality metric. That is, they are more predictive than the others with the same forecast quality metric.

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