processing of aggregate queries that are generally communication-intensive. In particular, we fo-cus on MIN/MAX queries that require both identity and value in the answer. We study how to provide an error bound to such answers, and how to design an “optimal ” sensor-contact policy that mini

max-min fair rate allocation constraint in the form of two coupled linear programs. We first show how the max-min rate can be computed efficiently for a given network. We then adopt a dual-based approach to maximize the network utilization. The analysis of the dual shows the sub

Understanding the optimal usage of fluctuating renewable energy in Wireless Sensor Networks (WSNs) is complex. Lexicographic Max-min (LM) rate allocation is a good solution, but is non-trivial for multi-hop WSNs, as both fairness and sensing rates have to be optimized through the exploration of all

We present Flush, a reliable, high goodput bulk data trans-port protocol for wireless sensor networks. Flush provides end-to-end reliability, reduces transfer time, and adapts to time-varying network conditions. It achieves these proper-ties using end-to-end acknowledgments, implicit snooping

of MIN, MAX, SUM, AVERAGE, and RANK of their values using O(n log log n) messages within O(log n log log n) rounds of communication. To the best of our knowledge, ours is the first result that shows how to compute these aggregates with high probability using only O(n log log n) messages. In contrast

Monitoring extreme values (MAX or MIN) is a fundamental problem in wireless sensor networks (and in general, complex dynamic systems). This problem presents very different algorithmic challenges from aggregate and selection queries, in the sense that an individual node cannot by itself determine

this problem for a ZigBee cluster tree by formulating it into a multi-dimensional multi-choice knapsack problem. Maximum overall query range and max-min fair query range objectives are investigated. Distributed algorithms are proposed which exploit the ZigBee cluster tree structure to keep the computation

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to the class of type-threshold functions (as defined in [2]), of which max, min, and indicator functions are important examples; our discussions are couched in terms of the max function. We view the problem as one of message passing distributed computation over a geometric random graph. The network is assumed

can be used to compute statistics such as max, min, and average accurately and energy-efficiently, but it does not work well for order statistics such as median. Current approximation techniques to compute order statistics cannot deliver both good accuracy and energy efficiency. This is unfortunate