STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Commuting with delay prone buses
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
Improved results for route planning in stochastic transportation
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Neuro-Dynamic Programming
Improved Algorithms and Analysis for Secretary Problems and Generalizations
SIAM Journal on Discrete Mathematics
Scheduling with Unexpected Machine Breakdowns
RANDOM-APPROX '99 Proceedings of the Third International Workshop on Approximation Algorithms for Combinatorial Optimization Problems: Randomization, Approximation, and Combinatorial Algorithms and Techniques
Fault-Tolerant Real-Time Scheduling
ESA '97 Proceedings of the 5th Annual European Symposium on Algorithms
Multidimensional balanced allocations
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Playing games in many possible worlds
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Latency constrained aggregation in sensor networks
ESA'06 Proceedings of the 14th conference on Annual European Symposium - Volume 14
Data aggregation in sensor networks: Balancing communication and delay costs
Theoretical Computer Science
Latency-constrained aggregation in sensor networks
ACM Transactions on Algorithms (TALG)
Optimal stochastic policies for distributed data aggregation in wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
Data aggregation in sensor networks: balancing communication and delay costs
SIROCCO'07 Proceedings of the 14th international conference on Structural information and communication complexity
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We consider the following problem, which arises in the context of distributed Web computations. An aggregator aims to combine specific data from n sources. The aggregator contacts all sources at once. The time for each source to return its data to the aggregator is independent and identically distributed according to a known distribution. The aggregator at some point stops waiting for data and returns an answer depending only on the data received so far. If the aggregator returns the aggregated information from k of the n sources at time t it obtains a reward Rk(t) that grows with k and decreases with t. The goal of the aggregator is to maximize its expected reward.We prove that for certain broad families of distributions and broad classes of reward functions, the optimal plan for the aggregator has a simple form and hence can be easily computed.