Emergent (Re)optimization for stream queries in grids

Query optimization in sensor grids have two major challenges: (a) optimizing in a multi-query environment, and (b) continuous re-optimization occurring due to new query registrations and de-queries, i.e. queries being stopped unexpectedly. Addressing this problem continuously on a system-wide basis is an infeasible option. In this work called EstuaryDB, we propose a notion of emergent optimization, where globally optimal configurations emerge as a result of a number of local autonomous decisions carried out in self-interest. Grid nodes act as self-interested autonomous agents that continuously seek to maximize their "wealth." The agents are unaware of system-wide issues such as when do queries arrive, what are they asking for, or when are they revoked. Every query brings with it a certain amount of wealth, and each agent continuously tries to save as much of the wealth as possible. The amount of latent wealth in the system at any time gives a quantitative measure of the efficiency achieved over naive stream retrieval.