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The combined effect of various problems such as congestion, collisions and route unavailability for data in Data-Intensive Sensor Networks is hard to estimate. This is one of the major reasons why existing solutions that try to optimize all these do not scale and have limited applicability. Our light-weight approach uses decisions made locally by individual sensors to efficiently discover desirable information processing patterns. Since, each node makes decisions based on local knowledge, this approach is more scalable.