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We present a bulk data collection service, Harvest, for energy constrainedwireless sensor nodes. To increase spatial reuse and thereby decrease latency,Harvest performs concurrent, pipelined exfiltration from multiple nodes to abase station. To this end, it uses a distance-k coloring of the nodes, notablywith a constant number of colors, which yields a TDMA schedule whereby nodescan communicate concurrently with low packet losses due to collision. Thiscoloring is based on a randomized CSMA approach which does not exploit locationknowledge. Given a bounded degree of the network, each node waits only O$(1)$time to obtain a unique color among its distance-k neighbors, in contrast tothe traditional deterministic distributed distance-k vertex coloring whereineach node waits O$(\Delta^{2})$ time to obtain a color.  Harvest offers the option of limiting memory use to only a small constantnumber of bytes or of improving latency with increased memory use; it can beused with or without additional mechanisms for reliability of messageforwarding. We experimentally evaluate the performance of Harvest using 51motes in the Kansei testbed. We also provide theoretical as well asTOSSIM-based comparison of Harvest with Straw, an extant data collectionservice implemented for TinyOS platforms that use one-node at a timeexfiltration. For networks with more than 3-hops, Harvest reduces the latencyby at least 33% as compared to that of Straw.

Harvest: A Reliable and Energy Efficient Bulk Data Collection Service for Large Scale Wireless Sensor Networks