Harvest: A Reliable and Energy Efficient Bulk Data Collection Service for Large Scale Wireless Sensor Networks
Vinayak Naik, Anish Arora

TL;DR
Harvest is a novel data collection service for wireless sensor networks that uses a randomized coloring scheme to enable concurrent data exfiltration, significantly reducing latency and energy consumption.
Contribution
It introduces a randomized, low-delay coloring approach for efficient, reliable, and energy-efficient bulk data collection in large-scale wireless sensor networks.
Findings
Harvest reduces latency by at least 33% compared to Straw.
It achieves low packet loss through a collision-resistant TDMA schedule.
The approach is validated with real-world experiments on 51 motes.
Abstract
We present a bulk data collection service, Harvest, for energy constrained wireless sensor nodes. To increase spatial reuse and thereby decrease latency, Harvest performs concurrent, pipelined exfiltration from multiple nodes to a base station. To this end, it uses a distance-k coloring of the nodes, notably with a constant number of colors, which yields a TDMA schedule whereby nodes can communicate concurrently with low packet losses due to collision. This coloring is based on a randomized CSMA approach which does not exploit location knowledge. Given a bounded degree of the network, each node waits only O time to obtain a unique color among its distance-k neighbors, in contrast to the traditional deterministic distributed distance-k vertex coloring wherein each node waits O time to obtain a color. Harvest offers the option of limiting memory use to only a small…
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Taxonomy
TopicsEnergy Efficient Wireless Sensor Networks · Mobile Ad Hoc Networks · Opportunistic and Delay-Tolerant Networks
