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

by   Vinayak Naik, et al.

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(1) 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(Δ^2) time to obtain a color. Harvest offers the option of limiting memory use to only a small constant number of bytes or of improving latency with increased memory use; it can be used with or without additional mechanisms for reliability of message forwarding. We experimentally evaluate the performance of Harvest using 51 motes in the Kansei testbed. We also provide theoretical as well as TOSSIM-based comparison of Harvest with Straw, an extant data collection service implemented for TinyOS platforms that use one-node at a time exfiltration. For networks with more than 3-hops, Harvest reduces the latency by at least 33



There are no comments yet.


page 1

page 2

page 3

page 4


Distance-2 Coloring in the CONGEST Model

We give efficient randomized and deterministic distributed algorithms fo...

Design and Implementation of a Wireless SensorNetwork for Agricultural Applications

We present the design and implementation of a shortest path tree based, ...

Challenges, Designs, and Performances of a Distributed Algorithm for Minimum-Latency of Data-Aggregation in Multi-Channel WSNs

In wireless sensor networks (WSNs), the sensed data by sensors need to b...

Superfast Coloring in CONGEST via Efficient Color Sampling

We present a procedure for efficiently sampling colors in the model. It...

Coloring Fast Without Learning Your Neighbors' Colors

We give an improved randomized CONGEST algorithm for distance-2 coloring...

Distributed Recoloring

Given two colorings of a graph, we consider the following problem: can w...

FREE - Fine-grained Scheduling for Reliable and Energy Efficient Data Collection in LoRaWAN

Collecting data from remote sensor devices with limited infrastructure i...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.