In wireless sensor networks, link metric estimation at each hop should not require a long history of packet exchanges. In this paper, we explore several approaches to link quality estimation. We report on the results of experiments on the Grenoble testbed of the FIT IoT-lab composed of a set of Cortex M3 nodes with IEEE 802.15.4 radios. Whereas the received signal power is a poor indication of PDR (Packet Delivery Ratio) that one can expect on a given link, LQI (Link Quality Indicator) gives more accurate information. We propose a two stage classification, in which a very large fraction of links are immediately either deemed usable or not, while the remaining ones need a bit more testing before they are advertised by the routing protocol as good or weak links.
Abstract-RPL (the IETF Routing Protocol for Low-Power and Lossy Networks) and LRP (Lightweight Routing Protocol) have in common to build a collection tree (or, more precisely, a DODAG) and "downward" host routes in the wireless sensor network. Additionally, the objective of LRP is to keep control overhead as low as possible. To substantiate this claim, we compare RPL and LRP using 40 nodes of the IoT-LAB testbed -and the results are telling.We then introduce asymmetric links, which are unavoidable in most deployments, and measure their impact on the considered protocols in another set of experiments. We present the mechanisms that were introduced in LRP to deal with such cases and we report on extensive experiments involving RPL and LRP to analyze the behavior of both protocols when the links change or they exhibit asymmetry.
Quick and accurate estimation of link quality, and more specifically packet loss probability, is the key element for efficient and effective communications in wireless multi-hop networks. We focus on IEEE 802.15.4 and we posit that losses only occur when noise and interference last long enough and are strong enough relatively to the received signal, to hinder packet reception. So, the key information for any ordered node pair is the signal to noise plus interference ratio distribution, which we obtain by combining the observed noise plus interference power at the receiver with the received signal strength. In this paper, we propose two novel schemes for the estimation of PER: Burst-NISI and Sample-NISI. Burst-NISI is based on high frequency measurements of the power level of ambient noise and interference around a given node. Sample-NISI sporadically samples the power level of ambient noise and interference when the radio operates according to a duty cycle. Using a large scale experimental platform, we show that our packet error rate estimation schemes are accurate for any packet length and diverse experimentation sites with different settings, for which the prediction is within 10 percentage points of the PER value measured a posteriori.
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