  Multi-hop networks become popular network topologies in various emerging
Internet of things applications. Batched network coding (BNC) is a solution to
reliable communications in such networks with packet loss. By grouping packets
into small batches and restricting recoding to the packets belonging to the
same batch, BNC has a much smaller computational and storage requirements at
the intermediate nodes compared with a direct application of random linear
network coding. In this paper, we propose a practical recoding scheme called
blockwise adaptive recoding (BAR) which learns the latest channel knowledge
from short observations so that BAR can adapt to the fluctuation of channel
conditions. We focus on investigating practical concerns such as the design of
efficient BAR algorithms. We also design and investigate feedback schemes for
BAR under imperfect feedback systems. Our numerical evaluations show that BAR
has significant throughput gain for small batch size compared with the existing
baseline recoding scheme. More importantly, this gain is insensitive to
inaccurate channel knowledge. This encouraging result suggests that BAR is
suitable to be realized in practice as the exact channel model and its
parameters could be unknown and subject to change from time to time.
