  A single-source network is said to be \textit{memory-free} if all of the
internal nodes (those except the source and the sinks) do not employ memory but
merely send linear combinations of the incoming symbols (received at their
incoming edges) on their outgoing edges. Memory-free networks with delay using
network coding are forced to do inter-generation network coding, as a result of
which the problem of some or all sinks requiring a large amount of memory for
decoding is faced. In this work, we address this problem by utilizing memory
elements at the internal nodes of the network also, which results in the
reduction of the number of memory elements used at the sinks. We give an
algorithm which employs memory at the nodes to achieve single-generation
network coding. For fixed latency, our algorithm reduces the total number of
memory elements used in the network to achieve single-generation network
coding. We also discuss the advantages of employing single-generation network
coding together with convolutional network-error correction codes (CNECCs) for
networks with unit-delay and illustrate the performance gain of CNECCs by using
memory at the intermediate nodes using simulations on an example network under
a probabilistic network error model.
