  Data gathering operations in remote locations often rely on relay drones,
which collect, store and deliver transmitted information to a ground control
station. The probability of the ground control station successfully
reconstructing the gathered data can be increased if random linear coding (RLC)
is used, especially when feedback channels between the drones and the
transmitter are not available. RLC decoding can be complemented by partial
packet recovery (PPR), which utilizes sparse recovery principles to repair
erroneously received data before RLC decoding takes place. We explain that the
spark of the transpose of the parity-check matrix of the linear code, that is,
the smallest number of linearly-dependent columns of the matrix, influences the
effectiveness of PPR. We formulate a spark optimization problem and obtain code
designs that achieve a gain over PPR-assisted RLC, in terms of the probability
that the ground control station will decode the delivered data.
