  Location awareness is a critical issue in wireless sensor network
applications. For more accurate location estimation, the two issues should be
considered extensively: 1) how to sufficiently utilize the connection
information between multiple nodes and 2) how to select a suitable solution
from multiple solutions obtained by the Euclidean distance loss. In this paper,
a DV-Hop localization based on the distance estimation using multinode (DEMN)
and the hop loss in WSNs is proposed to address the two issues. In DEMN, when
multiple anchor nodes can detect an unknown node, the distance expectation
between the unknown node and an anchor node is calculated using the
cross-domain information and is considered as the expected distance between
them, which narrows the search space. When minimizing the traditional Euclidean
distance loss, multiple solutions may exist. To select a suitable solution, the
hop loss is proposed, which minimizes the difference between the real and its
predicted hops. Finally, the Euclidean distance loss calculated by the DEMN and
the hop loss are embedded into the multi-objective optimization algorithm. The
experimental results show that the proposed method gains 86.11\% location
accuracy in the randomly distributed network, which is 6.05% better than the
DEM-DV-Hop, while DEMN and the hop loss can contribute 2.46% and 3.41%,
respectively.
