Wireless sensor networks usually obtain the location of an unknown node by measuring the distance between the
unknown node and its neighbouring anchors. To enhance both localisation accuracy and localisation success rates, the authors
introduce a new neural network-based node localisation scheme. The new scheme is distinct because it can make the trained
network model completely relevant to the topology via online training and correlated topology-trained data and therefore
attain more efficient application of the neural networks and more accurate inter-node distance estimation. It is also distinct in
adopting both received signal strength indication and hop counts to estimate the inter-node distances, to improve the distance
estimation accuracy as well as localisation accuracy at no additional cost. Experimental evaluation is conducted to measure
the performance of the proposed scheme and other artificial intelligent-based node localisation schemes. The results show
that, at reasonable cost, the new scheme constantly produces higher localisation success rates and smaller localisation errors
than other schemes.
