Abstract | Random walks in wireless sensor networks can serve as fully
local, very simple strategies for sink motion that reduce energy dissipa-
tion a lot but increase the latency of data collection. To achieve satis-
factory energy-latency trade-offs the sink walks can be made adaptive,
depending on network parameters such as density and/or history of past
visits in each network region; but this increases the memory require-
ments. Towards better balances of memory/performance, we propose two
new random walks: the Random Walk with Inertia and the Explore-and-
Go Random Walk; we also introduce a new metric (Proximity Varia-
tion) that captures the different way each walk gets close to the network
nodes. We implement the new walks and experimentally compare them
to known ones. The simulation findings demonstrate that the new walk¢s
performance (cover time) gets close to the one of the (much stronger)
biased walk, while in some other respects (partial cover time, proximity
variation) they even outperform it. We note that the proposed walks
have been fine-tuned in the light of experimental findings. |