An Efficient Communication Strategy for Ad-hoc Mobile Networks
DISC '01 Proceedings of the 15th International Conference on Distributed Computing
Discrete Applied Mathematics
Energy balanced data propagation in wireless sensor networks
Wireless Networks
Biased sink mobility with adaptive stop times for low latency data collection in sensor networks
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
The survival of the weakest in networks
WAOA'06 Proceedings of the 4th international conference on Approximation and Online Algorithms
Aggregated mobility-based topology inference for fast sensor data collection
Computer Communications
A new random walk for efficient data collection in sensor networks
Proceedings of the 9th ACM international symposium on Mobility management and wireless access
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Random walks in wireless sensor networks can serve as fully local, very simple strategies for sink motion that significantly reduce energy dissipation a lot but increase the latency of data collection. To achieve satisfactory 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 requirements. Towards better balances of memory/performance, we propose three new random walks: the Random Walk with Inertia, the Explore-and-Go Random Walk and the Curly Random Walk; we also introduce a new metric (Proximity Variation) that captures the different way each walk gets close to the network nodes over time. We implement the new walks and experimentally compare them to known ones. The simulation findings demonstrate that the new walks' performance (cover time) gets close to the one of the (much stronger) biased walk with memory, 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.