An Energy-Balanced Routing Algorithm in Wireless Seismic Sensor Network

American Scientific Publishers
Publication Type:
Journal Article
Journal of Computational and Theoretical Nanoscience, 2016, 13 (10), pp. 6823 - 6833
Issue Date:
Full metadata record
Files in This Item:
Filename Description Size
DocumentOpener (6).pdfPublished Version2.4 MB
Adobe PDF
Due to unpredictable geological outdoor environments and imbalances in energy consumption of seismometer nodes in the wireless seismic sensor networks (WSSN), some seismometer nodes fail much earlier than others due to power loss. This would cause hot spot problems, network partitions, and significantly shorten network lifetime. In this paper, we designed an energy-balanced routing algorithm (EBRA) to ensure balanced energy consumption from all seismometer nodes in the WSSN and to enhance the connectivity and lifetime of the WSSN. By aiming at minimizing the imbalance in the residual energy, we divide the routing algorithm into two parts: clustering formation and inter-cluster routing. In clustering formation, we design an energy-balanced clustering algorithm, which selects the cluster head dynamically, based on residual energy, distance between the seismometer node and data collector. The clustering algorithm mitigates hot spot problems by balancing energy consumption among seismometer nodes. In regards to inter-cluster routing, we can relate it to the pareto-candidate set. To reduce the average multi-hop delay from cluster heads to the data collector, we optimize the pareto-candidate set by Hamming distance. In the design of EBRA, we consider minute details such as energy consumed by transmitting bits and impact of average multi-hop delay. This adds to the novelty of this work compared to the existing studies. Simulation results demonstrated a reduction in the average multi-hop delay by 87.5% with network size of 200 nodes in ten different data collector locations. Our algorithm also improves the network lifetime over the others three schemes by 7.8%, 23% and 45.4%, respectively.
Please use this identifier to cite or link to this item: