General Framework for Network Throughput Maximization in Sink-Based Energy Harvesting Wireless Sensor Networks

Mehrabidavoodabadi, Abbas and Kim, Kiseon (2017) General Framework for Network Throughput Maximization in Sink-Based Energy Harvesting Wireless Sensor Networks. IEEE Transactions on Mobile Computing, 16 (7). pp. 1881-1896. ISSN 1536-1233

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Official URL: https://doi.org/10.1109/TMC.2016.2607716

Abstract

Due to the advancement in energy harvesting wireless sensor networks (EH-WSNs), the data collection from one-hop stationary sensor nodes using a path-constrained mobile sink has become one of the challenging issues. Toward the throughput improvement, we propose a general framework for network throughput maximization (NTM) problem by optimizing practically feasible parameters. For each proposed scenario, a mixed integer linear programming (MILP) optimization model is introduced for the problem formulation. Due to the NP-Hardness of the MILP models, we design two efficient algorithms namely as ODSAA and ODAA for two practically implementable scenarios. Having a preknowledge about the deployed location of nodes, the proposed algorithms run centrally by sink and find the sub-optimal solutions within a reasonable computation time. Furthermore, under the uniform distribution of energy harvesting, we find out two threshold points on, respectively, energy harvesting mean and battery capacity of nodes after which the network throughput reaches a stable point. Finally, simulations are conducted on a different set of node deployments, which the results confirm that the proposed algorithms significantly improve the data throughput collected by sink and also the theoretical thresholds provide a confidence interval of 90 percent.

Item Type: Article
Uncontrolled Keywords: Energy harvesting wireless sensor networks (EH-WSNs), network throughput, mixed integer linear programming (MILP), NP-hardness, energy harvesting mean, battery capacity threshold
Subjects: G400 Computer Science
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 04 Jun 2020 14:04
Last Modified: 04 Jun 2020 14:55
URI: http://nrl.northumbria.ac.uk/id/eprint/43344

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