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Wireless Communications and Mobile Computing
Volume 2018, Article ID 7494829, 13 pages
Research Article

Energy and Delay Optimization of Heterogeneous Multicore Wireless Multimedia Sensor Nodes by Adaptive Genetic-Simulated Annealing Algorithm

1Hubei Key Laboratory of Transport Internet of Things, School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China
2LIMOS Laboratory, UMR 6158 CNRS (Centre National de la Recherche Scientifique), Clermont-Ferrand, France
3School of Electrical & Information, Hubei University of Automotive Technology, Shiyan, China

Correspondence should be addressed to Shengwu Xiong; nc.ude.tuhw@wsgnoix

Received 1 September 2017; Accepted 17 October 2017; Published 22 January 2018

Academic Editor: Jun Huang

Copyright © 2018 Xing Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Energy efficiency and delay optimization are significant for the proliferation of wireless multimedia sensor network (WMSN). In this article, an energy-efficient, delay-efficient, hardware and software cooptimization platform is researched to minimize the energy cost while guaranteeing the deadline of the real-time WMSN tasks. First, a multicore reconfigurable WMSN hardware platform is designed and implemented. This platform uses both the heterogeneous multicore architecture and the dynamic voltage and frequency scaling (DVFS) technique. By this means, the nodes can adjust the hardware characteristics dynamically in terms of the software run-time contexts. Consequently, the software can be executed more efficiently with less energy cost and shorter execution time. Then, based on this hardware platform, an energy and delay multiobjective optimization algorithm and a DVFS adaption algorithm are investigated. These algorithms aim to search out the global energy optimization solution within the acceptable calculation time and strip the time redundancy in the task executing process. Thus, the energy efficiency of the WMSN node can be improved significantly even under strict constraint of the execution time. Simulation and real-world experiments proved that the proposed approaches can decrease the energy cost by more than 29% compared to the traditional single-core WMSN node. Moreover, the node can react quickly to the time-sensitive events.