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

Abstract

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.

Latency‐minimum offloading decision and resource allocation for fog‐enabled Internet of Things networks

“…Due to the NP‐hard in this kind of problems, there is a challenge for using the traditional iterative search method, that is, when the problem scale is large, it is unlikely to get the optimal solution at an acceptable cost. Recently, the intelligent heuristic algorithm was developed to overcome this challenge, such as the genetic algorithm (GA), 26 the simulated annealing (SA) algorithm, 27 the ant colony algorithm, tabu search, and so on.…”

Energy-aware Task Scheduling Strategies for Multi-core Embeddded Systems

“…However, few people pay attention to the application of machine learning algorithms in this field. The literature [18], [19], [20], [21] uses the existing heuristic algorithm for energy optimization. The literature [18] improved the genetic algorithm and combined it with the retiming technology to reduce the energy consumption of Real-Time Streaming Applications.…”

“…The literature [18] improved the genetic algorithm and combined it with the retiming technology to reduce the energy consumption of Real-Time Streaming Applications. In the literature [19], Adaptive Genetic-Simulated Annealing algorithm that integrates the genetic algorithm and the annealing algorithm, was used to minimize the energy consumption and time of WMSN equipment. The literature [20] studied the influence of genetic algorithm parameters on energy consumption optimization of multi-core processors and improved the fitness function of genetic algorithms.…”

Evaluation of Multimedia Classroom Teaching Effectiveness Based on RS-BP Neural Network

“…In terms of evaluation index system construction, Hiary used hierarchical analysis to construct an importance matrix and then determined the teaching quality evaluation index system [11]. Liu pointed out that teaching quality evaluation indexes should include teaching goal evaluation, teaching program evaluation, teaching process evaluation, and teaching effect evaluation [12]. Khedif et al used a fuzzy mathematical mining algorithm to analyze the satisfaction of teaching quality and derived key indicators affecting teaching evaluation [13].…”