Abstract
The mismatch of task scheduling results in rapid network energy consumption during data transmission in wireless sensor networks. To address this issue, the paper proposed an Energy-consumption Optimization-oriented Task Scheduling Algorithm (EOTS algorithm) which formally described the overall power dissipation in the network system. On this basis, a network model was built up such that both the idle energy consumption in sensor nodes and energy consumption during the execution of tasks were taken into account, with which the whole task was effectively decomposed into sub-task sequences. They underwent simulated annealing and iterative refinement, with the intention of improving sensor nodes’ utilization rate, reducing local idle energy cost, as well as cutting down the overall energy consumption accordingly. The experiment result shows that under the environment of multi-task operation, from the perspective of energy cost optimization, the proposed scheduling strategy recorded an increase of 21.24% compared with the FIFO algorithm, and an increase of 16.77% in comparison to the EMRSA algorithm; while in light of network lifetimes, the EOTS algorithm surpassed the ECTA algorithm by a gain of 19.21%. Therefore, the effectiveness of the proposed EOTS algorithm is verified.
Highlights
Wireless sensor networks are a new-type network system linked by tens of thousands of self-organized multihop sensor nodes
Compared to conventional task scheduling that pursues minimum makes pan, energyefficient-based scheduling features the main objective of a best-effort reduction of the times and time for data resources to exploit, so that the utilization rate of data resources increases at the same time when overall performance and energy consumption keep balance [8,9]
Without comprehensive consideration of overall data load rate and energy consumption utilization rate, tasks based on traditional load balance scheduling strategy tend to be allocated to overmuch sensor nodes, leading to energy dissipation
Summary
Wireless sensor networks are a new-type network system linked by tens of thousands of self-organized multihop sensor nodes. Pietri et al [11] established several models of data utilization rate at the state of overall load balance, and used the feedback theory to adjust and compute utility of sensor nodes, maintaining overall load balance at the same time when the total energy consumption is lowered It is an efficacy energy-saving approach to reducing energy consumption of sensor nodes based on characteristics of currently operating tasks. The energy-consumption optimization-oriented task scheduling algorithm was proposed based on which the whole task was effectively decomposed into sub-task sequences They underwent simulated annealing and iterative refinement for energy saving, cutting down overall energy cost
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