<p><span style="font-family: Times New Roman;"><strong>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 </strong><strong>E</strong><strong>nergy-consumption </strong><strong>O</strong><strong>ptimization-oriented </strong><strong>T</strong><strong>ask </strong><strong>S</strong><strong>cheduling </strong><strong>A</strong><strong>lgorithm (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.</strong></span></p>
Providing an Efficient Model for Wireless Sensor Networks Using the Scenario of the Variable Sink Counts Based on the Particle Swarm Algorithm
