Yan Qiao, Peng Wei, Zhang Guiqing
School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China.
The Key Laboratory of Intelligent Buildings Technology of Shandong Province, Shandong Jianzhu University, Jinan 250101, China.
Sensors (Basel). 2020 Feb 7;20(3):881. doi: 10.3390/s20030881.
Multi-radio technology is regarded as a promising way to improve the performance of Wireless Sensor Networks (WSNs) and has attracted much attention of researchers. It is very important to reduce energy consumption and to prolong the lifetime of Multi-Radio WSNs (MR-WSNs), since the node is generally battery-operated in MR-WSN environments. In this paper, two typical types of energy consumption process, the transmitting energy consumption and idle listening energy consumption, are analyzed firstly. Based on the above analysis, the energy consumption model of multi-radio nodes is built, and then it is considered as the optimization objective for the minimization energy consumption of multi-radio nodes. Furthermore, the heuristic optimal energy consumption task scheduling strategy based on the Particle Swarm Optimization (PSO) algorithm is proposed, and then the detailed steps of the proposed strategy are presented. Finally, the effectiveness and performance of the proposed strategy are evaluated through practical experiments and simulations. Evaluation results show that the proposed strategy outperforms some other algorithms, in terms of energy consumption, network lifetime, and tasks extensibility.
多无线电技术被视为提高无线传感器网络(WSNs)性能的一种有前途的方法,并已引起研究人员的广泛关注。由于在多无线电无线传感器网络(MR-WSNs)环境中节点通常由电池供电,因此降低能耗并延长其寿命非常重要。本文首先分析了两种典型的能耗过程,即发射能耗和空闲监听能耗。基于上述分析,建立了多无线电节点的能耗模型,并将其作为多无线电节点最小化能耗的优化目标。此外,提出了基于粒子群优化(PSO)算法的启发式最优能耗任务调度策略,并给出了该策略的详细步骤。最后,通过实际实验和仿真对所提策略的有效性和性能进行了评估。评估结果表明,在所提策略在能耗、网络寿命和任务扩展性方面优于其他一些算法。