Network lifetime is always a challenging issue in battery-powered networks due to the difficulty of recharging or replacing nodes in some scenarios. Clustering methods are a promising approach to tackle this challenge and prolong lifetime by efficiently distributing tasks among nodes in the cluster. The present study aimed to improve energy consumption in heterogeneous IoT devices using an energy-aware clustering method. In a heterogeneous IoT network, nodes (i.e., battery-powered IoT devices) can have a variety of energy profiles and communication capabilities. Most of the existing clustering algorithms have neglected the heterogeneity of energy capacity among nodes and assumed that they are of the same energy level. In this work, we present HetEng, a Cluster Head (CH) selection process that extended an existing clustering algorithm, named Smart-BEEM. To this end, we proposed a statistical approach that distributes energy consumption among highly energetic nodes in the network topology by constantly changing the CH role between the nodes based on their real energy levels (in joules). Experimental results showed that HetEng resulted in a 6.6% increase of alive nodes and 3% improvement in residual energy among the nodes in comparison with SmartBEEM. Moreover, our method reduced the total number of iterations by 1% on average.
翻译:在电池动力网络中,网络寿命始终是一个具有挑战性的问题,因为在某些情景中,很难再补给或更换节点。组合方法是一个很有希望的方法,可以应对这一挑战,并通过在集群中节点之间高效分配任务来延长寿命。本研究的目的是利用一种能觉聚集法,提高不同异异异异异异异异异异异异异异异异异异异异异异异异异异异功能装置的能源消耗量。在一个杂异异的IoT网络中,节点(即电池动力IoT装置)可以拥有多种能源配置和通信能力。大多数现有的组合算法忽视了节点之间能源能力的异质,并假定它们属于同一能源水平。在此工作中,我们介绍HetEng,一个聚类负责人(CH)选择过程,该过程扩展了现有的组合算法,名为Smart-BEM。为此,我们提出了一个统计方法,将能源消耗量分散在网络表层中的高能量节点,根据实际能源水平不断改变CH的作用(在焦耳目中)。实验结果表明,Hetng导致活节点节点总能量水平增加6.%,并在SmarimEM方法中改进了1。