An efficient energy-aware clustering method helps in reducing the battery depletion of the different resources in WSNs. The selection of suitable sensors for cluster head can be an effective way to increase the proficiency of the clustering process. In the past two decades, a number of clustering methods have been proposed. However, most of the methods are suffering from the uneven variation in the number of the Cluster Head (CH), irregular energy consumption by the nodes, transmission of the redundant data, and unequal load of the cluster heads. This paper resolves these problems by proposing an energy-aware data gathering technique based on nature-inspired optimization for both homogeneous and heterogeneous networks. It considers a fitness function by integrating four fitness parameters namely: energy efficiency, cluster node density, average distance of sensors to the CH, and distance from CH to Base Station (BS). This method considers a chain based data gathering and transmission process for intra and inter-cluster communication. A data aggregation process is also introduced for removing the redundant data which helps in decreasing the transmission cost and overhead of the networks. The performance of the proposed methods is evaluated against the state of the art protocols by considering the different performance matrices like network lifetime in terms of rounds, stability period in terms of first node dead, total energy consumption per round, throughput, number of CHs per round etc. The experimental results show the network lifetime and throughput of the proposed method are increased by 23.14%, 29.42%, 60.48%, & 80.16%, and 38.38%, 40.06%, 71.88%, & 95.58%, in respect of the Senthil and Kannapiran method (Wirel Pers Commun 94(4):2239–2258, 2017), ICSCA (Gupta, Procedia Comput Sci 125:234–240, 2018), Adnan et al. method (Lect Note Electric Eng 362: 621–634, 2016), DEEC (Qing et al., Comput Commun 29(12):2230–2237, 2016), respectively, for 100 J network energy in case of tier-3 heterogeneity, respectively.

中文翻译：

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WSN中基于自然启发式优化的能量感知型聚类和数据收集技术

一种有效的能量感知群集方法有助于减少WSN中不同资源的电池消耗。为簇头选择合适的传感器可能是提高聚类过程熟练程度的有效方法。在过去的二十年中，已经提出了许多聚类方法。但是，大多数方法都遭受簇头（CH）数量的不均匀变化，节点的不规则能耗，冗余数据的传输以及簇头的负载不平等。本文通过针对均质和异构网络提出了一种基于自然启发式优化的能量感知数据收集技术，从而解决了这些问题。它通过整合四个适应度参数来考虑适应度函数，即：能源效率，集群节点密度，传感器到CH的平均距离，以及CH到基站（BS）的距离。此方法考虑了用于集群内和集群间通信的基于链的数据收集和传输过程。还引入了数据聚合过程以去除冗余数据，这有助于降低网络的传输成本和开销。通过考虑不同的性能矩阵，如轮次的网络寿命，就第一个节点的死机而言的稳定期，每轮的总能耗，吞吐量，每个CH的数量，对照现有协议评估所提出方法的性能实验结果表明，所提方法的网络寿命和吞吐量分别提高了23.14％，29.42％，60.48％和80.16％，以及38.38％，40.06％，71.88％和95.58％。关于Senthil和Kannapiran方法（Wirel Pers Commun 94（4）：2239-2258，2017），ICSCA（Gupta，Procedia Comput Sci 125：234-240，2018），Adnan等人。方法（Lect Note Electric Eng 362：621–634，2016），DEEC（Qing等人，Comput Commun 29（12）：2230–2237，2016）分别针对第3层异构性情况下的100 J网络能量， 分别。