Sustainable Computing: Informatics and Systems 30 (2021) 100482 Available online 23 November 2020 2210-5379/© 2020 Elsevier Inc. All rights reserved. A robust clustering approach for extending the lifetime of wireless sensor networks in an optimized manner with a novel ftness function Ahmed Hassan a , Ahmed Anter b , Mohammed Kayed b, * a Faculty of Science, Beni-Suef University, Benisuef, 62511, Egypt b Faculty of Computers and Artifcial Intelligence, Beni-Suef University, Benisuef, 62511, Egypt A R T I C L E INFO Keywords: WSN Clustering Wireless power transfer Quantum behaved particle swarm optimization Game theory ABSTRACT The huge load on the cluster head (CH) nodes in the clustered Wireless Sensor Network (WSN) is still a problem that causes premature death for those overloaded nodes. In this paper, we have practically proved that CH-load reduction has the largest effect on enhancing the energy effciency of the clustering algorithms compared to some other energy effciency- enhancing factors. So, this paper also proposes a model that divides the load of the CH role into small parts/values, in an optimized manner, and allocates these values to the whole nodes in the cluster. Therefore, using the Wireless Power Transfer (WPT) strategy, each member node in the cluster will transfer a specifc amount of energy (equal to a part of the CH load that is assigned to the node) to the CH node which will also bear a suitable and a calculated part of the load instead of the whole load. So, the lifetime of the WSN is enhanced. Our simulation results show that our proposed model achieves high lifetime improvement over Leach and K-means clustering algorithms respectively. 1. Background WSN has achieved the recent technological progress in many vital felds such as healthcare, military and smart cities [1,2]. WSN consists of a set of small, cheap and smart devices that are called sensor nodes. Each sensor node performs three main tasks: sensing an interesting area, processing the sensed data and fnally transmitting the sensed data to the base station (BS). Each task performed by a sensor node requires (con- sumes) an amount of energy to complete. However, each sensor node is powered by a limited capacity battery that makes a limitation in the lifetime of the WSN. Many algorithms have been suggested to extend the lifetime of the WSN. One of the most energy effcient approaches is the clustering technique in which the sensor nodes are grouped into clusters. For each cluster, a CH node is selected. In each communication round, each CH node aggregates the sensed data from all cluster members in one data packet (data aggregation) and fnally sends the aggregated data to a BS [3]. The clustering approach has achieved many advantages (such as data aggregation and less number of transmissions which greatly reduce the communication overhead) that not only extend the lifetime of the WSN, but also they improve the performance and throughput of the WSN. This performance improvement is done by ensuring the connectivity between the nodes and the BS, facilitating the security of the whole WSN and fnally providing scalability [4]. In spite of the previous advantages of the clustering approach, CH nodes are exposed to a huge load (energy consumption) that causes premature death for these nodes and harming the lifetime of the WSN. Beside this, the death of the CH nodes is greatly more dangerous than the death of other ordinary nodes. Because if a CH node dies during a round, data obtained from all the cluster members could not be received and so cannot be sent to the BS. So, the lifetime of the member nodes or generally the WSN itself becomes useless. Energy harvesting is one of the hot topics in which the batteries of the sensor nodes are recharged from other energy sources. Traditional energy harvesting techniques convert the energy from the natural sources, such as solar or wind harvesting, into electric energy to recharge the batteries of the sensor nodes. However, traditional energy harvesting techniques are not reliable because they depend on the cur- rent environmental conditions that are not constant or always available. Another energy harvesting approach is WPT in which a node can transfer energy wirelessly to another node to recharge its battery. Wireless power transfer was frstly reported by Nicola Tesla a century ago. WPT techniques can be classifed into two basic classes: radiative or non-radiative depending on the energy transfer mechanisms. Radiative power can be emitted from an antenna and spreads through a medium * Corresponding author. E-mail addresses: ahmedhassancs22@yahoo.com (A. Hassan), AhmedAnter@fcis.bsu.edu.eg (A. Anter), mskayed@gmail.com (M. Kayed). Contents lists available at ScienceDirect Sustainable Computing: Informatics and Systems journal homepage: www.elsevier.com/locate/suscom https://doi.org/10.1016/j.suscom.2020.100482 Received 6 April 2020; Received in revised form 4 September 2020; Accepted 8 November 2020