###### Abstract  
Wireless sensor network (WSN) sensor nodes should have adequate energy. Reduced energy usage is essential to maximize the endurance of WSNs. Combining WSN with a more significant energy source, a cluster head (CH), is another effective strategy for extending WSN durability. A CH is dependent on the communication inside and between clusters. A CH's energy level extends the cluster's life for the complete WSN. Determining the energy required in WSNs while developing clustering algorithms is challenging. For maintaining energy efficiency in WSNs, this research offers K-medoids with sunflower-based clustering and a cross-layer-based optimal routing approach. An efficient fitness function generated from diverse objectives is used to choose the CH. After CH selection, sunflower optimization (SFO) indicates the best data transmission line to the sink node. The proposed protocol, SFO-CORP, increased the network lifetime by 19.6%, 13.63%, 11.13%, and 4.163% compared to the LEACH, EECRP, FEEC-IIR, and CL-IoT protocols, respectively. The experimental results showed that it performed better for packet delivery ratio, energy consumption, end-to-end delay, network lifetime, and computation efficiency.  
hybrid K-medoids; energy efficiency; cluster head; sunflower optimization algorithm; network lifetime +
Footnote †: journal: _Sustainability_  
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[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], S. Hybrid K-Medoids with Energy-Efficient Sundover Optimization Algorithm for Wireless Sensor Networks  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], S. Hybrid K-Medoids with Energy-Efficient Sundover Optimization Algorithm for Wireless Sensor Networks  
The energy of a sensor node determines its lifespan (battery). The main issue with WSNs is that the sensor nodes are quickly depleted and die. As a result, it is critical to develop an energy saving algorithm to extend the life of sensor nodes [6].  
Protocols for cluster-based hierarchical routing are critical for minimizing the energy consumption of WSNs. Clustering enables this to be accomplished effectively and straightforwardly. Clustering groups many WSN nodes and creates communication between the group and the BS via the group leader, referred to as the CH. Selecting clusters and related CHs is a complex and intimidating undertaking in and of itself. Numerous tactics have been employed over the years to optimize the choice of CHs. Multiple optimization strategies have been tried to determine the optimal group of CHs in a network [7]. Fuzzy logic [8], ant colony optimization [9], genetic algorithms [10], particle swarm intelligence [11], and the harmony search algorithm [12] were among these strategies. The CH selection problem for WSNs is solved in this article using a k-medoid with sunflower optimization (SFO) approach.  
Routing is critical in low-energy WSNs and is carried out using energy-efficient routing protocols [13]. A cross-layer optimized opportunistic routing approach is proposed to address the issues of effective clustering and data transmission in smart manufacturing. The issue of optimum cluster head (CH) selection is articulated in this study using the characteristics of various layers. Following the first cluster construction, the ideal CH for each cluster is determined by performing a cross-layer probability evaluation of each sensor node. Cross-layer parameters increase energy efficiency, while minimizing clustering and routing overheads. This research proposes a cross-layer optimized opportunistic routing protocol for WSNs that generates numerous pathways, measuring the node lifespan, communication reliability, and traffic intensity.  
The remaining sections of this article are organized as follows: In Section 2, the related works with respect to energy efficiency in WSNs are discussed. Section 3 presents the proposed hybrid K-medoids sunflower optimization algorithm. Section 4 gives the simulation results and a discussion about the proposed algorithm. Finally, the proposed work is concluded in Section 5.