## 2 Related Works  
A hybrid cross-layer routing protocol was inspired by WSN-assisted energy conservation in the Internet of Things (IoT). Routing is processed at three layers in the cross-layer routing strategy: the physical layer, the data link layer, and the transport layer. The protocol for Bionic Cross-Layer Routing (BiHCLR) is proposed in this paper for efficient and energy-efficient routing in WSN-IoT. Then, to conserve energy in BiHCLR, a fuzzy logic method is used to select a CH for each grid cell [11]. The routing path is then chosen using a hybrid bionic algorithm. The proposed BiHCLR's performance is assessed using a quality of service (QoS) analysis. Latency-aware heterogeneous cluster-based data acquisition (DA-HCDA) in the IoT was proposed [12]. A DA-HCDA algorithm is presented to ensure maximum coverage. As a result of the introduced DA-HCDA and quartile aggregation mechanisms, end-to-end latency and network lifetime are improved.  
A new Energy-Aware Adaptive Fuzzy Neural Clustering (EAANFC-MR) algorithm was implemented. The EAANFC-based clustering method chooses CHs based on residual energy (RE), node distance, and degree [13]. The QOBFO algorithm is used as a multihop routing technique to select the best route to the destination. The proposed EAANFC-MR algorithm has been simulated using MATLAB. Numerous tests demonstrate the advantages of the suggested algorithm when it is examined with various QoS parameters. The Multi-Objective Fuzzy Inference System (moFIS) and Bacterial Feeding Optimization (BFO) are effective algorithms for determining attributes such as degree difference, overall distance to neighbors, remaining energy, and distance to the BS for wireless sensor networks. The demonstrated outcomes revealed that the moFIS-BFO algorithm outperforms and extends network life.  
A new Natural Inspired Cross-Layer Clustering (NICC) protocol is investigated along with Bacterial Feeding Optimization (BFO) with an optimum fitness function that simulates the trade-off between data throughput and energy usage. A BFO algorithm selects optimal sensor nodes for routing and clustering issues based on cross-layer parameter fitness value calculations. In various WSN scenarios, demonstrated results revealed that the NICC protocol outperformed state-of-the-art clustering techniques [14]. The Naive Bayes, KNN, and Support Vector Machine (SVM) classifiers are used for classification and image processing applications. The conventional algorithm has less accuracy when compared with the proposed methodology [15].  
A new algorithm is proposed for determining the best CH in an IoT-WSN. The Improved Sumflower Optimization (ISFO) algorithm is the name given to this novel algorithm. The proposed algorithm's results show that it consumes less power than other algorithms and has a higher number of still-alive nodes than others. As a result, the ISFO algorithm demonstrates its superiority in terms of power consumption and network lifespan [16]. An optimal cross-layer-based CH selection is recommended to resolve the energy conservation issue in WSNs. The suggested algorithm with novel probabilistic decision rules is used as a fitness function to find the best path for data transfer. Compared to other methods, CH-IoT minimized power consumption, end-to-end latency, and communication overhead while optimizing network performance. Various performance parameters, such as the number of active nodes, temperature, load, remaining power, and cost function, have been used to select the best CH in an IoT network cluster. The proposed method is compared with several conventional optimization algorithms, including the artificial bee colony, the whale optimization algorithm (WOA), the genetic algorithm, and the adaptive gravity search algorithm [17].