# Empowering WBANs: Enhanced Energy Efficiency Through Cluster-Based Routing and Swarm Optimization
## 2 Related Work
[17] suggested an evolutionary approach to WBAN cluster selection to perform an energy-efficient routing algorithm for monitoring cattle conditions and habits. The proposed method employs the ant lion optimizer (ALO) to select the appropriate CH for different pasture sizes by leveraging SNs with diverse communication ranges and considering user desire for cluster volume. The proposed algorithm is compared against other algorithms, including grasshopper optimization, moth-flame optimization, and ant colony optimization. The comparison results reveal that the proposed solution uses an energy-efficient WBAN protocol for remote surveillance applications. [PERSON] et al. [18] proposed the energy harvested and cooperative-enabled efficient routing protocol (EHCRP) for IoT-WBANs, considering multiple WBAN parameters such as SN's aggregated hop count, node congestion level, residual energy, and network bandwidth. Using these parameters, computers pick repeater nodes using path cost estimation. The suggested method allows for cost-effective and efficient multi-hop data routing. The path cost estimating procedure improves network data transmission reliability and efficiency. After many simulations, the suggested approach findings were compared to those of existing approaches (EB-MADM, E-HARP, EERP, and PCRP). The findings show that NLT, range, and E2E latency have all improved significantly. [PERSON] et al. (2019) proposed an efficient and reliable routing scheme (ERRS), which increases dependability by achieving better security and network lifetime via the flexible static clustering routing method. Using a comprehensive analysis-based MATLAB simulation, the ERRS surpasses reference protocols by 26% in terms of network performance and stability. The ERRS provides a reliable and efficient routing solution for WBANs, even if the proposed method's E2E delay is 17% and 40% greater than the M-ATTEMPT and SIMPLE approaches, respectively. [PERSON] et al. (2019) suggested a scheme for WBAN spectrum allocation. This strategy aims to cluster via ML-based smart partitioning methodology in dense deployments to reduce cross-WBAN interference. They take into account human social mobility clustering and topological control. The experimental results show that the proposed technique performs effectively when changing fast topology between WBANs. [PERSON] et al. (2019) suggested a self-organizing dynamic cluster (SDC) algorithm for minimizing intrusion among various WBANs. The suggested approach uses the cellular network algorithm to assign channels among several WBANs. The proposed algorithm increases the dependability of data transfer among WBANs. They utilize time division multiple access (TDMA) and frequency division multiple access (FDMA), with TDMA having a huge framework and FDMA using a clustered frame. [PERSON] et al. (2019) introduced the E-HARP system, a harvest-aware cluster and cooperating routing system that uses less energy. Dynamic selection of CH and optimum routing path selection comprise two of the method's main benefits. The best cluster is chosen using a fitness function. A fitness function is created using the RE node, the essential transmission energy, the link SNR, and the power loss. Second, routing protocols are implemented through the collaboration of CM nodes. Experimental outcomes show that the suggested approach performs well regarding energy utilization, PDR, latency, and NLT. [PERSON] et al. (2019) suggested a dynamic layering technique to balance SN power consumption and NLT utilizing combinatorial optimization (DHCO). The objective of the DHCO algorithm is to create a viable route set for each SN, not to select a CH or the following node. Table 1 shows a summary of existing procedures used in WBANs. Static, established routing protocols and simple algorithms struggle to adapt to ever-changing network circumstances. However, the ABBSOA-MSSOA hybrid model optimizes energy use in real time by balancing exploration and exploitation (refining current solutions). It also makes dynamic adjustments to its judgments.