## 4 Results
### Results of Individual Studies and Synthesis
#### 4.1.2 Routing and Clustering Approaches  
Several studies have explored innovative routing and clustering algorithms to extend the operational life and reduce the energy consumption of IoT networks, particularly in Wireless Sensor Networks (WSNs) and Body Sensor Networks (BSNs). These approaches often involve adaptive strategies that respond dynamically to network conditions, ensuring efficient data transmission and balanced energy distribution across nodes. * [PERSON] et al. [24] developed a machine-learning-based classification model specifically designed for patient monitoring in Body Sensor Networks (BSNs). This model utilises patient status information to adjust transmission levels according to situational needs, reducing power consumption when high transmission rates are unnecessary. For instance, the system conserves energy during periods of low patient activity, such as rest, by lowering transmission frequency. In contrast, it increases the transmission rate in response to critical changes in patient vitals, ensuring timely data delivery to healthcare providers. This adaptive strategy is particularly beneficial for healthcare IoT applications, where balancing energy efficiency with reliable data delivery is crucial to ensure continuous patient monitoring without frequent device recharging. * [PERSON] et al. [25] introduced the Optimal Path Selection Method (OPSM), inspired by ant colony algorithms. OPSM uses the natural foraging behaviour of ants to identify optimal data paths, efficiently balancing load across the network. By selecting the shortest and least congested routes, OPSM minimises energy-intensive retransmissions and reduces latency, thereby enhancing both network performance and longevity. Simulation results demonstrated that OPSM reduced transmission delays and extended network lifetime, making it highly effective for applications requiring continuous data flow, such as environmental monitoring or smart agriculture. * [PERSON] et al. [26] proposed a routing approach that incorporates the Node Importance and Active Path (NIAP) metric. This metric prioritises nodes based on their remaining energy and role in the network topology, enabling more effective load balancing and extending the lifetime of WSNs. By focusing on power consumption at each routing decision, NIAP allows the network to avoid overburdening low-energy nodes, distributing energy use more evenly. The study found that implementing the NIAP metric led to a marked increase in network lifespan, making it particularly suitable for IoT applications in remote or challenging environments where battery replacement is difficult. * [PERSON] et al. [27] developed an energy-scheduling algorithm that organises sensors into clusters with designated brokers for improved message management. Unlike traditional clustering methods such as LEACH (Low-Energy Adaptive Clustering Hierarchy), which selects cluster heads randomly, this algorithm strategically selects cluster brokers based on their energy levels and proximity to other nodes. By employ ing a structured approach to cluster formation and message handling, this method enhances energy management and maintains more operational nodes over time. The study demonstrated that this algorithm outperformed LEACH in energy efficiency and network longevity, making it ideal for energy-constrained environments where maximising node uptime is critical. * [PERSON] et al. [28] proposed the Energy-Efficient Centroid-Based Routing Protocol (EECRP), which organises IoT nodes into clusters based on their spatial positions and distributes workload evenly. This protocol calculates the centroid of clusters and assigns it as the routing path, ensuring that each node participates equally in data transmission, which prevents premature energy depletion in any single node. they even workload distribution of EECRP allows for optimised energy usage across the network, extending its lifetime.