## 4 Results
### Results of Individual Studies and Synthesis
#### 4.1.1 Application Protocols: CoAP and MQTT  
Several studies examined the role of CoAP and MQTT protocols in enhancing energy efficiency in IoT networks, proposing various modifications and strategies to address the energy demands of data transmission and processing:  
* [PERSON] et al. [19] explored a sleep scheduling method for CoAP to improve energy efficiency by managing the sleep-wake cycles of IoT nodes. In this approach, nodes were programmed to enter low-power states when not actively transmitting or receiving data, significantly reducing energy use during idle periods. By integrating resource directories and message queues, this method allowed for better synchronisation across nodes, ensuring that they only activate when necessary for specific tasks. Although this strategy showed potential for energy savings, the study lacked experimental validation, limiting its robustness and calling for further empirical research to confirm the effectiveness of sleep scheduling in real-world applications.
* [PERSON] et al. [20] proposed a task offloading schema for MQTT, aimed at enhancing energy efficiency by categorising tasks based on their urgency and offloading them accordingly. This approach prioritised high-urgency tasks for immediate processing, while less critical tasks were deferred or offloaded to external resources, thus balancing workload distribution and reducing the energy strain on IoT devices. While the method demonstrated promise for conserving energy, especially in resource-constrained environments, the study noted that additional empirical testing is necessary to assess its practical implications and ensure that the offloading mechanism can adapt to varied network demands without compromising performance.
* [PERSON] [21] leveraged machine learning algorithms in MQTT gateway nodes to predict data transmission needs, proactively adjusting transmission schedules to avoid unnecessary data exchanges. This predictive analytics approach enabled the system to forecast data requirements and manage transmission times, which reduced redundant data exchanges and conserved device energy. By aligning data transmissions with actual needs, this method minimised power consumption, illustrating the value of predictive techniques in managing energy use in IoT networks. [PERSON]'s study underscores the potential of machine learning in optimising IoT communication protocols for energy efficiency, particularly in applications with fluctuating data demands.
* [PERSON] et al. [22] addressed the challenge of packet loss in wireless sensor networks (WSNs) by incorporating random linear network coding with MQTT. This coding approach was designed to improve data reliability by compensating for packet loss, which is common in remote or resource-constrained environments. By reducing the need for retransmissions, the approach decreased radio frequency updates, leading to a 38.24% reduction in energy consumption in agricultural IoT applications. This finding demonstrates the effectiveness of network coding techniques in conserving energy, especially in settings where consistent data transmission is crucial yet prone to disruption.
* [PERSON] et al. [23] introduced OSCoAP (Object Security for CoAP), a secure version of CoAP intended to enhance both data security and energy efficiency. This protocol incorporated lightweight encryption techniques that minimised processing demands while maintaining robust security measures, which are essential in sensitive IoT applications. By securing data with minimal overhead, OSCoAP not only protected information but also extended device battery life by 30%, demonstrating that security protocols can be designed to support energy efficiency. This dual benefit is particularly valuable in applications where data integrity and energy conservation are paramount, such as healthcare or industrial IoT.