## 8 Future Directions  
This systematic review has identified several key gaps in current research and areas that warrant further exploration to advance energy efficiency in IoT systems. Addressing these gaps can support the development of more robust, sustainable, and scalable IoT solutions. The following recommendations outline the primary areas for future research:  
* Many of the reviewed studies lack experimental validation, as proposed methods are often tested only in simulated environments. While simulations offer valuable insights, real-world testing is essential to assess the effectiveness and scalability of energy-saving techniques under practical conditions. Experimental validation would help determine how well proposed algorithms, protocols, and strategies perform in diverse settings, from industrial IoT to smart cities and healthcare applications. Conducting field studies and pilot projects will provide the IoT community with critical data on potential deployment challenges, practical energy savings, and long-term impacts on device lifespan and maintenance costs. * A significant barrier to synthesising findings across studies is the lack of standardised metrics for evaluating energy efficiency in IoT networks. Current studies use varying criteria and metrics to measure energy consumption, making it difficult to compare results and draw generalisable conclusions. Standardised metrics, such as energy per transmitted bit, energy per task completion, or lifetime energy usage, would facilitate a more uniform evaluation framework. Establishing a common set of performance indicators for energy efficiency will enable researchers to better assess the effectiveness of different approaches and support consistent, repeatable experiments that build on each other's findings. * Future studies should incorporate a wider range of contextual factors, such as environmental conditions, device density, network topology, and application-specific requirements. For instance, the energy demands of IoT systems can vary significantly based on environmental factors such as temperature and humidity, which may affect sensor accuracy and power consumption. Similarly, application requirements differ across fields--healthcare IoT systems, for instance, may prioritise real-time data processing and reliability, while agricultural IoT systems might prioritise low-cost, long-range deployments. By considering these diverse factors, researchers can develop more adaptive, context-aware solutions that perform well across various environments, contributing to more versatile and resilient IoT systems. * The integration of advanced technologies, including artificial intelligence (AI) and machine learning (ML), presents promising opportunities for adaptive energy management in IoT networks. AI and ML can enable real-time energy optimisation by dynamically predicting workload, adjusting task scheduling, and automating resource allocation based on historical data and current conditions. For example, machine learning algorithms could predict data transmission needs and optimise sleep-wake cycles, reducing unnecessary power consumption. AI-driven energy management systems could also facilitate self-healing networks that detect and mitigate issues such as node failure or congestion, improving both energy efficiency and system reliability. Future research should further explore how these technologies can be leveraged to make IoT systems more responsive and energy-efficient, especially in complex, large-scale deployments. * While most studies focus on optimising battery-based energy use, hybrid energy solutions that combine traditional batteries with renewable sources (e.g., solar or kinetic energy) have potential for extending IoT device lifespans. Research on integrating hybrid power systems with energy-efficient protocols and algorithms could enable IoT deployments in remote areas where regular maintenance or battery replacement is difficult. Hybrid solutions also align with global sustainability goals by reducing dependency on conventional power sources.