# Green IoT: A Review and Future Research Directions
## 8 Potential Future Directions
Additionally, the selection of an appropriate energy storage technology may impact the overall cost of the device, as well as the complexity of the device design. Despite these considerations, the use of energy storage solutions can provide numerous benefits for IoT devices. For example, the use of batteries or supercapacitors can enable IoT devices to operate without a constant power source, reducing the need for frequent charging or replacement. Additionally, energy storage solutions can improve the reliability and performance of IoT devices, providing a more stable power supply and improving overall device longevity. (ii) _Routing schemes_  
The development of \"smart\" routing schemes that can dynamically adjust routes based on network conditions, traffic patterns, and energy availability is an important area of research for the future of IoT networks. Traditional routing schemes in IoT networks are typically static and do not adapt to changing network conditions or energy availability, which can lead to inefficient use of network resources and increased energy consumption. \"Smart\" routing schemes aim to address these challenges by utilizing real-time data and algorithms to dynamically adjust network routes based on network conditions, traffic patterns, and energy availability. These schemes can be implemented at various levels of the network, from the sensor nodes to the edge devices and gateways. At the sensor node level, \"smart\" routing schemes can be used to optimize the energy consumption of individual nodes by dynamically adjusting their transmission power and frequency based on the energy availability and network conditions. For example, nodes with low energy levels can reduce their transmission power and frequency to conserve energy, while nodes with high energy levels can increase their transmission power and frequency to improve network coverage. At the edge device and gateway level, \"smart\" routing schemes can be used to optimize the routing of data packets based on the network conditions and traffic patterns. For example, packets can be routed through nodes that have higher energy levels or better connectivity to reduce packet loss and improve network efficiency. One example of a \"smart\" routing scheme is the energy-aware routing protocol (EARP), which is designed for IoT networks with energy harvesting capabilities. EARP utilizes a decentralized approach to dynamically adjust network routes based on the energy availability and network conditions of individual nodes. The protocol uses a combination of metrics, including residual energy levels and link quality, to determine the optimal routing path for data packets. Another example of a \"smart\" routing scheme is the traffic-aware energy-efficient routing (TEER) protocol, which is designed for IoT networks with limited energy resources. TEER utilizes a centralized approach to dynamically adjust network routes based on traffic patterns and energy availability. The protocol uses a combination of metrics, including traffic density and residual energy levels, to determine the optimal routing path for data packets. Overall, the development of \"smart\" routing schemes that can dynamically adjust routes based on network conditions, traffic patterns, and energy availability is an important area of research for the future of IoT networks. These schemes can help to improve network efficiency, reduce energy consumption, and increase the longevity of IoT devices. 3. _Adaptive AI and ML_ The development of AI-based energy management systems and machine learning algorithms for IoT devices is a rapidly evolving field that can significantly improve the energy efficiency and sustainability of IoT networks. These systems and algorithms can dynamically adjust and optimize power usage based on usage patterns, network conditions, and energy availability. AI-based energy management systems use machine learning algorithms to analyze data from IoT devices, such as usage patterns and energy consumption, to identify opportunities for energy optimization.