## 7 Conclusions, Home Recommendations, and Future Directions  
The majority of IoT SNs utilized in smart city applications have limited battery life and are not rechargeable. This characteristic makes IoT networks highly vulnerable to sudden spikes in energy consumption, emphasizing the critical need for energy conservation to ensure prolonged network operation. Addressing this challenge, our work proposes a novel IoT routing protocol incorporating innovative energy-efficient data fusion grouping and routing algorithms. At the core of this protocol is the data fusion grouping algorithm, which plays a pivotal role in enhancing energy efficiency by uniformly dividing the network sensing area into hexagonal DFGs. This not only minimizes redundant data transmission but also balances energy consumption across the network, thereby extending the operational lifespan of the proposed protocol. Each SN responds with an information message containing its ID, location, and RE when the sink node sends a request information message. Using the DFG algorithm, the sensing area is structured systematically, while the MPA is employed to determine the PDFH, PSDFH, and SSDFH. Five critical RMs--the SN's RE, average distance between DFGMs, distance from the sink node, ASBO, and the ratio of the average rounds all the DFGMs served as PDFHs to the rounds served by the CDFH\({}_{i}\)--were incorporated to optimize routing and resource allocation. Further, we developed a new TDMA scheme, a novel RDCF, and an innovative DFF. The comprehensive simulation results demonstrate the superior performance of the proposed protocol, particularly in terms of network lifetime, total remaining energy, throughput, and average delay over several rounds, both with and without multi-hop routing. Comparative analyses against relevant protocols, including LDIWPSOC, PSO-EEC, OFCA, and NPSOP, confirm the significant improvements achieved due to the enhancements introduced in our approach. Moreover, our work addresses critical real-world challenges such as building occlusions at various heights, device/link failures, and data redundancy obstacles. These considerations place our protocol ahead of its competitors, particularly in challenging environmental settings. The robust data fusion algorithm incorporated in our protocol not only optimizes data transmission efficiency but also effectively mitigates data redundancy, making it a cornerstone of the protocol's success in extending network longevity and reliability. By addressing these critical aspects comprehensively, our proposed protocol establishes a strong foundation for energy-efficient and reliable IoT networks in smart city applications. Therefore, we enthusiastically urge the Greater Amman Municipality (GAM) and the Information and Communication Technology (ICT) industry to take the adoption of these models into consideration and implement them in reality, given the remarkable outcomes attained with the smart models that have been presented. This advice is especially important as the ICT industry and GAM have not yet proposed or implemented any smart model projects for Amman, the capital of Jordan. Future research could focus on further enhancing the PDFH selection and routing mechanisms in IoT networks for smart cities. One promising direction is to incorporate machine learning techniques, which could enable predictive analytics to anticipate energy spikes and optimize routing paths preemptively. Another avenue for improvement is to test the protocol's effectiveness in larger-scale smart city environments with more diverse obstacles and real-time data requirements, which would provide insights into its scalability and robustness. Conceptualization, K.A.D. ; Methodology, K.A.D. ; Software, M.A.-A. ; Formal analysis, K.A.D. ; Writing--original draft, K.A.D. and M.A.-A. ; Writing--review & editing, K.A.D. and M.A.-A. All authors have read and agreed to the published version of the manuscript.