# Offloaded Computation for QoS Routing in Wireless Sensor Networks
## 6 Conclusions and Perspectives  
This work presents a scalable standard-compliant framework for offloaded multi-constrained QoS routing in RPL-based Wireless Sensor Networks (WSNs). By shifting the computational burden from constrained nodes to edge servers, the proposed architecture enables centralized DODAG construction while preserving RPL compatibility and minimizing protocol disruption.  
The methodology comprises three key algorithms: Algorithm 1 for computing feasible non-dominated paths; Algorithm 2 for conflict-free DODAG construction; and Algorithm 3 for redundancy augmentation to support local repair. The experimental evaluation demonstrates substantial advantages over the heuristic baselines regarding QoS feasibility and routing efficiency. Specifically, it preserved between **142.90% and 1282.05% more QoS-satisfying nodes**, with an average gain of **563.45%**, across a wide range of constraint thresholds. This performance far exceeds the previously reported margins and affirms the approach's scalability under real-time constraint-intensive deployments. Furthermore, the framework supports conflict-resilient DODAG construction, dynamic adaptation to multiple BRs, and a lightweight redundancy mechanism, making it robust under both static and evolving network conditions. These capabilities ensure that high-QoS coverage is maintained even as network parameters shift.  
The prospective research directions include (i) integrating approximate multi-constrained routing algorithms such as TAMCRA to accelerate DODAG computation, (ii) extending the framework to hybrid fog/edge-cloud architectures, and (iii) evaluating runtime responsiveness and fault tolerance under dynamic workloads and monitoring-driven triggers. The emerging tools from graph-based learning and AI also present promising opportunities for predictive QoS-aware routing scenarios. Although \(H_{1}\) and \(H_{2}\) reflect common heuristics, future work will expand the comparison to include RPL-based QoS protocols such as mRPL [5] and CA-RPL [10]. These algorithms incorporate objective function tuning, composite metrics, and constraint-awareness--each offering complementary insights. Adapting and benchmarking them within our experimental framework will help to clarify the operational trade-offs between centralized and distributed decision-making under real-world conditions.  
Overall, the proposed framework bridges a critical gap between theoretical multi-constrained routing models and their practical implementation in WSNs. It offers a deployable solution for QoS-sensitive industrial and smart infrastructure applications.  
Conceptualization, B.M. and M.M.; Methodology, B.M. and M.M.; Formal analysis, M.M.; Investigation, B.M.; Writing--original draft, B.M. and M.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding and the APC was funded by MDPI.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest**: The authors declare no conflicts of interest.