# Offloaded Computation for QoS Routing in Wireless Sensor Networks
## 2 Related Work
### QoS Routing in Wireless Sensor Networks  
Extensive surveys have been conducted to analyze QoS-aware routing protocols for WSNs [23; 24; 25; 26]. Table 2 provides a comparative overview of existing approaches, highlighting their objectives, methodologies, and observed limitations. Figure 1 categorizes QoS routing protocols over Wireless Multimedia Sensor Networks (WMSNs) into four main categories. The first category, **real-time requirements,** classifies protocols based on timing constraints, distinguishing between hard and soft real-time requirements. The second category, **decision-making methodology**, defines how routing decisions are made, either probabilistically or deterministically. The third category, **network topology**, organizes protocols based on network structure, including hierarchical, flat, and location-based approaches. Finally, **protocol operation** groups protocols according to their functional approach, such as QoS-aware, negotiation-based, or multi-path. The categories presented in Figure 1 represent key classifications of QoS routing protocols in Wireless Multimedia Sensor Networks (WMSNs). However, these are not the only possible categorizations. Other classifications exist based on mobility support, fault tolerance, security, and energy efficiency. For a more comprehensive discussion, readers can refer to [26; 27; 28; 29]. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Aspect** & **Gaddour** & **Ghaleb** & **GRPAR** & **OMC-RPL** & **TDL-Based** & **Proposed** \\  & **et al.**[16] & **et al.**[5] & [19] & [20] & & **Protocol [21]** & **Approach** \\ \hline Modification of Objective Function & Yes & Yes (Composite metrics) & No & Yes (custom multi-metric) & No (cross-layer based) & No \\ \hline Increase in Control Overhead & Yes (Fuzzy logic extension) & Moderate (multi-metric dissemination) & Low (greedy heuristic) & Moderate (multi-class weighting) & Moderate (cross-layer metrics) & No (cross-layer metrics) & No \\ \hline External Offloading to Edge & No & No & No & No & No & Yes \\ \hline Lightweight Conflict Detection & No & No & No & No & No & Yes \\ \hline Redundancy Augmentation Mechanism & No & No & No & No & No & Yes \\ \hline Standard Compliance with RPL & Partial & Partial (Objective Function modifications) & Partial & Partial & No (non-RPL based) & Full based) \\ \hline \hline \end{tabular} This comparison highlights key architectural and functional improvements of our proposed model over prominent RPL-based QoS-aware routing solutions regarding scalability, protocol compliance, and offloading support.  
\end{table} 
Table 1: Comparison of the proposed work with representative QoS-aware RPL-based routing solutions. One of the paramount applications of WSNs lies within the Internet of Things (IoT). In these networks, sources are often located at considerable distances from the sink, gateway, or BR, necessitating multi-hop communication. According to [PERSON] et al. [33], intelligent routing in WSNs is critical to enhancing QoS in IoT environments. They proposed a hierarchical structure utilizing K-means clustering, where a Cluster Head (CH) is selected within each cluster through a fuzzy inference system. Subsequently, sensors transmit data packets to their respective CHs, which forward the data to the sink node. To determine suitable CHs, [PERSON] et al. [31] introduced an evolutionary (genetic algorithm)-based routing approach with a novel fitness function. Additionally, [PERSON] et al. [36] combined