# Offloaded Computation for QoS Routing in Wireless Sensor Networks
## 2 Related Work
### QoS Routing in Wireless Sensor Networks
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Ref.** & **Objectives and Contributions** & **Limitations** \\ \hline \multirow{3}{*}{[PERSON] et al. [30]} & Introduced reinforcement learning for multi-objective QoS optimization in edge-enabled WSN-IoT systems. & complexity of simultaneous multi-objective optimization. \\ \hline \multirow{3}{*}{[PERSON] et al. [31]} & Proposed an evolutionary genetic algorithm for Cluster Head (CH) selection in WSNs; energy-efficient CH selection and routing. & Fitness function tuning can be computationally intensive. \\ \hline \multirow{3}{*}{[PERSON] et al. [32]} & Proposed a hybrid protocol for data aggregation and reactive routing to improve QoS at the network layer in WSNs. Enhances routing efficiency and data collection. & Not explicitly tested for energy efficiency in large-scale deployments. Primarily simulation-based, and lacks implementation in diverse environments. \\ \hline \multirow{3}{*}{[PERSON] et al. [33]} & Proposed hierarchical routing for IoT using k-means clustering and fuzzy inference. Effective CH selection and reduced intra-cluster communication. & Computational complexity of CH selection in resource-constrained networks. \\ \hline \multirow{3}{*}{[PERSON] et al. [34]} & Developed a multi-path protocol using Particle Swarm Optimization (PSO) for traffic fairness and load balancing in WSNs. & PSO may require high computation time and lacks real-world testing on dynamic WSN topologies. \\ \hline \multirow{3}{*}{[PERSON] et al. [35]} & Comprehensive analysis of QoS parameters and frameworks of machine-learning-based QoS techniques in WSNs. & Focused on literature review without detailed implementation. \\ \hline \multirow{3}{*}{[PERSON] et al. [36]} & Combined K-means clustering with a mobile data collector (MDC) for QoS improvement in large-scale WSNs. & MDC introduces additional complexity and latency; mobility optimization is not addressed. \\ \hline \multirow{3}{*}{[PERSON] et al. [37]} & Proposed an energy-efficient MAC protocol using Q-learning and adaptive modulation for QoS improvement. & Limited to specific scenarios; may lack scalability in diverse WSN applications. \\ \hline \multirow{3}{*}{[PERSON] et al. [38]} & Compared SDN-enabled WSNs to conventional WSNs using the RPL and identified overhead trade-offs. & Limited to small-scale testbeds. \\ \hline \multirow{3}{*}{[PERSON] et al. [39]} & Highlighted SD-WSN improvements via OpenFlow for QoS provisioning in resource-constrained environments. & Design flaws identified in SDN-based WSNs, with high computational overhead. \\ \hline \multirow{3}{*}{Cisco [22]} & Differentiated Services (DiffServ) approach for prioritizing traffic classes to meet QoS requirements. & Less suited for resource-constrained WSNs; focused on Internet-scale networks. \\ \hline \multirow{3}{*}{[PERSON] et al. [40]} & Provided a detailed overview of SDN architecture for centralized and programmable flow management to enhance QoS and optimize resource allocation. & Overhead in resource-limited WSNs and latency in centralized SDN designs. \\ \hline \end{tabular}
\end{table} 
Table 2: Summary of literature review on QoS in WSNs and IoT. the K-means method with a mobile data collector (MDC) to enhance the clustering efficiency in large-scale WSNs. In parallel, [PERSON] et al. [41] addressed the challenges of real-time data delivery in smart city applications by proposing an environment-aware, energy-efficient, and reliable multi-sink routing scheme. Similarly, [PERSON] et al. [42] introduced an adaptive swarm-based fuzzy optimization (ASFO) technique integrated with a cross-layer expedient routing protocol, effectively improving both the energy efficiency and QoS in WSNs. QoS requirements are typically contingent upon several parameters or metrics. The anticipated outcomes of QoS can be articulated as constraints, suggesting that the pertinent QoS parameters should remain within predefined intervals. The most straightforward method for specifying QoS is establishing a set of such constraints.