QoS‐Aware Relay Node Deployment for Wireless Sensor Networks Using a Repair‐Based Multiobjective Evolutionary Algorithm
TL;DR
Investigating QoS‐aware relay node deployment by incorporating probabilistic link reliability, retransmission‐induced expected delay, and deadline satisfaction into the deployment optimization stage suggests that deployment‐stage QoS optimization is useful for latency‐sensitive WSN applications such as industrial monitoring, disaster surveillance, and mission‐critical IoT.
Abstract
Wireless sensor network deployment is a fundamental design problem because the physical placement of relay nodes directly affects network connectivity, communication reliability, and end‐to‐end delay. Although many deployment studies focus on coverage, connectivity, and deployment cost, delay‐related quality‐of‐service (QoS) requirements are often addressed mainly at the routing or protocol stage after the network topology has been fixed. This paper investigates QoS‐aware relay node deployment by incorporating probabilistic link reliability, retransmission‐induced expected delay, and deadline satisfaction into the deployment optimization stage. The relay deployment problem is formulated as a constrained multiobjective optimization problem that minimizes deployment cost while maximizing the deadline satisfaction ratio (DSR). Rather than replacing the classical NSGA‐II algorithm, this work develops a QoS‐aware constrained deployment framework tailored to relay placement in wireless sensor networks. NSGA‐II is used as the multiobjective search backbone, and problem‐specific sequential repair mechanisms are embedded into the search process. Structural feasibility repair first enforces coverage and connectivity requirements, while QoS‐oriented delay repair then refines deadline‐violating communication paths. Simulation experiments are conducted under uniform, clustered, and obstacle‐based deployment scenarios. The proposed framework is compared with greedy relay placement, standard NSGA‐II, MOEA/D, and MOPSO. Ablation, scalability, and parameter sensitivity analyses are also performed to examine the contribution of the repair modules and the robustness of the framework. The results show that the repair‐guided framework improves deadline satisfaction and Pareto solution quality while maintaining structural feasibility under constrained deployment settings. These findings suggest that deployment‐stage QoS optimization is useful for latency‐sensitive WSN applications such as industrial monitoring, disaster surveillance, and mission‐critical IoT.