Abstract

Wireless Sensor Networks (WSNs) are a fundamental component of the Internet of Things (IoT), supporting a wide range of applications from environmental monitoring to structural health assessment. Among the different WSN paradigms, event-driven networks have gained increasing attention because they report data only when significant events occur, thereby reducing unnecessary communication and conserving energy. A critical aspect of these networks is geographic routing, where routing decisions are based on node locations rather than identifiers, reflecting the spatial relevance of detected events. However, conventional geographic routing algorithms face significant challenges, including workload imbalance, network congestion, and high energy consumption, which are particularly problematic given the limited power resources of sensor nodes. These limitations directly impact network lifetime, reliability, and overall performance, underscoring the need for more intelligent routing strategies. To address these issues, this study proposes a novel geographic routing algorithm that leverages the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a widely recognized Multi-Criteria Decision-Making (MCDM) approach. Unlike traditional shortest-path routing methods that primarily minimize hop count, the proposed algorithm introduces additional decision criteria to enhance energy efficiency, balance node workload, and reduce latency. Specifically, it selects the optimal forwarding node by considering multiple factors, including residual energy, hop distance, delivery ratio, and a newly defined Exclusive Routing Share (ERS) metric, which prevents network bottlenecks by identifying over-utilized nodes. This approach not only extends the network lifetime but also ensures more reliable and balanced data delivery throughout the network. The effectiveness of the proposed algorithm was evaluated through extensive simulation experiments, comparing its performance against the benchmark Fault-Tolerant Routing (FTR) algorithm. Simulations were conducted under various network densities, event frequencies, and fault conditions to ensure a comprehensive assessment. The results demonstrate significant performance improvements: an average reduction of 15.4% in packet error rate, a 14.9% increase in network lifetime, and a 1.46% improvement in packet delivery ratio. These findings highlight the potential of the TOPSIS-based approach in optimizing routing decisions and mitigating common challenges in event-driven WSNs.