Abstract—Wireless Sensor Networks (WSNs) have revolutionized the ability to monitor environments, track events, and prevent intrusions through node-based barriers in both deterministic and uncertain settings. However, achieving optimal coverage—ensuring that sensor nodes effectively monitor a target area—remains a significant challenge due to factors such as dynamic environmental conditions, energy constraints, and network connectivity issues. This paper presents a comparative study of two coverage models in WSNs: (a) deterministic models, which assume predictable environmental conditions and node behavior, and (b) uncertainty-based models, which account for unpredictable factors such as node failures, environmental disturbances, and measurement inaccuracies. We propose novel strategies for each model and evaluate their performance in terms of connectivity, network lifetime, and coverage efficiency. Our results demonstrate that deterministic models excel in stable environments, achieving a coverage efficiency of up to 95%, while uncertainty-based models provide greater resilience in dynamic or hostile environments, maintaining a coverage efficiency of 85% even under adverse conditions. The study also identifies key trade-offs between the two approaches, such as the higher energy consumption of uncertainty-based models compared to deterministic ones. Based on these findings, we outline promising research directions for addressing coverage challenges in WSNs, particularly in the context of emerging technologies such as Internet of Things (IoT) and 5G networks. This work contributes to the field by providing a comprehensive framework for selecting and optimizing coverage strategies based on environmental conditions and application requirements.