This paper presents a novel methodology for deploying wireless sensor nodes in the Industrial Internet of Things (IIoT) to address the safety and efficiency challenges in underground coal mining. The methodology is intended to support long-term planning on mitigating the risks in occupational health and safety policies. To ensure realistic and accurate deployment, we propose a software tool that generates mine models based on geolocation data or blueprints in image format, allowing precise adaptation to the specific conditions of each mine. Furthermore, the process is based on sensing and communication range values obtained through simulations and on-site experiments. The deployment strategy is articulated in two complementary steps: a deterministic deployment, where nodes are strategically placed according to the structure of the tunnels, followed by a random stage to include additional nodes that ensure optimal coverage and connectivity inside the mine by comparing different methodologies for deploying sensor networks using coverage density as a performance metric. We analyze coverage and connectivity based on the three probability density functions (PDFs) for the random deployment of nodes: uniform, normal, and exponential, evaluating both the degree of coverage (k-coverage) and the degree of connectivity (k-connectivity). The results show that our proposed methodology stands out for its lower density of sensors per square meter, which translates into a reduction of between 20.81% and 23.46% for uniform and exponential PDFs, respectively, concerning the number of sensors compared to the analyzed methodologies. In this way, it is possible to determine which distribution is suitable to cover the elongated area with the smallest number of nodes, considering the coverage and connectivity requirements, to reduce the deployment cost. The uniform PDF minimizes the number of sensors needed by 44.70% in small mines and 46.27% in medium ones compared to the exponential PDF. These findings provide valuable information to optimize node deployment regarding cost and efficiency; a uniform function is a good option depending on prices. The exponential distribution reached the highest values of k-coverage and k-connectivity for small and medium-sized mines; in addition, it has greater robustness and tolerance to faults like signal network intermittence. This methodology not only improves the collection of critical information for the mining operation but also plays a vital role in reducing the risks to the health and safety of workers by providing a more robust and adaptive monitoring system. The approach can be used to plan IIoT systems based on Wireless Sensor Networks (WSN) for underground mining exploitation, offering a more reliable and adaptable strategy for monitoring and managing complex work environments.

Wireless sensor networks (WSN) are essential for monitoring operations, environmental conditions, and ensuring worker safety in underground mining environments. A key challenge in deploying these networks lies in achieving sufficient sensor coverage and reliable connectivity to enable early incident detection and timely response. While existing approaches primarily focus on deployment efficiency, this paper introduces a safety-driven optimization model that accounts for the complete underground mine structure and its specific operational conditions. The proposed algorithm minimizes the number of deployed nodes—reducing capital expenditure (CAPEX)—while meeting occupational safety standards by ensuring sensor coverage at critical points of interest and maintaining network connectivity. Compared to existing models, our method reduces the number of required nodes by up to 82%, while satisfying both k-coverage and k-connectivity requirements. Unlike conventional strategies based on idealized assumptions, our approach is tailored to the real-world conditions of Colombian underground mines. Simulation results demonstrate that achieving connectivity demands more nodes than coverage when both are evaluated under equivalent conditions. The algorithm was validated in two prototype mines, achieving k = 2 for both coverage and connectivity—ensuring that each point of interest is monitored by at least two sensors and connected via two link-disjoint communication paths. This redundancy enhances fault tolerance by providing backup for both sensing and communication. Finally, we introduce two new metrics for evaluating k-coverage and k-connectivity, designed to assess the robustness of WSN in underground mining and tunnel environments.