The paper addresses the challenges of queue stability and dynamic throughput maximization in multi-agent heterogeneous wireless networks, particularly in the context of the Industrial Internet of Things (IIoT). The authors propose a distributed power control algorithm that leverages the wireless channel's nature to approximate the centralized maximum-weight scheduling (MWS) algorithm. This approach uses ubiquitous multi-protocol mobile devices as intermediaries to improve interoperability and facilitate data relay, translation, and forwarding from end IoT devices. The focus is on large-scale network stability and queue management, formulating a long-term time-averaged optimization problem that incorporates end-to-end rate control, routing, link scheduling, and resource allocation to ensure network-wide throughput. A real-time decomposition-based approximation algorithm is presented to ensure adaptive resource allocation, queue stability, and meeting Quality of Service (QoS) constraints with high energy efficiency. Numerical results demonstrate significant energy efficiency improvements across diverse traffic models, maintaining throughput requirements for both uniform and hotspot User Equipment (UE) distribution patterns. The work offers a comprehensive solution to enhance IIoT performance and address evolving challenges in industrial applications.The paper addresses the challenges of queue stability and dynamic throughput maximization in multi-agent heterogeneous wireless networks, particularly in the context of the Industrial Internet of Things (IIoT). The authors propose a distributed power control algorithm that leverages the wireless channel's nature to approximate the centralized maximum-weight scheduling (MWS) algorithm. This approach uses ubiquitous multi-protocol mobile devices as intermediaries to improve interoperability and facilitate data relay, translation, and forwarding from end IoT devices. The focus is on large-scale network stability and queue management, formulating a long-term time-averaged optimization problem that incorporates end-to-end rate control, routing, link scheduling, and resource allocation to ensure network-wide throughput. A real-time decomposition-based approximation algorithm is presented to ensure adaptive resource allocation, queue stability, and meeting Quality of Service (QoS) constraints with high energy efficiency. Numerical results demonstrate significant energy efficiency improvements across diverse traffic models, maintaining throughput requirements for both uniform and hotspot User Equipment (UE) distribution patterns. The work offers a comprehensive solution to enhance IIoT performance and address evolving challenges in industrial applications.