Collaborative Service Provisioning in IIoT Systems via Service Urgency and Situation-Adaptive Goal Modeling: A Dynamic Service–Energy Tradeoff

Efficient use of scarce communication and computing resources is critical for meeting the diverse requirements of vertical industrial applications in the Industrial Internet of Things (IIoT). However, the randomness of concurrent service arrivals and the diversity of service demands result in dynamic competition for limited resources at a specific time, presenting significant challenges for effective service provisioning in IIoT systems with changing conditions. To address this challenge, this article investigates a collaborative service provisioning scheme that incorporates service urgency and situation-adaptive system goals to meet diverse vertical applications. Specifically, a novel concept, urgency of service, is introduced to characterize the sensitivity of service to limited resources, thereby mitigating resource competition by prioritizing concurrent services. To cope with system uncertainty, we design a situation-adaptive system goal that enables a dynamic tradeoff between service demands and system energy consumption. For this purpose, we develop a comprehensive metric that integrates the Value of Service (VoS) and system energy requirements, termed VoSE, to customize the time-varying system goals. The overall goal is to maximize the long-term VoSE, which is formulated as a mixed-integer nonlinear programming (MINLP) problem. Since it is NP-hard, we decompose it into a service provisioning subproblem (SPP) and a dynamic system goal subproblem (DGP). A reverse auction-based and urgency-driven service provisioning algorithm is first developed to solve the SPP. Furthermore, a dynamic system operation goal determination algorithm based on the VoSE ratio is proposed for the DGP. Extensive simulation results validate the effectiveness of the proposed algorithm in various performance parameters and demonstrate its significant superiority over baseline schemes.