Shapley value is a widely used tool in explainable artificial intelligence (XAI), as it provides a principled way to attribute contributions of input features to model outputs. However, estimation of Shapley value requires capturing conditional dependencies among all feature combinations, which poses significant challenges in complex data environments. In this article, EmSHAP (Energy-based model for Shapley value estimation), an accurate Shapley value estimation method, is proposed to estimate the expectation of Shapley contribution function under the arbitrary subset of features given the rest. By utilizing the ability of energy-based model (EBM) to model complex distributions, EmSHAP provides an effective solution for estimating the required conditional probabilities. To further improve estimation accuracy, a GRU (Gated Recurrent Unit)-coupled partition function estimation method is introduced. The GRU network captures long-term dependencies with a lightweight parameterization and maps input features into a latent space to mitigate the influence of feature ordering. Additionally, a dynamic masking mechanism is incorporated to further enhance the robustness and accuracy by progressively increasing the masking rate. Theoretical analysis on the error bound as well as application to four case studies verified the higher accuracy and better scalability of EmSHAP in contrast to competitive methods.

The efficiency of production and equipment maintenance costs in the Industrial Internet of Things (IIoT) are directly impacted by equipment lifetime, making it an important concern. Mobile edge computing (MEC) can enhance network performance, extend device lifetime, and effectively reduce carbon emissions by integrating energy harvesting (EH) technology. However, when the two are combined, the coupling effect of energy and the system's communication resource management pose a great challenge to the development of computational offloading strategies. This paper investigates the problem of maximizing the energy efficiency of computation offloading in a two-tier MEC network powered by wireless power transfer (WPT). First, the corresponding mathematical models are developed for local computing, edge server processing, communication, and EH. The proposed fractional problem is transformed into a stochastic optimization problem by Dinkelbach method. In addition, virtual power queues are introduced to eliminate energy coupling effects by maintaining the stability of the battery power queues. Next, the problem is then resolved through the utilization of both Lyapunov optimization and convex optimization method. Consequently, a wireless energy transmission-based algorithm for maximizing energy efficiency is proposed. Finally, energy efficiency, an important parameter of network performance, is used as an indicator. The excellent performance of the EEMA-WET algorithm is verified through extensive extension and comparison experiments.

In the Internet of Everything era, various Internet of Things (IoT) devices have become popular, and the number of computing-intensive applications has increased substantially. As an emerging technology, mobile-edge computing (MEC) gives network edge nodes stronger computing and storage capabilities, bringing users a good Quality of Experience (QoE). By offloading some computing tasks to the edge for processing, the burden on IoT devices can be effectively reduced. However, this approach exacerbates the computing and storage resource depletion of the MEC server and the bandwidth and transmission cost of the wireless link used to offload computing tasks. Additionally, making an offloading decision online without future system status information is a considerable challenge. Therefore, we should study and design a reasonable offloading strategy to reduce the additional overhead, which is of significance. We establish a virtual queue model to describe the workload offloading problem of IoT devices in a two-layer MEC network. This is a stochastic optimization problem. Based on Lyapunov optimization, we transform the research problem into a deterministic optimization problem. A Lyapunov online energy consumption optimization algorithm (LOECOA) is proposed to effectively balance the system’s queue backlog and energy consumption. Based on theoretical analysis and a large number of experimental and numerical results, our algorithm performs better on energy consumption while satisfying the system constraints under a dynamic task arrival rate.