With the rapid development of Internet of Things (IoT) technology, Terminal Devices (TDs) are more inclined to offload computing tasks to higher-performance computing servers, thereby solving the problems of insufficient computing capacity and rapid battery consumption of TD. The emergence of Multi-access Edge Computing (MEC) technology provides new opportunities for IoT task offloading. It allows TDs to access computing networks through multiple communication technologies and supports more mobility of terminal devices. Review studies on IoT task offloading and MEC have been extensive, but none of them focus on IoT task offloading in MEC. To fill this gap, this paper provides a comprehensive and in-depth understanding of the algorithms and mechanisms of multiple IoT task offloading in the MEC network. For each paper, the main problems solved by the mechanism, technical classification, evaluation methods, and supported parameters are extracted and analyzed. Furthermore, shortcomings of current research and future research trends are discussed. This review will help potential and new researchers quickly understand the panorama of IoT task offloading approaches in MEC and find appropriate research paths.

Utilizing the Kolmogorov–Arnold representation theorem, KANs have emerged as a mathematically rigorous and easily interpretable alternative to traditional neural networks. These networks decompose high-dimensional functions into sums of univariate continuous functions using adaptive activation functions. Compared to MLPs, KANs exhibit superior or comparable performance in accuracy, parameter efficiency, and interpretability. Applications highlight the advantages of KANs in solving complex partial differential equations with enhanced convergence and uncertainty quantification, modeling dynamic systems in a meaningful manner, and making reliable forecasts in the areas of power systems, environmental monitoring, and demand prediction. Based on current research on KANs, they demonstrate a promising frontier in interpretable deep learning, with increasing influence across numerous interdisciplinary scientific and engineering fields.

The rapid development of Internet of Things (IoT) technology has led to a significant increase in the computational task load of Terminal Devices (TDs). TDs reduce response latency and energy consumption with the support of task-offloading in Multi-access Edge Computing (MEC). However, existing task-offloading optimization methods typically assume that MEC’s computing resources are unlimited, and there is a lack of research on the optimization of task-offloading when MEC resources are exhausted. In addition, existing solutions only decide whether to accept the offloaded task request based on the single decision result of the current time slot, but lack support for multiple retry in subsequent time slots. It is resulting in TD missing potential offloading opportunities in the future. To fill this gap, we propose a Two-Stage Offloading Decision-making Framework (TSODF) with request holding and dynamic eviction. Long Short-Term Memory (LSTM)-based task-offloading request prediction and MEC resource release estimation are integrated to infer the probability of a request being accepted in the subsequent time slot. The framework learns optimized decision-making experiences continuously to increase the success rate of task offloading based on deep learning technology. Simulation results show that TSODF reduces total TD’s energy consumption and delay for task execution and improves task offloading rate and system resource utilization compared to the benchmark method.

In recent years, task offloading and its scheduling optimization have emerged as widely discussed and significant topics. The multi-objective optimization problems inherent in this domain, particularly those related to resource allocation, have been extensively investigated. However, existing studies predominantly focus on matching suitable computational resources for task offloading requests, often overlooking the optimization of the task data transmission process. This inefficiency in data transmission leads to delays in the arrival of task data at computational nodes within the edge network, resulting in increased service times due to elevated network transmission latencies and idle computational resources. To address this gap, we propose an Asynchronous Data Transmission Policy (ADTP) for optimizing data transmission for task offloading in edge-computing enabled ultra-dense IoT. ADTP dynamically generates data transmission scheduling strategies by jointly considering task offloading decisions and the fluctuating operational states of edge computing-enabled IoT networks. In contrast to existing methods, the Deep Deterministic Policy Gradient (DDPG) based task data transmission scheduling module works asynchronously with the Deep Q-Network (DQN) based Virtual Machine (VM) selection module in ADTP. This significantly reduces the computational space required for the scheduling algorithm. The continuous dynamic adjustment of data transmission bandwidth ensures timely delivery of task data and optimal utilization of network bandwidth resources. This reduces the task completion time and minimizes the failure rate caused by timeouts. Moreover, the VM selection module only performs the next inference step when a new task arrives or when a task finishes its computation. As a result, the wastage of computational resources is further reduced. The simulation results indicate that the proposed ADTP reduced average data transmission delay and service time by 7.11% and 8.09%, respectively. Furthermore, the task failure rate due to network congestion decreased by 68.73%.

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