Forecast-driven task offloading for reliable and adaptive mobile edge computing

The global drone market has grown from US$4 billion in 2023 to a projected US$4.8 billion by 2029. Concomitant with this growth, integrating drones into Mobile Edge Computing (MEC) environments poses critical challenges in scalability, resource allocation, and latency-aware distributed processing, which directly affect overall system performance. Addressing these issues through physical prototyping is costly and complex, which motivates the use of analytical models that can predict system behavior under different conditions. In this work, we propose a performance evaluation approach based on Stochastic Petri Nets (SPNs) to model the admission, load balancing, and distributed task processing among drones acting as mobile edge nodes. The proposed model enables resource scaling and scalability analysis without physically implementing the architecture, reducing deployment risks and costs. Simulation results demonstrate that the model accurately captures key performance metrics, including Mean Response Time (MRT), throughput, and task drop rate, providing quantitative insights for designing efficient and resilient UAV-supported MEC systems.

The proliferation of distributed energy resources (DERs) and the ubiquity of Internet of Things (IoT) devices are driving the integration of mobile edge computing (MEC) into smart grids. This convergence enables real-time data processing for prosumers but introduces a complex cyber-physical coupling: computational offloading decisions directly impact local energy consumption, thereby altering the prosumer’s status in the peer-to-peer (P2P) energy market. Conversely, dynamic market prices influence the economic viability of offloading. This paper addresses the joint optimization of computational task offloading and P2P energy trading in an edge-assisted smart grid ecosystem. We formulate the problem as a mixed-integer nonlinear programming (MINLP) model aimed at maximizing long-term system utility, balancing throughput, latency, and economic incentives under strict edge server capacity and community energy neutrality constraints. To tackle the curse of dimensionality and system stochasticity, we propose a hybrid framework combining Deep Q-Networks (DQN) with a constraint-aware heuristic mechanism. The DQN agent learns adaptive offloading policies from high-dimensional states, while a deterministic rule-based layer ensures strict adherence to community energy balance. Simulation results based on real-world solar generation and market data demonstrate that our proposed method outperforms baseline strategies—including local-only execution and greedy heuristics—improving average utility by 12.3% and reducing task delay by 16.5%, while maintaining robust operational feasibility.

This paper proposes a federated learning (FL) framework that incorporates adaptive gradient compression and dynamic aggregation to address communication efficiency and data privacy issues in the context of FL with limited sample size and non-IID data distributions in edge devices and resource-scarce environments. This proposed framework incorporates dynamic gradient compression techniques that compress gradients based on their magnitude and variance to ensure high communication efficiency with minimal loss in model accuracy. Meanwhile, the proposed framework incorporates dynamic aggregation techniques that assign different weights to clients based on their reliability to ensure effective model convergence in heterogeneous and scarce data distributions. Data privacy in the proposed framework is ensured through secure aggregation and Differential Privacy (DP) techniques. Experimental results on various datasets, including LEAF, FEMNIST, Reddit, and Shakespeare, show that the proposed framework ensures communication efficiency of over 70%, preserves model accuracy with minimal loss at 1–2%, and achieves 30% faster convergence speed compared to traditional FL techniques. These results show that the proposed framework is applicable in real-world scenarios in mobile edge computing and IoT applications, where communication efficiency and data privacy are significant factors for model convergence and deployment. The combination of gradient compression and dynamic aggregation in FL with strong privacy guarantees makes this framework a powerful tool for model convergence in heterogeneous scenarios.

Sixth-generation (6G) networks are likely to support advanced Internet of Vehicles (IoV) applications that have rigid latency, reliability, and computation demands. Nevertheless, efficient task offloading is a challenging problem because vehicle environments are characterized by mobility, changing channels, varying task requirements, constrained edge resources, and growing energy demands. To address these issues, this study presents an Optimized Multi-Tier Task Offloading Strategy (OMTOS) for sustainable IoV systems. The proposed framework comprises a four-tier computing architecture comprising vehicles, roadside units (RSUs), mobile edge computing (MEC) servers, and cloud infrastructure. The generalized latency-energy optimization problem is formulated to allocate tasks across these levels, accounting for task due dates, resource capacity, communication delay, computation delay, and energy consumption. To address dynamic offloading, OMTOS employs a centralized training and decentralized execution (CTDE) based multi-agent Soft Actor-Critic (SAC) method, where the vehicle agents can make decentralized offloading decisions with centralized critics guiding the coordinated learning process during training. It is tested against rule-based and heuristic as well as deep reinforcement learning and various multi-agent reinforcement learning baselines, including LE, EO, RO, GO, DQN, DDPG, SAC, MADDPG, and MAPPO. The aforementioned results reveal that OMTOS achieves low average delay, low energy consumption, a high task success rate, and high convergence compared to the competing methods. Sensitivity analysis also indicates that the latency and energy weightings can be changed to suit various IoV service requirements, including delay-critical safety services, and energy-conscious delay-tolerant services. These results show that OMTOS offers an adaptive and sustainable task-offloading tool in 6G-enabled IoV environments.

Currently, multiple unmanned aerial vehicles (UAVs) can cooperatively work as mobile edge computing (MEC) servers in the sky to provide computation services to ground terminals (GTs). Such an MEC-enabled multi-UAV system will greatly benefit the GTs, each of which can offload its tasks on demand to a nearby UAV. In particular, if a GT has to process computation-intensive deep learning tasks in a catastrophic environment, it can partially offload these tasks to UAVs using a scheme like Partial Program Offloading (PPO). This ensures the quick processing of the deep learning tasks while saving computing resources on both the GT and UAV sides. Nevertheless, UAV–GT offloading links are frequently blocked by ground obstacles in complicated environments, and individual UAVs may have limited computation capacity. Moreover, UAVs lack a constant propulsion energy supply to sustain a long mission time. All these factors lead to a degraded Quality of Service (QoS) for GTs in terms of task latency. To address this issue, we propose to jointly optimize the UAV trajectories, computing resource allocation, and the partial offloading of deep learning tasks. The formulated joint optimization problem is challenging to solve optimally, as it is non-convex and involves multiple coupled constraints. We propose utilizing the Successive Convex Approximation (SCA) method alongside a Block Coordinate Descent (BCD) approach to tackle this joint problem. Numerical results demonstrate that the proposed joint optimization scheme significantly outperforms the benchmark solutions.

Cooperative path scheduling and resource allocation for LEO satellite-enabled mobile edge computing network

Access selection and service placement in mobile edge computing networks

Digital twin is emerging as a key technology to monitor the status of complex industry systems. Valuable insights, such as running statuses and anomalies, can be analyzed from the collected system status timely. Considering that the data updating from each system component (known as a physical object) to its digital twin is performed continuously, timely and accurate streaming analytics based on machine learning models is a key technology to analyze such data efficiently. In this paper, we focus on enabling low-delay yet highly-accurate streaming analytics for digital twin applications in mobile edge computing (MEC) networks. Specifically, we formulate a fundamental optimization problem of digital twin placements and model selections for streaming analytics, with the aim of minimizing both the analytic loss and the processing delay. To this end, we first consider the problem with a single query, for which, we propose an approximation algorithm with provable approximation ratio for a special case, and then devise an efficient algorithm for the original problem with a single query. We then study the online digital twin placement and model selection problem for streaming analytics with multiple queries under real scenarios, where resource demands of arrival queries and resource availability of MEC network are uncertain. We propose an online learning algorithm with a bounded regret to make admission policies. We finally evaluate the performance of the proposed algorithms by extensive simulations. Results show that the weighted sums of the total processing delay and the cumulative loss in the solution delivered by the proposed algorithms outperform their counterparts by 12.5% with a single query and 13.3% with multiple queries, respectively.

Enhancing computer education through IoT-Enabled learning environments leveraging mobile edge computing for real-time feedback

Classifying encrypted sensor traffic is critical for the security and management of Internet of Things networks, particularly in Mobile Edge Computing (MEC) environments. Existing methods often require extensive task-specific labeled data to adapt to emerging traffic categories and may also fail to distinguish intrinsic traffic behaviors from patterns introduced by shared communication libraries, which can degrade classification accuracy under distribution shifts. To address these issues, we propose CDTF, a contrastive dual-task framework for transferable and few-shot traffic representation learning. CDTF adopts a hybrid pre-training strategy that jointly optimizes supervised triplet pretraining (STP) and self-supervised dynamic burst masking (DBM). STP uses base-class labels as structural anchors to explicitly constrain distance relationships by aligning intra-class samples and separating inter-class samples, thereby mitigating interference from shared network components. DBM models global semantic structures and enhances the robustness of traffic representations against network noise and distribution shifts. By learning discriminative and contextual representations in a shared embedding space via these two tasks, CDTF can rapidly adapt to novel categories through lightweight fine-tuning, thereby substantially reducing the reliance on large-scale fine-grained supervision in downstream tasks. Experimental results across seven public and two custom datasets, across diverse environments, show that the proposed framework outperforms state-of-the-art methods. Under the few-shot setting, CDTF improves Precision by 4.61 percentage points over the strongest baseline, with statistical significance confirmed by a paired t-test ().

HighlightsWhat are the main findings?A training-free geometric method is proposed for LiDAR point cloud registration on resource-constrained edge platforms.The modular pipeline synergistically combines asymmetric candidate expansion with uncertainty-aware refinement to effectively handle sensor noise and sparse correspondences.What are the implications of the main findings?The framework provides a plug-and-play solution for real-time robotics perception, bridging the gap between theoretical accuracy and practical deployment on low-power hardware.The results validate that optimized geometric methods remain superior in interpretability and generalization for industrial LiDAR sensing under challenging low-overlap conditions.Accurate LiDAR point cloud registration on resource-constrained edge platforms is a prerequisite for intelligent robotics and industrial automation, yet it remains challenging because low-overlap matching, false correspondences, and fine alignment must be handled under limited computing budgets without GPU acceleration. While learning-based methods have advanced the field, their heavy hardware dependency and training requirements often hinder their practical deployment on mobile edge devices. To bridge this gap, this paper proposes GeoRescue, a training-free geometric registration framework designed for high-precision perception under stringent hardware limits. The method consists of three modular stages: Asymmetric Correspondence Expansion (ACE), which enlarges the candidate correspondence set to reduce the loss of true matches; Dynamic Geometric Topology Gating (DGTG), which suppresses false matches through distance-consistency-based hypothesis filtering; and Uncertainty-Aware Manifold Refinement (UAMR), which improves fine alignment by explicitly modeling local anisotropic noise via covariance-guided optimization. Experiments on 3DMatch, 3DLoMatch, and KITTI show that GeoRescue achieves registration recall rates of 84.84% and 41.27%, respectively, and a 94.95% success rate on KITTI. Remarkably, the framework matches the accuracy of high-capacity learning models while running on a GPU-free, 15 W edge CPU platform (Intel Core i5-8265U). These results indicate that GeoRescue provides a deployment-ready solution with an optimal efficiency–accuracy trade-off for LiDAR sensing and robotics perception in complex, real-world scenarios.

The deployment of Unmanned Aerial Vehicles (UAVs) in conjunction with Mobile Edge Computing (MEC) has come to be a viable approach to solve some challenges that face the internet of things systems, including energy consumption, latency, and data processing efficiency. However, trajectory planning optimization for UAVs in the MEC systems remains a challenging issue due to energy restrictions. This study introduces a trajectory planning algorithm, Fungal Growth–Differential Evolution (FGODE), seeking to minimize overall energy consumption without compromising task offloading efficiency and UAV mobility. The approach employs a hybrid optimization algorithm that combines the Fungal Growth Optimizer (FGO) and Differential Evolution (DE) algorithms to effectively maintain between searching new regions and refining promising solutions. The method also utilizes an optimized population size-based encoding mechanism to properly represent candidate solutions. Furthermore, a low-complexity greedy mechanism is employed to sequence the stop points along each UAV’s trajectory, while elite opposition-based learning and Gaussian mutation are utilized to accelerate convergence and mitigate premature stagnation. Several experiments have been conducted to compare with several algorithms. Experimental findings show that FGODE delivers more competitive results than state-of-the-art algorithms across several performance metrics, displaying higher optimization capability.

The underlying challenges in the wearable electronic market are the limited power and processing capability often resulting in failure to handle complex computations. In order to balance the computational demands with resource constraints, the potential of intelligent reflecting surfaces (IRSs) are exploited in this paper. The paper presents a wearable electronics network in which the wearable nodes communicate with the assistance of double faced active (DFA) IRSs. By simultaneously controlling reflection and transmission links with active amplification, DFA-IRS enables reliable mobile edge computing (MEC)-based task offloading from wearable devices to nearby processing nodes. A resource utilization (RU) algorithm is proposed that associates the devices with DFA-IRSs. The optimal phase shifts of DFA-IRSs are obtained. Further, the impact of transmit power [Formula: see text], number of DFA-IRSs [Formula: see text], number of DFA-IRS elements N, per element amplification [Formula: see text], power budget [Formula: see text] on the average sum rate of the system is evaluated. It is observed that the DFA-IRS aided system offers average sum rate of 8.2bps/Hz with N =120 and [Formula: see text] of 20dBm with optimal phase shifts [Formula: see text] and [Formula: see text] in the reflection and transmission space respectively. Also, there is an improvement of 5.80% in average sum rate over random phase shifts. The comparison with conventional IRS, single faced active (SFA)-IRS and simultaneously transmitting and reflecting (STAR) IRS is also presented. In the end, the use case of proposed network for personalized healthcare is discussed.

Nowadays, farmers across the globe are gradually adopting intelligent farming, which is facilitated by a variety of cutting-edge technologies. The advancement of intelligent farming applications is greatly aided by the internet of farming things (IoFT). Massive IoFT devices generally possess constrained resources, making it challenging to meet the battery and computational requirements of intelligent farming applications through local computation. RF energy harvesting enabled mobile edge computing (RFE-MEC) addresses this issue by harvesting RF energy from an access point, offloading and computing tasks at the edge in a nearby access point. In the proposed scheme, multiuser nonorthogonal multiple access allows the IoFT devices to simultaneously offload computationally intensive tasks to the MEC server for processing. The delay outage probability closed-form expression is formulated for the RFE-NOMA-MEC intelligent farming system under a Rayleigh fading channel. The impact of imperfect channel state information on the RFE-NOMA-MEC is considered. Tunicate enhanced northern goshawk optimization algorithm (TNGO) has been proposed to discover the optimal parameter set to minimize delay outage probability. The results indicate that the system performance is enhanced using TNGO when the optimal time switching factor, power allocation coefficient and task allocation ratio are utilized.

Ultra-high-speed railway communication systems face several technical challenges due to extremely high mobility, including Doppler-induced channel variations, frequent handovers, and increasing network traffic. These challenges not only degrade communication reliability but also negatively affect the efficiency of network resource utilization. In this paper, we review the key technical challenges in ultra-high-speed railway communication environments and investigate artificial intelligence (AI)-based intelligent network control techniques to address these issues. In particular, we examine mobility management approaches focusing on AI-based predictive handover schemes and intelligent network control architectures based on the Open Radio Access Network (O-RAN). In addition, network resource management strategies are discussed through mobile edge computing (MEC)-enabled traffic offloading and task migration techniques. Through this analysis, we discuss the potential applicability of intelligent network control technologies for improving communication reliability and enhancing network resource utilization efficiency in ultra-high-speed railway communication environments.

Applying Computational Intelligence in medical forensics.

Computation offloading at lower time and lower energy consumption is crucial for resource-constrained mobile devices. This paper proposes an offloading decision-making model using federated learning. Based on the device configuration, task type, and input, the proposed decision-making model predicts whether the task is computationally intensive or not. If the predicted result is computationally intensive , then based on the network parameters the proposed decision-making model predicts whether to offload or locally execute the task. The experimental results show that the proposed method achieves above 90 % prediction accuracy in offloading decision-making, and reduces the response time and energy consumption of the user device by ∼ 11-31 %. A secure partial computation offloading method for federated learning is also proposed to deal with the Straggler effect of federated learning. The results present that the proposed partial computation offloading method for federated learning has achieved a prediction accuracy of above 98 % for the global model.

Effective task offloading among multiple self-interested service providers in mobile edge computing

Deadline-Aware joint optimization of task offloading and resource allocation for cell-Free mobile edge computing networks