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.

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 ().

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.

Energy-aware trajectory and resource management for NOMA-enabled MEC in UAV-based airborne maneuvering networks: A PPO-driven approach

Bounty hunter optimizer: A novel metaheuristic with an application to multi-UAV mobile edge computing and path planning

Mobile Edge Computing (MEC) has become increasingly critical for latency-sensitive applications, including Augmented/Extended Reality (AR/XR), Cloud Gaming, Real-Time Video Analytics, and Interactive Enterprise Services. Existing edge steering mechanisms remain largely reactive by relying on static policies, nearest-edge selection, or compute-only information that usually fail under user mobility, fluctuating radio conditions, dynamic user-plane paths, and edge resource contention rather than being more proactive. This paper presents SCONE-AEGIS framework that extends the Standard Communication with Network Elements (SCONE) paradigm beyond throughput advisories to support joint network-compute steering of MEC applications. SCONE-AEGIS introduces an Edge Steering Advice (ESA) that communicates recommendations that can be consumed by the applications, which have been derived from a combination of RAN, UPF, and MEC telemetry. The framework is a combination of a two-stage AI/ML engine, the first being a Spatio-Temporal Graph Predictor that is uncertaintyaware and models the evolving relationships among radio access network nodes, user-plane functions, edge sites, and mobile users, and the second stage is a Safe Contextual Bandit Steering Policy (SCBSP) that selects execution sites subject to SLA constraints, migration hysteresis, and prediction confidence. The proposed framework provides a standards-compatible, privacypreserving path for exposing joint network-compute intelligence to applications without breaking transport encryption.

Mobile Edge Computing (MEC) plays a key role in supporting latency-sensitive Internet of Things (IoT) services by enabling computation to be performed closer to end devices.However, accurate task offloading, which involves deciding whether a task should be executed locally on the mobile device or offloaded to an edge cloud remains challenging due to rapidly varying network quality, fluctuating traffic demand, heterogeneous access technologies and time-varying edge resource availability.Conventional rule-based and simplified optimization strategies struggle to adapt to such environments and often fail to capture complex, nonlinear dependencies among system variables.To address these challenges, this study proposes a hybrid Deep Cross Network-TabNet framework for intelligent task offloading decision-making, formulated as a supervised binary classification problem that predicts the execution mode (Edge Cloud vs. Mobile Device) from heterogeneous tabular inputs.The model fuses two complementary learners: a Deep & Cross Network (DCN) branch that explicitly models bounded-order feature interactions using three cross layers alongside a deep multilayer perceptron and a TabNet branch that performs instance-wise sparse feature selection through sequential attention masks across five decision steps with a 32-dimensional feature transformer.Representations from both branches are concatenated and passed to a fusion head with a sigmoid output.Experiments on a publicly available dataset demonstrate excellent predictive performance, achieving 0.986 test accuracy with weighted precision, recall and F1 score of 0.986 and ROC-AUC of 0.994.Comparative evaluation against strong tabular baselines confirms the incremental advantage of the proposed hybrid architecture.Beyond classification metrics, an objective-based evaluation using a principled latency-energy cost model shows that the proposed approach reduces the cost gap relative to a derived cost-optimal policy compared with the observed execution behavior.Ablation confirms the hybrid advantage over standalone baselines and stratified 5-fold cross-validation reports 0.986 0.001 mean accuracy, indicating stable generalization within the evaluated setting.

The deep integration of mobile Internet and artificial intelligence (AI) is reshaping tourism services, with mobile virtual tourism assistants enhancing travel experiences. However, existing systems have limitations in interaction paradigms, contextual awareness, and cross-cultural adaptability while struggling to balance real-time and lightweight performance and service depth on mobile platforms. To address these challenges, a mobile virtual tourism assistant optimized by machine learning (ML) was proposed, focusing on adaptive interaction and cross-cultural performance optimization. A three-layer end–to–edge collaborative architecture—comprising data perception, intelligent processing, and application presentation—was constructed to accommodate multimodal inputs and the resource constraints inherent to mobile devices. Three core technical innovations were introduced. First, an end-edge collaborative multimodal adaptive interaction mechanism was developed, transitioning from passive question-answering to proactive service delivery through lightweight hybrid dialogue management and contextual prediction algorithms. Second, a device behavior– driven cross-cultural ML model was established, with quantifiable cultural feature vectors and adaptive interface generation logic constructed, supporting dynamic cross-cultural service adaptation. Third, a joint optimization model integrated with mobile edge computing (MEC) was designed, incorporating a real-time replanning algorithm to balance itinerary personalization with mobile resource efficiency. This study bridges the gap between deep personalized interaction on mobile platforms and systematic cross-cultural evaluation, providing technical foundations and theoretical insights for the global deployment of intelligent mobile tourism services.

In mobile edge computing (MEC), frequent server handovers due to user mobility increase latency and degrade quality of service (QoS). This study enhances MEC service stability by predicting user mobility for efficient server transitions. The proposed spacio-temporal (ST)-Mamba model combines Mamba (state-space encoder) and a gated recurrent unit (GRU) in parallel to capture both long-term and short-term dependencies, while Fourier feature embedding enriches spatial-temporal representation. Experiments show that ST-Mamba achieves about 9–10% lower root mean square error (RMSE) and mean absolute error (MAE) than long short-term memory (LSTM), GRU, and Transformer baselines, with statistically significant improvements confirmed by Welch’s t-test. These results demonstrate that hybrid state space model (SSM)–RNN architectures are promising for mobility-aware QoS optimization in MEC, with future work extending to real-world and multi-user settings.

Mobile edge computing (MEC) has become a key paradigm for supporting latency-sensitive and bandwidth-intensive applications. However, existing server recommendation methods rely on static heuristics and lack adaptability to dynamic environments with incomplete quality of service (QoS) data. This study aims to address these limitations by enabling adaptive and context-aware server recommendations that effectively manage user mobility and missing QoS information in real time. We propose an intelligent MEC server recommendation framework built on a multi-agent architecture spanning mobile, edge, and cloud layers. The mobility layer predicts user movement, the edge layer performs LLM-based decision-making, and the cloud layer imputes QoS through multi-source data fusion. Lightweight gRPC and WebSocket protocols ensure scalability across multi-user environments. Experiments demonstrate that the proposed system outperforms the baseline, achieving 85% Top-1 accuracy and confirming its effectiveness and scalability for real-world MEC applications.

Mobile edge computing (MEC) allows ground vehicles (GVs) to offload computationally intensive tasks to edge servers, offering advantages over centralized cloud computing by reducing the energy consumption and network congestion. However, the dynamic nature of communication links often results in suboptimal offloading performance due to signal occlusion and interference. Reconfigurable intelligent surface (RIS) technology, a promising component of sixth-generation (6G) communication networks, can enhance wireless network capabilities by modifying the phase and amplitude of reflective components. In this paper, we propose a RIS-assisted MEC strategy to provide a distributed edge intelligence (DEI) solution for systems providing fast wireless connectivity and low latency to ground vehicles in dynamic environments. The RIS-assisted vehicular networks model has recently showed promising results when the delay was minimized by jointly optimizing the computation offloading strategy and the RIS phase shift. The delay optimization problem was modeled as a markov decision process (MDP), and a delay minimization algorithm (DDPG-DM) based on deep reinforcement learning (DRL) was proposed. Simulation results demonstrate that the proposed algorithm significantly outperforms existing non-RIS learning algorithms and classical methods, achieving superior performance in reducing delay. The findings suggest that integrating RIS with MEC can substantially improve the efficiency of computation offloading in dynamic vehicular environments.