In mission-critical IoT-MEC environments, jointly optimizing service placement and resource allocation is intractable due to the high-dimensional coupling of discrete topological decisions with continuous resource dimensioning. Furthermore, traditional methods oversimplify dependencies, overlooking multi-source “Wait-for-All” synchronization and the stochastic variance of bursty workloads. To bridge these gaps, we propose HyDK, a variance-aware framework synergizing Deep Reinforcement Learning (DRL) with convex optimization. The core innovation is our Action Space Pruning mechanism. We theoretically decompose the hybrid decision space by solving the continuous sub-problem to optimality via a KKT-based convex optimization routine. This acts as a deterministic optimality backstop, effectively pruning continuous dimensions and allowing the agent to focus exclusively on the complex discrete topological search. To address physical realities, we construct a finegrained Directed Acyclic Graph (DAG) model to capture data aggregation bottlenecks and integrate an M/G/1 queuing model incorporating the second moment of service time to mitigate longtail latency risks. Trace-driven simulations using the Edge-IIoTset demonstrate that HyDK improves system responsiveness by up to 25.6% with significantly tighter confidence intervals compared to existing baselines.

Federated learning (FL) provides a decentralized framework that enables universal model training through collaborative efforts on mobile nodes, such as smart vehicles in the Internet of Vehicles (IoV). Each smart vehicle acts as a mobile client, contributing to the process without uploading local data. This method leverages nonindependent and identically distributed (non-IID) training data from different vehicles, influenced by various driving patterns and environmental conditions, which can significantly impact model convergence and accuracy. Although client selection can be a feasible solution for non-IID issues, it faces challenges related to selection metrics. Traditional metrics evaluate client data quality independently per round and require client selection after all clients complete local training, leading to resource wastage from unused training results. In the IoV context, where vehicles have limited connectivity and computational resources, information asymmetry in client selection risks clients submitting false information, potentially making the selection ineffective. To tackle these challenges, we propose a novel long-term client-selection federated learning based on truthful auction (LCSFLA). This scheme maximizes social welfare with consideration of long-term data quality using a new assessment mechanism and energy costs, and the advised auction mechanism with a deposit requirement incentivizes client participation and ensures information truthfulness. We theoretically prove the incentive compatibility and individual rationality of the advised incentive mechanism. Experimental results on various datasets,including those from IoV scenarios, demonstrate its effectiveness in mitigating performance degradation caused by non-IID data.

Federated learning (FL) emerges as a potential solution for enabling multiple terminal devices to collaboratively accomplish computational tasks within an autonomous aerial vehicle (AAV) swarm. However, traditional FL approaches, predicated on synchronous data aggregation, are not feasible for a AAV swarm owing to the inherently variable and dynamic nature of their communication networks compared with terrestrial systems. Furthermore, the data procured by AAVs is often highly heterogeneous, attributable to disparities in deployment environments and device attributes. Considering the distinct flight paths and unique operational conditions encountered by different AAVs, a considerable amount of data remains unlabeled. To tackle the challenges associated with asynchronous operations and the prevalence of unlabeled data, we introduce a novel framework termed semi-synchronous FL with cross-sharpness and layer-freezing (SFedXL), tailored for a AAV swarm. In particular, we devise a cross-sharpness model training strategy aimed at optimizing the utilization of both labeled and unlabeled datasets. Additionally, we propose an innovative semi-synchronous model aggregation protocol, complemented by client-specific layer-freezing and client cluster scheduling, designed to expedite the training process. Our simulation results indicate that the proposed algorithm surpasses current FL methods in terms of object recognition accuracy and communication efficiency, albeit with a tradeoff of increased local computation latency.

Mobile-Edge Cloud Computing (MECC) plays a crucial role in balancing low-latency services at the edge with the computational capabilities of cloud data centers (DCs). However, many existing studies focus on single-provider settings or limit their analysis to interactions between mobile devices (MDs) and edge servers (ESs), often overlooking the competition that occurs among ESs from different providers. This article introduces an innovative two-layer game framework that captures independent self-interested competition among MDs and ESs, providing a more accurate reflection of multi-vendor environments. Additionally, the framework explores the influence of cloud-edge collaboration on ES competition, offering new insights into these dynamics. The proposed model extends previous research by developing algorithms that optimize task offloading and resource allocation strategies for both MDs and ESs, ensuring the convergence to Nash equilibrium in both layers. Simulation results demonstrate the potential of the framework to improve resource efficiency and system responsiveness in multi-provider MECC environments.

In Uncrewed Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) networks, the security of transmission faces significant challenges due to the vulnerabilities of line-of-sight links and potential eavesdropping on two-hop links. This paper addresses these challenges with an innovative Cooperative Secure Transmission and Computation strategy (CSTC), specifically engineered for time-slotted UAV-assisted MEC networks plagued by mobile collusive eavesdroppers. These eavesdroppers significantly bolster their interception capabilities through coordinated and optimized movements, escalating the security threats. To neutralize these risks, the proposed CSTC employs the UAV and remote devices as helper nodes to emit jamming signals, thereby thwarting eavesdropping activities, while simultaneously facilitating the efficient relay of users’ tasks to the base station for advanced processing. The CSTC aims to maximize the sum Secrecy Transmission Rate (STR) satisfying task latency constraints. It involves a joint optimization of UAV trajectory, jamming beamformers, transmit power, and data offloading strategy to expedite task transmission. Additionally, a real-time computation scheduling approach is developed based on a newly defined metric, the Urgency Degree of Users (UDoU), to enhance task processing efficiency. Our extensive simulations validate that the CSTC not only elevates the sum STR but also consistently meets latency constraints, demonstrating its robustness against advanced mobile eavesdropping techniques.

UAV-assisted MEC revolutionizes edge computing by deploying UAVs for real-time data processing in areas lacking infrastructure, supporting a wide range of applications from emergency responses to smart cities. Unlike edge servers, UAVs face substantial computational constraints, necessitating a comprehensive strategy that integrates UAV trajectory with task offloading, caching, and migration. Existing studies often overlook the synergy among these strategies, impacting their overall effectiveness. Furthermore, the focus on content pre-caching overlooks task caching’s critical role in addressing high computational demands with limited UAV resources. This research aims to jointly optimize UAV trajectories and task management strategies, including offloading, caching, and migration. Utilizing the Lyapunov optimization framework, we break down the complex optimization problem into manageable subproblems: UAV placement, user-UAV association, task offloading, scheduling, and bandwidth allocation, addressed iteratively using the Block Coordinate Descent method. Specifically, the scheduling subproblem is transformed into a non-convex quadratically constrained quadratic programming problem, managed effectively through semidefinite relaxation and a probabilistic mapping approach. Our simulations show that this integrated approach significantly boosts system throughput and reduces execution times compared to conventional methods. This study enhances the understanding of the interplay between UAV trajectory planning and task management, offering vital theoretical insights for advancing UAV-assisted MEC systems.

This paper explores an unmanned aerial vehicle (UAV)-assisted wireless sensor network (WSN), in which one UAV-enabled mobile data collector periodically collects data from a set of ground sensor nodes (SNs) to the data center (DC) and then DC transmits the processed data back to a group of ground users to fulfill their diverse needs. To accurately evaluate information freshness, we introduce the Age of Multi-Sensor Association Information (AomaI) metric by incorporating the multi-source and up-downlink aspects. Under this framework, we formulate the optimization problem aiming to minimize the average AomaI for all users. To tackle this non-convex problem, we decompose it into two sub-problems: the SN-side optimization problem and the UAV-side optimization problem. For the first subproblem, we propose parallel optimization and primal-dual methods to obtain the optimal solution. For the second subproblem, we first determine the optimal UAV transmission power, then develop the data processing and results distribution scheduling strategies for the DC, and lastly propose the task-associated genetic algorithm (TAGA) and the improved Nawas-Enscore-Ham (INEH) algorithm to design the UAV’s visiting order. Simulation results demonstrate that uplink and downlink AoI influence each other, and the consideration of up-downlink AoI can effectively enhance the freshness of AomaI.

This paper explores an unmanned aerial vehicle (UAV)-assisted wireless sensor network (WSN), where one UAV-enabled mobile data collector periodically collects data from a set of ground sensor nodes (SNs) to the data center (DC), intending to fulfill the users’ diverse needs. To accurately evaluate information freshness, we introduce the Age of Multi-Source Information (AomsI) metric. Under this framework, we formulate the optimization problem aiming to minimize the average AomsI for all users. To tackle this non-convex problem, we decompose it into two sub-problems: the SN-side optimization problem and the UAV-side optimization problem. For the first subproblem, we propose the parallel optimization method to obtain the optimal solution. For the second subproblem, we first determine the optimal UAV transmission power, and then propose the task-associated genetic algorithm (TAGA) to design the UAV’s visiting order. Simulation results demonstrate that the UAV tends to prioritize the collection of all SN data required by the same user during data collection.

Recent research in price-based Mobile Edge Computing (MEC) has predominantly aimed at maximizing edge server revenue by efficiently allocating computing resources. While this operational approach has certainly strengthened the edge computing industry, the practical deployment scenario's impact on server hardware lifespan has not been fully explored. In this paper, we introduce a novel server pricing strategy that accounts for the influence of CPU temperature on the server's longevity, all while ensuring the quality of service requirements for time-sensitive User Equipments (UEs). Leveraging these insights, we establish an MEC system model with a single MEC server and multiple UEs, which we then formulate as a Stackelberg game. We derive two closed-form solutions considering various UEs' conditions, closely aligning with the expectations of time-sensitive UEs. Our simulation results showcase the effectiveness of our proposed method in safeguarding hardware equipment while minimizing revenue loss.

In response to the escalating data volumes and the pressing need for reduced network latency in Mobile Edge Computing (MEC), this paper delves into the sphere of edge computing. Many MEC platforms are turning to container-based virtualization and leveraging image layering to cut down transmission costs. Amid this shift, applications are growing more complex, composed of multiple tasks and demanding diverse execution environments. However, existing research has mainly concentrated on task scheduling within edge systems, sidelining the vital aspect of preparing runtime environments on MEC servers. To bridge this gap, our paper tackles task scheduling complexities, including data and layer dependencies. It offers an integrated solution that optimizes task scheduling and layer loading across MEC servers, all with the goal of minimizing the total computation completion time. To address this NP-hard problem, we present a heuristic task-scheduling algorithm rooted in the genetic algorithm (GA) and complement it with an in-depth exploration of layer-loading policies. Our experiments conclusively demonstrate the substantial reduction in total computation completion time achievable through this approach.

Although numerous state-of-the-art deep neural networks have recently been proposed for malware classification, effectively detecting malware on a large-scale sample set and identifying zero-day or new malware variants still pose significant challenges. To address this issue, a deep hashing-based malware classification model is designed for malware identification, including two parts: ResNet50-based deep hashing for malware retrieval and voting-based malware classification. Specifically, multiple deep hashing models are developed by extracting the high-layer outputs (feature maps) from the ResNet50 trained with malware gray-scale images in the first part. In this case, to maximize the Hamming distance or dissimilarity among hash values computed with malware samples under different families, a ResNet50-based deep polarized network (RNDPN) is designed to return Top K similar samples. In the second part, we propose a majority-voting and a Hamming-distance-based voting for malware identification according to the retrieved results. The experiment results show that RNDPN outperforms the other six deep hashing models with 97.54% mean average precision (mAP) for malware retrieval when only 40 similar examples are retrieved, where the best results for all deep hashing models are observed with 48 bits hashing code length. Furthermore, the Hamming distance-based voting method implemented with RNDPN demonstrates unparalleled performance in malware classification compared to other models. Notably, it achieves exceptional results in two key aspects: malware classification accuracy with an impressive accuracy rate of 96.5%, and the identification of new or zero-day malware with a commendable accuracy of 85.7%.

The rapid expansion of Mobile Edge Computing (MEC), driven by the escalating data volume and the demand for minimal network latency, underscores the need for efficient data processing. To address the growing complexity of neural networks and applications, segmentation into smaller components (e.g., neural network layers, subnetworks, and subtasks) for parallel computation across diverse nodes is common. However, effective data transmission between these segments necessitates optimized task scheduling among edge servers. Many platforms leverage container-based OS-level virtualization to enhance edge computing efficiency, leveraging container image layers to cut storage and transmission costs. However, previous research predominantly emphasizes task scheduling, overlooking runtime environment preparation on edge servers and potential collaboration among edge nodes. This paper introduces an innovative approach that adeptly manages task data and image layer dependencies collaboratively. It formulates an NP-hard problem: minimizing total computation completion time by jointly determining downlink transmission rate allocation, task-offloading strategies, and layer-loading schemes, allowing for thoughtful decoupling and iterative refinement. The Gray Wolf Optimizer and Cellular Automata are introduced for dynamic task scheduling, complemented by a low-complexity algorithm inspired by the Nawas-Enscore-Ham method. For layer downloading, the paper explores a partial-layer loading policy, considering storage constraints, and establishes a full-layer loading strategy with the Peer-to-Peer mechanism, significantly reducing computational complexity. Rigorous experimental results underscore the remarkable efficacy of these approaches in curtailing total computation completion time, positioning them as benchmarks for comparison against alternative solutions.

Due to high flexibility and ease of deployment, Unmanned Aerial Vehicle (UAV)-enabled mobile edge computing (MEC) has recently emerged to provide services for users to meet the demands of computing-intensive tasks at edge. However, the MEC-server mounted UAV is inappropriate for heavy-computation tasks owing to the limitation of energy supply and hardware cost, which may make for excessively high CPU temperature. To tackle this issue, this paper considers a UAV-and-basestation (BS) hybrid-assisted MEC network, where a hover-fly-hover mode is adopted to facilitate the provisioning of MEC services with the help of BS. Furthermore, a temperature control constraint is introduced to ensure the reliability of CPU at UAV. We aim to minimize the cruise duration of UAV with thermal-aware constraint by jointly optimizing UAV hovering trajectory, task scheduling strategy, and computation-and-communication resource allocation strategy. Although the formulated problem is non-convex, we decouple it into three subproblems and solve them in an iterative manner. Simulation results demonstrate that the proposed algorithm can not only satisfy the CPU temperature constraint but also help save the cruise duration.

As an emerging computing paradigm, cloud-edge collaborative computing (CECC) combines computing resources at the back-end and the edge of the network to provide more flexible service delivery, thus striking a good balance between abundant computing resources and high responsiveness. However, mobile devices (MDs) must make strategic offloading decisions in such an environment. Although existing research has made remarkable progress in computation offloading strategies, most works ignore multi-priority settings in complex application scenarios. In this article, we focus on the impact of multi-priority settings and mixed queue disciplines on offloading decisions in CECC. First, we utilize queueing models to characterize all computing nodes in the environment and establish mathematical models to describe the considered scenario. Second, we formulate offloading decisions of the target MD into three multi-variable optimization problems to investigate the cost-performance tradeoff. Third, we propose numerical algorithms based on the Karush-Kuhn-Tucke conditions to address these problems. Finally, we construct numerical examples, a comparative experiment, and a simulation experiment to demonstrate the effectiveness of our methods. Our work provides important insights into the optimization of computation offloading for MDs in complex application scenarios, which can help achieve a better cost-performance tradeoff in CECC.

As the power density of modern CPUs is gradually increasing, thermal management has become one of the primary concerns for multicore systems, where task scheduling and dynamic voltage/frequency scaling (DVFS) play a pivotal role in effectively managing the system temperature. In this article, we proposeCARTAD, a new reinforcement learning (RL)-based task scheduling and DVFS method for temperature minimization and latency guarantee on multicore systems. The novelty ofCARTADframework is that we exploit the machine learning technique to analyze the applications’ intermediate representations (IRs) generated by a compiler and identify an important feature which is critical for predicting the application’s performance. With the newly explored feature, we construct an RL-based scheduler with the more effective state representation and reward function such that the system temperature can be minimized while guaranteeing applications’ latency. We implement and evaluateCARTADon real platforms in comparison with the state-of-the-art approaches. Experimental results showCARTADcan reduce the maximum temperature by up to 16 °C and the average temperature by up to 10 °C.

Higher communication rates are required given that cloud radio access network (C-RAN) becomes a significant component of 5G wireless communication, yet the problem of using sparse beamforming to maximize the achievable sum rate in the long term subject to transmit power constraints still remains open in C-RAN. Inspired by the success of Deep Reinforcement Learning (DRL) in solving dynamic programming problems, we propose a DRL-based framework for online sparse beamforming in C-RAN. Particularly, the DRL agent is in charge of remote radio head (RRH) activation based on the defined state space, action space, and reward function, and meanwhile makes a decision on transmit beamforming at active RRHs in each decision period. Through simulations, we evaluate the performance of the proposed framework by comparing it with traditional ways and show that it can achieve higher sum rate in time-varying network environment.

Mobile edge computing (MEC) provides users with a high quality of experience (QoE) by placing servers with rich services close to the end users. Compared with local computing, MEC can contribute to energy saving, but results in increased communication latency. In this paper, we jointly optimize task offloading and resource allocation to minimize the energy consumption in an orthogonal frequency division multiple access (OFDMA)-based MEC networks, where the time-sensitive tasks can be processed at both local users and MEC server via partial offloading. Since the optimization variables of the problem are strongly coupled, we first decompose the original problem into two subproblems named as offloading selection (PO), and subcarriers and computing resource allocation (PS), and then propose an iterative algorithm to deal with them in a sequence. To be specific, we derive the closed-form solution for PO, and deal with PSby an alternating way in the dual domain due to its NP-hardness. Simulation results demonstrate that the proposed algorithm outperforms the existing schemes.

In this paper, we investigate offloading scheme and resource allocation strategy for Orthogonal Frequency-Division Multiple Access (OFDMA) based multi-access edge computing (MEC) network to minimize the total system energy consumption. Partial data offloading is studied where mobile date can be computed at both local devices and the edge cloud with the consideration of time-sensitive tasks for users. Since the NP-hardness of the considered optimization problem, we propose an iterative algorithm to decide the proportion of data to offload and design the resource allocation strategy in a sequence. Simulation results show that the proposed algorithm achieves better performance than the reference schemes.

For a user cooperation system with multiple antennas, we consider trust degree-based cooperation techniques to explore the influence of the trustworthiness between users on the communication systems. For the system with two communication pairs, when one communication pair achieves its quality of service (QoS) requirement, they can help the transmission of the other communication pair according to the trust degree, which quantifies the trustworthiness between users in the cooperation. For given trust degree, we investigate the user cooperation strategies, which include the power allocation and precoder design for various antenna configurations. For single-input-single-output and multiple-input-single-output (MISO) cases, we provide the optimal power allocation and beamformer design that maximize the expected achievable rates while guaranteeing the QoS requirement. For a single-input-multiple-output (SIMO) case, we resort to semidefinite relaxation technique and block coordinate update method to solve the corresponding problem, and guarantee the rank-one solutions at each step. For a multiple-input-multiple-output (MIMO) case, as MIMO is the generalization of MISO and SIMO, the similarities among their problem structures inspire us to combine the methods from MISO and SIMO together to efficiently tackle MIMO case. Simulation results show that the trust degree information has a great effect on the performance of the user cooperation in terms of the expected achievable rate, and the proposed user cooperation strategies achieve high achievable rates for give trust degree.