Emerging multimodal systems present new requirements for mobile edge networks to handle multimodal data. In this paper, a novel multimodal mobile edge computing (MEC) framework is proposed, which synchronizes the multimodal data acquisition, communication, and computation to ensure both consistency and efficiency. The key objective is to simultaneously maximize multimodal data throughput and minimize the energy consumption of mobile terminals (MTs) under synchronization and resource constraints. A multi-objective optimization (MOP) is formulated, where the sensor activation time, computation offloading, and resource allocation are jointly optimized. To solve this nonconvex problem, a dual-layer Lagrangian multiplier method (D-LMM) is developed. It decouples the optimization into an upper-level throughput maximization and a lower-level energy minimization. The former is converted into a convex problem via quadratic transformation, yielding a stationary solution for sensor activation times, while the latter is solved by alternating optimization. The D-LMM algorithm is proven to converge to a local optimum. Simulation results verify that the proposed framework significantly improves throughput and reduces MT energy consumption. The synchronization-aware multimodal coordination further ensures sufficient data collection and robust performance across varying network scales and resource conditions, enabling reliable downstream operations.

Traffic scheduling plays a critical role in Time-Sensitive Networking (TSN) for ensuring high reliability and deterministic latency. In this paper, we propose a novel window-based scheduling approach for the Time-Aware Shaper (TAS). By allowing packets to wait in egress queues before forwarding, our approach relaxes the strict timing constraints imposed by existing packet-based schedulers. We employ a generalized Network Calculus (NC) framework built on an End-to-End (E2E) network model, to analyze the upper-bound latency, which is then used to assess the schedulability of Time-Critical (TC) traffic. Inspired by the Proportional–Integral–Derivative (PID) closed-loop control architecture, we introduce an Incremental PID-based Search (IPS) algorithm to optimize schedulability, where the P, I, and D terms are leveraged to scale update steps, maintain search momentum, and dampen the oscillations, respectively. To accommodate various traffic classes, throughput constraints for non-TC traffic are incorporated as bounds on window lengths. Simulation experiments were performed on a multi-node network topology carrying large traffic volumes. Under optimal PID settings, the proposed IPS algorithm was evaluated against the well-validated Simulated Annealing (SA) method under a unified scheduling framework with identical decision variables and constraints to ensure a fair comparison. Results show that IPS consistently achieves higher schedulability and requires fewer iterations for flow counts ranging from 100 to 600. Furthermore, a real-time simulation platform based on OMNeT++ was developed, and the effectiveness of the proposed wait-allowed scheduling model was validated through optimized GCL configurations.

Mobile edge computing (MEC) facilitates high reliability and low-latency applications by bringing computation and data storage closer to end-users. Intelligent computing is an important application of MEC, where computing resources are used to solve intelligent task-related problems based on task requirements. However, efficiently offloading computing and allocating resources for intelligent tasks in MEC systems is a challenging problem due to complex interactions between task requirements and MEC resources. To address this challenge, we investigate joint computing offloading and resource allocation for classification intelligence tasks (CITs) in MEC systems. Our goal is to optimize system utility by jointly considering computing accuracy and task delay to achieve maximum utility of our system. We focus on CITs and formulate an optimization problem that considers task characteristics including the accuracy requirements and the parallel computing capabilities in MEC systems. To solve the proposed problem, we decompose it into three subproblems: subcarrier allocation, computing capacity allocation and compression offloading. We use successive convex approximation and convex optimization method to derive optimized feasible solutions for the subcarrier allocation, offloading variable, computing capacity allocation, and compression ratio. Based on our solutions, we design an efficient joint computing offloading and resource allocation algorithm for CITs in MEC systems. Our simulation demonstrates that the proposed algorithm significantly improves the performance by 16.4% on average and achieves a flexible trade-off between system revenue and cost considering CITs compared with benchmarks.

Computation offloading and resource allocation are critical in mobile edge computing (MEC) systems to handle the massive and complex requirements of applications restricted by limited resources. In a multiuser multiserver MEC network, the mobility of terminals causes computing requests to be dynamically distributed in space. At the same time, the non-negligible dependencies among tasks in some specific applications impose temporal correlation constraints on the solution as well, leading the time-adjacent tasks to experience varying resource availability and competition from parallel counterparts. To address such dynamic spatial-temporal characteristics as a challenge in the allocation of communication and computation resources, we formulate a long-term delay-energy tradeoff cost minimization problem in the view of jointly optimizing task offloading and resource allocation. We begin by designing a priority evaluation scheme to decouple task dependencies and then develop a grouped Knapsack problem for channel allocation considering the current data load and channel status. Afterward, in order to meet the rapid response needs of MEC systems, we exploit the double duel deep Q network (D3QN) to make offloading decisions and integrate channel allocation results into the reward as part of the dynamic environment feedback in D3QN, constituting the joint optimization of task offloading and channel allocation. Finally, comprehensive simulations demonstrate the performance of the proposed algorithm in the delay-energy tradeoff cost and its adaptability for various applications.

The demands for industrial ubiquitous communications promote the development of real-time and high-reliability wireless communication techniques. Accurate time synchronization is a critical foundation for deterministic communications. However, many wireless time synchronization methods achieve poor accuracy, while others take the high hardware costs and can not be used in practice. How to design high precision wireless time synchronization method with reasonable hardware costs is still a big challenge. Therefore, without affecting Wi-Fi protocol stack, a new medium access control (MAC) layer-based approach is proposed in this article to implement precision time synchronization with an open-source Wi-Fi design. The software protocol stack only needs to send handshake messages carrying identifiers, and timestamps are inserted and extracted from handshake messages as they pass through the MAC synchronization architecture designed in field programmable gate array. In the single-hop synchronization experiment, the synchronization accuracy is tested with and without network load. Comparing with other methods in several literatures, the results of the proposed solution unequivocally demonstrate the effectiveness and excellent wireless time synchronization precision, with 99% absolute time synchronization errors under 50% and 100% loads within 200 ns and 1$\mu$s, respectively.

Mobile edge computing (MEC) enables the provision of high-reliability and low-latency applications by offering computation and storage resources in close proximity to end-users. Different from traditional computation task offloading in MEC systems, the large data volume and complex task computation of artificial intelligence involved intelligent computation task offloading have increased greatly. To address this challenge, we propose a MEC system for multiple base stations and multiple terminals, which exploits semantic transmission and early exit of inference. Based on this, we investigate a joint semantic transmission and resource allocation problem for maximizing system reward combined with analysis of semantic transmission and intelligent computation process. To solve the formulated problem, we decompose it into communication resource allocation subproblem, semantic transmission subproblem, and computation capacity allocation subproblem. Then, we use 3D matching and convex optimization method to solve subproblems based on the block coordinate descent (BCD) framework. The optimized feasible solutions are derived from an efficient BCD based joint semantic transmission and resource allocation algorithm in MEC systems. Our simulation demonstrates that: 1) The proposed algorithm significantly improves the delay performance for MEC systems compared with benchmarks; 2) The design of transmission mode and early exit of inference greatly increases system reward during offloading; and 3) Our proposed system achieves efficient utilization of resources from the perspective of system reward in the intelligent scenario.

The advent of mobile edge computing (MEC) technology offers new prospects for executing demanding applications close to the user. However, complex applications like intelligent transportation and autonomous driving pose modeling and problem-solving challenges due to inter-user service logic correlations. Therefore, we construct a model that considers the terminal's mobility, time-varying channel status, and inter-user task dependencies and formulate a problem aiming to optimize the task completion delay and the energy consumption weighted cost in a dynamic MEC scenario. To resolve this problem, a Double Deep Q Network (DDQN)-based algorithm is developed for task offloading, while integrated sub channel allocation and transmit power control constitute part of the interaction with the dynamic environment to generate the reward signal, optimizing the long-term system performance. Comprehensive simulations verify that the proposed algorithm outperforms the comparative methods in terms of reducing the cost, and its adaptability in different scenarios has also been validated and analyzed.

Location-based service (LBS) is the core of internet of things (IoTs), which serves tracking, navigation and monitoring. The ubiquitous magnetic signals are temporally stable and spatially distinguishable, and can achieve high-precision and ubiquitous positioning results without additional infrastructure, which is favored by researchers and has become a major research hotspot. Although there has been extensive research in the field of indoor magnetic positioning, there is still room for optimization in terms of positioning accuracy and robustness. Aiming at the problem that the magnetometer is offset and susceptible to environmental interference, we propose an online magnetometer calibration algorithm without user perception. Aiming at the inconsistency of magnetic data spatial scale problem caused by differences in device sampling frequency and user walking speed, we leverage different scales to segment the magnetic data, extract the magnetic sequence features of the corresponding scales through Transformer, utilize the attention mechanism to score the weights of the different scale features, and finally fuse the multiple scale features for positioning. We conduct extensive and well-designed experiments on public datasets and self-collected datasets. The experimental results indicate that the proposed method effectively solves the magnetic spatial scale problem and improves indoor magnetic positioning accuracy.

This paper investigates the intelligent computing task-oriented computing offloading and semantic compression in mobile edge computing (MEC) systems. With the popularity of intelligent applications in various industries, terminals increasingly need to offload intelligent computing tasks with complex demands to MEC servers for computing, which is a great challenge for bandwidth and computing capacity allocation in MEC systems. Considering the accuracy requirement of intelligent computing tasks, we formulate an optimization problem of computing offloading and semantic compression. We jointly optimize the system utility which are represented as computing accuracy and task delay respectively to acquire the optimized system utility. To solve the proposed optimization problem, we decompose it into computing capacity allocation subproblem and compression offloading subproblem and obtain solutions through convex optimization and successive convex approximation. After that, the offloading decisions, computing capacity and compressed ratio are obtained in closed forms. We design the computing offloading and semantic compression algorithm for intelligent computing tasks in MEC systems then. Simulation results represent that our algorithm converges quickly and acquires better performance and resource utilization efficiency through the trend with total number of users and computing capacity compared with benchmarks.

Network Function Virtualization (NFV) is an emerging technology that extracts network functions from dedicated devices and instantiates them in the form of Virtual Network Functions (VNFs). In this paper, we focus on the multi-traffic scheduling in VNF-based service orchestration. We propose a dynamic multi-service Quality of Service (QoS) Guarantee approach, which aims to reduce data coupling between multiple services and bandwidth preemption. Then we devise a service scheduling algorithm to allocate link resources for network services. The simulation results demonstrate that our method efficiently reduces network congestion and ensures high-priority services' trouble-free running.

Enabled by Network Function Virtualization (NFV) and Software-Defined Networks (SDN), 5G networks benefit various industries (the so-called verticals) by supporting their technological and business needs flexibly and swiftly. However, a critical challenge is making high-quality joint optimal decisions for vertical demand mapping, involving Virtual Network Function (VNF) placement and optimization of network resources. In particular, to devise VNF placement schemes, network operators need to consider different objectives, such as minimizing operational costs or network latency, which are optimization objectives traditionally addressed separately. This paper studies the VNF placement for service function chains to minimize energy and traffic costs jointly. First, the problem is formulated as an optimization problem. Then we propose a joint optimization function to measure the energy consumption of physical nodes and traffic cost on links. Then, we improve the biogeography-based evolutionary algorithm to solve the proposed problem. Simulation results show that our method is effective for the proposed problem and outperforms existing methods in terms of performance.

In this paper, the energy efficiency of cooperative ARQ transmission in Amplify-and-Forward (AF) relay networks is discussed. The average total energy consumed per bit for cooperative ARQ under Quality of Service (QoS) constraints is formulated. With numerical method, the objective is optimized over the transmission data rate, the transmit power of the source and the relay, given fixed maximum retransmission number. Then, performances of various maximum retransmission numbers are analyzed. Simulation results demonstrate that cooperative ARQ with large maximum retransmission number has solid capability to save energy when the transmission energy plays a dominant role compared to circuit energy. However, for energy saving, small maximum retransmission number should be adopted on condition that tight maximum average transmission delay constraint is imposed.