Discrete event network simulator (DES) is a fundamental service for the design, validation and optimization of various networked systems, including traditional computer networks and the recent LLM training/inference systems. Efficient DES is strongly demanded by the community yet not delivered. The reason is: while DES allows for node-level parallelism, its concurrency potential has been significantly under-utilized (usually <5%) due to the limited number of CPU cores. Meanwhile, GPUs, with thousands of cores, have long been overlooked in the design of DES systems due to the incompatibility between GPUs' SIMT architecture and DES's sequential model. In this paper, we aim to achieve ground-breaking performance improvement for DES by designing a novel GPU-driven simulation engine. By carefully manipulating the life cycles of network events and orchestrating them with long- and short-term alignments, we overcome the incompatibility barriers and established GeDES, a high-level parallel and cost-effective DES system. Extensive simulation experiments show that GeDES significantly boosts DES by 33-2400X speedups compared to the SOTA works Unison and DONS. The code is available at https://github.com/mobinets/GeDES.

LoRa networks have become a key enabling technology for the Industrial Internet of Things (IIoT) due to the low cost, wide coverage, and easy deployment. However, the lifetime of Industrial LoRa Networks (ILNs) leaves much to be desired due to the star topology used, i.e., the energy consumption of nodes at network edges is much higher than those near the gateway. Existing works deploy additional gateways to improve network lifetime, however, this is often limited by hardware cost and geographical constraints. In this article, we propose to trade the long communication range for better network performance by introducing intranetwork relays into ILNs. The fine-grained global views and strict time synchronization in ILNs allow us to establish multihop relays within a network, thus supporting more energy-efficient parameters at edge nodes for enhancing network lifetime. We investigate the unique challenges of LoRa relays in ILNs, and then propose HybLoRa, a novel hybrid framework which adaptively assign relays and configure nodes based on a gain-driven minimum spanning tree (MST). We conduct real-world experiments and large-scale simulations. The results show that compared with single-hop EXPLoRa, HybLoRa can reduce total network energy consumption by 36.1% and improve network lifetime by 67.2% .

The recent breakthrough in Wireless Power Transfer (WPT) provides a promising way to prolong network lifetime by employing a charging vehicle to replenish energy. Data transmissions from nodes typically happen in response to physical sensory events, leading to time-varying energy consumption. To improve charging efficiency, the existing schemes collect energy information by employing a data-gathering vehicle or data collection protocol. However, in duty cycle networks, these schemes either incur extra vehicles or high data collection delay. To solve this problem, we propose an mobile adaptive charging scheme with rapid data sharing (rShare), which establishes multi-layer collection trees and collects overall energy data to the vehicle. A spatial predicted active sending (SPAS) algorithm is proposed for distant nodes to actively estimate the future position and transmit their data to cover potential positions of the charging vehicle, which significantly reduces data collection delay. We also propose an estimated time of arrival (ETA)-aware scheme based on the TSP Nearest Neighbor algorithm that updates the charging path based on the collected data. Extensive simulation results demonstrate that our scheme outperforms the state-of-the-arts in terms of dead node avoidance with less communication overhead.

One fundamental problem of content caching in edge computing is how to replace contents in edge servers with limited capacities to meet the dynamic requirements of users without knowing their preferences in advance. Recently, online deep reinforcement learning (DRL)-based caching methods have been developed to address this problem by learning an edge cache replacement policy using samples collected from continuous interactions (trial and error) with the environment. However, in practice, the online data collection phase is often expensive and time-consuming, thus hindering the practical deployment of online DRL-based methods. To bridge this gap, we propose a novel Agile edge Cache replacement method based on Offline-online deep Reinforcement learNing (ACORN), which can efficiently learn an edge cache replacement policy offline from a training dataset collected by a behavior policy (e.g., Least Recently Used) and then improve it with fast online fine-tuning. We also design a specific convolutional neural network structure with multiple branches to effectively extract content popularity knowledge from the dataset. Experimental results show that the offline policy generated by ACORN outperforms the behavior policy by up to 38%. Through online fine-tuning, ACORN also achieves the number of cache hits as good as that of several advanced DRL-based methods while significantly reducing the number of training epochs by up to 40%.

Federated learning (FL) has shown its great potential for achieving distributed intelligence in privacy-sensitive IoT. However, popular FL approaches, such as FedAvg and its variants share model parameters among clients during the training process and thus cause significant communication overhead in IoT. Moreover, nonindependent and identically distributed (non-IID) data across learning devices severely affect the convergence and speed of FL. To address these challenges, we propose a communication-efficient FL framework based on Two-step Knowledge Distillation, Fed2KD, which boosts the classification accuracy through privacy-preserving data generation while improving communication efficiency through a new knowledge distillation scheme empowered by an attention mechanism and metric learning. The generalization ability of Fed2KD is analyzed from the view of domain adaption. Extensive simulation experiments are conducted on Fashion-MNIST, CIFAR-10, and ImageNet data sets with various non-IID data distributions. The performance results show that Fed2KD can reduce the communication overhead and improve classification accuracy compared to FedAvg and its latest variants.

During the past decade, Industry 4.0 has greatly promoted the improvement of industrial productivity by introducing advanced communication and network technologies in the manufacturing process. With the continuous emergence of new communication technologies and networking facilities, especially the rapid evolution of cellular networks for 5G and beyond, the requirements for smarter, more reliable, and more efficient cellular network services have been raised from the Industry 5.0 blueprint. To meet these increasingly challenging requirements, proactive and effective allocation of cellular network resources becomes essential. As an integral part of the cellular network resource management system, cellular traffic prediction faces severe challenges with stringent requirements for accuracy and reliability. One of the most critical problems is how to improve the prediction performance by jointly exploring the spatial and temporal information within the cellular traffic data. A promising solution to this problem is provided by graph neural networks (GNNs), which can jointly leverage the cellular traffic in the temporal domain and the physical or logical topology of cellular networks in the spatial domain to make accurate predictions. In this article, we present the spatial-temporal analysis of a real-world cellular network traffic dataset and review the state-of-the-art research works in this field. Based on this, we further propose a time-series similarity-based graph attention network, TSGAN, for the spatial-temporal cellular traffic prediction. The simulation results show that our proposed TSGAN outperforms three classic prediction models based on GNNs or GRU on a real-world cellular network dataset in short-term, mid-term, and long-term prediction scenarios.

Over the past few years, fog radio access networks (F-RANs) have become a promising paradigm to support the tremendously increasing demands of multimedia services, by pushing computation and storage functionalities toward the edge of networks, closer to users. In F-RANs, distributed edge caching among fog access points (F-APs) can effectively reduce network traffic and service latency as it places popular contents at local caches of F-APs rather than the remote cloud. Due to the limited caching resources of F-APs and spatiotemporally fluctuant content demands from users, many cooperative caching schemes were designed to decide which contents are popular and how to cache them. However, these approaches often collect and analyze the data from Internet-of-Things (IoT) devices at a central server to predict the content popularity for caching, which raises serious privacy issues. To tackle this challenge, we propose a federated learning-based cooperative hierarchical caching scheme (FLCH), which keeps data locally and employs IoT devices to train a shared learning model for content popularity prediction. FLCH exploits horizontal cooperation between neighbor F-APs and vertical cooperation between the baseband unit (BBU) pool and F-APs to cache contents with different degrees of popularity. Moreover, FLCH integrates a differential privacy mechanism to achieve a strict privacy guarantee. Experimental results demonstrate that FLCH outperforms five important baseline schemes in terms of the cache hit ratio, while preserving data privacy. Moreover, the results show the effectiveness of the proposed cooperative hierarchical caching mechanism for FLCH.

With the massive deployment of 5G cellular infrastructures, traffic prediction has become an indispensable part of the cellular resource management system in order to provide reliable and fast communication services that can meet the increasing quality-of-service requirements of smart city. A promising approach for handling this problem is to introduce intelligent methods to implement a highly effective and efficient cellular traffic prediction model. Meanwhile, integrating the multiaccess edge computing framework in 5G cellular networks facilitates the application of intelligent traffic prediction models by enabling their implementation at the network edge. However, the data shortage and privacy issues may still be obstacles for training a robust and accurate prediction model at the edge. To address these issues, we propose a data-augmentation-based cellular traffic prediction model (ctGAN-S2S), where an effective data augmentation submodel based on generative adversarial networks is proposed to improve the prediction performance while protecting data privacy, and a long-short-term-memory-based sequence-to-sequence submodel is used to achieve the flexible multistep cellular traffic prediction. The experimental results on a real-world city-scale cellular traffic dataset reveal that our ctGAN-S2S model achieves up to 48.49% improvement of the prediction accuracy compared to four typical reference models.