The forecasting of mobile data not only helps operators proactively perceive the network status, enabling them to arrange and schedule network resources in advance to improve user service quality, but also allows for the on-demand, flexible extrapolation of network changes under different strategies, effectively reducing the trial-and-error costs in the live network. Traditional prediction methods with tailored models for exclusive data types undoubtedly increase the design complexity and deployment costs. In this paper, we propose a Mobile Foundation Model (MobiFM) for data forecasting, which adopts a unified framework to forecast mobile data with diverse types (mobile traffic, users, and wireless channel), various time granularities (hourly and minute-level), and multiple spatial scales (cell-level and grid-level). MobiFM is a generative model built on diffusion and Transformer backbones. It incorporates a memory-network core that flexibly stores large amounts of contextual knowledge from urban environments, network configuration parameters, and spatio-temporal features. In parallel, MobiFM employs Mixture-of-Experts (MoE) networks to specialize and exploit the distinct characteristics of heterogeneous mobile data. We train the MobiFM using 10 real-world datasets with over 200,000 time-series points, totaling more than 1 billion tokens. The experimental results demonstrate that MobiFM achieves improvements of 20.24%, 10.92%, and 6.52% in the forecasting of mobile traffic, users, and wireless channel data, respectively, exhibiting good generalization performance compared to the baselines. Furthermore, based on MobiFM’s forecasting capability, we formulate an energy-saving optimization case, where the experimental results show the MobiFM-based scheme can improve energy efficiency up to 17.9%.

With the widespread deployment of 5G networks, efficient network optimization and planning have become increasingly important. The generation of mobile traffic and user data can help network operators understand and grasp the network’s operational status from different perspectives, enabling customized strategies such as wireless resource allocation and user access control. However, existing research primarily focuses on the generation of single-type data and lacks exploration of the interplay between mobile traffic and user data. Moreover, current generative models struggle to capture the spatio-temporal correlations between multimodal environmental data and mobile data. In this paper, we propose a Multimodal Context-aware Diffusion Model (MCDiff) for simultaneously generating mobile traffic and users. The model incorporates an interplay perception module to capture the correlation between mobile traffic and users. To better characterize the complex and dynamic features of urban environments, we innovatively propose extracting both spatial and temporal variations from multimodal contextual data and employing contrastive learning to align multimodal contextual features with mobile data. Extensive experiments on two real-world datasets demonstrate that MCDiff can accurately generate both mobile traffic and users, achieving up to a 23.11% improvement in fidelity metrics. Facilitated by our multimodal contextual fusion module, MCDiff exhibits strong controllable and generalization capabilities, with a minimal transfer gap of only 1.19%. Furthermore, by leveraging the generated mobile traffic and user data, we formulate network planning and optimization strategies. Experimental results highlight the superiority and practicality of our method.

Mobile traffic generation helps analysts predict network performance and synthesize high-fidelity traffic data based on environmental factors, supporting optimization and planning in 5G networks, such as base station sleep management and digital twins. However, existing methods mainly explore spatio-temporal (ST) relationships implicitly and lack explicit modeling of regional features. In this paper, we introduce a Regional Features Enhanced Diffusion Model (Re-Diff) to explicitly models regional features for accurate mobile traffic generation. We integrate an average mobile traffic predictor into the diffusion model’s denoising network. By using POI and AOI distributions, we establish relationships between the environment and average network traffic usage, thereby explicitly representing regional features. We then utilize a cross-attention mechanism to actively explore the dependencies at different scales between mobile traffic’s periodic characteristics and regional features, further improving the generation accuracy of mobile traffic. We validate Re-Diff on two real-world datasets, showing a 9.20% improvement in generation accuracy over current baselines. More importantly, the model’s transferability across different cities improves by over 14.57%, which highlights the importance of explicitly modeling the relationship between traffic and the environment. This capability helps understand the universal correlations between POI, AOI, and mobile traffic in different regions, enhancing the model’s generalizability. Our code is now available at https://github.com/tsinghua-fib-lab/Re-Diff.

Mobile traffic forecasting allows operators to anticipate network dynamics and performance in advance, offering substantial potential for enhancing service quality and improving user experience. It involves multiple tasks, including long-term prediction, short-term prediction, and generation tasks that do not rely on historical data. By leveraging the different types of mobile network data generated from these tasks, operators can perform a variety of network optimizations and planning activities, such as base station (BS) deployment, resource allocation, energy optimization, etc. However, existing models are often designed for specific tasks and trained with specialized data, and there is a lack of universal models for traffic forecasting across different urban environments. In this paper, we propose a Universal model for Mobile traffic forecasting (UoMo), aiming to handle diverse forecasting tasks of short/long-term predictions and distribution generation across multiple cities to support network planning and optimization. UoMo combines diffusion models and transformers, where various spatio-temporal masks are proposed to enable UoMo to learn intrinsic features of different tasks, and a contrastive learning strategy is developed to capture the correlations between mobile traffic and urban contexts, thereby improving its transfer learning capability. Extensive evaluations on 9 real-world datasets demonstrate that UoMo outperforms current models in various forecasting tasks and zero/few-shot learning. It shows an average accuracy improvement of 27.85%, 18.57%, and 15.6% in long-term prediction, short-term prediction, and generation tasks, respectively, showcasing its strong forecasting capability. We deploy UoMo on China Mobile's JiuTian platform, leveraging the predicted mobile data to optimize live networks. This optimization includes BS deployment, resulting in a 25.3% increase in served users, and BS sleep control, which reduces equipment depreciation by 40.7%. The source code is available online: https://github.com/tsinghua-fib-lab/UoMo.

In large-scale Internet of Things (IoT) networks, generating high-fidelity packet trace data (e.g., packet size, packet time interval) is crucial for developing more powerful and precise network analysis tools. The packet data facilitates improved monitoring, threat detection, and the design of tailored resource allocation strategies to enhance user experiences. However, due to the inherent variability of IoT services and the complex temporal dependencies between network packets, producing detailed packet trace data for multiple services remains a challenging task. In this paper, we propose PacketDiff, a novel diffusion model that generates network packet trace data for diverse services based on statistical network data (i.e., network flow data). We first design an IP graph to characterize and learn the preference relationships between different IoT devices in terms of service usage. Afterward, a classifier-free guidance denoising network that integrates network flow information is designed with dual-layer transformer architectures to enhance the controllability of the generation process. Unlike traditional methods, which often rely on the simple replication of statistical patterns within IoT traffic data and fail to account for the dynamic and varied nature of IoT networks, our approach offers a more robust and accurate solution. Extensive experiments conducted on two real-world IoT datasets demonstrate that PacketDiff closely approximates real network packet data, particularly concerning key characteristics such as average packet size and time interval, with improvements of 43.4% and 39.02%.

Generating mobile traffic in urban environments is important for network planning and optimization. However, existing models show weakness in capturing spatial-temporal dynamics between mobile traffic and urban environments. This makes it difficult for the models to generate high-fidelity traffic data and control the generation process across different regions in large-scale urban environments, ultimately affecting the effectiveness of optimization strategies. In this paper, we propose a Spatio-Temporal Knowledge-driven Diffusion model (STK-Diff) for controllable mobile traffic generation. We construct an Urban Knowledge Graph (UKG) to fully characterize the urban features, which incorporates both the spatial and semantic relations of different entities, such as base stations, business areas, and functional regions. Based on the constructed UKG, we design the denoising network of diffusion model with a temporal extraction module and a spatial connection module. These two modules capture the correlations of mobile traffic and environment features via a frequency attention mechanism and spatial graph learning scheme, so as to make a strong controllability on the generated mobile traffic. Extensive experiments on three real-world datasets show that the proposed framework not only improves generation fidelity by up to 19%, but also enhances the controllability to generate specific patterns, with a gain of surpassing 15%.

The fifth-generation (5G) mobile network has significantly enhanced people's lives with faster internet speed and more reliable connections. However, there is still insufficient coverage of 5G networks worldwide, requiring telecom operators to deploy more base stations to meet the increasing demand for 5G's further commercialization. In this regard, a major challenge is understanding user network behaviors and traffic demands in target areas where 5G has not yet been deployed, which is crucial for developing a more efficient base station deployment strategy. Mobile traffic generation is a potential approach that enables operators to preemptively estimate user network demands in target areas, thereby specifying corresponding deployment strategies to enhance network performance. However, existing methods have limitations in capturing spatio-temporal features of 5G mobile traffic, particularly in areas with insufficient 5G coverage and limited historical 5G traffic data. To fill this gap, we introduce a regional feature conditioned diffusion framework for 5G network traffic generation. Our models explore the relationship between 5G traffic and existing 4G traffic, utilizing a customized cross attention mechanism and graph convolutional networks (GCN) to capture the correlation between network traffic and regional features. Based on this relationship, the framework can characterize mobile network traffic demands, thereby achieving high-fidelity 5G traffic generation in target regions with insufficient 5G coverage. Extensive experiments on real-world datasets have shown that the proposed scheme outperforms state-of-the-art baselines by more than 10%, demonstrating its high-fidelity generation capability, controllability, and generalizability. Moreover, we have deployed our scheme on China Mobile's Jiutian Platform as a network traffic simulator to improve 5G base station deployment strategies.

With the rapid development of the Fifth Generation Mobile Communication Technology (5G) networks, network planning and optimization have become increasingly crucial. Generating high-fidelity network traffic data can preemptively estimate the network demands of mobile users, which holds potential for network operators to improve network performance. However, the data required by existing generation methods is predominantly inaccessible to the public, resulting in a lack of reproducibility for the models and high deployment costs in practice. In this article, we propose an Open data-based Diffusion model for mobile traffic generation (OpenDiff), where a multi-positive contrastive learning algorithm is designed to construct conditional information for the diffusion model using entirely publicly available satellite remote sensing images, Point of Interest (POI), and population data. The conditional information contains relevant human activities in geographical areas, which can effectively guide the generation of network traffic data. We further design an attention-based fusion mechanism to capture the implicit correlations between network traffic and human activity features, enhancing the model's controllable generation capability. We conduct evaluations on three different cities with varying scales, where experimental results verify that our proposed model outperforms existing methods by 14.36% and 13.05% in terms of generation fidelity and controllability. To further validate the effectiveness of the model, we leverage the generated traffic data to assist the operators with network planning on a real-world network optimization platform of China Mobile Communications Corporation. The source code is available online:https://github.com/impchai/OpenDiff-diffusion-model-with-open-data.

Accurately predicting base station traffic volumes and understanding mobile traffic patterns is essential for smart city development, enabling efficient resource allocation and ensuring high-quality communication services. However, existing works have limitations in capturing spatial information, though the surrounding environment plays a critical role in mobile traffic prediction. In this paper, we utilize a spatial knowledge graph to represent spatial information and add important urban components to augment it making it a more effective tool for capturing environmental information. we further propose a multi-relational knowledge graph convolutional network model for mobile traffic prediction, which consists of three parts. The environmental context modelling captures spatial information from the augmented spatial knowledge graph using tucker decomposition and relational graph convolutional network. The semantic relationship modelling extracts semantic relationships between base stations and employs transformer and causal convolution to capture temporal features. The inter-attentional fusion modelling utilizes the self-attention mechanism to further capture base station relationships and predict future traffic volumes. Extensive experiments demonstrate that our proposed model significantly outperforms the state-of-the-art models by over 10% in mobile traffic prediction. The code is available at https://github.com/tsinghua-fiblab/Mobile-Traffic-Prediction-sigspatial23

Cooperative autonomous driving has emerged as an appealing paradigm to expand the perception range of vehicles and improve driving safety by sharing local sensing data and driving intentions. However, the constrained communication resource and unstable link quality seriously restrict the coordination and reliability of driving decisions. The distributed consensus mechanism is a potential approach to address the problem. This paper proposes a fast and efficient vehicular consensus framework to improve the coordination and reliability of driving decisions in delay-sensitive applications. We first design a Raft empowered two-hop consensus mechanism with dynamic negotiation. Moreover, we theoretically analyze the performance of the mechanism in terms of successful consensus ratio, latency, and link quality by leveraging Jensen's inequality and binomial distribution. In addition, an adaptive joint design algorithm for consensus process and communication is put forward to minimize the consensus delay while satisfying the requirements of vehicular resources and coordination degree. Simulation results demonstrate that our proposed scheme can improve the reliability of critical decisions by 15.4% compared with existing approaches.

With a large number of devices and a wealth of user data sets, the Internet of Things (IoT) has become a great host for federated learning (FL). At the same time, the massive amount of user data in IoT results in desperate demand for privacy preserving. The onion router (Tor) is a promising method to solve the privacy issue in IoT-based FL by user anonymity. However, IoT devices’ resource is too limited to execute the cryptographic operations in Tor. Moreover, network traffics in Tor can be easily controlled by malicious routers with a fake high self-reported bandwidth. In this article, taking advantage of the Tor, we will introduce an anonymous FL framework in IoT called FedTor. To decrease the cryptographic cost in conventional Tor, we propose a lightweight shared key generation scheme for resource-limited IoT devices. Furthermore, we use the difference between the self-reported bandwidth and the bandwidth observed from others to measure the reputation of onion routers. A reputation-based router selection (RBRS) scheme is then brought up to defend traffic control from malicious routers. We conducted extensive simulations to compare FedTor with related works. The results show that the RBRS scheme can decrease the malicious rate of onion routers and the lightweight shared key has a cost advantage over other schemes.

The rapidly growing number of Internet-of-Things (IoT) devices poses new challenges for spectrum management in future wireless communication networks. It is critical to achieve efficient and dynamic spectrum management in the sixth-generation (6G) wireless communication networks era. To tackle the challenges of managing a large-scale IoT network with heterogeneous devices, we propose a directed acyclic graph (DAG) blockchain-enhanced user-autonomy spectrum sharing model. As the proposed consensus rule is closely related to system utility, the swarm intelligence of users gradually reaches the point of convergence in the process of blockchain consensus. We analyze the effect of the tip selection method of the DAG blockchain on spectrum allocation utility. A dynamic tip selection method is proposed to enhance the global utility, which is related to the spectrum supply–demand. In addition, the ring signature technique is utilized to realize privacy protection during the sharing process. Simulation indicates that the proposed tip selection method achieves a 10% enhancement in terms of the global utility. Furthermore, significant reductions in administrative expense and reliability improvement are demonstrated by simulation results. The stability of the tip number in the proposed model has been proved theoretically, which is also validated by simulation experiments.

Edge computing has emerged as a prospective paradigm to meet ever-increasing computation demands in mobile target tracking wireless sensor networks (MTT-WSNs). This paradigm can offload time-sensitive tasks to sink nodes to improve computing efficiency. Nevertheless, it is intractable to execute dynamic and critical missions in the MTT-WSN network due to static property. Besides, the network cannot ensure consecutive tracking with limited energy. To address the problems, this article proposes a new hierarchical tracking structure based on the edge intelligence (EI) technology. The structure can integrate the computing resource of both mobile nodes and edge servers to provide high-efficient computing for real-time tracking. Based on the proposed structure, we propose a long-term dynamic resource allocation algorithm to obtain the optimal resource scheduling solution for accurate and consecutive tracking. Simulation results demonstrate that our algorithm outperforms the deep${Q}$-learning over 14.5% in terms of systematic energy consumption. It can also obtain a significant enhancement in tracking accuracy compared with the noncooperative scheme.

The emergence of Intelligent Connected Vehicles (ICVs) shows great potential for future intelligent traffic systems, enhancing both traffic safety and road efficiency. However, the ICVs relying on data driven perception and driving models face many challenges, such as the lack of comprehensive knowledge to deal with complicated driving context. In this paper, we investigate cooperative knowledge sharing for ICVs. We propose a secure and efficient blockchain based knowledge sharing framework, wherein a distributed learning based scheme is utilized to enhance the efficiency of knowledge sharing and a directed acyclic graph (DAG) system is designed to guarantee the security of shared learning models. To cater for the time-intense demand of highly dynamic vehicular networks, a lightweight DAG is designed to reduce the operation latency in terms of fast consensus and authentication. Moreover, to further enhance model accuracy as well as minimizing bandwidth consumption, an adaptive asynchronous distributed learning (ADL) based scheme is proposed for model uploading and downloading. Experiment results show that the DAG based framework is lightweight and secure, which reduces both chosen and confirmation delay as well as resisting malicious attacks. In addition, the proposed adaptive ADL scheme enhances driving safety related performance compared to several existing algorithms.

Knowledge sharing in IoV shows great potential for future vehicular networks. Vehicles, platoons and even traffic infrastructures can exchange the driving experiences or sensing data to facilitate intelligent transportation applications such as autodriving and traffic analysis. However, it is challenging for vehicular knowledge-sharing systems to address the issues brought by information security and vehicular mobility. Although blockchain technology shows defensibility in dealing with trust issues, it is difficult to be applied in large-scale vehicular networks due to the computation consumption of mining process and frequent synchronization of ledger. In this paper, we propose a directed acyclic graph (DAG) enabled knowledge-sharing framework in which vehicular knowledge is encapsulated as a site in the DAG. A new tip selection algorithm (TSA) and a fast authentication scheme for cross-regional vehicles are designed to reduce computation and storage expenditure. Simulation results show that the proposed DAG framework can achieve a higher knowledge sharing quality and lower authentication latency compared with traditional DAG systems.

Internet of Vehicles (IoVs) is highly characterized by collaborative environment data sensing, computing and processing. Emerging Big Data and Artificial Intelligence (AI) technologies show significant advantages and efficiency for knowledge sharing among intelligent vehicles. However, it is challenging to guarantee the security and privacy of knowledge during the sharing process. Moreover, conventional AI-based algorithms cannot work properly in distributed vehicular networks. In this paper, a hierarchical blockchain framework and a hierarchical federated learning algorithm are proposed for knowledge sharing, by which vehicles learn environmental data through machine learning methods and share the learning knowledge with each others. The proposed hierarchical blockchain framework is feasible for the large scale vehicular networks. The hierarchical federated learning algorithm is designed to meet the distributed pattern and privacy requirement of IoVs. Knowledge sharing is then modeled as a trading market process to stimulate sharing behaviours, and the trading process is formulated as a multi-leader and multi-player game. Simulation results show that the proposed hierarchical algorithm can improve the sharing efficiency and learning quality. Furthermore, the blockchain-enabled framework is able to deal with certain malicious attacks effectively.

With the increasing requirement of spectral efficiency in 6G mobile networks, the Citizens Broadband Radio Service (CBRS) proposed by the Federal Communications Commission (FCC) is considered as the potential dynamic spectrum sharing solution. Traditional CBRS suffers high administrative expense and privacy risk. In addition, conventional consensus methods in blockchain consume excessive computing power or lack well-founded consensus standard. In this paper, we propose a distributed CBRS-Blockchain model with a specialized consensus method, which is able to reduce administrative expense of dynamic access system. Based on the ring signature techniques, a privacy protection method is proposed. Furthermore, we design a new consensus method named as proof-of-strategy, which combines with the process of spectrum allocation. The proposed method not only provides well-founded consensus mechanism, but also prevents spectrum allocation system from the event of single point failure. Simulation results show the divergent privacy protection for legal users and malicious users, as well as the system utility under the proposed consensus method.

The emerging blockchain technology provides a new paradigm for maintaining data integrity and unforgeability in a distributed manner. However conventional public blockchain systems suffer large consensus latency thus cannot be well applied to Internet of Vehicles (IoV) with the high mobility of vehicles and low latency requirement. In addition, not all messages in IoV should be stored in a global ledger. In this paper, we propose a novel Hierarchical Blockchain (HB) which divides the system into two layers and each layer maintains an exclusive ledger. Sensing information of vehicles are recorded in differnet layer according to its influence scope. Furthermore, based on the transactions recorded in the hierarchical blockchain, we design the proactive file duplicate caching scheme considering not only popularities but influence scopes for the enhancement of overall system performance. Simulation results shows the superiority of the proposed architecture compared with conventional vehicular systems, in terms of failure rate, latency and system utility.

This paper proposes a new device-to-device edge computing and networks (D2D-ECN) framework which facilitates low-latency execution of real-time Internet-of Things applications through computation offloading with minimal overhead. Our framework accounts for key challenges of D2D-ECN in terms of the efficiency of the resource management and the resulting security concerns caused by lacking trustworthy between task owners and resource providers. In particular, we propose to use a blockchain-empowered framework for implementing resource trading and task assigment as the smart contracts. However, the existing Proof-of-Work (PoW) is impractical for the resource-constrained IoT devices due to high computational complexity of the mining process. Thus, we present a reputation-based consensus mechanism called proof-of-reputation (PoR), where the device with the highest reputation score is responsible for packaging the resource transactions and reputation records in the blockchain. Furthermore, we evaluate the reputation score of each device according to the current computation performance and history reputation. Security, feasibility analysis and numerical results show that our proposed computation offloading scheme can be deployed in the decentralized D2D-ECN system safely and effectively.