Digital Communications and Networks最新文献_第10页

A deep-learning-based MAC for integrating channel access, rate adaptation, and channel switch 一种基于深度学习的MAC，用于集成信道访问、速率自适应和信道切换

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-08-01 DOI: 10.1016/j.dcan.2024.10.010

Jiantao Xin , Wei Xu , Bin Cao , Taotao Wang , Shengli Zhang

With increasing density and heterogeneity in unlicensed wireless networks, traditional MAC protocols, such as Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) in Wi-Fi networks, are experiencing performance degradation. This is manifested in increased collisions and extended backoff times, leading to diminished spectrum efficiency and protocol coordination. Addressing these issues, this paper proposes a deep-learning-based MAC paradigm, dubbed DL-MAC, which leverages spectrum data readily available from energy detection modules in wireless devices to achieve the MAC functionalities of channel access, rate adaptation, and channel switch. First, we utilize DL-MAC to realize a joint design of channel access and rate adaptation. Subsequently, we integrate the capability of channel switching into DL-MAC, enhancing its functionality from single-channel to multi-channel operations. Specifically, the DL-MAC protocol incorporates a Deep Neural Network (DNN) for channel selection and a Recurrent Neural Network (RNN) for the joint design of channel access and rate adaptation. We conducted real-world data collection within the 2.4 GHz frequency band to validate the effectiveness of DL-MAC. Experimental results demonstrate that DL-MAC exhibits significantly superior performance compared to traditional algorithms in both single and multi-channel environments, and also outperforms single-function designs. Additionally, the performance of DL-MAC remains robust, unaffected by channel switch overheads within the evaluation range.

随着非授权无线网络的密度和异构性的增加，传统的MAC协议，如Wi-Fi网络中的载波感知避碰多址（CSMA/CA），正在经历性能下降。这表现在碰撞增加和后退时间延长，导致频谱效率和协议协调降低。为了解决这些问题，本文提出了一种基于深度学习的MAC范式，称为DL-MAC，它利用无线设备中能量检测模块中现成的频谱数据来实现信道接入、速率适应和信道切换的MAC功能。首先，我们利用DL-MAC实现信道接入和速率自适应的联合设计。随后，我们将通道切换功能集成到DL-MAC中，增强了其从单通道到多通道操作的功能。具体来说，DL-MAC协议结合了用于信道选择的深度神经网络（DNN）和用于信道接入和速率自适应联合设计的循环神经网络（RNN）。我们在2.4 GHz频段内进行了实际数据收集，以验证DL-MAC的有效性。实验结果表明，与传统算法相比，DL-MAC在单通道和多通道环境下都表现出显著的性能优势，并且优于单功能设计。此外，DL-MAC的性能保持稳健，在评估范围内不受信道交换开销的影响。

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引用次数: 0

Generalized spatial modulation detector assisted by reconfigurable intelligent surface based on deep learning 基于深度学习的可重构智能曲面辅助广义空间调制检测器

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-08-01 DOI: 10.1016/j.dcan.2024.11.015

Chiya Zhang , Qinggeng Huang , Chunlong He , Gaojie Chen , Xingquan Li

Reconfigurable Intelligent Surface (RIS) is regarded as a cutting-edge technology for the development of future wireless communication networks with improved frequency efficiency and reduced energy consumption. This paper proposes an architecture by combining RIS with Generalized Spatial Modulation (GSM) and then presents a Multi-Residual Deep Neural Network (MR-DNN) scheme, where the active antennas and their transmitted constellation symbols are detected by sub-DNNs in the detection block. Simulation results demonstrate that the proposed MR-DNN detection algorithm performs considerably better than the traditional Zero-Forcing (ZF) and the Minimum Mean Squared Error (MMSE) detection algorithms in terms of Bit Error Rate (BER). Moreover, the MR-DNN detection algorithm has less time complexity than the traditional detection algorithms.

可重构智能表面（RIS）被认为是未来无线通信网络发展的前沿技术，具有提高频率效率和降低能耗的特点。本文提出了一种将RIS与广义空间调制（GSM）相结合的结构，并在此基础上提出了一种多残差深度神经网络（MR-DNN）方案，该方案通过检测块中的子dnn检测有源天线及其发射星座符号。仿真结果表明，提出的MR-DNN检测算法在误码率（BER）方面明显优于传统的零强迫（Zero-Forcing， ZF）和最小均方误差（Minimum Mean Squared Error， MMSE）检测算法。此外，MR-DNN检测算法比传统检测算法具有更低的时间复杂度。

引用次数: 0

Interest-aware joint caching, computing, and communication optimization for mobile VR delivery in MEC networks MEC网络中移动VR传输的兴趣感知联合缓存、计算和通信优化

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-08-01 DOI: 10.1016/j.dcan.2024.10.018

Baojie Fu , Tong Tang , Dapeng Wu , Ruyan Wang

In the upcoming B5G/6G era, Virtual Reality (VR) over wireless has become a typical application, which is an inevitable trend in the development of video. However, in immersive and interactive VR experiences, VR services typically exhibit high delay, while simultaneously posing challenges for the energy consumption of local devices. To address these issues, this paper aims to improve the performance of VR service in the edge-terminal cooperative system. Specifically, we formulate a joint Caching, Computing, and Communication (3C) VR service policy problem by optimizing the weighted sum of the total VR delivery delay and the energy consumption of local devices. To design the optimal VR service policy, the optimization problem is decoupled into three independent subproblems to be solved separately. To improve the caching efficiency within the network, a Bert-based user interest analysis method is first proposed to accurately characterize the content request behavior. Based on this, a service cost minimum-maximization problem is formulated under the consideration of performance fairness among users. Then, the joint caching and computing scheme is derived for each user with a given allocation of communication resources while a bisection-based communication scheme is acquired with the given information on the joint caching and computing policy. With alternative optimization, an optimal policy for joint 3C based on user interest can be finally obtained. Simulation results are presented to demonstrate the superiority of the proposed user interest-aware caching scheme and the effectiveness of the joint 3C optimization policy while considering user fairness. Our code is available at https://github.com/mrfuqaq1108/Interest-Aware-Joint-3C-Optimization.

在即将到来的B5G/6G时代，基于无线的虚拟现实（VR）已经成为一种典型的应用，这是视频发展的必然趋势。然而，在沉浸式和交互式VR体验中，VR服务通常具有高延迟，同时对本地设备的能耗提出了挑战。针对这些问题，本文旨在提高边缘终端协同系统中虚拟现实服务的性能。具体而言，我们通过优化VR总交付延迟和本地设备能耗的加权和，制定了一个联合缓存、计算和通信（3C） VR服务策略问题。为了设计最优的虚拟现实服务策略，将优化问题解耦为三个独立的子问题分别求解。为了提高网络内的缓存效率，首先提出了一种基于bert的用户兴趣分析方法来准确表征内容请求行为。在此基础上，提出了考虑用户间性能公平性的服务成本最小最大化问题。然后，在给定通信资源分配的情况下，推导出每个用户的联合缓存和计算方案，并利用给定的联合缓存和计算策略信息获得基于切分的通信方案。通过备选优化，最终得到基于用户兴趣的联合3C最优策略。仿真结果证明了所提出的用户兴趣感知缓存方案的优越性，以及在考虑用户公平性的情况下联合3C优化策略的有效性。我们的代码可在https://github.com/mrfuqaq1108/Interest-Aware-Joint-3C-Optimization上获得。

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引用次数: 0

Enhancing generalized receive spatial modulation by symbol-level precoding: Design guidelines with or without intelligent reflecting surfaces 通过符号级预编码增强广义接收空间调制：有或没有智能反射面的设计指南

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-08-01 DOI: 10.1016/j.dcan.2025.01.003

Lei Zhang , Miaowen Wen , Qiang Li , Guangyuan Zheng , Lixia Xiao

Existing Generalized Receive Spatial Modulation (GRSM) with Symbol-Level Precoding (SLP) forces the received signals (excluding noise) at unintended antennas to be zero, which restricts the generation of strong constructive interference to intended receive antennas and thus limits the performance improvement over conventional GRSM with Zero-Forcing (ZF) precoding. In this paper, we propose a novel GRSM-SLP scheme that relaxes the zero receive power constraint and achieves superior performance by integrating Intelligent Reflecting Surfaces (IRSs). Specifically, our advanced GRSM-RSLP jointly exploits SLP at the transmitter and passive beamforming at the IRS to maximize the power difference between intended and unintended receive antennas, where the received signals at unintended antennas are relaxed to lie in a sphere centered at origin with a preset radius that depends on the Signal-to-Noise Ratio (SNR) value. The precoding matrix and passive beamforming vectors are optimized alternately by considering both phase shift keying and quadrature amplitude modulation signaling. It is worth emphasizing that GRSM-RSLP is a universal solution, also applicable to systems without IRS, although it performs better in IRS-assisted systems. We finally conduct extensive simulations to prove the superiority of GRSM-RSLP over GRSM-ZF and GRSM-SLP. Simulation results show that the performance of GRSM-RSLP is significantly influenced by the number of unintended antennas, and the larger the number, the better its performance. In the best-case scenario, GRSM-RSLP can achieve SNR gains of up to 10.5 dB and 12.5 dB over GRSM-SLP and GRSM-ZF, respectively.

现有的带有符号级预编码（SLP）的广义接收空间调制（GRSM）将非预期天线处的接收信号（不含噪声）强制为零，这限制了对预期接收天线产生强建设性干扰，从而限制了采用零强制（ZF）预编码的GRSM性能的提高。在本文中，我们提出了一种新的GRSM-SLP方案，该方案放松了零接收功率约束，并通过集成智能反射面（IRSs）来获得优越的性能。具体来说，我们先进的GRSM-RSLP联合利用发射器处的SLP和IRS处的无源波束形成来最大限度地提高预期和非预期接收天线之间的功率差，其中非预期天线处的接收信号被放松到以原点为中心的球体中，该球体的半径取决于信噪比（SNR）值。通过相移键控和正交调幅信号交替优化预编码矩阵和无源波束形成矢量。值得强调的是，GRSM-RSLP是一个通用的解决方案，也适用于没有IRS的系统，尽管它在IRS辅助系统中表现更好。最后，我们进行了大量的仿真，以证明GRSM-RSLP优于GRSM-ZF和GRSM-SLP。仿真结果表明，GRSM-RSLP的性能受非预期天线数量的影响较大，且非预期天线数量越大，其性能越好。在最佳情况下，与GRSM-SLP和GRSM-ZF相比，GRSM-RSLP的信噪比增益分别可达10.5 dB和12.5 dB。

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引用次数: 0

Performance analysis of RIS-assisted dual-hop mixed FSO-RF UAV communication systems ris辅助双跳混合FSO-RF无人机通信系统性能分析

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-08-01 DOI: 10.1016/j.dcan.2025.02.001

Donghyun Kim , Hwi Sung Park , Bang Chul Jung

In this paper, we investigate a Reconfigurable Intelligent Surface (RIS)-assisted Free-Space Optics–Radio Frequency (FSO–RF) mixed dual-hop communication system for Unmanned Aerial Vehicles (UAVs). In the first hop, a source UAV transmits data to a relay UAV using the FSO technique. In the second hop, the relay UAV forwards data to a destination Mobile Station (MS) via an RF channel, with the RIS enhancing coverage and performance. The relay UAV operates in a Decode-and-Forward (DF) mode. As the main contribution, we provide a mathematical performance analysis of the RIS-assisted FSO–RF mixed dual-hop UAV system, evaluating outage probability, Bit-Error Rate (BER), and average capacity. The analysis accounts for factors such as atmospheric attenuation, turbulence, geometric losses, and link interruptions caused by UAV hovering behaviors. To the best of our knowledge, this is the first theoretical investigation of RIS-assisted FSO–RF mixed dual-hop UAV communication systems. Our analytical results show strong agreement with Monte Carlo simulation outcomes. Furthermore, simulation results demonstrate that RIS significantly enhances the performance of UAV-aided mixed RF/FSO systems, although performance saturation is observed due to uncertainties stemming from UAV hovering behavior.

本文研究了一种用于无人机的可重构智能表面（RIS）辅助自由空间光学-射频（FSO-RF）混合双跳通信系统。在第一跳中，源无人机使用FSO技术向中继无人机传输数据。在第二跳中，中继无人机通过射频信道将数据转发到目标移动站（MS）， RIS增强了覆盖范围和性能。中继无人机以解码和转发（DF）模式操作。作为主要贡献，我们提供了ris辅助FSO-RF混合双跳无人机系统的数学性能分析，评估了中断概率，误码率（BER）和平均容量。该分析考虑了由无人机悬停行为引起的大气衰减、湍流、几何损失和链路中断等因素。据我们所知，这是ris辅助FSO-RF混合双跳无人机通信系统的第一个理论研究。我们的分析结果与蒙特卡罗模拟结果非常吻合。此外，仿真结果表明，RIS显著提高了无人机辅助混合RF/FSO系统的性能，尽管由于无人机悬停行为的不确定性导致性能饱和。

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引用次数: 0

Environment-aware streaming media transmission method in high-speed mobile networks 高速移动网络中环境感知流媒体传输方法

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-08-01 DOI: 10.1016/j.dcan.2025.03.007

Jia Guo, Jinqi Zhu, Xiang Li, Bowen Sun, Qian Gao, Weijia Feng

With technological advancements, high-speed rail has emerged as a prevalent mode of transportation. During travel, passengers exhibit a growing demand for streaming media services. However, the high-speed mobile networks environment poses challenges, including frequent base station handoffs, which significantly degrade wireless network transmission performance. Improving transmission efficiency in high-speed mobile networks and optimizing spatiotemporal wireless resource allocation to enhance passengers' media experiences are key research priorities. To address these issues, we propose an Adaptive Cross-Layer Optimization Transmission Method with Environment Awareness (ACOTM-EA) tailored for high-speed rail streaming media. Within this framework, we develop a channel quality prediction model utilizing Kalman filtering and an algorithm to identify packet loss causes. Additionally, we introduce a proactive base station handoff strategy to minimize handoff-related disruptions and optimize resource distribution across adjacent base stations. Moreover, this study presents a wireless resource allocation approach based on an enhanced genetic algorithm, coupled with an adaptive bitrate selection mechanism, to maximize passenger Quality of Experience (QoE). To evaluate the proposed method, we designed a simulation experiment and compared ACOTM-EA with established algorithms. Results indicate that ACOTM-EA improves throughput by 11% and enhances passengers' media experience by 5%.

随着科技的进步，高速铁路已经成为一种普遍的交通方式。在旅行中，乘客对流媒体服务的需求日益增长。然而，高速移动网络环境带来了挑战，包括频繁的基站切换，这大大降低了无线网络的传输性能。提高高速移动网络的传输效率，优化无线资源的时空分配，以增强乘客的媒体体验是研究的重点。为了解决这些问题，我们提出了一种针对高铁流媒体的环境意识自适应跨层优化传输方法（ACOTM-EA）。在这个框架内，我们开发了一个信道质量预测模型，利用卡尔曼滤波和一种算法来识别数据包丢失的原因。此外，我们引入了一种主动的基站切换策略，以最大限度地减少与切换相关的中断，并优化相邻基站之间的资源分配。此外，本研究提出了一种基于增强型遗传算法的无线资源分配方法，结合自适应比特率选择机制，以最大限度地提高乘客体验质量（QoE）。为了评估所提出的方法，我们设计了一个仿真实验，并将ACOTM-EA与已有算法进行了比较。结果表明，ACOTM-EA提高了11%的吞吐量，提高了5%的乘客媒体体验。

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引用次数: 0

A knowledge graph-based reinforcement learning approach for cooperative caching in MEC-enabled heterogeneous networks 基于知识图的异构网络协同缓存强化学习方法

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-08-01 DOI: 10.1016/j.dcan.2024.12.006

Dan Wang, Yalu Bai, Bin Song

Existing wireless networks are flooded with video data transmissions, and the demand for high-speed and low-latency video services continues to surge. This has brought with it challenges to networks in the form of congestion as well as the need for more resources and more dedicated caching schemes. Recently, Multi-access Edge Computing (MEC)-enabled heterogeneous networks, which leverage edge caches for proximity delivery, have emerged as a promising solution to all of these problems. Designing an effective edge caching scheme is critical to its success, however, in the face of limited resources. We propose a novel Knowledge Graph (KG)-based Dueling Deep Q-Network (KG-DDQN) for cooperative caching in MEC-enabled heterogeneous networks. The KG-DDQN scheme leverages a KG to uncover video relations, providing valuable insights into user preferences for the caching scheme. Specifically, the KG guides the selection of related videos as caching candidates (i.e., actions in the DDQN), thus providing a rich reference for implementing a personalized caching scheme while also improving the decision efficiency of the DDQN. Extensive simulation results validate the convergence effectiveness of the KG-DDQN, and it also outperforms baselines regarding cache hit rate and service delay.

现有的无线网络充斥着视频数据传输，对高速和低延迟视频服务的需求持续激增。这给网络带来了拥堵的挑战，也需要更多的资源和更专用的缓存方案。最近，支持多访问边缘计算（MEC）的异构网络，利用边缘缓存进行近距离传输，已经成为解决所有这些问题的有希望的解决方案。然而，在资源有限的情况下，设计一个有效的边缘缓存方案是其成功的关键。我们提出了一种新的基于知识图（KG）的Dueling Deep Q-Network (KG- ddqn)，用于支持mec的异构网络中的协同缓存。KG- ddqn方案利用KG来发现视频关系，为用户对缓存方案的偏好提供有价值的见解。具体来说，KG指导选择相关视频作为缓存候选（即DDQN中的动作），从而为实现个性化缓存方案提供了丰富的参考，同时也提高了DDQN的决策效率。大量的仿真结果验证了KG-DDQN的收敛有效性，并且在缓存命中率和服务延迟方面也优于基线。

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引用次数: 0

FlyCache: Recommendation-driven edge caching architecture for full life cycle of video streaming FlyCache：推荐驱动的边缘缓存架构，用于视频流的全生命周期

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-08-01 DOI: 10.1016/j.dcan.2025.01.001

Shaohua Cao , Quancheng Zheng , Zijun Zhan , Yansheng Yang , Huaqi Lv , Danyang Zheng , Weishan Zhang

With the rapid development of 5G technology, the proportion of video traffic on the Internet is increasing, bringing pressure on the network infrastructure. Edge computing technology provides a feasible solution for optimizing video content distribution. However, the limited edge node cache capacity and dynamic user requests make edge caching more complex. Therefore, we propose a recommendation-driven edge Caching network architecture for the Full life cycle of video streaming (FlyCache) designed to improve users' Quality of Experience (QoE) and reduce backhaul traffic consumption. FlyCache implements intelligent caching management across three key stages: before-playback, during-playback, and after-playback. Specifically, we introduce a cache placement policy for the before-playback stage, a dynamic prefetching and cache admission policy for the during-playback stage, and a progressive cache eviction policy for the after-playback stage. To validate the effectiveness of FlyCache, we developed a user behavior-driven edge caching simulation framework incorporating recommendation mechanisms. Experiments conducted on the MovieLens and synthetic datasets demonstrate that FlyCache outperforms other caching strategies in terms of byte hit rate, backhaul traffic, and delayed startup rate.

随着5G技术的快速发展，互联网上视频流量的比例越来越大，给网络基础设施带来了压力。边缘计算技术为优化视频内容分发提供了可行的解决方案。然而，有限的边缘节点缓存容量和动态用户请求使得边缘缓存更加复杂。因此，我们提出了一种推荐驱动的边缘缓存网络架构，用于视频流的全生命周期（FlyCache），旨在提高用户的体验质量（QoE）并减少回程流量消耗。FlyCache在三个关键阶段实现智能缓存管理：播放前、播放期间和播放后。具体来说，我们为播放前阶段引入了一个缓存放置策略，为播放期间阶段引入了一个动态预取和缓存准入策略，并为播放后阶段引入了一个渐进的缓存清除策略。为了验证FlyCache的有效性，我们开发了一个包含推荐机制的用户行为驱动的边缘缓存模拟框架。在MovieLens和合成数据集上进行的实验表明，FlyCache在字节命中率、回程流量和延迟启动率方面优于其他缓存策略。

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引用次数: 0

Sum rate maximization in UAV-assisted data harvesting network supported by CF-mMIMO system exploiting statistical CSI 利用统计CSI的CF-mMIMO系统支持的无人机辅助数据采集网络的和速率最大化

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-08-01 DOI: 10.1016/j.dcan.2024.06.009

Linlin Xu , Qi Zhu , Wenchao Xia , Jun Zhang , Gan Zheng , Hongbo Zhu

Unmanned Aerial Vehicles (UAVs) have been considered to have great potential in supporting reliable and timely data harvesting for Sensor Nodes (SNs) from an Internet of Things (IoT) perspective. However, due to physical limitations, UAVs are unable to further process the harvested data and have to rely on terrestrial servers, thus extra spectrum resource is needed to convey the harvested data. To avoid the cost of extra servers and spectrum resources, in this paper, we consider a UAV-based data harvesting network supported by a Cell-Free massive Multiple-Input-Multiple-Output (CF-mMIMO) system, where a UAV is used to collect and transmit data from SNs to the central processing unit of CF-mMIMO system for processing. In order to avoid using additional spectrum resources, the entire bandwidth is shared among radio access networks and wireless fronthaul links. Moreover, considering the limited capacity of the fronthaul links, the compress-and-forward scheme is adopted. In this work, in order to maximize the ergodically achievable sum rate of SNs, the power allocation of ground access points, the compression of fronthaul links, and also the bandwidth fraction between radio access networks and wireless fronthaul links are jointly optimized. To avoid the high overhead introduced by computing ergodically achievable rates, we introduce an approximate problem, using the large-dimensional random matrix theory, which relies only on statistical channel state information. We solve the nontrivial problem in three steps and propose an algorithm based on weighted minimum mean square error and Dinkelbach's methods to find solutions. Finally, simulation results show that the proposed algorithm converges quickly and outperforms the baseline algorithms.

从物联网（IoT）的角度来看，无人驾驶飞行器（uav）在支持传感器节点（SNs）可靠和及时的数据收集方面具有巨大的潜力。然而，由于物理限制，无人机无法对采集的数据进行进一步处理，必须依赖地面服务器，因此需要额外的频谱资源来传输采集的数据。为了避免额外的服务器和频谱资源成本，本文考虑了一种基于无人机的数据采集网络，该网络由无单元大规模多输入多输出（CF-mMIMO）系统支持，其中无人机用于从SNs收集数据并将其传输到CF-mMIMO系统的中央处理单元进行处理。为了避免使用额外的频谱资源，整个带宽在无线接入网和无线前传链路之间共享。此外，考虑到前传链路的容量有限，采用了压缩转发方案。为了最大限度地提高网络传输速率，对地面接入点的功率分配、前传链路的压缩以及无线接入网和无线前传链路之间的带宽比例进行了联合优化。为了避免计算遍历可达速率带来的高开销，我们引入了一个近似问题，使用大维随机矩阵理论，它只依赖于统计信道状态信息。我们分三步求解非平凡问题，并提出了一种基于加权最小均方误差和Dinkelbach方法的求解算法。最后，仿真结果表明，该算法收敛速度快，优于基准算法。

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引用次数: 0

Profit-driven distributed trading mechanism for IoT data 利益驱动的物联网数据分布式交易机制

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-08-01 DOI: 10.1016/j.dcan.2024.10.014

Chang Liu , Zhili Wang , Qun Zhang , Shaoyong Guo , Xuesong Qiu

Data trading is a crucial means of unlocking the value of Internet of Things (IoT) data. However, IoT data differs from traditional material goods due to its intangible and replicable nature. This difference leads to ambiguous data rights, confusing pricing, and challenges in matching. Additionally, centralized IoT data trading platforms pose risks such as privacy leakage. To address these issues, we propose a profit-driven distributed trading mechanism for IoT data. First, a blockchain-based trading architecture for IoT data, leveraging the transparent and tamper-proof features of blockchain technology, is proposed to establish trust between data owners and data requesters. Second, an IoT data registration method that encompasses both rights confirmation and pricing is designed. The data right confirmation method uses non-fungible token to record ownership and authenticate IoT data. For pricing, we develop an IoT data value assessment index system and introduce a pricing model based on a combination of the sparrow search algorithm and the back propagation neural network. Finally, an IoT data matching method is designed based on the Stackelberg game. This establishes a Stackelberg game model involving multiple data owners and requesters, employing a hierarchical optimization method to determine the optimal purchase strategy. The security of the mechanism is analyzed and the performance of both the pricing method and matching method is evaluated. Experiments demonstrate that both methods outperform traditional approaches in terms of error rates and profit maximization.

数据交易是释放物联网（IoT）数据价值的重要手段。然而，物联网数据不同于传统的物质产品，因为它的无形和可复制性。这种差异导致了模糊的数据权利、令人困惑的定价和匹配方面的挑战。此外，集中式物联网数据交易平台存在隐私泄露等风险。为了解决这些问题，我们提出了一种利润驱动的物联网数据分布式交易机制。首先，提出了一种基于区块链的物联网数据交易架构，利用区块链技术的透明和防篡改特性，在数据所有者和数据请求者之间建立信任。其次，设计了一种包含权利确认和定价的物联网数据登记方法。数据权利确认方法使用不可替代的令牌来记录所有权并对物联网数据进行认证。在定价方面，我们开发了物联网数据价值评估指标体系，并引入了基于麻雀搜索算法和反向传播神经网络相结合的定价模型。最后，设计了一种基于Stackelberg博弈的物联网数据匹配方法。建立了涉及多个数据所有者和请求者的Stackelberg博弈模型，采用层次优化方法确定最优购买策略。分析了该机制的安全性，并对定价方法和匹配方法的性能进行了评价。实验表明，这两种方法在错误率和利润最大化方面都优于传统方法。

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引用次数: 0