Wireless Networks最新文献

An EEG signal-based music treatment system for autistic children using edge computing devices 利用边缘计算设备为自闭症儿童设计基于脑电信号的音乐治疗系统

IF 3 4区计算机科学

Wireless Networks Pub Date : 2024-09-10 DOI: 10.1007/s11276-024-03826-x

Mingxu Sun, Lingfeng Xiao, Xiujin Zhu, Peng Zhang, Xianping Niu, Tao Shen, Bin Sun, Yuan Xu

{"title":"An EEG signal-based music treatment system for autistic children using edge computing devices","authors":"Mingxu Sun, Lingfeng Xiao, Xiujin Zhu, Peng Zhang, Xianping Niu, Tao Shen, Bin Sun, Yuan Xu","doi":"10.1007/s11276-024-03826-x","DOIUrl":"https://doi.org/10.1007/s11276-024-03826-x","url":null,"abstract":"This paper proposes a system that applies electroencephalogram (EEG) technology to achieve music intervention therapy. The system can identify emotions of autistic children in real-time and play music considering their emotions as a musical treatment to assist the treatment of music therapists and the principle of playing homogenous music is to finally calm people down. The proposed method firstly collects EEG of autistic children using a 14-channel EMOTIV EPOC + and preprocesses signals through bandpass filtering, wavelet decomposition and reconstruction, then extracts frequency band-power characteristics of reconstructed EEG signals. Later, the data are classified as one of the three types of emotions (positive, middle and negative) using a support vector machine (SVM). The system also displays the recognized emotion type on a user interface and gives real-time emotional state feedback on emotional changes, which helps music therapists to evaluate the treatment and results more conveniently and effectively. Real EEG data are used to conduct the verification of system feasibility which reaches a classification accuracy of 88%. As the Internet of Things develops, the combination of edge computing with Wise Information Technology of 120 (WIT120) becomes a new trend. In this work, we propose a system to combine edge computing devices with cloud computing resources to form the music regulation system for autistic children to meet processing requirements for EEG signals in terms of timeliness and computational performance. In the designed system, preprocessing EEG signals is done in edge nodes then the preprocessed signals are sent to the cloud where frequency band-power characteristics can be extracted as features to be used in SVM. At last, the results are sent to a mobile app or computer software for therapists to evaluate.","PeriodicalId":23750,"journal":{"name":"Wireless Networks","volume":"4 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142216677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A DV-Hop localization algorithm corrected based on multi-strategy sparrow algorithm in sea-surface wireless sensor networks 海面无线传感器网络中基于多策略麻雀算法的 DV-Hop 定位算法校正

IF 3 4区计算机科学

Wireless Networks Pub Date : 2024-09-10 DOI: 10.1007/s11276-024-03827-w

Lei Zhang, Yujing Deng, Jia Fu, Lei Li, Jinhua Hu, Kangjian Di

{"title":"A DV-Hop localization algorithm corrected based on multi-strategy sparrow algorithm in sea-surface wireless sensor networks","authors":"Lei Zhang, Yujing Deng, Jia Fu, Lei Li, Jinhua Hu, Kangjian Di","doi":"10.1007/s11276-024-03827-w","DOIUrl":"https://doi.org/10.1007/s11276-024-03827-w","url":null,"abstract":"Sea surface sensor node localization accuracy is often hindered by seawater flow, while sea storms affect the transmission of radio signals. To improve the localization accuracy of the Distance Vector-Hop (DV-Hop) algorithm in Sea surface wireless sensor networks, we propose a DV-Hop localization algorithm enhanced through a multi-strategy sparrow search algorithm. The sea surface communication model is established, with drones as sink nodes, and the number of hops between nodes in the Sea Surface network is subdivided using non-uniform communication radii. Then, the average hop distance of the node is corrected by combining the weighted minimum mean square error and the cosine theorem. Finally, the calculated localization error is used as the fitness function. The localization of unknown nodes is initialized using the elite reversal strategy, and the Harris Hawk optimization method combined with the differential evolution algorithm is used to update the localization of the sparrow population discoverer to improve the population diversity. In the simulation experiments, the effectiveness of our algorithm is verified in anisotropic topologies. After that, we compared DV-Hop, Sparrow Search Algorithm for Optimizing DV-Hop (SSA-DV-Hop), Whale Optimization Algorithm for Optimizing DV-Hop (WOA-DV-Hop), and Harris Hawk Optimization Algorithm for Optimizing DV-Hop (HHO-DV-Hop) with our algorithm to verify the accuracy of the algorithm. The results show that, across various communication radii, the average localization error exhibited a reduction of 66.91% in comparison to DV-Hop. In addition, in different scenarios with different numbers of beacon nodes, the average localization error decreased by 66.78% compared to DV-Hop. Therefore, the proposed algorithm can effectively improve localization accuracy.","PeriodicalId":23750,"journal":{"name":"Wireless Networks","volume":"419 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142216678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Layer Collaborative Federated Learning architecture for 6G Open RAN 面向 6G 开放式 RAN 的多层协作联邦学习架构

IF 3 4区计算机科学

Wireless Networks Pub Date : 2024-09-05 DOI: 10.1007/s11276-024-03823-0

Borui Zhao, Qimei Cui, Wei Ni, Xueqi Li, Shengyuan Liang

引用次数: 0

Cloud-edge collaboration-based task offloading strategy in railway IoT for intelligent detection 铁路物联网中基于云边协作的任务卸载策略，实现智能检测

IF 3 4区计算机科学

Wireless Networks Pub Date : 2024-09-04 DOI: 10.1007/s11276-024-03824-z

Qichang Guo, Zhanyue Xu, Jiabin Yuan, Yifei Wei

{"title":"Cloud-edge collaboration-based task offloading strategy in railway IoT for intelligent detection","authors":"Qichang Guo, Zhanyue Xu, Jiabin Yuan, Yifei Wei","doi":"10.1007/s11276-024-03824-z","DOIUrl":"https://doi.org/10.1007/s11276-024-03824-z","url":null,"abstract":"Driven by technologies such as deep learning, online detection equipment can perform comprehensive and continuous monitoring of high-speed railways (HSR). However, these detection tasks in the railway Internet of Things (IoT) are typically computation-intensive and delay-sensitive, that makes task processing challenging. Meanwhile, the dynamic and resource-constrained nature of HSR scenarios poses significant challenges for effective resource allocation. In this paper, we propose a cloud-edge collaboration architecture for deep learning-based detection tasks in railway IoT. Within this system model, we introduce a distributed inference mode that partitions tasks into two parts, offloading task processing to the edge side. Then we jointly optimize the computing offloading strategy and model partitioning strategy to minimize the average delay while ensuring accuracy requirements. However, this optimization problem is a complex mixed-integer nonlinear programming (MINLP) issue. We divide it into two sub-problems: computing offloading decisions and model partitioning decisions. For model partitioning, we propose a Partition Point Selection (PPS) algorithm; for computing offloading decisions, we formulate it as a Markov Decision Process (MDP) and solve it using DDPG. Simulation results demonstrate that PPS can rapidly select the globally optimal partition points, and combined with DDPG, it can better adapt to the offloading challenges of detection tasks in HSR scenarios.","PeriodicalId":23750,"journal":{"name":"Wireless Networks","volume":"17 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142216679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploiting data transmission for route discoveries in mobile ad hoc networks 利用数据传输在移动特设网络中发现路由

IF 3 4区计算机科学

Wireless Networks Pub Date : 2024-08-31 DOI: 10.1007/s11276-024-03796-0

Xin Yu

{"title":"Exploiting data transmission for route discoveries in mobile ad hoc networks","authors":"Xin Yu","doi":"10.1007/s11276-024-03796-0","DOIUrl":"https://doi.org/10.1007/s11276-024-03796-0","url":null,"abstract":"On-demand routing protocols discover routes through network-wide searches. Route requests are broadcast to a large number of nodes, and route replies may contain long routes. In this paper, we address the route discovery problem and aim to reduce route discovery overhead. We propose using data packets to discover routes. A source sets a boolean variable in a data packet to be true when it has only one route to the destination. This variable is a new form of a route request. The nodes forwarding the data packet send route replies containing cached routes. To prevent nodes from sending duplicate routes to the source, we define a forward list and a backward list in the data packet. The node sending a route reply records route diverging and converging information about the route in the route reply. Subsequent nodes use the information in the data packet to decide whether to send a route reply. Our algorithm reduces route discovery latency and discovers routes shorter than or having the same length as the active data path. Due to these shorter routes, it reduces the total size of route requests and route replies significantly. Routing overhead increases slowly as mobility or network load increases. Our algorithm is independent of node movement. It improves packet delivery ratio by 15% and reduces latency by 54% for the 100-node networks at node mean speed of 20 m/s.","PeriodicalId":23750,"journal":{"name":"Wireless Networks","volume":"319 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142226972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Coarse-to-fine label propagation with hybrid representation for deep semi-supervised bot detection 利用混合表示进行粗到细标签传播，实现深度半监督机器人检测

IF 3 4区计算机科学

Wireless Networks Pub Date : 2024-08-14 DOI: 10.1007/s11276-024-03821-2

Huailiang Peng, Yujun Zhang, Xu Bai, Qiong Dai

{"title":"Coarse-to-fine label propagation with hybrid representation for deep semi-supervised bot detection","authors":"Huailiang Peng, Yujun Zhang, Xu Bai, Qiong Dai","doi":"10.1007/s11276-024-03821-2","DOIUrl":"https://doi.org/10.1007/s11276-024-03821-2","url":null,"abstract":"Social bot detection is crucial for ensuring the active participation of digital twins and edge intelligence in future social media platforms. Nevertheless, the performance of existing detection methods is impeded by the limited availability of labeled accounts. Despite the notable progress made in some fields by deep semi-supervised learning with label propagation, which utilizes unlabeled data to enhance method performance, its effectiveness is significantly hindered in social bot detection due to the misdistribution of individuation users (MIU). To address these challenges, we propose a novel deep semi-supervised bot detection method, which adopts a coarse-to-fine label propagation (LP-CF) with the hybridized representation models over multi-relational graphs (HR-MRG) to enhance the accuracy of label propagation, thereby improving the effectiveness of unlabeled data in supporting the detection task. Specifically, considering the potential confusion among accounts in the MIU phenomenon, we utilize HR-MRG to obtain high-quality user representations. Subsequently, we introduce a sample selection strategy to partition unlabeled samples into two subsets and apply LP-CF to generate pseudo labels for each subset. Finally, the predicted pseudo labels of unlabeled samples, combined with labeled samples, are used to fine-tune the detection models. Comprehensive experiments on two widely used real datasets demonstrate that our method outperforms other semi-supervised approaches and achieves comparable performance to the fully supervised social bot detection method.","PeriodicalId":23750,"journal":{"name":"Wireless Networks","volume":"420 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142216684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EtherVote: a secure smart contract-based e-voting system EtherVote：基于智能合约的安全电子投票系统

IF 3 4区计算机科学

Wireless Networks Pub Date : 2024-08-07 DOI: 10.1007/s11276-024-03818-x

Achilleas Spanos, Ioanna Kantzavelou

引用次数: 0

Deep intrusion net: an efficient framework for network intrusion detection using hybrid deep TCN and GRU with integral features 深度入侵网：利用混合深度 TCN 和具有积分特征的 GRU 进行网络入侵检测的高效框架

IF 3 4区计算机科学

Wireless Networks Pub Date : 2024-08-03 DOI: 10.1007/s11276-024-03800-7

Y. Alekya Rani, E. Sreenivasa Reddy

{"title":"Deep intrusion net: an efficient framework for network intrusion detection using hybrid deep TCN and GRU with integral features","authors":"Y. Alekya Rani, E. Sreenivasa Reddy","doi":"10.1007/s11276-024-03800-7","DOIUrl":"https://doi.org/10.1007/s11276-024-03800-7","url":null,"abstract":"In recent times, the several cyber attacks are occurred on the network and thus, essential tools are needed for detecting intrusion over the network. Moreover, the network intrusion detection systems become an important tool thus, it has the ability to safeguard the source data from all malicious activities or threats as well as protect the insecurity among individual privacy. Moreover, many existing research works are explored to detect the network intrusion model but it fails to protect the target network efficiently based on the statistical features. A major issue in the designed model is regarded as the robustness or generalization that has the capability to control the working performance when the data is attained from various distributions. To handle all the difficulties, a new meta-heuristic hybrid-based deep learning model is introduced to detect the intrusion. Initially, the input data is garnered from the standard data sources. It is then undergone the pre-processing phase, which is accomplished through duplicate removal, replacing the NAN values, and normalization. With the resultant of pre-processed data, the auto encoder is utilized for extracting the significant features. To further improve the performance, it requires choosing the optimal features with the help of an Improved chimp optimization algorithm known as IChOA. Subsequently, the optimal features are subjected to the newly developed hybrid deep learning model. The hybrid model is built by incorporating the deep temporal convolution network and gated recurrent unit, and it is termed as DINet, in which the hyper parameters are tuned by an improved IChOA algorithm for attaining optimal solutions. Finally, the proposed detection model is evaluated and compared with the former detection approaches. The analysis shows the developed model is suggested to provide 97% in terms of accuracy and precision. Thus, the enhanced model elucidates that to effectively detect malware, which tends to improve data transmission significantly and securely.","PeriodicalId":23750,"journal":{"name":"Wireless Networks","volume":"55 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141885699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ets-ddpg: an energy-efficient and QoS-guaranteed edge task scheduling approach based on deep reinforcement learning Ets-ddpg：基于深度强化学习的高能效和 QoS 保证边缘任务调度方法

IF 3 4区计算机科学

Wireless Networks Pub Date : 2024-07-30 DOI: 10.1007/s11276-024-03820-3

Jiale Zhao, Yunni Xia, Xiaoning Sun, Tingyan Long, Qinglan Peng, Shangzhi Guo, Fei Meng, Yumin Dong, Qing Xia

{"title":"Ets-ddpg: an energy-efficient and QoS-guaranteed edge task scheduling approach based on deep reinforcement learning","authors":"Jiale Zhao, Yunni Xia, Xiaoning Sun, Tingyan Long, Qinglan Peng, Shangzhi Guo, Fei Meng, Yumin Dong, Qing Xia","doi":"10.1007/s11276-024-03820-3","DOIUrl":"https://doi.org/10.1007/s11276-024-03820-3","url":null,"abstract":"With the development of 5 G communication and Internet of Things (IoT) technology, increasing data is generated by a large number of IoT devices at edge networks. Therefore, increasing need for distributed Data Centers (DCs) are seen from enterprises and building elastic applications upon DCs deployed over decentralized edge infrastructures is becoming popular. Nevertheless, it remains a great difficulty to effectively schedule computational tasks to appropriate DCs at the edge end with low energy consumption and satisfactory user-perceived Quality of Service. It is especially true when DCs deployed over an edge environment, which can be highly inhomogeneous in terms of resource configurations and computing capabilities. To this end, we develop an edge task scheduling method by synthesizing a M/G/1/PR queuing model for characterizing the workload distribution and a Deep Deterministic Policy Gradient algorithm for yielding high-quality schedules with low energy cost. We conduct extensive numerical analysis as well and show that our proposed method outperforms state-of-the-art methods in terms of average task response time and energy consumption.","PeriodicalId":23750,"journal":{"name":"Wireless Networks","volume":"14 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141869949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mobile computing power trading decision-making method for vehicle-mounted devices in multi-task edge federated learning 多任务边缘联合学习中车载设备的移动计算能力交易决策方法

IF 3 4区计算机科学

Wireless Networks Pub Date : 2024-07-26 DOI: 10.1007/s11276-024-03819-w

Huidan Zhang, Li Feng

{"title":"Mobile computing power trading decision-making method for vehicle-mounted devices in multi-task edge federated learning","authors":"Huidan Zhang, Li Feng","doi":"10.1007/s11276-024-03819-w","DOIUrl":"https://doi.org/10.1007/s11276-024-03819-w","url":null,"abstract":"With the development of edge computing and artificial intelligence technology, edge federated learning (EFL) has been widely applied in the Internet of Vehicles (IOV) due to its distributed characteristics and advantages in privacy protection. In this paper, we study the mobile computing power trading between edge servers (ES) and mobile vehicle-mounted equipment (MVE) in the IOV scene. In order to reduce the influence of MVEs’ flexibility, which can easily lead to single point failure or offline problem, we propose semi-synchronous FL aggregation. Considering that multiple federated learning (FL) tasks have different budgets and MVEs have different computing resources, we design an incentive mechanism to encourage selfish MVEs to actively participate in FL task training, so as to obtain higher quality FL models. Furthermore, we propose a fast association decision method based on dynamic state space Markov decision process (DSS-MDP). Simulation experiment data show that, MVEs can obtain higher quality local models at the same energy consumption, thus gaining higher utility. Semi-synchronous FL aggregation is able to improve the accuracy of FL global model by 0.764% on average and reduce the idle time of MVEs by 90.44% compared with the way of allocating aggregation weights according to the data volume.","PeriodicalId":23750,"journal":{"name":"Wireless Networks","volume":"67 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141772781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0