Clustered federated learning (CFL) is a promising solution to address the performance degradation posed by inconsistent data distribution, enhancing model collaborative training through homogeneous data clustering. However, the dynamic nature of user mobility in mobile edge computing (MEC) scenarios results in highly inconsistent data distributions, further complicating collaborative training. So far, existing CFL schemes rely on a central server for clustering, while the prominent clustering algorithms require a predetermined number of clusters. This inevitably leads to a relatively fixed cluster structure, which fails to adapt to user mobility, thereby hurting the model's performance. To capture the inconsistent data distribution in the dynamic user mobility scenario, this paper proposes a decentralized clustered federated learning framework (FedCVM) based on the voter model, which comprises two modules: an iterative adaptive clustering algorithm (ACAS) and a transition probability-based neighbor aggregation algorithm (TPNA). Specifically, ACAS is designed based on the greedy strategy of minimum spanning forest, which achieves optimal cluster division without predetermining the number of clusters, to realize user dynamic clustering. To further improve the model accuracy, the proposed TPNA sets the optimal exchange weights based on the neighbor interaction principle of the voter model to implement precise updates of similar user parameters. We have conducted the theoretical analysis to demonstrate that FedCVM can select the optimal cluster head and ensure model convergence. Extensive experiments on MNIST, CIFAR-10, DermaMNIST, and HARBox demonstrate that FedCVM consistently achieves the highest accuracy while maintaining a favorable trade-off between accuracy and time cost. Moreover, under simulated mobile scenarios, it yields at least a 5.1% improvement in accuracy and achieves at least a 1.29x reduction in communication overhead compared to SOTA methods. We further validate FedCVM through real-world deployment on a heterogeneous edge testbed, demonstrating its effectiveness in dynamic, decentralized mobile scenarios.

Accurate recognition of driver drowsiness is essential for preventing accidents caused by reduced alertness, yet cross-subject electroencephalography (EEG) analysis remains hindered by noisy and individually variable brain activity. Existing methods lack the capacity to discern the contribution of each electrode, leading to suboptimal feature selection and degraded recognition accuracy. Here we introduce an auditory-inspired electrode attention (AEA) mechanism that emulates human selective hearing to isolate the most informative drowsinessrelated electrodes from noisy EEG signals. AEA integrates a fluctuation feature extraction module (FFEM) and an electrode value estimation module (EVEM), supported by an electrode contribution (EC) metric that quantifies electrode importance. FFEM captures spatiotemporal fluctuation dynamics, while EVEM models their global correlations, jointly enhancing robustness to individual variability and motion artifacts. When combined with conventional backbones, AEA achieves accuracies of 83.31 % and 80.45 % on the SADT and SEED-VIG datasets, respectively-surpassing state-of-the-art methods by up to 4.96 % in accuracy and$\mathbf{1 2. 5 5 \%}$in F1-score. Code available here.

Wi-Fi base Human activity recognition (HAR) plays a critical role in healthcare, security, and other scenarios by analyze Channel State information (CSI) from wireless signals perturbed by human movements. Existing approaches overlook Wi-Fi signal's intrinsic physical properties such as multipath fading, subcarrier orthogonality leading to vulnerability to noise, inadequate performance and high computational cost. To address these drawbacks, we propose a novel dual-domain Fourier transform model(FourierHAR). It composed by frequency domain mixture-of-experts with multiscale convolutional neural network(FMMC) and Extended LSTM(xLSTM). FMMC utilizes a mixture-of-Experts to capture both frequency domain and time domain features, and use the multi-scale convolution encoder augmented to extract noise-robust spectral patterns, while xLSTM aggregates temporal respectively. Experiments on three datasets show that FourierHAR achieves accuracies of 96.39 %, 96.40 %, and 99.62 %, respectively-surpassing state-of-the-art benchmarks by up to 26.73 % in F1-score and demonstrating outstanding computational efficiency.

Invariant molecular representation models provide potential solutions to guarantee accurate prediction of molecular properties under distribution shifts out-of-distribution (OOD) by identifying and leveraging invariant substructures inherent to the molecules. However, due to the complex entanglement of molecular functional groups and the frequent display of activity cliffs by molecular properties, the separation of molecules becomes inaccurate and tricky. This results in inconsistent semantics among the invariant substructures identified by existing models, which means molecules sharing identical invariant structures may exhibit drastically different properties. Focusing on the aforementioned challenges, in the semantic space, this paper explores the potential correlation between the consistent semantic-expressing the same information within different molecular representation forms-and the molecular property prediction problem. To enhance the performance of OOD molecular property prediction, this paper proposes a consistent semantic representation learning (CSRL) framework without separating molecules, which comprises two modules: a semantic uni-code (SUC) module and a consistent semantic extractor (CSE). To address inconsistent mapping of semantic in different molecular representation forms, SUC adjusts incorrect embeddings into the correct embeddings of two molecular representation forms. Then, CSE leverages non-semantic information as training labels to guide the discriminator's learning, thereby suppressing the reliance of CSE on the non-semantic information in different molecular representation embeddings. Extensive experiments demonstrate that the consistent semantic can guarantee the performance of models. Overall, CSRL can improve the model's average Receiver Operating Characteristic - Area Under the Curve (ROC-AUC) by 6.43%, when comparing with 11 state-of-the-art models on 12 datasets.

Received Signal Strength Indicator (RSSI)-based indoor localization offers a cost-effective solution for autonomous mobile robot navigation in 3D indoor environments, including cross-floor and multi-building structures. However, localization accuracy is fundamentally constrained by the low sampling density and unstable measurement of RSSI data. So far, existing methods neglect cross-environment RSSI coherence (e.g., repeated signal patterns in geometrically similar areas), resulting in unreliable fingerprint databases. What’s more, most approaches fail to model the spatial hierarchy of buildings, floors, and coordinates, which leads to lower accuracy in indoor positioning model predictions. To address these issues, we propose EP-3DLoc, a novel 3D indoor localization framework that combines an Environment-Invariant feature-based Data Completion (EIC) method with a Position-Related feature-based Localization (PRL) method. The EIC enhances data quality by filling in sparse RSSI data using environment-invariant features, which are recurring RSSI patterns found in similar environmental structures. The PRL module combines multi-scale RSSI signal processing (raw data and image-like data) with a multi-task network that analyzes location relationships, enhancing localization accuracy in 3D environments. Experimental results on public datasets (TUT2018, UTSIndoorLoc, and UJIIndoorLoc) have demonstrated that EP-3DLoc achieves state-of-the-art performance on indoor localization in multi-building environments. Further testing on the self-constructed dataset HZAUIndoorLoc have revealed that EP-3DLoc not only outperforms existing methods in localization accuracy but also maintains low energy consumption and strong resistance to interference. The dataset HZAUIndoorLoc is available at https://github.com/Hanzoe/HZAUIndoorLoc-Dataset.

The Agricultural Digital Twin Network (ADTN) represents one of the key enabling technologies of Agriculture 4.0. However, the unstructured nature of agricultural scenarios poses significant challenges to the geometric fidelity and scalability of ADTNs, as the morphological diversity and entity uniqueness of crops further reduce the rendering accuracy of virtual entities and increase modeling costs. Existing research lacks low-cost solutions for digital twin generation in unstructured agricultural environments, as implicit-based models struggle to capture global invariant features critical to crop structure and texture. To address these challenges, a crop-based ADTN (CBDTN) framework is designed, which incorporates low-cost image acquisition terminals, edge devices, and cloud platforms, thereby optimizing resource utilization through implicit modeling and minimizing costs in resource constrained scenarios. Furthermore, to improve the accuracy of virtual entity generation, a texture prior feature-enabled neural radiance field (TPFNeRF) is proposed, inspired by the human visual system. It emulates the processing mechanism of the visual cortex to extract consistent prior features from multi-view images, thereby providing robust rendering guidance that enhances perceptual fidelity in 3D reconstruction tasks. The experimental results show that the CBDTN with TPFNeRF exhibits superior performance in generating objects with complex textures and structures. In synthetic dataset, it significantly improves reconstruction quality, achieving 6.52 dB PSNR and 4.3% SSIM improvements over the baseline, and delivers overall performance superior to recent methods such as GFB-NeRF and DiSRNeRF. In real-world scenarios, TPFNeRF effectively reconstructs object geometry and color from fixed viewpoints using only 5 MB of model weights, significantly reducing CBDTN deployment costs.

Person re-identification (Re-ID) is a critical technology in security systems and video surveillance. However, most of the existing methods focused on precise Re-ID, which not only neglect the transmission overheads, computing energy consumption and carbon emissions, but are unsustainable. Furthermore, the personal semantics is usually blurred and distorted in real-world scenarios due to the bird's eye view (BEV) of cameras. Crossillumination and face-coverings also weakened the key personal semantics. Such deficiencies have resulted in a substantial amount of carbon emissions and poor Re-ID performance. To reduce the video transmission overheads, computing energy consumption and carbon emissions yet guaranteeing the accuracy of Re-ID, this paper proposes a novel semantic communication-based lowcarbon sustainable framework (SC-LCSF) for Re-ID. SC-LCSF adopts the semantic encoder based on an enhanced semanticsaware attention mechanism (ESA-SE) to extract the personal semantics. Only semantic information is transmitted at the semantic layer, which is then decoded into personal IDs by the multi-granularity semantic decoder (MG-SD). Two widely used public datasets, Market-1501 and CUHK03, and a newly curated real-world dataset, HZAU-SCUEC01, are used to train SC-LCSF and to evaluate its performance. Experimental results show that compared to the state-of-the-art (SOTA) methods, SC-LCSF achieves the best Rank-1 and mAP accuracy on all the datasets. Furthermore, SC-LCSF has a significant performance enhancement in low-carbon sustainable computing – the transmission data amount, CPU power consumption, CPU temperature, GPU power consumption, GPU temperature and Re-ID delay have a reduction of 96.8%, 39.6%, 27.9%, 40.9%, 29.7% and 76.6%, respectively.

Molecular representation learning is widely used in the field of drug discovery, due to its ability to accurately capture the complex features of compounds in high-dimensional space. However, existing molecular representation learning models are prone to be influenced by spurious parts during distribution shifts (also known as out-of-distribution, or OOD), which results in models mistakenly treating these spurious parts as crucial features of molecules, thereby limiting the generalization capability of the models. To tackle this issue, a novel invariant molecular representation learning model, called Causal Invariant Hierarchical Molecular Representation Graph Neural Networks (CHiMoGNN), is proposed for OOD molecular property prediction. In CHiMoGNN, a Feature Enhancement (FE) module is designed to leverage the multi-level molecular parts to enhance the expression of invariant features, thereby enhancing the model’s capability to capture key molecular information. In addition, a Cartesian Product based Environmental Impact (EI) module is adopted to generate counterfactual samples with environmental diversity. Consequently, these samples are utilized to train a classifier that maintains consistent performance across various environments. Extensive experiments on seven real-world datasets demonstrate that CHiMoGNN outperforms 9 state-of-the-art models, achieving a 5.73% increase in average ROC-AUC, and the results also show that CHiMoGNN can effectively maintain generalization in various distribution shifts. Code and datasets are available at https://github.com/Chertuion/CHiMoGNN.

Facial emotion recognition (FER) is crucial in advancing health, security, and human-computer interaction. Nowadays, The mainstream method for FER relies on computer vision, specifically utilizing deep learning models to extract facial features, enabling the identification of emotions. However, due to the local similarities and global differences inherent in facial features, the extracted features exhibit significant variations. Existing methods primarily focus on how to extract both types of features fully, neglecting the fact that the variations between them can lead to feature dependence in classifiers, ultimately resulting in lower recognition accuracy. Therefore, this paper proposes an emotion recognition convolutional model integrating local and global facial features (ELG). It includes the multi-scale local feature extraction module (MLF) and the global feature shrinkage attention module (GFS). The MLF employs multi-scale parallel convolutions to extract local features while preserving the integrity of global features. The GFS, based on attention mechanisms and feature shrinking techniques, reduces the disparities between the two types of features, mitigating the model’s feature dependence, and thereby enhancing the model's accuracy in emotion recognition. On public datasets FER-2013 and CK+, ELG outperforms state-of-the-art emotion recognition models, with accuracy and F1-score higher by 1.2% and 2.4%, respectively. Specifically, ELG achieves an accuracy of 80.2% and an F1 score of 80% on FER 2013, while reaching an accuracy of 99.5% and an F1-score of 98.4% on CK+.

Radio frequency identification (RFID) system has been widely employed in warehouse management and supply logistics. A fundamental systematic functionality is to determine the presence or absence of tagged items, referred to as missing tag identification. Although this research has attracted exten-sive attention, prior methods predominantly address scenarios involving a single user. In this paper, we extend the research to more common multi-user scenarios, which raise two concerns: time sensitivity and privacy protection. We propose a novel Parallel Missing tag Identification protocol (PaMI) that leverages lightweight and anonymous bit-vector techniques to specify the data and order of tag responses. This effectively prevents both intra- and inter-user tag collisions, enabling the identification of missing tags across multiple users in one shot while safeguarding user privacy. We carry out a comprehensive theoretical analysis to optimize the proposed method's performance. Extensive experiments show that our protocol strikingly outperforms state-of - the-art baselines.

As the Internet of Vehicles (IoV) has become the critical part of Intelligent Vehicular Transportation Systems (IVTS), massive IoV entities (e.g., RSU, OBU, pedestrians’ mobile devices, etc.) get involved into IVTS. At present, one of the biggest challenges with IoV/IVTS is how to maintain a balance between security and privacy. The receivers need to be sure that they are receiving reliable messages from the origin and could trace or link the attacker’s identity, but the tracing or linking may work against the sender’s need for identity privacy. To solve the security and privacy problem, most of current works have proposed authentication solutions to provide anonymous, traceable and unlinkable schemes, which are still vulnerable to either Sybil attacks or quantum attacks. Therefore, we propose the blockchain-based post-quantum anonymous, traceable and linkable authentication scheme by utilizing NIST winner post-quantum algorithms and related post-quantum linkable ring signature. Grounded on the authentication scheme, we also develop key exchange mechanism, which help IoV entities perform efficient message authentication encryption/decryption during P2P communication and broadcast. The security analysis shows that our proposal is resistant to Sybil attack and provides other essential security characteristics including man-in-the-middle-proof and anti-replay. Finally, we perform detailed performance evaluation including each on-chain API execution time, the off-chain communication time and the on-board/on-chain storage requirements. To further evaluate the feasibility of our scheme in the IoV/IVTS environment, we also show the effectiveness of our proposal in a blockchain-based simulation study.

Generative adversarial networks (GANs)-based person re-identification (re-id) schemes provide potential ways to augment data in practical applications. However, existing solutions perform poorly because of the separation of data generation and re-id training and a lack of diverse data in real-world scenarios. In this paper, a person re-id model (IDGAN) based on semantic map guided identity transfer GAN is proposed to improve the person re-id performance. With the aid of the semantic map, IDGAN generates pedestrian images with varying poses, perspectives, and backgrounds efficiently and accurately, improving the diversity of training data. To increase the visual realism, IDGAN utilizes a gradient augmentation method based on local quality attention to refine the generated image locally. Then, a two-stage joint training framework is employed to allow the GAN and the person re-id network to learn from each other to better use the generated data. Detailed experimental results demonstrate that, compared with the existing state-of-the-art methods, IDGAN is capable of producing high-quality images and significantly enhancing re-id performance, with the FID of generated images on the Market-1501 dataset being reduced by 1.15, and mAP on the Market-1501 and DukeMTMC-reID datasets being increased by 3.3% and 2.6%, respectively.

Obtaining accurate and effective information for drug molecules is a crucial and challenging task, which relies on high-quality chemical knowledge. However, chemical knowledge has been accumulated over the past 100 years from various regions, laboratories, and experimental purposes, which contains a lot of noise and inconsistency, leading to the out-of-distribution (OOD) problem. OOD may results in weak robustness and unsatisfied performance. In order to solve OOD learning problem with noise, a novel benchmark: ADMEOOD is proposed, which is a systematic OOD dataset curator and specifically designed for drug property prediction. ADMEOOD screens 27 Absorption, Distribution, Metabolism and Excretion (ADME) drug properties from Chembl and relevant literature. This paper explicitly make distinctions between two kinds of OOD data shifts: Noise Shift and Concept Conflict Drift (CCD). Overall, ADME contains 6 domain annotations combined with noise, CCD and no shifts, resulting in 18 different splits in total. ADMEOOD provides performance results on a variety of SOTA OOD models. The results demonstrate a significant difference performance between in-distribution and OOD data. Moreover, Empirical Risk Minimization and other models exhibit distinct trends in different domains and measurement types. The ADMEOOD benchmark can be accessed via https://github.com/qweasdzxc-wsy/ADMEOOD/.

Indoor localization technology based on the Received Signal Strength Indicator (RSSI) holds significant practical promise for mobile robots. However, accuracy is directly diminished due to the challenge of precisely establishing correlations among 3D positional data (buildings, floors, and coordinates) from RSSI, as well as RSSI fluctuations caused by numerous signal interferences. This demonstration proposes MMLoc, a 3D indoor localization system for mobile robots. To enhance positional features, MMLoc reshapes one-dimensional RSSI data into images and jointly utilizes them as inputs to a prediction model. To further optimize the model's performance, the building prediction task works as a prerequisite for floor and coordinate prediction tasks, followed by staged feature extraction and multidimensional data fusion. Experimental results on a JetBot demonstrate that MMLoc has achieved high-precision 3D indoor localization.

With the advent of the Internet of everything era, people's demand for intelligent Internet of Things (IoT) devices is steadily increasing. A more intelligent cyber-physical system (CPS) is needed to meet the diverse business requirements of users, such as ultra-reliable low-latency communication, high quality of services (QoS), and quality of experience (QoE). Edge intelligence (EI) is recognized by academia and industry as one of the key emerging technologies for the CPS, which provides the ability to analyze data at the edge rather than sending it to the cloud for analysis, and will be a key enabler to realize a world of a trillion hyperconnected smart sensing devices.As a distributed intelligent computing paradigm in which computation is largely or completely performed at distributed nodes, EI provides for the rapid development of artificial intelligence (AI) and edge computing resources to support real-time insight and analysis for applications in CPS, which brings memory, computing power and processing ability closer to the location where it is needed, reduces the volumes of data that must be moved, the consequent traffic, and the distance the data must travel. As an emerging intelligent computing paradigm, EI can accelerate content delivery and improve the QoS of applications, which is attracting more and more research attentions from academia and industry because of its advantages in throughput, delay, network scalability and intelligence in CPS.

This article proposes an electrical signature analysis method for open-circuit faults (OCFs) detection of inverter with various disturbances in distribution grid. According to the fault mechanism, the fundamental value, rated harmonics, and direct current component of three-phase currents are used as fault electrical signatures. The signatures are estimated by unscented Kalman filter (UKF) and recognized by extreme learning machine (ELM) for fault detection. Both OCF of single switch and OCFs of multiple switches are tested with consideration of direct disturbances such as load change, overcurrent and bias current, and indirect disturbances such as grid frequency variation, background harmonics, and unbalanced voltage dip. The simulations and experiments show that the OCF detection of the new method is still accurate even with these disturbances, and reveal that only the signatures of the faulty phase current are immune to the disturbances while the ones of unfaulty phases are not. The robustness when facing the various disturbances and all explainable detection results make the new method suitable and effective for OCF of inverter detection in complicated distribution grid environment.

Cognitive radio (CR) provides an effective solution to meet the huge bandwidth requirements in intelligent transportation systems (ITS), which enables secondary users (SUs) to access the idle spectrum of the primary users (PUs). However, the high mobility of users and real-time service requirements result in the additional transmission collisions and interference, which degrades the spectrum access rate and the quality of service (QoS) of users in ITS. This paper proposes a spectrum access algorithm (Feilin) based on federated deep reinforcement learning (FDRL) to improve spectrum access rate, which maximizes the QoS reward function with considering the hybrid benefits of delay, transmission power and utility of SUs. To guarantee the utility of SUs, the warranty contract is designed for SUs to obtain compensation for data transmission failure, which promotes SUs to compete for more spectrum resources. To meet the real-time requirements and improve QoS in ITS, a spectrum access model called FDQN-W is proposed based on federated deep Q-network (DQN), which adopts the asynchronous federated weighted learning algorithm (AFWLA) to share and update the weights of DQN in multiple agents to decrease time cost and accelerate the convergence. Detailed simulation results show that, in the multiuser scenario, compared with the existing methods, the proposed algorithm Feilin increases the spectrum access success rate by 15.1%, and reduces the collision rate with SUs and the collision rate with PUs by 46.4% and 6.8%, respectively.

Federated learning (FL) provides a promising solution to meet the requirements of data privacy and security in intelligent transportation systems (ITS), which enables edge devices and road side units (RSUs) to collaboratively train learning models without exposing the raw data. However, the deep leakage from gradients (DLG) still leads to the risk of divulging the original data. Meanwhile, the existing gradient protection methods based on secure multi-party computation (SMC) result in huge communication overheads and latency, which are difficult to satisfy the real-time demands of both FL and the diverse services in ITS. Focusing on improving edge data security in ITS, this paper proposes an enhanced federated learning (FL) model (SemBroc-RF) with reinforcement learning, which considers the advantages of both end-to-end homomorphic encryption (HE) and SMC. To reduce communication overheads and strengthen data security in RSUs and end devices simultaneously, a partially encrypted secure multi-party broadcast computation algorithm (SemBroc) is designed, which achieves the time complexity O(n) by constructing the decoding function and sharing the gradients among the local models. To improve the model accuracy by gradients aggregation, a FL algorithm (GreFLa) with reinforcement learning is proposed based on the adaptive assigned weight of the local gradients. Theoretical analysis and detailed simulation results verify that SemBroc-RF can effectively prevent gradient leakage. On the MNIST and CIFAR-10 datasets, compared with the benchmark, the accuracy of SemBroc-RF is increased by 3.63% and 1.35%, and the training round of SemBroc-RF is reduced by 70.8% and 45.6%, respectively.

With the access of massive mobile devices, spectrum resources are becoming increasingly scarce. How to effectively and securely utilize the limited spectrum resources has become a fundamental challenge for future mobile communication systems. Focusing on intelligent sensing and sharing, this article proposes a blockchain-based two-stage secure spectrum intelligent sensing and sharing auction mechanism (BISA), which selects appropriate base stations to form a consortium blockchain to guarantee secure and efficient spectrum auction with low complexity. In the first stage, a reverse-auction-based incentive mechanism is presented to provide bidding strategies for the primary users (PUs) and secondary users (SUs) selecting the PU that maximizes the utility. In the second stage, a unit-utility-based auction algorithm is proposed to achieve a stable match between PUs and SUs. PUs will select the SU with the maximum unit utility to complete the auction. Then, the transaction records are formed into blocks and uploaded to guarantee the security of transactions. Simulation results show that, compared with the existing methods, the proposed BISA increases the total utility and throughput of SUs by 216.4% and 189.3%, respectively.

Due to the distributed and dynamic characteristics of the Internet of Vehicles (IoV) and the continuous growth in the number of devices, content-centric decentralized vehicular named data networking (VNDN) has become more suitable for content-oriented applications in IoV. However, the existing centralized architecture is prone to the failure of single points, which results in trust problems in key verification between cross-domain nodes and consuming more power and reducing the lifetime. Focusing on secure key management and power-efficient routing, this article proposes a blockchain-based key management and green routing scheme for VNDN. A blockchain-based key management scheme is presented to achieve secure and efficient distribution and verification of keys. Specifically, all trusted agencies (TAs) form a consortium blockchain for storing public key hashes to ensure the authenticity of users’ public keys. A green global routing scheme based on node relaying pressure (GGNRP) is proposed to save power consumption and reduce the forwarding delay. A new node relay pressure metric is introduced to assist with routing decisions. Detailed experiments and analysis show that, compared with the existing scheme, the proposed scheme can achieve secure key management and GGNRP can decrease the power consumption and average delay by 15.8% and 63.2%, respectively.

Power equipment is one kind of basic element in smart grid, and how to design an efficient detection and analysis scheme of electric signature (ES) for power equipment failure (PEF) monitoring is a key and challenging issue. This article proposes an ES detection and analysis method which can monitor multiple kinds of PEF in smart substation. The bottleneck of ES analysis is explored in the view of Heisenberg uncertainty, and an optimal time–frequency analysis method is designed to solve the problems. The proposed method (PM) is based on union of time and frequency bases whose decomposition is realized by Bayesian compressive sensing using Laplace prior. Simulated and field ESs are employed to test PM with comparisons of existing methods. Also, PM is applied in a smart substation of China. Several typical PEFs and measurement soft failures caused by electromagnetic interference are discussed. The results indicate that the PM can accurately monitor PEFs whose mechanism can be revealed by time–frequency features of ESs, if the required sampling rate and sampling time are satisfied because of its immunity of the uncertainty principle restriction. The robustness in noise environment and optimal time–frequency representation of ESs make the PM an efficient general-purpose PEF monitoring in smart grid by time–frequency analysis.

Due to the defects caused by limited energy, storage capacity, and computing ability, the increasing amount of sensing data has become a challenge in wireless sensor networks (WSNs). To decrease the additional power consumption and extend the lifetime of a WSN, a multistage hierarchical clustering deredundancy algorithm is proposed. In the first stage, a dual‐metric distance is employed, and redundant nodes are preliminarily identified by the improved k‐means algorithm to obtain clusters of similar nodes. Then, a Gaussian hybrid clustering classification algorithm is presented to implement data similarity clustering for edge sensing data in the second stage. In the third stage, the clustered sensing data is randomly weighted to deduplicate the spatial correlation data. Detailed experimental results show that, compared with the existing schemes, the proposed deredundancy algorithm can achieve better performance in terms of redundant data ratio, energy consumption, and network lifetime.

In the field of social computing, influence-based propagation only studies the maximized propagation of a single piece of information. However, in the actual network environment, there are more than one piece of competing information spreading in the network, and the information will influence each other in the process of spreading. This paper focuses on the problem of competitive propagation of multiple similar information, which considers the influence of communities on information propagation, and establishes overlapping interest communities based on label propagation. Based on users' interests and preferences, the influence probability between nodes of different types of information is calculated, and combining the characteristics of the community structure, the influence calculation method of nodes is proposed. Specifically, aiming at the shortcomings of strong randomness in existing overlapping community detection methods that are based on label propagation, this paper proposes the User Interest Overlapping Community Detection Algorithm based on Label Propagation (UICDLP). Furthermore, when the seed node set of competition information is known, this paper proposes the Influence Maximization Algorithm of Node Avoidance (IMNA). Finally, the experimental results verified that the proposed algorithms are effective and feasible.

Due to the popularity of onboard geographic devices, a large number of spatial-textual objects are generated in the Internet of Vehicles (IoV). This development calls for approximate spatial keyword queries with numeric attributes in IoV (A2SKIV), which takes into account the locations, textual descriptions, and numeric attributes of spatial-textual objects. Considering large amounts of objects involved in the query processing, this article comes up with the idea of utilizing vehicles as fog-computing resource and proposes the network structure called FCV, and based on which the fog-based top-k A2SKIV query is explored and formulated. In order to effectively support network distance pruning, textual semantic pruning, and numerical attribute pruning, simultaneously, a two-level spatial-textual hybrid index STAG-tree is designed. Based on STAG-tree, an efficient top-k A2SKIV query processing algorithm is presented. The simulation results show that our STAG-based approach is about 1.87× (17.1×, resp.) faster in search time than the compared ILM (DBM, resp.) method, and our approach is scalable.

This paper proposes a highly efficient approach for power disturbance waveform (PDW) compression in the view of Heisenberg uncertainty. The key idea is to represent each signal component of PDW using as few nonzero coefficients as possible by the uncertainty principle restriction. PDWs are projected in a union of bases (UB), and each signal component of the PDWs can be represented very sparsely. The UB decomposition is solved by orthogonal matching pursuit. The features and cross correlation of subbases of the UB guarantee the PDW compression with high a compression ratio and recovered accuracy. With various simulated and field PDWs tests, the compressed data size of the new method is proven with good characteristics such as low sensitivity to sampling frequency increment and types of signal components contained in PDWs. Moreover, it is found that the new method and methods that employ wavelet techniques share the similar effect of noise for PDW compression. The proposed method is also applied at a 220-kV power substation for field PDW compression from fault recorders. The comparisons, analyses, and experiments indicate that the proposed method has a high PDW compression efficiency for future power grid monitoring.

Structured publish/subscribe (pub/sub) is a promising technique adopted on kinds of vehicle applications of Internet of industrial vehicles (IoIV), which uses Boolean expressions to capture the items with thousands of different attributes, values and spatial locations, and then processes and analyzes the vast amounts of data collected to obtain users' interests. However, existing pub/sub work with Boolean expressions either ignores spatial requirement or focuses on Euclidean space. This paper aims to fill this gap by addressing the issue of fog-based spatial-textual pub/sub problem with Boolean expressions in IoIV. A novel hybrid index called RnetBE is proposed, which exquisitely organizes traffic network structure, Boolean expressions, and spatial information of subscriptions. And RnetBE can prune huge numbers of unqualified subscriptions based on both spatial constraint and Boolean expressions, thus achieving high efficiency in indexing and matching. Moreover, range-tree deletion and orderly group processing optimization techniques are proposed to save storage space and further improve the subscription pruning efficiency. Simulation results show that RnetBE and the proposed algorithm are efficient in terms of memory consumption and matching time.

Interference alignment (IA) is widely regarded as a promising interference management technique in wireless networks. Despite its rapid advances in cellular networks, most results of IA are limited to information-theoretic exploration or physical-layer signal design. Little progress has been made so far to advance IA in cellular networks from a networking perspective. In this paper, we aim to fill this gap by studying IA in large-scale cellular networks. For the uplink, we propose an OFDM-based IA scheme and prove its feasibility at the physical layer by showing that all data streams in the IA scheme can be transported free of interference. Based on the IA scheme, we develop a cross-layer IA optimization framework that can fully translate the benefits of IA to throughput gain in cellular networks. Furthermore, we show that the IA optimization problem in the downlink can be solved in the exactly same way as that in the uplink. Simulation results show that our OFDM-based IA scheme can significantly increase the user throughput and the throughput gain increases with user density in the network.

Recently, a new MIMO degree-of-freedom (DoF) model was proposed to allocate DoF resources for spatial multiplexing (SM) and interference cancellation (IC) in a multi-hop network. Although this DoF model promises many benefits, it hinges upon a global node ordering to keep track of IC responsibilities among all the nodes. An open question about this model is whether its global ordering property can be achieved among the nodes in the network through distributed operations. In this paper, we explore this question by studying DoF scheduling in a multi-hop MIMO network, with the objective of maximizing the minimum throughput among a set of sessions. We propose an efficient DoF scheduling algorithm to solve it and show that our algorithm only requires local operations. We prove that the resulting DoF scheduling solution is globally feasible and show that there exists a corresponding feasible global node ordering for IC, albeit such global ordering is implicit. Simulation results show that the solution values obtained by our algorithm are relatively close to the upper bound values computed by CPLEX solver, thereby indicating that our algorithm is highly competitive.

Recent advances in information theory (IT) have shown great promises of interference alignment (IA) for cellular networks. However, due to a number of assumptions, these IT results cannot be directly applied to address practical problems. The goal of this paper is to fill in this gap by studying IA for cellular networks with more practical settings. We propose an IA scheme that includes constraints at each user and each base station (BS) for the uplink communication of a cellular network. We prove the feasibility of the IA scheme by constructing the encoding and decoding vectors for each data stream so that it can be transported free of interference. Based on this IA scheme, we study an uplink user throughput maximization problem and show the throughput improvement of the IA scheme over two other schemes.