Circuit graph discovery has emerged as a fundamental approach to elucidating the skill mechanistic of language models. Despite the output faithfulness of circuit graphs, they suffer from atomic ablation, which causes the loss of causal dependencies between connected components. In addition, their discovery process, designed to preserve output faithfulness, inadvertently captures extraneous effects other than an isolated target skill. To alleviate these challenges, we introduce skill paths, which offer a more refined and compact representation by isolating individual skills within a linear chain of components. To enable skill path extracting from circuit graphs, we propose a three-step framework, consisting of decomposition, pruning, and post-hoc causal mediation. In particular, we offer a complete linear decomposition of the transformer model which leads to a disentangled computation graph. After pruning, we further adopt causal analysis techniques, including counterfactuals and interventions, to extract the final skill paths from the circuit graph. To underscore the significance of skill paths, we investigate three generic language skills—Previous Token Skill, Induction Skill, and In-Context Learning Skill—using our framework. Experiments support two crucial properties of these skills, namely stratification and inclusiveness.

Domain generalization (DG) and algorithmic fairness are two key challenges in machine learning. However, most DG methods focus solely on minimizing expected risk in the unseen target domain, without considering algorithmic fairness. Conversely, fairness methods typically do not account for domain shifts, so the fairness achieved during training may not generalize to unseen test domains. In this work, we bridge these gaps by studying the problem of Fair Domain Generalization (FairDG), which aims to minimize both expected risk and fairness violations in unseen target domains. We derive novel mutual information-based upper bounds for expected risk and fairness violations in multi-class classification tasks with multi-group sensitive attributes. These bounds provide key insights for algorithm design from an information-theoretic perspective. Guided by these insights, we propose a practical method that solves the FairDG problem through Pareto optimization. Experiments on real-world vision and language datasets show that our method achieves superior utility–fairness trade-offs compared to existing approaches.

Understanding human affect via Valence-Arousal, Expressions, and Action Unit is essential for human-machine interaction. While recent multi-task learning (MTL) methods seek to unify these tasks, they overlook three key challenges: (i) the absence of unified modeling all three affective task types: regression, detection, and classification; (ii) reliance on complete annotations for all tasks, leaving disjoint single-task datasets underutilized; and (iii) task conflicts caused by Noisy Gradients, Negative Transfer (NT), and Task-specific Performance Misalignment (TPM). We introduce COIN, a novel two-stage MTL framework that bridges Cognitive Priors and Instance Semantics for robust training. First, we design a cognitively guided cross-task label induction strategy to propagate supervision under sparse annotations and mitigate NT, yielding strong task-specific CogXperts. Second, we introduce two complementary branches to address TPM: (i) Task-Specific Branch: transferring cognitive knowledge from task-optimal CogXperts to jointly optimize objectives under partial supervision, and (ii) Semantic Alignment Branch: enhancing instance-level semantic representations via Class-Conditioned and Instance-Adaptive Prompts. Experiments across six diverse datasets demonstrate COIN’s robustness and generalization. Code is available at https://github.com/imhgy/COIN.

Temporal Action Segmentation (TAS) is fundamental to video understanding, aiming to recognize and segment actions within untrimmed videos. While over-segmentation errors have received considerable attention, the equally critical issue of out of-context errors stemming from the neglect of implicit causal relationships between actions remains under-explored. However, constraining these unknown causal relationships at the frame level—including identifying causal links and strengths—presents a significant challenge, leading to notable research gaps. To tackle these challenges, we define a Temporal Action Causal Model (TACM) for the TAS task as declarative guidance, which efficiently injects the causal relationship between actions into frame variables in a generated manner. To this end, we propose the Causal Action Segmentation Refiner (CaASR), a refinement framework designed to mitigate out-of-context errors in segmentation results of backbone models. By reconstructing the causal generation process of post-segmentation frame variables, CaASR constrains causal relationships between actions and ensures the reliability of generated causal relationships. Extensive experiments demonstrate that CaASR significantly enhances both segmentation performance and interpretability across various backbone models, including state-of-the-art methods.

Large language models (LLMs) are widely recognized for their exceptional capacity to capture semantics meaning.Yet, there remains no established metric to quantify this capability.In this work, we introduce a quantitative metric, Information Emergence (IE), designed to measure LLMs' ability to extract semantics from input tokens.We formalize "semantics" as the meaningful information abstracted from a sequence of tokens and quantify this by comparing the entropy reduction observed for a sequence of tokens (macro-level) and individual tokens (micro-level).To achieve this, we design a lightweight estimator to compute the mutual information at each transformer layer, which is agnostic to different tasks and language model architectures.We apply IE in both synthetic in-context learning (ICL) scenarios and natural sentence contexts.Experiments demonstrate informativeness and patterns about semantics.While some of these patterns confirm the conventional prior linguistic knowledge, the rest are relatively unexpected, which may provide new insights.

Fine-grained classification via LLMs is susceptible to more complex label biases compared to traditional classification tasks. Existing bias mitigation strategies, such as retraining, post-hoc adjustment, and parameter-efficient fine-tuning (PEFT) are primarily effective for simple classification biases, such as stereotypes, but fail to adequately address prediction propensity and discriminative ability biases. In this paper, we analyze these two bias phenomena and observe their progressive accumulation from intermediate to deeper layers within LLMs. To mitigate this issue, we propose a bias-aware optimization framework that incorporates two distinct label balance constraints with a PEFT strategy targeting an intermediate layer. Our approach adjusts less than 1% of the model’s parameters while effectively curbing bias amplification in deeper layers. Extensive experiments conducted across 12 datasets and 5 LLMs demonstrate that our method consistently outperforms or matches the performance of full-parameter fine-tuning and LoRA, achieving superior results with lower perplexity.

While contemporary speech separation technologies adeptly process lengthy mixed audio waveforms, they are frequently challenged by the intricacies of real-world environments, including noisy and reverberant settings, which can result in artifacts or distortions in the separated speech. To overcome these limitations, we introduce SepALM, a pioneering approach that employs audio language models (ALMs) to rectify and re-synthesize speech within the text domain following preliminary separation. SepALM comprises four core components: a separator, a corrector, a synthesizer, and an aligner. By integrating an ALM-based end-to-end error correction mechanism, we mitigate the risk of error accumulation and circumvent the optimization hurdles typically encountered in conventional methods that amalgamate automatic speech recognition (ASR) with large language models (LLMs). Additionally, we have developed Chain-of-Thought (CoT) prompting and knowledge distillation techniques to facilitate the reasoning and training processes of the ALM. Our experiments substantiate that SepALM not only elevates the precision of speech separation but also markedly bolsters adaptability in novel acoustic environments.

Multimodal Emotion Recognition in Conversations remains a challenging task due to the complex interplay of textual, acoustic and visual signals. While recent models have improved performance via advanced fusion strategies, they often lack psychologically meaningful priors to guide multimodal alignment. In this paper, we revisit the use of CLIP and propose a novel Visual Emotion Guided Anchoring (VEGA) mechanism that introduces class-level visual semantics into the fusion and classification process. Distinct from prior work that primarily utilizes CLIP's textual encoder, our approach leverages its image encoder to construct emotion-specific visual anchors based on facial exemplars. These anchors guide unimodal and multimodal features toward a perceptually grounded and psychologically aligned representation space, drawing inspiration from cognitive theories (prototypical emotion categories and multisensory integration). A stochastic anchor sampling strategy further enhances robustness by balancing semantic stability and intra-class diversity. Integrated into a dual-branch architecture with self-distillation, our VEGA-augmented model achieves sota performance on IEMOCAP and MELD. Code is available at: https://github.com/dkollias/VEGA

Efficient and precise open-vocabulary 3D scene segmentation remains a critical challenge in computer vision. While current leading methods encode CLIP language features into 3D Gaussians to achieve high segmentation accuracy and fast inference speeds, they suffer from point ambiguity issues caused by separately training on multi-level 2D semantic masks. This approach not only compromises time and space efficiency but also degrades accuracy when selecting optimal semantic levels. To overcome these limitations, we propose Voxel-Aware Fusion Language Gaussian Splatting (VaF-LangSplat), a novel framework that jointly optimizes geometric and semantic representations. Our approach first voxelizes 3D Gaussians using sparse point clouds and lightweight MLP decoders, effectively disentangling language features from geometric attributes. This enables simultaneous training across arbitrary semantic levels with minimal overhead. Crucially, we introduce Fusion Language Splatting, which aligns geometric and multi-level semantic distributions to sharpen boundary definitions while eliminating redundant Gaussian expansions. The voxel-aware representation further enhances robustness against motion blur and lighting variations. Experiments on open-vocabulary 3D localization and segmentation tasks demonstrate that VaF-LangSplat outperforms LangSplat (the prior state-of-the-art) with significant improvements in both segmentation/localization accuracy and efficiency: 4X faster training and 15X reduced storage requirements.

This paper introduces OmniGSE, a novel general speech enhancement (GSE) framework designed to mitigate the diverse distortions that speech signals encounter in real-world scenarios. These distortions include background noise, reverberation, bandwidth limitations, signal clipping, and network packet loss. Existing methods typically focus on optimizing for a single type of distortion, often struggling to effectively handle the simultaneous presence of multiple distortions in complex scenarios. OmniGSE bridges this gap by integrating the strengths of discriminative and generative approaches through a two-stage architecture that enables cross-domain collaborative optimization. In the first stage, continuous features are enhanced using a lightweight channel-split NAC-RoFormer. In the second stage, discrete tokens are generated to reconstruct high-quality speech through language models. Specifically, we designed a hierarchical language model structure consisting of a RootLM and multiple BranchLMs. The RootLM models general acoustic features across codebook layers, while the BranchLMs explicitly capture the progressive relationships between different codebook levels. Experimental results demonstrate that OmniGSE surpasses existing models across multiple benchmarks, particularly excelling in scenarios involving compound distortions. These findings underscore the framework's potential for robust and versatile speech enhancement in real-world applications.

Circuit discovery has gradually become one of the prominent methods for mechanistic interpretability, and research on circuit completeness has also garnered increasing attention. Methods of circuit discovery that do not guarantee completeness not only result in circuits that are not fixed across different runs but also cause key mechanisms to be omitted. The nature of incompleteness arises from the presence of OR gates within the circuit, which are often only partially detected in standard circuit discovery methods. To this end, we systematically introduce three types of logic gates: AND, OR, and ADDER gates, and decompose the circuit into combinations of these logical gates. Through the concept of these gates, we derive the minimum requirements necessary to achieve faithfulness and completeness. Furthermore, we propose a framework that combines noising-based and denoising-based interventions, which can be easily integrated into existing circuit discovery methods without significantly increasing computational complexity. This framework is capable of fully identifying the logic gates and distinguishing them within the circuit. In addition to the extensive experimental validation of the framework's ability to restore the faithfulness, completeness, and sparsity of circuits, using this framework, we uncover fundamental properties of the three logic gates, such as their proportions and contributions to the output, and explore how they behave among the functionalities of language models.

Existing methods, including large language models (LLMs), excel at capturing semantic correlations between utterances, but often struggle to accurately distinguish specific causal relationships. This limitation poses a significant challenge for reasoning-intensive tasks in affective computing, where precise identification of emotional triggers and their effects is crucial. Our preliminary work demonstrated the potential of introducing i.i.d. noise terms within Structural Causal Models (SCMs) for the Emotion-Cause Pair Extraction (ECPE) task. However, this approach relied on end-to-end learning of high-dimensional latent representations, which hindered both scalability to LLMs and model interpretability. To address these issues, we conceptualize i.i.d. noise terms as token-level implicit causes—natural language expressions that reflect a speaker's underlying emotions, intentions, or situational context. Building on this insight, we introduce ICE (Implicit-Cause-Enhanced), an instruction-based framework that leverages implicit causes to enhance causal reasoning in LLMs. First, we design prompts that heuristically guide LLMs to generate implicit causes, which are then iteratively refined via an external evaluation mechanism. Second, by incorporating these implicit causes as intermediate reasoning steps, ICE improves the accuracy of emotion-cause pair prediction. Moreover, we distill the rationales produced by ICE into lightweight generative models, demonstrating that even small models can benefit from implicit-cause-driven reasoning. Extensive experiments in both instruction-based and distillation-based settings confirm the effectiveness, robustness, and interpretability of our approach.

Generating music with emotion is an important task in automatic music generation, in which emotion is evoked through a variety of musical elements (such as pitch and duration) that change over time and collaborate with each other. However, prior research on deep learning-based emotional music generation has rarely explored the contribution of different musical elements to emotions, let alone the deliberate manipulation of these elements to alter the emotion of music, which is not conducive to fine-grained element-level control over emotions. To address this gap, we present a novel approach employing musical element-based regularization in the latent space to disentangle distinct elements, investigate their roles in distinguishing emotions, and further manipulate elements to alter musical emotions. Specifically, we propose a novel VQ-VAE-based model named MusER. MusER incorporates a regularization loss to enforce the correspondence between the musical element sequences and the specific dimensions of latent variable sequences, providing a new solution for disentangling discrete sequences. Taking advantage of the disentangled latent vectors, a two-level decoding strategy that includes multiple decoders attending to latent vectors with different semantics is devised to better predict the elements. By visualizing latent space, we conclude that MusER yields a disentangled and interpretable latent space and gain insights into the contribution of distinct elements to the emotional dimensions (i.e., arousal and valence). Experimental results demonstrate that MusER outperforms the state-of-the-art models for generating emotional music in both objective and subjective evaluation. Besides, we rearrange music through element transfer and attempt to alter the emotion of music by transferring emotion-distinguishable elements.

Speech signals are inherently complex as they encompass both global acoustic characteristics and local semantic information. However, in the task of target speech extraction, certain elements of global and local semantic information in the reference speech, which are irrelevant to speaker identity, can lead to speaker confusion within the speech extraction network. To overcome this challenge, we propose a self-supervised disentangled representation learning method. Our approach tackles this issue through a two-phase process, utilizing a reference speech encoding network and a global information disentanglement network to gradually disentangle the speaker identity information from other irrelevant factors. We exclusively employ the disentangled speaker identity information to guide the speech extraction network. Moreover, we introduce the adaptive modulation Transformer to ensure that the acoustic representation of the mixed signal remains undisturbed by the speaker embeddings. This component incorporates speaker embeddings as conditional information, facilitating natural and efficient guidance for the speech extraction network. Experimental results substantiate the effectiveness of our meticulously crafted approach, showcasing a substantial reduction in the likelihood of speaker confusion.

The increasing integration of machine learning algorithms in daily life underscores the critical need for fairness and equity in their deployment. As these technologies play a pivotal role in decision-making, addressing biases across diverse subpopulation groups, including age, gender, and race, becomes paramount. Automatic affect analysis, at the inter-section of physiology, psychology, and machine learning, has seen significant development. However, existing databases and methodologies lack uniformity, leading to biased evaluations. This work addresses these issues by analyzing six affective databases, annotating demographic attributes, and proposing a common protocol for database partitioning. Emphasis is placed on fairness in evaluations. Extensive experiments with baseline and state-of-the-art methods demonstrate the impact of these changes, revealing the inadequacy of prior assessments. The findings underscore the importance of considering demographic attributes in affect analysis research and provide a foundation for more equitable methodologies. Our annotations, code and pre-trained models are available at: https://github.com/dkollias/Fair-Consistent-Affect-Analysis

The objective of the Multiple Appropriate Facial Reaction Generation (MAFRG) task is to produce contextually appropriate and diverse listener facial behavioural responses based on the multimodal behavioural data of the conversational partner (i.e., the speaker). Current methodologies typically assume continuous availability of speech and facial modality data, neglecting real-world scenarios where these data may be intermittently unavailable, which often results in model failures. Furthermore, despite utilising advanced deep learning models to extract information from the speaker’s multimodal inputs, these models fail to adequately leverage the speaker’s emotional context, which is vital for eliciting appropriate facial reactions from human listeners. To address these limitations, we propose an Emotion-aware Modality Compensatory (EMC) framework. This versatile solution can be seamlessly integrated into existing models, thereby preserving their advantages while significantly enhancing performance and robustness in scenarios with missing modalities. Our framework ensures resilience when faced with missing modality data through the Compensatory Modality Alignment (CMA) module. It also generates more appropriate emotion-aware reactions via the Emotion-aware Attention (EA) module, which incorporates the speaker’s emotional information throughout the entire encoding and decoding process. Experimental results demonstrate that our framework improves the appropriateness metric FRCorr by an average of 57.2% compared to the original model structure. In scenarios where speech modality data is missing, the performance of appropriate generation shows an improvement, and when facial data is missing, it only exhibits minimal degradation.

Reasoning, a crucial aspect of NLP research, has not been adequately addressed by prevailing models including Large Language Model. Conversation reasoning, as a critical component of it, remains largely unexplored due to the absence of a welldesigned cognitive model. In this paper, inspired by intuition theory on conversation cognition, we develop a conversation cognitive model (CCM) that explains how each utterance receives and activates channels of information recursively. Besides, we algebraically transformed CCM into a structural causal model (SCM) under some mild assumptions, rendering it compatible with various causal discovery methods. We further propose a probabilistic implementation of the SCM for utterance-level relation reasoning. By leveraging variational inference, it explores substitutes for implicit causes, addresses the issue of their unobservability, and reconstructs the causal representations of utterances through the evidence lower bounds. Moreover, we constructed synthetic and simulated datasets incorporating implicit causes and complete cause labels, alleviating the current situation where all available datasets are implicit-causesagnostic. Extensive experiments demonstrate that our proposed method significantly outperforms existing methods on synthetic, simulated, and real-world datasets. Finally, we analyze the performance of CCM under latent confounders and propose theoretical ideas for addressing this currently unresolved issue.

Emotion is one of the most crucial attributes of music. However, due to the scarcity of emotional music datasets, emotion-conditioned symbolic music generation using deep learning techniques has not been investigated in depth. In particular, no study explores conditional music generation with the guidance of emotion, and few studies adopt time-varying emotional conditions. To address these issues, first, we endow three public lead sheet datasets with fine-grained emotions by automatically computing the valence labels from the chord progressions. Second, we propose a novel and effective encoder-decoder architecture named EmoMusicTV to explore the impact of emotional conditions on multiple music generation tasks and to capture the rich variability of musical sequences. EmoMusicTV is a transformer-based variational autoencoder (VAE) that contains a hierarchical latent variable structure to model holistic properties of the music segments and short-term variations within bars. The piece-level and bar-level emotional labels are embedded in their corresponding latent spaces to guide music generation. Third, we pretrain EmoMusicTV with the lead sheet continuation task to further improve its performance on conditional melody or harmony generation. Experimental results demonstrate that EmoMusicTV outperforms previous methods on three tasks, i.e., melody harmonization, melody generation given harmony, and lead sheet generation. Ablation studies verify the significant roles of emotional conditions and hierarchical latent variable structure on conditional music generation. Human listening shows that the lead sheets generated by EmoMusicTV are closer to the ground truth (GT) and perform slightly worse than the GT in conveying emotional polarity.

Automatic music generation is the combination of artificial intelligence and art, in which melody harmonization is a significant and challenging task. However, previous recurrent neural network (RNN)-based work fails to maintain long-term dependency and neglects the guidance of music theory. In this article, we first devise a universal chord representation with a fixed small dimension, which can cover most existing chords and is easy to expand. Then a novel melody harmonization system based on reinforcement learning (RL), RL-Chord, is proposed to generate high-quality chord progressions. Specifically, a melody conditional LSTM (CLSTM) model is put forward that learns the transition and duration of chords well, based on which RL algorithms with three well-designed reward modules are combined to construct RL-Chord. We compare three widely used RL algorithms (i.e., policy gradient, Q -learning, and actor-critic algorithms) on the melody harmonization task for the first time and prove the superiority of deep Q -network (DQN). Furthermore, a style classifier is devised to fine-tune the pretrained DQN-Chord for zero-shot Chinese folk (CF) melody harmonization. Experimental results demonstrate that the proposed model can generate harmonious and fluent chord progressions for diverse melodies. Quantitatively, DQN-Chord achieves better performance than the compared methods on multiple evaluation metrics, such as chord histogram similarity (CHS), chord tonal distance (CTD), and melody-chord tonal distance (MCTD).

Our investigation into the Affective Reasoning in Conversation (ARC) task highlights the challenge of causal discrimination.Almost all existing models, including large language models (LLMs), excel at capturing semantic correlations within utterance embeddings but fall short in determining the specific causal relationships.To overcome this limitation, we propose the incorporation of i.i.d.noise terms into the conversation process, thereby constructing a structural causal model (SCM).It explores how distinct causal relationships of fitted embeddings can be discerned through independent conditions.To facilitate the implementation of deep learning, we introduce the cogn frameworks to handle unstructured conversation data, and employ an autoencoder architecture to regard the unobservable noise as learnable "implicit causes."Moreover, we curate a synthetic dataset that includes i.i.d.noise.Through comprehensive experiments, we validate the effectiveness and interpretability of our approach.Our code is available in https://github.com/Zodiark-ch/ mater-of-our-EMNLP2023-paper.

In noisy and reverberant environments, the performance of deep learning-based speech separation methods drops dramatically because previous methods are not designed and optimized for such situations. To address this issue, we propose a multi-stage end-to-end learning method that decouples the difficult speech separation problem in noisy and reverberant environments into three sub-problems: speech denoising, separation, and de-reverberation. The probability and speed of searching for the optimal solution of the speech separation model are improved by reducing the solution space. Moreover, since the channel information of the audio sequence in the time domain is crucial for speech separation, we propose a triple-path structure capable of modeling the channel dimension of audio sequences. Experimental results show that the proposed multi-stage triple-path method can improve the performance of speech separation models at the cost of little model parameter increment.

Transformer has shown advanced performance in speech separation, benefiting from its ability to capture global features. However, capturing local features and channel information of audio sequences in speech separation is equally important. In this paper, we present a novel approach named Intra-SE-Conformer and Inter-Transformer (ISCIT) for speech separation. Specifically, we design a new network SE-Conformer that can model audio sequences in multiple dimensions and scales, and apply it to the dual-path speech separation framework. Furthermore, we propose Multi-Block Feature Aggregation to improve the separation effect by selectively utilizing information from the intermediate blocks of the separation network. Meanwhile, we propose a speaker similarity discriminative loss to optimize the speech separation model to address the problem of poor performance when speakers have similar voices. Experimental results on the bench-mark datasets WSJ0-2mix and WHAM! show that ISCIT can achieve state-of-the-art results.

Emotion recognition in conversations (ERC) has gained more attention, where contextual information modeling and multimodal fusion have been the focus and challenges in recent years. In this paper, we proposed a Multi-Scale Receptive Field Graph model (MSRFG) to tackle the challenges of ERC. Specifically, MSRFG constructs multi-scale perception graphs and learns contextual information via parallel multi-scale receptive field paths. To compensate for the deficiency of temporal information learning by the graph network, MSRFG injects temporal dependencies into the graph network to model the temporal relationships between utterances. Moreover, to achieve the effective fusion of multimodal information, MSRFG converges the multi-scale features of each modality separately and performs the learning of attention weights after the integration of converged features. We carried out experiments on IEMOCAP and MELD datasets to validate the effectiveness of the proposed method, and the results proved the superiority of our model over the existing SOTA methods. The code is available at https://github.com/Janie1996/MSRFG1.

Existing melody harmonization models have made great progress in improving the quality of generated harmonies, but most of them ignored the emotions beneath the music. Meanwhile, the variability of harmonies generated by previous methods is insufficient. To solve these problems, we propose a novel LSTM-based Hierarchical Variational Auto-Encoder (LHVAE) to investigate the influence of emotional conditions on melody harmonization, while improving the quality of generated harmonies and capturing the abundant variability of chord progressions. Specifically, LHVAE incorporates latent variables and emotional conditions at different levels (piece-and bar-level) to model the global and local music properties. Additionally, we introduce an attention-based melody context vector at each step to better learn the correspondence between melodies and harmonies. Objective experimental results show that our proposed model outperforms other LSTM-based models. Through subjective evaluation, we conclude that only altering the types of chords hardly changes the overall emotion of the music. The qualitative analysis demonstrates the ability of our model to generate variable harmonies.

Vehicular edge networks involve edge servers that are close to mobile devices to provide extra computation resource to complete the computation tasks of mobile devices with low latency and high reliability. Considerable efforts on computation offloading in vehicular edge networks have been developed to reduce the energy consumption and computation latency, in which roadside units (RSUs) are usually considered as the fixed edge servers (FESs). Nonetheless, the computation offloading with considering mobile vehicles as mobile edge servers (MESs) in vehicular edge networks still needs to be further investigated. To this end, in this article, we propose a Deep-Reinforcement-Learning-based computation offloading with mobile vehicles in vehicular edge computing, namely, Deep-Reinforcement-Learning-based computation offloading scheme (DRL-COMV), in which some vehicles (such as autonomous vehicle) are deployed and considered as the MESs that move in vehicular edge networks and cooperate with FESs to provide extra computation resource for mobile devices, in order to assist in completing the computation tasks of these mobile devices with great Quality of Experience (QoE) (i.e., low latency) for mobile devices. Particularly, the computation offloading model with considering both mobile and FESs is conducted to achieve the computation tasks offloading through vehicle-to-vehicle (V2V) communications, and a collaborative route planning is considered for these MESs to move in vehicular edge networks with objective of improving efficiency of computation offloading. Then, a Deep-Reinforcement-Learning approach with designing rational reward function is proposed to determine the effective computation offloading strategies for multiple mobile devices and multiple edge servers with objective of maximizing both QoE (i.e., low latency) for mobile devices. Through performance evaluations, our results show that our proposed DRL-COMV scheme can achieve a great convergence and stability. Additionally, our results also demonstrate that our DRL-COMV scheme also can achieve better both QoE and task offloading requests hit ratio for mobile devices in comparison with existing approaches (i.e., DDPG, IMOPSOQ, and GABDOS).

Music emotion recognition (MER), a sub-task of music information retrieval (MIR), has developed rapidly in recent years. However, the learning of affect-salient features remains a challenge. In this paper, we propose an end-to-end attention-based deep feature fusion (ADFF) approach for MER. Only taking log Mel-spectrogram as input, this method uses adapted VGGNet as spatial feature learning module (SFLM) to obtain spatial features across different levels. Then, these features are fed into squeeze-and-excitation (SE) attention-based temporal feature learning module (TFLM) to get multi-level emotion-related spatial-temporal features (ESTFs), which can discriminate emotions well in the final emotion space. In addition, a novel data processing is devised to cut the single-channel input into multi-channel to improve calculative efficiency while ensuring the quality of MER. Experiments show that our proposed method achieves 10.43% and 4.82% relative improvement of valence and arousal respectively on the R2 score compared to the state-of-the-art model, meanwhile, performs better on datasets with distinct scales and in multi-task learning.

The rapid and wide adoption of microgrids (MGs) and the increasing popularity of electric vehicles (EVs) have created a unique opportunity for the integration of these technologies. In this article, we address the issue of demand response of EVs during MG outages by leveraging Vehicle-to-Grid (V2G) technology. Particularly, we investigate an auction trading market that allows EVs with surplus energy to act as sellers, and EVs that want to be charged to act as buyers. A novel distributed double auction scheme is proposed to allow each buyer EV to submit multiple bids to seller EVs in different parking lots. Nonetheless, the locations of buyer EVs could be inferred by an adversary through analyzing the valuations, posing serious privacy and security risks. In this regard, a valuation-based attack scheme is investigated to validate the potential privacy risk. To defend against such an attack, we present a location privacy-preserving double auction scheme, in which the MicroGrid Central Controller (MGCC) acts as the auctioneer, solving the social welfare maximization problem of matching buyers to sellers, and the cloud is used to conduct calculations for the auctioneer, protecting the privacy of participants via homomorphic encryption. Theoretical analysis is conducted to validate our auction scheme in satisfying the designed economic and privacy properties (e.g., strategy-proofness and$k$-anonymity). The experimental results show that our auction scheme can not only mitigate the demand response problem in MGs, but also provides good performance with respect to social welfare, satisfaction ratio, computational and communication overhead, and privacy leakage.

An edge-cloud collaborative intelligent (ECCI) platform is of great significance for the agile development and rapid deployment of ECCI applications, which are essential for realizing smart industry in the era of Industry 4.0. However, the existing platforms lack considering the high real-time latency demand of industrial operations, which severely hinders the development of smart industry and may even lead to severe industrial accidents. To effectively reduce the response latency of industrial applications, in this article, we propose an ECCI platform IndustEdge. It takes time-sensitive networking as the deterministic transport for the link layer, and provides an extensible ECCI orchestration component to reduce the system level latency. Furthermore, IndustEdge has an ECCI algorithm library for different collaborative modes and provides the complete life cycle management for ECCI applications. We implement platforms for both the real-world prototype and emulated-world emulation, and conduct two case studies to evaluate the effectiveness of IndustEdge.

With the increasing popularity of crowdsourcing services, high-dimensional crowdsourced data provides a wealth of knowledge. Nonetheless, unprecedented privacy threats to participants have emerged, due to complex correlations among multiple attributes and the vulnerabilities of untrusted crowdsourcing servers. Differential privacy-based paradigms have been proposed to release privacy-preserving datasets with statistical approximation. Nonetheless, most existing schemes are limited when facing highly correlated attributes, and cannot prevent privacy threats from untrusted crowdsourcing servers. To address this issue, we propose two novel solutions, namelyLoCopandDR_LoCop, which guarantee local differential privacy based on the randomized response technique while synthesizing and releasing high-dimensional crowdsourced data with high data utility. Particularly,LoCopleverages copula theory to synthesize high-dimensional crowdsourced data via univariate marginal distribution and attribute dependence. Univariate marginal distribution is estimated by the Lasso-based regression algorithm from aggregated privacy-preserving bit strings. Dependencies among attributes are modeled as multivariate Gaussian copula. Based onLoCop, the enhanced solutionDR_LoCopnot only takes advantage of C-vine copula to reflect conditional dependencies among high-dimensional attributes, but also achieves dimension reduction. Extensive experiments on real-world datasets demonstrate that our solutions substantially outperform the state-of-the-art techniques in terms of both data utility and computational overhead.

The densified deployment of heterogeneous networks coexisting with a variety of overlapping cells has emerged as a viable solution for next generation wireless networks. Despite numerous advantages, the heterogeneity and denseness also raise complicated handover management issue. Nonetheless, most existing handover methods generally depend on one or more objective attributes, and rarely consider the subjective demands of personalized users and specific applications that demand differentiated services. Through decomposing the control plane and data plane, software defined network(SDN) offers a flexible architectural paradigm to overcome these challenges. In this article, we first develop an SDN-driven handover architecture that is capable of perceiving global network status and requirements from various perspectives, including the physical layer, users, and applications. Then, a context-aware multi-criteria handover mechanism is developed in the SDN edge to provide differentiated services. Considering the numerous complicated factors, the handover decision is made based on a hierarchical fuzzy inference system to process diverse attributes and vague requirements described in natural language. Finally, we evaluate the performance of our proposed scheme through a combination of extensive simulations and real-world experiments. The results demonstrate that our solution outperforms the baseline handover schemes, more efficiently providing differentiated services with respect to throughput, bandwidth cost, and application satisfaction, and is efficient and feasible in practice.

In Internet-of-Things (IoT)-driven smart-world systems, real-time crowdsourced databases from multiple distributed servers can be aggregated to extract dynamic statistics from a larger population, thus providing more reliable knowledge for our society. Particularly, multiple distributed servers in a decentralized network can realize real-time collaborative statistical estimation by disseminating statistics from their separate databases. Despite no raw data sharing, the real-time statistics could still expose the data privacy of crowdsourcing participants. For mitigating the privacy concern, while the traditional differential privacy (DP) mechanism can be simply implemented to perturb the statistics in each timestamp and independently for each dimension, this may suffer a great utility loss from the real-time and multidimensional crowdsourced data. Also, the real-time broadcasting would bring significant overheads in the whole network. To tackle the issues, we propose a novel privacy preserving and communication-efficient decentralized statistical estimation algorithm (DPCrowd), which only requires intermittently sharing the DP protected parameters with one-hop neighbors by exploiting the temporal correlations in real-time crowdsourced data. Then, with further consideration of spatial correlations, we develop an enhanced algorithm, DPCrowd+, to deal with multidimensional infinite crowd-data streams. Extensive experiments on several data sets demonstrate that our proposed schemes DPCrowd and DPCrowd+ can significantly outperform existing schemes in providing accurate and consensus estimation with rigorous privacy protection and great communication efficiency.

The massive generation, extensive sharing, and deep exploitation of data in the big data era have raised unprecedented privacy threats. To address privacy concerns, various privacy paradigms have been proposed to achieve a good tradeoff between privacy and data utility. Particularly, differential privacy has been well accepted as one of the de facto standard for privacy preservation, and numerous schemes guaranteeing differential privacy have been proposed. Nonetheless, most of the existing works claiming a superior utility-privacy tradeoff only present specific methods, with distinct perspectives, and a complete comparative analysis and evaluation study has not been fully investigated. To this end, in this paper we review and investigate existing schemes on providing differential privacy from a broad and encompassing perspective to provide a comprehensive survey with respect to both the privacy guarantee and the effectiveness and efficiency in utility improvement. We categorize the existing schemes into distribution optimization, sensitivity calibration, transformation, decomposition, and correlations exploitation, based on their mechanisms in improving data utility. We also conduct some analysis and comparison of their various concepts and principles, focusing on improvements to data utility. Finally, we outline some challenges and provide future research directions.

Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for hidden semantic discovery of text data and serves as a fundamental tool for text analysis in various applications. However, the LDA model as well as the training process of LDA may expose the text information in the training data, thus bringing significant privacy concerns. To address the privacy issue in LDA, we systematically investigate the privacy protection of the main-stream LDA training algorithm based on Collapsed Gibbs Sampling (CGS) and propose several differentially private LDA algorithms for typical training scenarios. In particular, we present the first theoretical analysis on the inherent differential privacy guarantee of CGS based LDA training and further propose a centralized privacy-preserving algorithm (HDP-LDA) that can prevent data inference from the intermediate statistics in the CGS training. Also, we propose a locally private LDA training algorithm (LP-LDA) on crowdsourced data to provide local differential privacy for individual data contributors. Furthermore, we extend LP-LDA to an online version as OLP-LDA to achieve LDA training on locally private mini-batches in a streaming setting. Extensive analysis and experiment results validate both the effectiveness and efficiency of our proposed privacy-preserving LDA training algorithms.

Latency-aware service placement is promising in reducing the overall service response latency of proliferating edge-cloud collaborative smart manufacturing systems. However, intuitive latency estimators used by existing service placement approaches cannot accurately depict the nonlinear end-to-end (E2E) latency of multihop microservices with complex dependencies, which is severely hindering the effectiveness of latency-aware service placement. To address this issue, in this article, we present a microservice placement mechanism for edge-cloud collaborative smart manufacturing (MPCSM), where a microservice placement algorithm latency-aware edge-cloud collaborative placement supported by an accurate data-driven E2E latency estimation method is proposed. We build a real-world collaborative prototype, and conduct a case study on semiconductor manufacturing to elaborate the construction of our latency estimator. Results of extensive experiments demonstrate that the error of our E2E latency estimator is up to 10× less than that of existing ones, and the overall service latency with MPCSM is up to 10× less than that with existing service placement approaches.

Finding where and what objects to put into an existing scene is a common task for scene synthesis and robot/character motion planning. Existing frameworks require development of hand-crafted features suitable for the task, or full volumetric analysis that could be memory intensive and imprecise. In this paper, we propose a data-driven framework to discover a suitable location and then place the appropriate objects in a scene. Our approach is inspired by computer vision techniques for localizing objects in images: using an all directional depth image (ADD-image) that encodes the 360-degree field of view from samples in the scene, our system regresses the images to the positions where the new object can be located. Given several candidate areas around the host object in the scene, our system predicts the partner object whose geometry fits well to the host object. Our approach is highly parallel and memory efficient, and is especially suitable for handling interactions between large and small objects. We show examples where the system can hang bags on hooks, fit chairs in front of desks, put objects into shelves, insert flowers into vases, and put hangers onto laundry rack.

With the rapid development of artificial intelligence (AI), ethical issues surrounding AI have attracted increasing attention. In particular, autonomous vehicles may face moral dilemmas in accident scenarios, such as staying the course resulting in hurting pedestrians or swerving leading to hurting passengers. To investigate such ethical dilemmas, recent studies have adopted preference aggregation, in which each voter expresses her/his preferences over decisions for the possible ethical dilemma scenarios, and a centralized system aggregates these preferences to obtain the winning decision. Although a useful methodology for building ethical AI systems, such an approach can potentially violate the privacy of voters since moral preferences are sensitive information and their disclosure can be exploited by malicious parties resulting in negative consequences. In this paper, we report a first-of-its-kind privacy-preserving crowd-guided AI decision-making approach in ethical dilemmas. We adopt the formal and popular notion of differential privacy to quantify privacy, and consider four granularities of privacy protection by taking voter-/record-level privacy protection and centralized/distributed perturbation into account, resulting in four approaches VLCP, RLCP, VLDP, and RLDP, respectively. Moreover, we propose different algorithms to achieve these privacy protection granularities, while retaining the accuracy of the learned moral preference model. Specifically, VLCP and RLCP are implemented with the data aggregator setting a universal privacy parameter and perturbing the averaged moral preference to protect the privacy of voters' data. VLDP and RLDP are implemented in such a way that each voter perturbs her/his local moral preference with a personalized privacy parameter. Extensive experiments based on both synthetic data and real-world data of voters' moral decisions demonstrate that the proposed approaches achieve high accuracy of preference aggregation while protecting individual voter's privacy.

The electrocardiogram (ECG) signal, as one of the most important vital signs, can provide indications of many heart-related diseases. Nonetheless, in the case of telehealth context, the automated analysis and accurate detection of ECG signals remain unsolved issues, because the poor data quality collected by the wearable devices and unprofessional users further increases the complexity of hand-crafted feature extraction, ultimately affecting the efficiency of feature extraction and the detection accuracy. To address this issue and improve the detection accuracy, in this paper we present a novel detection scheme with the raw ECG signal in wearable telehealth system. Our system benefits from the concept of big data, sensing and pervasive computing and the emerging deep learning technology. In particular, a Deep Heartbeat Classification (DHC) scheme is proposed to analyze the ECG signal for arrhythmia detection. Distinct from existing solutions, the detection model in DHC can be trained directly on the raw ECG signal without hand-crafted feature extraction. A cloud-based prototypical system is also designed and implemented with the functions of data acquisition, wireless transmission, back-end data management, and ECG detection. The experimental results demonstrate that our prototypical system is feasible and effective in real-world practice, and extensive experimentation based on the MIT-BIH database demonstrates that the proposed DHC scheme outperforms baseline schemes.

Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for discovery of hidden semantic architecture of text datasets, and plays a fundamental role in many machine learning applications. However, like many other machine learning algorithms, the process of training a LDA model may leak the sensitive information of the training datasets and bring significant privacy risks. To mitigate the privacy issues in LDA, we focus on studying privacy-preserving algorithms of LDA model training in this paper. In particular, we first develop a privacy monitoring algorithm to investigate the privacy guarantee obtained from the inherent randomness of the Collapsed Gibbs Sampling (CGS) process in a typical LDA training algorithm on centralized curated datasets. Then, we further propose a locally private LDA training algorithm on crowdsourced data to provide local differential privacy for individual data contributors. The experimental results on real-world datasets demonstrate the effectiveness of our proposed algorithms.

Spatio-temporal monitoring of the Internet of Things (IoT) has enabled the development and proliferation of third-party computing services by extensively exploiting the massive amount of sensing data. In particular, continuously generated data stream are monitored in real-time and exploited to facilitate people's daily lives, such as traffic monitoring and epidemic prevention. In its simplest way of deployment, the direct publishing of various streams could seriously compromise the privacy of participating users. Hence, a more sophisticated scheme is needed to regulate the privately publishing of data streams, which may possibly require control to be applied dynamically. However, most existing solutions are non-adaptive to dynamic changes of the streams due to constraints of predefined parameters, thus are vulnerable to low data utility. In this paper, we present AdaPub, a data-adaptive framework for infinite multidimensional stream real-time publishing with ω-event differential privacy while ensuring high data utility. Without predefining the parameters, AdaPub could learn and update the parameters that reflect the spatio-temporal correlations of the stream in a data-adaptive manner. Specifically, we propose two modules DimParti and AdaCluster which are seamlessly incorporated into AdaPub to simultaneously learn dimension correlations and time correlations in a data-adaptive way, thus greatly improving the data utility of the sanitized streams. Extensive experiments on real-world datasets demonstrate that our solution substantially outperforms state-of-the-art solutions with much lower errors while achieving strong privacy guarantees.

In this paper, we address the issue of demand response (DR) in microgrids via vehicle-to-vehicle technology in the smart grid with consideration for location privacy protection supported by Internet of Vehicles. To enable effective DR, the online double auction is a viable approach to support energy trading between electric vehicles (EVs) that have surplus or insufficient energy, while the utility of each participant can be considered. Nonetheless, there are three primary challenges in designing such an online double auction approach. First, as EVs are allowed to enter the market at any time, the auctioneer should make the best decision without further information about bids and asks. Second, as EVs are allowed to enter the market in different places, the auctioneer should perform routing optimization for EV charging after determining the winner. Third, there is a risk of leakage in the location of EVs that needs to be protected. To tackle these issues, we present a new truthful online double auction scheme, which features multiunit energy trading among EVs, routing optimization for EV charging, and location privacy protection. We conduct a theoretical analysis and demonstrate that our online double auction scheme is capable of achieving several important economic properties as well as the privacy guarantee (i.e., k-anonymity). Our experimental results show that the proposed scheme can achieve good performance with respect to social welfare, satisfaction ratio, total profit of EV owners, peak load shifting, state of charge, driving distance satisfaction, and computing time, and can further ensure location privacy protection.

Abstract The growing number of 3D scene data available online brings in new challenges for scene retrieval, understanding, and synthesis. Traditional shape processing methods have difficulty to manage 3D scenes because such methods ignore the contextual information, that is, the spatial relationship between the objects or groups of objects, which plays a significant role in describing scenes. Therefore, a context‐aware representation is needed to deal with such a problem. In this paper, we propose a method to build scene hierarchies based on contextual information. Given a 3D scene, we first use the interaction bisector surface to measure the affinity between different objects/elements of the scene and then apply the normalized cut method to build a hierarchical structure for the whole scene. The resulting hierarchical structure contains not only the relationship between the individual objects but also the relationship between object groups, which provides much richer information of the scene compared with a flat structure that only describes the contacts or affinity between the individual objects. We test our method using several public databases and show that the resulting structure is more consistent with the ground truth. We also show that our method can be used for point cloud segmentation and outperforms previous methods.

It has been widely understood that differential privacy can guarantee rigorous privacy against adversaries with arbitrary prior knowledge. However, recent studies demonstrate that this may not be true for correlated data, and indicate that three factors could influence privacy leakage: the data correlation pattern, prior knowledge of adversaries, and sensitivity of the query function. This poses a fundamental problem: what is the mathematical relationship between the three factors and privacy leakage? In this paper, we present a unified analysis of this problem. A new privacy definition, named prior differential privacy (PDP), is proposed to evaluate privacy leakage considering the exact prior knowledge possessed by the adversary. We use two models, the weighted hierarchical graph and the multivariate Gaussian model, to analyze discrete and continuous data, respectively. We demonstrate that positive, negative, and hybrid correlations have distinct impacts on privacy leakage. Considering general correlations, a closed-form expression of privacy leakage is derived for continuous data, and a chain rule is presented for discrete data. Our results are valid for general linear queries, including count, sum, mean, and histogram. Numerical experiments are presented to verify our theoretical analysis.

Music emotion recognition, which enables effective and efficient music organization and retrieval, is a challenging subject in the field of music information retrieval. In this paper, we propose a new bidirectional convolutional recurrent sparse network (BCRSN) for music emotion recognition based on convolutional neural networks and recurrent neural networks. Our model adaptively learns the sequential-information-included affect-salient features (SII-ASF) from the 2-D time–frequency representation (i.e., spectrogram) of music audio signals. By combining feature extraction, ASF selection, and emotion prediction, the BCRSN can achieve continuous emotion prediction of audio files. To reduce the high computational complexity caused by the numerical-type ground truth, we propose a weighted hybrid binary representation (WHBR) method that converts the regression prediction process into a weighted combination of multiple binary classification problems. We test our method on two benchmark databases, that is, the Database for Emotional Analysis in Music and MoodSwings Turk. The results show that the WHBR method can greatly reduce the training time and improve the prediction accuracy. The extracted SII-ASF is robust to genre, timbre, and noise variation and is sensitive to emotion. It achieves significant improvement compared to the best performing feature sets in MediaEval 2015. Meanwhile, extensive experiments demonstrate that the proposed method outperforms the state-of-the-art methods.

With the explosive growth of mobile devices and subsequent traffic volume, densified deployment of Heterogeneous Network (HetNet) coexisting with a variety of cells with overlay coverage has emerged as a viable solution for future 5G networks. Despite many advantages, this new architecture also introduces numerous new network management issues, such as frequent handovers. Although a number of handover mechanisms have been proposed, these methods generally depend on one or more objective attributes from the perspective of the users and network, and do not consider the subjective demands of personalized users and specific applications that demand differentiated network services. In this paper, we first develop an software defined networking (SDN)-driven handover architecture that is capable of perceiving global network statements and requirements from all perspectives, including the physical layer, users, and applications. Then, a context-aware multi-criteria handover mechanism is developed in the SDN controller to provide differentiated services. Considering the many complicated factors, the handover decision is made based on a hierarchical fuzzy inference system to process diverse attributes and fuzzy information described in natural language. The evaluation results demonstrate that our scheme outperforms the baseline Received Signal Strength Indicator (RSSI)-based handover scheme, more efficiently providing differentiated services with respect to throughput, bandwidth cost, and application satisfaction.

Unmanned Aerial Vehicles (UAVs), also known as drones, have become a new paradigm to provide emergency wireless communication infrastructure when conventional base stations are damaged or unavailable. In this paper, we propose new schemes to enable the 3D deployment of drones, which can provide network coverage and connectivity services for users located in uneven terrain. We formalize two models, including optimal coverage model and optimal connectivity model, which belong to NP-hard. To be specific, we first consider both the quality of service (QoS) requirements of users and the capacity of drones. We then formalize the problem and design a heuristic scheme, called Particle Swarm Optimization (PSO) algorithm to achieve a cost-effective solution. We also address the optimal connectivity problem in a scenario, in which a number of isolated local networks have been established by users through ad hoc communication and/or device-to-device (D2D) communication. We further develop the cost-effective heuristic algorithm to effectively minimize the total number of required drones. Via extensive performance evaluation, our experimental results demonstrate that the proposed schemes can achieve the effective deployment of drones for users in uneven terrain with respect to the number of required drones.

The development of the Internet of Things (IoT), cyber physical systems, and ubiquitous mobile terminals enable video content to be shared and consumed in new and innovative ways. Nonetheless, the stochastic and unpredictable nature of heterogeneous wireless networks with mobile clients presents a significant challenge to the increasing demand of quality of experience (QoE). In this paper, we study the problem of maximizing the user's QoE of viewing streaming video via automatic bitrate adaptation in heterogeneous wireless networks. To be specific, a stochastic optimization problem is first formulated by considering fundamental uncertainties of heterogeneous wireless networks (i.e., the stochastic throughput). A dynamic bitrate adaptation scheme is then designed based on the Lyapunov optimization framework. Our proposed scheme conducts video bitrate selection based on the current queue buffer state and real-time throughput, and is capable of balancing the tradeoff between a user's QoE and buffer occupation (i.e., memory utilization). The performance of our proposed scheme is investigated through a combination of extensive analysis, simulations, and experiments in a real-world testbed. Simulation results demonstrate that our scheme outperforms the baseline with regard to QoE and bandwidth utilization. In addition, the experimental results in real-world testbed validate the scheme's efficiency and practicality in real-world system.

High-dimensional crowdsourced data collected from numerous users produces rich knowledge about our society; however, it also brings unprecedented privacy threats to the participants. Local differential privacy (LDP), a variant of differential privacy, is recently proposed as a state-of-the-art privacy notion. Unfortunately, achieving LDP on high-dimensional crowdsourced data publication raises great challenges in terms of both computational efficiency and data utility. To this end, based on the expectation maximization (EM) algorithm and Lasso regression, we first propose efficient multi-dimensional joint distribution estimation algorithms with LDP. Then, we develop a local differentially private high-dimensional data publication algorithm (LoPub) by taking advantage of our distribution estimation techniques. In particular, correlations among multiple attributes are identified to reduce the dimensionality of crowdsourced data, thus speeding up the distribution learning process and achieving high data utility. Extensive experiments on real-world datasets demonstrate that our multivariate distribution estimation scheme significantly outperforms existing estimation schemes in terms of both communication overhead and estimation speed. Moreover, LoPub can keep, on average, 80% and 60% accuracy over the released datasets in terms of support vector machine and random forest classification, respectively.

In this paper, we present theory and a design of the online double auction for the trading of energy within a smart grid with microgrids (MGs). The online double auction has the potential to enable the allocation of surplus electricity to the MGs that need electricity with the highest gain in the real-time market. Nonetheless, two critical issues remain challenging when designing an effective online double auction scheme in such a system. First, as the agents are allowed to arrive and depart at any time, the auctioneer needs to make decisions without the information of further bids and asks. Second, the economic properties of strategy-proof, individual rational, and (weak) budget balance should be satisfied. To address these issues and enable multiunit electricity trading among local MGs, in this paper, we propose a strategy-proof online double auction (SODA) scheme, in which the surplus and insufficient MGs in the system are treated as sellers and buyers, respectively, and the MG center controller is capable of maximizing the social welfare of MGs by appropriately matching buyers and sellers. Via theoretical analysis, we prove that SODA can achieve the properties of individual rationality, (weak) budget balance, strategy-proofness, and computational efficiency. Experiments also show that SODA is capable of reducing the energy purchasing cost of the MGs and shifting the peak-load, while achieving great performance with respect to social welfare, seller/buyer satisfaction ratio, social efficiency, and computation overhead.

Transportation-based Cyber-Physical Systems (TCPS), also known as Intelligent Transportation Systems (ITS), have been introduced to increase traffic efficiency and safety. To reduce traffic congestion and traveling time, a number of real-time route guidance schemes have been developed to assist travelers in determining the optimal route for their transit. In this paper, we address the vulnerability issue of the route guiding process and study data integrity attacks against route guidance schemes. To be specific, we consider a generic attack, in which the adversary may compromise vehicles via wireless communication networks and then manipulate the real-time traffic information generated or forwarded by these vehicles, and finally broadcast the forged real-time traffic information into vehicular networks. We formally model the attack and quantitatively analyze its impact on the effectiveness of route guidance schemes. Our findings show that the investigated data integrity attack can effectively disrupt route guidance, resulting in significant traffic congestion, the increase of travel time, and the imbalanced use of transportation resources.

In this paper, we investigate the data integrity attack against Optimal Power Flow (OPF) with the least effort from the adversary's perspective. The investigated attack can first select the minimum number of target nodes to compromise by analyzing the difference between the capacity of transmission line and the real transmission power, and then search for a critical attack vector (with a goal to minimize the amount of information to manipulate) as an optimal attack strategy. To defend against such an attack, we develop the defensive scheme by protecting the critical nodes. Based on various IEEE standard systems, we show the effectiveness of our investigated attack scheme and the corresponding defense schemes.

Mobile Crowdsensing is a promising paradigm for ubiquitous sensing, which explores the tremendous data collected by mobile smart devices with prominent spatial-temporal coverage. As a fundamental property of Mobile Crowdsensing Systems, temporally recruited mobile users can provide agile, fine-grained, and economical sensing labors, however their self-interest cannot guarantee the quality of the sensing data, even when there is a fair return. Therefore, a mechanism is required for the system server to recruit well-behaving users for credible sensing, and to stimulate and reward more contributive users based on sensing truth discovery to further increase credible reporting. In this paper, we develop a novel Cheating-Resilient Incentive (CRI) scheme for Mobile Crowdsensing Systems, which achieves credibility-driven user recruitment and payback maximization for honest users with quality data. Via theoretical analysis, we demonstrate the correctness of our design. The performance of our scheme is evaluated based on extensive real-world trace-driven simulations. Our evaluation results show that our scheme is proven to be effective in terms of both guaranteeing sensing accuracy and resisting potential cheating behaviors, as demonstrated in practical scenarios, as well as those that are intentionally harsher.

Various paradigms, based on differential privacy, have been proposed to release a privacy-preserving dataset with statistical approximation. Nonetheless, most existing schemes are limited when facing highly correlated attributes, and cannot prevent privacy threats from untrusted servers. In this paper, we propose a novel Copula- based scheme to efficiently synthesize and release multi-dimensional crowdsourced data with local differential privacy. In our scheme, each participant's (or user's) data is locally transformed into bit strings based on a randomized response technique, which guarantees a participant's privacy on the participant (user) side. Then, Copula theory is leveraged to synthesize multi-dimensional crowdsourced data based on univariate marginal distribution and attribute dependence. Univariate marginal distribution is estimated by the Lasso-based regression algorithm from the aggregated privacy- preserving bit strings. Dependencies among attributes are modeled as multivariate Gaussian Copula, of which parameter is estimated by Pearson correlation coefficients. We conduct experiments to validate the effectiveness of our scheme. Our experimental results demonstrate that our scheme is effective for the release of multi-dimensional data with local differential privacy guaranteed to distributed participants.

In this paper, we address the problem of demand response of electrical vehicles (EVs) during microgrid outages in the smart grid through the application of Vehicle-to-Grid (V2G) technology. Particularly, we present a novel privacy-preserving double auction scheme. In our auction market, the MicroGrid Center Controller (MGCC) acts as the auctioneer, solving the social welfare maximization problem of matching buyers to sellers, and the cloud is used as a broker between bidders and the auctioneer, protecting privacy through homomorphic encryption. Theoretical analysis is conducted to validate our auction scheme in satisfying the intended economic and privacy properties (e.g., strategy-proofness and k-anonymity). We also evaluate the performance of the proposed scheme to confirm its practical effectiveness.

Microgrids (MGs) have attracted growing attention due to self-sufficiency and self-healing properties. Nonetheless, the intermittent nature and uncertainty of distributed energy resources and load demands remain challenging issues in balancing demands and managing energy resources in MGs. Existing research efforts mainly focus on developing techniques to enable interactions between local MGs and the utility grid, which leads to high line power losses and operation costs. In this paper, we present the Sto2Auc framework to address the issue of stochastic optimal bidding problem for a system with MGs. First, the optimal bidding problem is formulated as a two-stage stochastic programming process, which aims to minimize the system operation cost and obtain optimal energy capacity of MGs by the MG center controller (MGCC). Uncertainties arise from both energy supply and demand, which are considered in the stochastic model, and random parameters representing those uncertainties are captured by using the Monte Carlo method. Second, to enable optimal electricity trading between the insufficient and surplus MGs, we propose a distributed double auction (DDA)-based scheme, which is proven to converge to the optimal social welfare of the system with MGs, and achieves the economical properties of being strategy-proof, individually rational, and (weak) budget balanced. Extensive experiments on an MG system composed of IEEE-33 buses demonstrate the effectiveness of proposed scheme. The experimental results show that Sto2Auc framework is capable of reducing the operational cost of MG systems, while the implemented DDA scheme achieves good performance with respect to social welfare, demand insufficiency, and MGCC profit.

Fog/edge computing has been proposed to be integrated with Internet of Things (IoT) to enable computing services devices deployed at network edge, aiming to improve the user's experience and resilience of the services in case of failures. With the advantage of distributed architecture and close to end-users, fog/edge computing can provide faster response and greater quality of service for IoT applications. Thus, fog/edge computing-based IoT becomes future infrastructure on IoT development. To develop fog/edge computing-based IoT infrastructure, the architecture, enabling techniques, and issues related to IoT should be investigated first, and then the integration of fog/edge computing and IoT should be explored. To this end, this paper conducts a comprehensive overview of IoT with respect to system architecture, enabling technologies, security and privacy issues, and present the integration of fog/edge computing and IoT, and applications. Particularly, this paper first explores the relationship between cyber-physical systems and IoT, both of which play important roles in realizing an intelligent cyber-physical world. Then, existing architectures, enabling technologies, and security and privacy issues in IoT are presented to enhance the understanding of the state of the art IoT development. To investigate the fog/edge computing-based IoT, this paper also investigate the relationship between IoT and fog/edge computing, and discuss issues in fog/edge computing-based IoT. Finally, several applications, including the smart grid, smart transportation, and smart cities, are presented to demonstrate how fog/edge computing-based IoT to be implemented in real-world applications.

In this paper, we address the security issue of optimal power flow (OPF) (as a key component in the smart grid). To be specific, we investigate the data integrity attack against OPF with the least effort from the adversary's perspective, and propose effectively defense schemes to combat the data integrity attack, with respect to the number of nodes to compromise and the amount of information to manipulate. The investigated attack can first select the minimum number of target nodes to compromise by analyzing the difference between the capacity of transmission line and the real transmission power, and then search for a critical attack vector as an optimal attack strategy. To defend against such an attack, we develop the defensive schemes by not only protecting the critical nodes but also detecting the existence of attacks based on false measurement detection schemes. Based on various IEEE standard systems, we show the effectiveness of our investigated attack scheme and the corresponding defense schemes. The experimental results show that the discovered compromised nodes and critical attack vector could lead to the increase of the fuel cost from the power generation by compromising the least number of nodes and injecting the least amount of false information, in comparison with the random attack as the baseline attack strategy. In addition, our two developed defensive schemes are capable of making OPF resilient to the data integrity attack via protecting critical nodes and identifying the falsified measurements accurately in the system.

In recent years, the smart grid has been recognized as an important form of the Internet of Things application. In the smart grid, as an energy-based cyber-physical system, the advanced metering infrastructure (AMI) will be developed to monitor and control the power grid by integrating computing and networking components to ensure stable and efficient operation. The AMI is vulnerable to cyber attacks, especially data integrity attacks. There have been a number of research efforts on detecting such attacks. Nonetheless, most of existing schemes either rely on predefined thresholds or require external knowledge. This may lead to low detection accuracy when the thresholds are improperly defined, and where there is a lack of the external knowledge. To address these issues, in this paper, we propose a Gaussian-mixture model-based detection scheme to mitigate data integrity attacks. Not relying upon the predefined thresholds or external knowledge, our developed scheme operates through narrowing the range of normal data, which can be obtained through clustering the historical data and learning minimum and maximum values or distance values to each center of individual clusters. To evaluate the effectiveness of our proposed scheme, we conduct performance simulation based on the ElectricityLoadDiagrams20112014 data set, and then analyze the effectiveness of the proposed scheme with respect to detection accuracy and overhead. The results of our investigation show that our scheme could achieve a higher detection rate, and a lower error rate, in comparison to existing schemes based on the Min-Max model.

State estimation plays a critical role in self-detection and control of the smart grid. Data integrity attacks (also known as false data injection attacks) have shown significant potential in undermining the state estimation of power systems, and corresponding countermeasures have drawn increased scholarly interest. Nonetheless, leveraging optimal phasor measurement unit (PMU) placement to defend against these attacks, while simultaneously ensuring the system observability, has yet to be addressed without incurring significant overhead. In this paper, we enhance the least-effort attack model, which computes the minimum number of sensors that must be compromised to manipulate a given number of states, and develop an effective greedy algorithm for optimal PMU placement to defend against data integrity attacks. Regarding the least-effort attack model, we prove the existence of smallest set of sensors to compromise and propose a feasible reduced row echelon form (RRE)-based method to efficiently compute the optimal attack vector. Based on the IEEE standard systems, we validate the efficiency of the RRE algorithm, in terms of a low computation complexity. Regarding the defense strategy, we propose an effective PMU-based greedy algorithm, which cannot only defend against data integrity attacks, but also ensure the system observability with low overhead. The experimental results obtained based on various IEEE standard systems show the effectiveness of the proposed defense scheme against data integrity attacks.

Mobile Crowdsourcing is a promising service paradigm utilizing ubiquitous mobile devices to facilitate large-scale crowdsourcing tasks (e.g., urban sensing and collaborative computing). Many applications in this domain require Device-to-Device (D2D) communications between participating devices for interactive operations such as task collaborations and file transmissions. Considering the private participating devices and their opportunistic encountering behaviors, it is highly desired to establish secure and trustworthy D2D connections in a fast and autonomous way, which is vital for implementing practical Mobile Crowdsourcing Systems (MCSs). In this paper, we develop an efficient scheme, Trustworthy Device Pairing (TDP), which achieves user-transparent secure D2D connections and reliable peer device selections for trustworthy D2D communications. Through rigorous analysis, we demonstrate the effectiveness and security intensity of TDP in theory. The performance of TDP is evaluated based on both real-world prototype experiments and extensive trace-driven simulations. Evaluation results verify our theoretical analysis and show that TDP significantly outperforms existing approaches in terms of pairing speed, stability, and security.

In this paper, we investigate a novel real-time pricing scheme, which considers both renewable energy resources and traditional power resources and could effectively guide the participants to achieve individual welfare maximization in the system. To be specific, we develop a Lagrangian-based approach to transform the global optimization conducted by the power company into distributed optimization problems to obtain explicit energy consumption, supply, and price decisions for individual participants. Also, we show that these distributed problems derived from the global optimization by the power company are consistent with individual welfare maximization problems for end-users and traditional power plants. We also investigate and formalize the vulnerabilities of the real-time pricing scheme by considering two types of data integrity attacks: Ex-ante attacks and Ex-post attacks, which are launched by the adversary before or after the decision-making process. We systematically analyze the welfare impacts of these attacks on the real-time pricing scheme. Through a combination of theoretical analysis and performance evaluation, our data shows that the real-time pricing scheme could effectively guide the participants to achieve welfare maximization, while cyber-attacks could significantly disrupt the results of real-time pricing decisions, imposing welfare reduction on the participants.

In the smart grid, the Advanced Metering Infrastructure (AMI) will be deployed to monitor and control the power grid by integrating both computing and networking components to achieve stable and efficient operation. The AMI is vulnerable to cyber attacks, especially in the form of data integrity attacks. A number of research efforts have been devoted to detecting such attacks. Nonetheless, the majority of existing schemes either rely on a pre-defined threshold, or require external knowledge. This leaves open the possibility for low detection accuracy when the threshold is improperly defined, and where there is a lack of the requisite external knowledge. To address this issue, in this paper we propose a Gaussian-Mixture Model-based Detection (GMMD) scheme to combat data integrity attacks. Not relying upon the pre-defined threshold or external knowledge, our scheme operates by narrowing the range of normal data that can be obtained by clustering the historical data and learning the minimum and maximum values of individual clusters. To validate the effectiveness of our scheme, we conduct performance evaluation based on the ElectricityLoadDiagrams20112014 data set, and analyze the effectiveness of the proposed scheme with respect to detection accuracy.The results of our investigation demonstrate that our scheme can achieve a higher detection rate, and lower error rate, in comparison with existing schemes based on the Min-Max model.

In this paper, we address the issue of designing an effective distributed real-time pricing scheme in the smart grid and investigating its security resilience when the data integrity attack is in place. Different from existing research efforts, in this paper we develop a distributed real-time pricing scheme, which can maximize the welfare of all participants and improve the resilience to system failures, as well as consider both renewable and traditional power resources. By leveraging the distributed approach, we leverage the gradient projection mechanism to solve the distributed real-time pricing problem in participants' smart meters to improve the resilience to system failures. We also investigate the vulnerabilities of the distributed real-time pricing scheme by considering one typical data integrity attack, which can inject false data into communication interfaces. Via a combination of both theoretical analysis and performance evaluation, we demonstrate that the proposed distributed scheme can effectively guide the participants to achieve individual welfare maximization. Our findings also show that data integrity attacks can disrupt the distributed real-time pricing, posing a damage to the welfare of participants.

In the smart grid, as a large-scale distributed cyber-physical system, Demand Response (DR) plays an important role in the electricity market. Various demand response schemes have been developed to improve the efficiency and economy of power utilization. Nonetheless, most existing schemes, including both market-led and system-led schemes, do not carefully take the information security into account in the DR process so that the power grid could suffer from cyber attacks (data integrity attacks, etc.). To address this issue, in this paper we proposed a resilient demand response scheme based on microgrids, which can achieve both great effectiveness of energy use and security resilience against data integrity attacks. In our scheme, the DR process considers two power distribution stages. In the intra-microgrid stage, the DR providers generate the list of possible electricity prices and schedules for power delivery. In the inter-microgrid stage, utilities select the proper electricity price and the schedule for power delivery. In this way, the damage impact of attacks on the power grid can be limited only within isolated microgrids that are compromised, while other microgrids that are not compromised can operate effectively. Our experimental results show that our scheme can not only bring better benefits to all participants, but also achieve a greater security resilience in the DR process in comparison with existing schemes.

Islanded Microgrids (IMG) have attracted much attention in the research and development of the smart grid, which is a large-scale distributed cyber-physical system. To overcome the limitations of the single IMG on energy efficiency and economic, In this paper, we first proposed a novel self-sufficient system, namely “Archipelago microgrid (MG)”, which is comprised of multi-microgrids while disconnected with the utility grid. We formalized the EV (electric vehicle) penetration problem as an optimization mixed integer nonlinear programming, which aims to minimize the emission and operation cost in the system. To enable a reasonable deployment of EV in each MGs, we developed two scheduling schemes, namely Unlimited Coordinated Scheme (UCS) and Limited Coordinated Scheme (LCS), respectively. A decentralized algorithm was also developed to solve the optimization model in LCS. A simulation study based on an modified IEEE-9 bus system with three MGs show that our proposed schemes can reduce both the environmental pollution created by CO2 emission and operation cost. Especially, with the consideration of peak load limits and resident preferences, the LCS scheme can obtain better results than the UCS scheme, leading to the reduction of the environmental pollution by 15.2% raised by CO2 emission, as well as the total cost by 10.8% in the system.

The multistep electricity price (MEP) policy has been introduced by many countries to promote energy saving, load balancing, and fairness in electricity consumption. Nonetheless, with the development of the smart grid, how to determine the quantity of electricity and at what price in a step-like fashion has not been fully investigated in the past. To address this issue, in this paper, we introduce two types of MEP models: a one-dimensional MEP model and a two-dimensional MEP model, which can be used to formally analyze and determine the desirable quantities of electricity and pricing in multiple steps. Particularly, in the one-dimensional MEP model, the steps are scaled only by the quantity of electricity whereas in the two-dimensional MEP model, the steps are scaled by both the quantity of electricity and the time when the electricity is used. Based on the proposed MEP models, we further investigate the vulnerability of the electricity market operation and investigate false data injection attacks against electricity prices and charges to consumers. Through an extensive simulation study, our data shows that the proposed MEP models can achieve fairness in electricity consumption, balance loads between peak and non-peak times, and improve electricity resource utilization. Our data also indicates that false data injection attacks can only partially compromise prices in our MEP models, leading to a limited impact on users' charges.

In real-time monitoring systems, fine-grained measurements would pose great privacy threats to the participants as real-time measurements could disclose accurate people-centric activities. Differential privacy has been proposed to formalize and guide the design of privacy-preserving schemes. Nonetheless, due to the correlations and high fluctuations in time-series data, it is hard to achieve an effective privacy and utility tradeoff by differential privacy mechanisms. To address this issue, in this paper, we first proposed novel multi-dimensional decomposition based schemes to compress the noise and enhance the utility in differential privacy. The key idea is to decompose the measurements into multi-dimensional records and to achieve differential privacy in bounded dimensions so that the error caused by unbounded measurements can be significantly reduced. We then extended our developed scheme and developed a binary decomposition scheme for privacy-preserving time-series aggregation in real-time monitoring systems. Through a combination of extensive theoretical analysis and experiments, our data shows that our proposed schemes can effectively improve usability while achieving the same level of differential privacy than existing schemes.

In this paper, we investigate a novel real-time pricing scheme, which considers both renewable energy resources and traditional power resources, and can effectively guide the participants to achieve individual welfare maximization. Particularly, we develop a Lagrangian- based approach that transforms the global optimization conducted by the power company to distributed optimization problems. We show that these distributed problems are consistent with individual welfare maximization problems for end-users and traditional power plants. We also investigate vulnerabilities of the real-time pricing scheme by considering two types of data integrity attacks, i.e., injecting false data into demand-users and injecting false data into supply- users. Through a combination of theoretical analysis and performance evaluation, our data shows that the proposed real-time pricing scheme can effectively guide the participants to achieve welfare maximization. Our data also shows that data integrity attacks can effectively disrupt the results of real-time pricing decisions, posing welfare reduction on participants.

Real-time monitoring systems can introduce numerous benefits to the participants in terms of performing data mining and analysis. Nonetheless, due to the correlations in time-series data, it is hard to achieve an effective privacy and utility tradeoff through a normal differential privacy mechanism. To address this issue, we propose novel multi-dimensional decomposition based schemes, which can greatly improve the utility in differential privacy. After extending the developed scheme, we then develop a binary decomposition scheme for time-series aggregation in real-time monitoring systems. Through both extensive theoretical analysis and experiments, our data shows that our proposed schemes can effectively improve usability while achieving the same level of differential privacy than existing schemes.

Security plays a vital role in promoting the practicality of Wireless Body Sensor Networks (BSNs), which provides a promising solution to precise human physiological status monitoring. A fundamental security issue in BSN is key management, including establishment and maintenance of the key system. However, current BSN key management solutions are either designed for specific phases of a BSN's life-time or restricted to strong assumptions such as homogeneous BSN composition, pre-deployed key materials, and existing secure path, which limits their applications in real-world BSNs. In this paper, we develop the Systematic Key Management (SKM) for practical BSNs, where basic human interactions are conducted for non-predeployed secure BSN initialization, and authenticated key agreement is achieved using lightweight non-pairing certificateless public key cryptography. We construct a BSN prototype consisting of self-designed motes and Android phones to evaluate the real-world performance of SKM. Through extensive simulations and test-bed experiments, we demonstrate that our lightweight SKM scheme manages to provide high security guarantee while outperforming state-of-the-art approaches in terms of both computation and storage efficiency.

To enhance the reliability and efficiency of energy service in the smart grid, the concept of the microgrid has been proposed. Nonetheless, how to secure the dynamic microgrid partition process is essential in the smart grid. In this paper, we address the security issue of the dynamic microgrid partition process and systematically investigate three false data injection attacks against the dynamic microgrid partition process. Particularly, we first discussed the dynamic microgrid partition problem based on a Connected Graph Constrained Knapsack Problem (CGKP) algorithm. We then developed a theoretical model and carried out simulations to investigate the impacts of these false data injection attacks on the effectiveness of the dynamic microgrid partition process. Our theoretical and simulation results show that the investigated false data injection attacks can disrupt the dynamic microgrid partition process and pose negative impacts on the balance of energy demand and supply within microgrids such as an increased number of lack-nodes and increased energy loss in microgrids.

Both opportunistic routing and inter-flow network coding are useful mechanisms for improving the performance of wireless networks. Both of them exploit the broadcast nature of the wireless medium and the spatial diversity of multi-hop wireless networks. In this paper, we aim at incorporating interflow network coding into opportunistic routing for further improving the performance of wireless mesh networks (WMNs). The main issue in designing such a scheme is candidate set selection and prioritization based on a proper metric for opportunistic routing. To this end, in this paper, we first present a new metric to determine the prioritization of the forwarders in the set of candidates and then design an Inter-flow Network Coding-based Opportunistic Routing (INCOR) scheme using the defined metric. Our proposed INCOR scheme can integrate the characteristics of inter-flow network coding and opportunistic routing effectively to make full use of the broadcast nature of the wireless medium. We carry out extensive simulations to evaluate the effectiveness of the INCOR method. Our data shows that INCOR outperforms both opportunistic routing and inter-flow network coding schemes.

Wireless Mesh Networks (WMNs) have potential to provide convenient broadband wireless Internet access to mobile users. With the emergence of Software-Defined Networking (SDN) paradigm that separates control plane and data plane, WMNs can be easily deployed and managed. In addition, by exploiting the broadcast nature of the wireless medium and the spatial diversity of multi-hop wireless networks, intra-flow network coding has shown a greater benefit in comparison with traditional routing paradigms in data transmission for WMNs. In this paper, we develop a novel OpenCoding protocol, which combines the SDN technique with intra-flow network coding for WMNs. Our developed protocol can simplify the deployment and management of the network and improve network performance. In OpenCoding, a controller working on the control plane makes routing decisions for mesh routers and the hop-by-hop forwarding function is replaced by network coding functions in data plane. Through a simulation study, we show the effectiveness of the OpenCoding protocol in comparison with existing schemes. Our data shows that OpenCoding outperforms both traditional routing and intra-flow network coding schemes.

In an intelligence transportation system (ITS), to increase traffic efficiency, a number of dynamic route guidance schemes have been designed to assist drivers in determining the optimal route for their travels. In order to determine optimal routes, it is critical to effectively predict the traffic condition of roads along the guided routes based on real-time traffic information to mitigate traffic congestion and improve traffic efficiency. In this paper, we propose a Dynamic En-route Decision real-time Route guidance (DEDR) scheme to effectively mitigate road congestion caused by the sudden increase of vehicles and reduce travel time. Particularly, DEDR considers real-time traffic information generation and transmission. Based on the shared traffic information, DEDR introduces Trust Probability to predict traffic conditions and dynamically en-route determine alternative optimal routes. In addition, DEDR considers multiple metrics to comprehensively assess traffic conditions and drivers can determine optimal route with individual preference of these metrics during travel. DEDR also considers effects of external factors (e.g., Bad weather, incidents, etc.) on traffic conditions. Through a combination of extensive theoretical analysis and simulation experiments, our data shows that DEDR can greatly increase the efficiency of an ITS in terms of great time efficiency and balancing efficiency in comparison with existing schemes.

The smart grid is a new type of energy-based cyber-physical system (CPS), which enables interactions between the utility provider and customers through smart meters and advanced metering infrastructures (AMI). Nonetheless, an adversary can inject misleading energy usage information to the utility provider through compromised smart meters and disrupt the grid and electricity market operations. To address this issue, in this paper, we propose an Energy Dispatching False Data Defense (EDF2D) approach, which can effectively detect the forged interactive information between customers and the utility provider with a great accuracy and mitigate the damage raised by attacks on grid operations. Particularly, EDF2D uses the historical interactive information of normal users to determine the conditional probabilities of data anomalies. Based on these conditional probabilities, a Bayesian network designed for detecting false data can be established by EDF2D, and this network is then used to confirm the authenticity of interactive information received by the utility provider originally transmitted from customers. Through a combination of theoretical analysis and performance evaluation, our experimental data shows that EDF2D can effectively detect harmful false interactive data forged by the adversary and mitigate false data injection attacks on smart grid operations.

In this paper, we addressed the issue of a stochastic optimal bidding problem for a system with microgrids (MGs). The optimal bidding problem is formulated as a two-stage stochastic programming process, which aims to minimize the system operation cost and to expand energy interactions among local MGs that are geographically close. Uncertainties come from both energy supply and demand sides (e.g., wind, solar, and load demand) are considered in the stochastic model and random parameters to represent those uncertainties are captured by using the Monte Carlo method. To enable an optimal electricity trading between local MGs, we presented two bidding schemes: (i) Cournot equilibrium based Dynamic Backtrack Energy Trading (DBET), and (ii) double auction based Dual Decomposition Auction (DDA). Experimental results on an IEEE-33 bus based system with MGs were presented to show the effectiveness of our proposed schemes. Experimental results show that our proposed bidding schemes can reduce the operation cost of the system, while the DDA scheme achieves better performance in terms of system social welfare than the DBET scheme.

In Cyber-Physical Networked Systems (CPNS), the adversary can inject false measurements into the controller through compromised sensor nodes, which not only threaten the security of the system, but also consume network resources. To deal with this issue, a number of en-route filtering schemes have been designed for wireless sensor networks. However, these schemes either lack resilience to the number of compromised nodes or depend on the statically configured routes and node localization, which are not suitable for CPNS. In this paper, we propose a Polynomial-based Compromise-Resilient En-route Filtering scheme (PCREF), which can filter false injected data effectively and achieve a high resilience to the number of compromised nodes without relying on static routes and node localization. PCREF adopts polynomials instead of Message Authentication Codes (MACs) for endorsing measurement reports to achieve resilience to attacks. Each node stores two types of polynomials: authentication polynomial and check polynomial, derived from the primitive polynomial, and used for endorsing and verifying the measurement reports. Through extensive theoretical analysis and experiments, our data shows that PCREF achieves better filtering capacity and resilience to the large number of compromised nodes in comparison to the existing schemes.

Concomitant to the growing popularity of Internet enabled mobile devices such as smartphones, tablets, PDAs, portable media players etc., however, are the concerns about availability of Internet access points for these devices. Mobile users often either overpay for service availability such as (3G or LTE) or suffer incapability of accessing Internet services due to limited hardware resources (3G or LTE) or exhaustion of carrier enforced data plans. In this paper we introduce Collab Assure, an auction based, ad-hoc market model assuring service for users with no Internet access capability. Collab Assure framework provides service assurance through opportunistic ad-hoc networks formed by spatio-temporally co-existing mobile users. The system allows users to "sublet" their surplus data plans to the users without Internet access. We discuss the design and implementation of Collab Assure technology in Android framework. Our simulation results advocate the success of this approach on real world traces, where mobile users need to participate in auctions for achieving on-demand and low-cost data service.

Pollution attacks refer to ones where attackers modify and inject corrupted data packets into the wireless network with network coding to disrupt the decoding process. In the context of network coding, the epidemic effect of pollution attacks can degrade network throughput significantly because of the mixing nature of network coding. To address this issue, a number of malicious nodes identification schemes have been developed in the past. However, these schemes have their limitations and cannot effectively deal with pollution attacks. In this paper, we propose a novel light-weight Self-checking Pollution Attackers Identification Scheme (SPAIS), which can identify the pollution attackers effectively and efficiently. Through making full use of the broadcast nature of wireless media and insight that a well-behaved node can monitor its downstream neighboring nodes locally by cooperating with other nodes, SPAIS hierarchically organizes the network as levels such that the nodes in the same level can monitor their downstream level nodes cooperatively. Through the combination of theoretical analysis and extensive simulations, our experimental data demonstrates that SPAIS can more effectively identify pollution attackers with a lower cost in comparison with the existing representative schemes. For example, even if the quality of the network connection is not in good condition and the malicious nodes send only one corrupted packet, the pollution attackers can be identified with a high probability.

Cooperative communication mechanisms have been proposed as an effective way of exploiting the spatial diversity to improve the quality of wireless transmissions. To the best of our knowledge, a number of research efforts have been paid to study how to employ diversity to the network layer routing design, realizing the minimum energy expenditure in the data transmission. However, there is a lack of a systematic strategy for evaluating the existing schemes. To address this issue, we first develop a taxonomy to summarize the existing energy-efficient cooperative routing algorithms and compare their pros and cons. In particular, we focus on the relay set selection strategies, which have great impact on energy saving. To fairly compare the performance of those schemes, we conduct theoretical analysis and derive three theorems to instruct energy-efficient cooperative routing. Our extensive experiments validate our findings. Our research summarizes the state-of-art research development and lay out future directions in this area.

The concept of Multistep Electricity Price (MEP) policy has been introduced by many countries to promote energy saving, load balance and fairness in electricity consumption. However, with the development of smart grid, how to determine the electricity quantity and price scaled to multiple steps has not been fully investigated. To address this issue, in this paper we study a two-dimensional MEP model to formally analyze and determine the desirable electricity quantity and price in multiple steps. In this model, the step is scaled by both electricity quantity and the time when the electricity is used. Based on the proposed MEP model, we further investigate the vulnerability of the electricity market operation and investigate false data injection attacks against electricity price and charges to consumers. Through simulation study, our data show that the proposed MEP models can achieve the fairness in electricity consumption, balance in load between peak time and non-peak time and improvement of resource utilization. Our data also indicates that false data injection attacks can only partially compromise prices in the MEP model, leading to a limited impact on users' charges.

The smart grid shall not only integrate the intermittent resources (IRs) to meet the diverse demands of users and reduce the greenhouse gas emission, but also integrate Electricity Vehicles (EVs) as the energy storage facility to smooth the bulk power generation over time. In this paper, we model and analyze the impact of integrating IRs and EVs on the bulk power generation in the smart grid. In particular, we introduce the reliability ratio to quantify the power generation capacity of intermittent resources and model the process of charging and discharging of EVs as a queuing system. We extend the Security-Constrained Economic Dispatch (SCED) and include the reliability limit of IRs and the number of EVs in the power generation dispatch process and formally analyze the effect of IRs and EVs on the bulk power generation. We conduct extensive simulation and our data shows that increasing IRs can decrease the bulk generation and the curve of bulk generation over time becomes smooth as the number of EVs increases.

The smart grid poses great concern about the exposure of consumers' privacy as the fine-grained measurements in the smart metering system can expose consumer's privacy through the disclosure of accurate load profiles of home energy usage. To address this issue, in this paper we propose novel scaling perturbation based privacy-preserving schemes that can achieve great utility for fine-grained measurements in a privacy-friendly and cost-effective manner. Our schemes adopt the measurement-based scaling perturbation to hide original measurements with low cost. Through a combination of both extensive theoretical analysis and experiments, our results show that the proposed schemes can preserve consumers' privacy through fine-grained measurements and achieve a better utility-privacy tradeoff in comparison with the existing schemes.

Improving TCP performance over Wireless network is critical to enhancing the Quality of Service of mobile users. However, most conventional approaches can neither discover the hidden drawbacks, nor provide both fast and accurate mechanisms to adapt to unique Wireless TCP specifications, thus cannot guarantee desirable performance. In this article, we propose WiTracer, a novel solution for wireless TCP performance enhancement. WiTracer uses several criterion to extract packet losses and Round-Trip-Time (RTT) information, thus to infer potential channel variations. Hereafter, WiTracer builds a simple and robust design to decouple packet loss recovery from TCP congestion control, and mitigates spurious performance degradation. Therefore, satisfactory throughput is maintained, with no modification to existing Reno compatible TCP suite. Extensive simulation and implementation results show that WiTracer outperforms other benchmark approaches and performs well in varying wireless environments.

Recent advances in solar harvesting technologies pave the way for sustainable environmental-monitoring applications in the emerging solar powered wireless sensor networks (SP-WSNs). The complexities associated with the low-resourced, highly-dynamic, and vulnerable sensor nodes operating in potentially unattended or hostile environments require a high degree of self-management and automation. Guided by autonomic communication principles, this paper presents AutoSP-WSN, a novel distributed framework to achieve sustainable data collection while also optimizing end-to-end network performance for SP-WSNs. Initially, we present the energy-aware support component that provides reliable energy monitoring and prediction. This drives the power management component, which is adaptive to time-varying solar power, avoiding battery exhaustion as well as maximizing the per-node utility. Finally, to demonstrate the key design issues of the network protocol component, we propose two self-adaptive network protocols, a routing protocol SP-BCP and a rate control scheme PEA-DLEX. We show that the individual components seamlessly highly integrate as a whole, and the AutoSP-WSN framework exhibits the properties of context-awareness, distributed operation, self-configuration, self-optimization, self-protection and self-healing. Through extensive experiments on a real SP-WSN platform, and hardware-driven simulations, we show that the proposed schemes achieve substantial improvements over previous work, in terms of reliability, sustainable operation, and network utility.

Admission control (AC) is an effective mechanism for providing bandwidth assurance in wireless mesh networks. Early AC schemes over multirate WMNs typically use a pre-chosen rate or a MAC-layer adapted rate for each link, denying data sessions that could have been admitted should a better multirate AC be available. Taking full advantage of multirate WMNs, we present a rate-adaptive admission control protocol (RaAC) for IEEE 802.11-based WMNs. RaAC consists of three major components: (1) a rate adaption algorithm to meet the bandwidth requirement of the data session and satisfy the channel condition of the PHY layer; (2) a new path-selection metric to balance between hop counts, bandwidth, rates, and other network parameters; and (3) a routing-coupled, distributed, rate-adaptive admission control algorithm to admit data sessions with bandwidth assurance. Through simulations, we show that RaAC is efficient and effective in meeting bandwidth requirements.

Network coding provides a promising alternative to the traditional store-and-forward transmission paradigm. For the system using random linear network coding, the adversary could inject corrupted messages into the networks by compromising the network nodes, which is known as the pollution attack. Corrupted messages injected by the adversary, if undetected, could cause a devastating impact to the network performance. To address this issue, a number of pollution attack defense schemes for network coding have been developed in the recent years. The overhead caused by defensive techniques against pollution attacks should be low especially in wireless networks with limited resources. However, there is lack of a systematical strategy for evaluating those schemes and establishing a foundation for designing attack pollution defense schemes for network coding in wireless networks. Towards this end, we first develop the taxonomy of existing network coding authentication schemes. To fairly compare the effectiveness of those schemes, we conduct theoretical analysis and implement different schemes within the same security level. Our extensive simulation and implementation results validate our findings well. Our research summarizes the state-of-art research development and lay out future directions in this area.

In Cyber-Physical Networked Systems (CPNS), attackers could inject false measurements to the controller through compromised sensor nodes, which not only threaten the security of the system, but also consumes network resources. To deal with this issue, a number of en-route filtering schemes have been designed for wireless sensor networks. However, these schemes either lack resilience to the number of compromised nodes or depend on the statically configured routes and node localization, which are not suitable for CPNS. In this paper, we propose a Polynomial-based Compromised-Resilient En-route Filtering scheme (PCREF), which can filter false injected data effectively and achieve a high resilience to the number of compromised nodes without relying on static routes and node localization. Particularly, PCREF adopts polynomials instead of MACs (message authentication codes) for endorsing measurement reports to achieve the resilience to attacks. Each node stores two types of polynomials: authentication polynomial and check polynomial derived from the primitive polynomial, and used for endorsing and verifying the measurement reports. Via extensive theoretical analysis and simulation experiments, our data show that PCREF achieves better filtering capacity and resilience to the large number of compromised nodes in comparison to the existing schemes.

Opportunistic routing (OR) is a viable approach for improving performance of wireless communications. Previous studies on OR have focused on cost minimization, performance of multiple rates, congestion control, and other issues. Bandwidth assurance over OR, however, has not been adequately investigated. To bridge this gap, we present a bandwidth-aware opportunistic routing (BOR) with admission control (AC) protocol named BOR/AC. In particular, by analyzing the expected available bandwidth (EAB) and the expected transmission cost (ETC) in OR, we first devise a new metric called BCR (bandwidth-cost ratio) to determine the priority of relays in the forwarding candidates set. Admission control is then applied to admit or reject traffic flows based on estimated expected available bandwidth. Extensive simulation results show that BOR/AC consistently achieves much better performance than existing opportunistic routing protocols.

In wireless sensor networks (WSNs), attackers could inject false data into the networks by compromising the sensor nodes. False data injected by the compromised nodes, if undetected, could not only cause false alarms but also consume the limited energy of the sensor nodes, posing serious threats to the lifetime of networks. To mitigate this type of attacks, a number of en-route filtering schemes to filter false data inside the networks have been developed in the past. However, there is lack of a systematical strategy to evaluate those schemes and establishing a foundation for designing en-route filtering techniques. To address these issues, we compare the pros and cons of the existing enroute filtering schemes. To fairly compare the performance of those schemes, we conduct theoretical analysis and derive a set of closed formulae for them. Our extensive simulations validate our findings. Our research summarizes the state-of-art research development and lay out future directions in this area.

Accurately measuring the available bandwidth information is critical for providing QoS assurance, especially for the bandwidth-limited 802.11-based wireless networks. However, the shared nature of wireless medium and IEEE 802.11 MAC pose great challenges for estimating the bandwidth accurately. This paper tends to tackle this issue. In particular, we first formally define the available bandwidth in IEEE 802.11 network by considering its unique characteristics. We then present our solution, Passive Available Bandwidth Estimation (PABE). In PABE, the effective link capacity is analyzed by considering the random factors in transmission, and the available channel idle time is estimated by passively monitoring the medium based on a new, lower threshold bandwidth obtained during the normal operations of IEEE 802.11. Our approach incurs very low cost to the network without any explicit message overhead. Through extensive simulation, our data validate that our approach consistently achieves much better performance than other existing algorithms in term of estimation accuracy.

Admission control plays an important role in providing Quality of Service (QoS) guarantees for wireless mesh networks (WMNs). Multipath routing can improve network performance in reliability and load balancing. However, when the multipath routing are adopted in 802.11-based WMNs, the transmission with bandwidth assurance is facing rigorous challenges. In this paper, a novel joint design of multipath routing and admission control protocol is presented, named MRAC. In MRAC, the multipath routing with bandwidth assurance is formulated as an optimization problem based on the analysis of available bandwidth and the bandwidth consumption. Based on the formulation, a heuristic solution is proposed to admit the data session through two parallel paths with bandwidth assurance. Through extensive simulations, the effectiveness of the MRAC is demonstrated in term of satisfying bandwidth requirement.

In mobile ad hoc networks, location service (LS) is critical and provides the fundamental service for geographic routing. However, most existing schemes for location service incur a high overhead because of the periodical updates of location information. In this paper, we intend to address this issue. Using the temporal relationship among historical locations of mobiles in the network, we propose a novel History information based Light Location Service (HLLS). In HLLS, location information of mobiles is propagated via Hello beacons locally, and location query is performed in a tracing manner with the aid of historical locations. In such a way, HLLS can eliminate the tremendous periodical location updates and significantly reduce the overhead for location service. Using extensive simulations, we demonstrate the effectiveness of HLLS in terms of high accuracy and low overhead.