Mobile-edge computing (MEC) has received wide attention recently due to its efficacy in alleviating the computation stress of mobile devices (MDs), which is realized by offloading workloads from MD users to nearby edge servers (ESs). Prior work has studied related task offloading and scheduling problems and proposed many approaches. However, none of these approaches considers the reliability issue in MEC systems which may suffer soft errors during task execution as well as bit errors during task offloading simultaneously. Targeting optimization on a multi-user MEC system, in this article we investigate the task offloading and scheduling problem of maximizing system quality of experience (QoE) under a certain reliability requirement. With the consideration of the combinatorial nature of this problem, we propose to decompose the original problem into i) a task-to-ES assignment problem with fixed task offloading decision, for satisfying system reliability constraint, ii) a computing resource allocation problem with fixed task offloading and assignment decisions, for maximizing system QoE, and iii) a task offloading optimization problem to find the best offloading decision that achieves the maximum QoE under the reliability constraint using our task assignment and resource allocation methods. In order to solve these sub-problems, we further design a reliability-aware task-to-ES assignment algorithm, a QoE-optimum resource allocation algorithm, and a binary particle swarm optimization based task offloading algorithm. We perform extensive simulations and testbed experiments to validate the efficacy of the proposed scheme. Simulation and testbed results show that the proposed scheme greatly outperforms four benchmark approaches and it achieves up to 63.2% and 43.1% increase in the average QoE (quantified by offloading utility), respectively.

The network system deployed in hazardous environments is a key component of hazard-aware cyber-physical systems (CPSs) and its performance highly depends on surrounding environments. Due to the mobility of network nodes (e.g., portable IoT devices), frequently changeable network topology and links, as well as other external interferences such as electromagnetic interference, ensuring adaptivity and reliability of hazard-aware CPSs is of utmost importance. Meanwhile, the timeliness of message transmission is stringent in hazardous environments because the violation of timing requirements may lead to serious consequences. Last but not least, portable IoT devices are typically energy limited, thus ensuring a sustainable message transmission is highly necessary. In this paper, we aim at optimizing the reliability of hazard-aware CPSs while meeting the timing and energy constraints. To this end, we develop the first hazard-aware CPS model and study the impacts of surrounding environments (i.e., physical side) to the network infrastructure of a hazard-aware CPS (i.e., cyber side) with respect to reliability. We also propose a new scheme that adaptively tunes the fault tolerance strategies and admission strategies for real-time messages, to increase the reliability of hazard-aware CPSs under the energy constraint. Extensive simulation results demonstrate that our proposed scheme is capable of increasing system reliability by up to 4.21× with a lower deadline miss rate and runtime overhead compared with the state-of-the-art approaches.

With the recent advances in mobile sensing, computing, and communication technologies, mobile cyber–physical systems (MCPSs) become a promising networking paradigm that provides mobile users with various applications and services from cyber space to the physical world. One of the main challenges in the MCPSs is to defend security threats launched by adversaries. Exploiting security services to defend threats is energy consuming, whereas the energy of mobile devices is generally limited since most of mobile devices are battery powered. This necessitates the need to design new methodologies to tackle the tradeoff between security and energy of MCPSs. To this end, this article aims to maximize MCPSs’ security under the constraints of energy and deadline. In this article, we first formulate the MCPS security maximization problem as a mixed-integer nonlinear programming (MINLP) problem and then transform it into a mixed-integer linear programming (MILP) problem without performance degradation. To solve the transformed MILP problem efficiently, we propose a decomposed algorithm to derive the optimum task scheduling solution instead of using MILP solvers that may be very time consuming for MCPSs of large granularity. The derived task scheduling solution decides the assignment, operating frequency, execution order, as well as security service selection for all tasks. We implement a series of simulation-based experiments to validate the proposed decomposed task scheduling scheme. Simulation results demonstrate that the proposed scheme increases system security level by 20.38% and 65.11% on average as compared to a state-of-art approach and a baseline method.

Real-world data in emerging applications may suffer from highly-skewed class imbalanced distribution, however how to deal with this kind of problem appropriately through deep learning needs further investigation. In this paper, we mainly propose a novel cross-entropy based loss function, referred to as Additive Scale Loss (ASL), for deep representation learning and imbalanced classification. To deal with the class imbalanced problem, ASL aims at increasing the loss in case of misclassification, which can avoid the superimposed loss values caused by the large amount of easily classified data in the unbalanced database to dominate the loss value of misclassified data. Moreover, in real-world applications, one data source may be used for multiple scenarios, such as classification and embedding learning, however training two separable models to handle these problems is costly, especially in deep learning area. To tackle this issue, we present and integrate a discriminative inter-class separation term into ASL, and propose a discriminative ASL (D-ASL), which can not only improve the classification performance, but also obtain discriminative representations simultaneously. The discriminative inter-class separation term is general, and can be easily integrated to other loss functions, such as CE and FL, as the byproducts. Finally, a new deep convolutional neural network equipped with D-ASL and a fully-connected (FC) layer is proposed, which can classify the imbalanced image data and obtain the discriminative representations at the same time. Extensive experimental results verified the superior performance of our method.

We propose a joint subspace recovery and enhanced locality-based robust flexible label consistent dictionary learning method called Robust Flexible Discriminative Dictionary Learning (RFDDL). The RFDDL mainly improves the data representation and classification abilities by enhancing the robust property to sparse errors and encoding the locality, reconstruction error, and label consistency more accurately. First, for the robustness to noise and sparse errors in data and atoms, the RFDDL aims at recovering the underlying clean data and clean atom subspaces jointly, and then performs DL and encodes the locality in the recovered subspaces. Second, to enable the data sampled from a nonlinear manifold to be handled potentially and obtain the accurate reconstruction by avoiding the overfitting, the RFDDL minimizes the reconstruction error in a flexible manner. Third, to encode the label consistency accurately, the RFDDL involves a discriminative flexible sparse code error to encourage the coefficients to be soft. Fourth, to encode the locality well, the RFDDL defines the Laplacian matrix over recovered atoms, includes label information of atoms in terms of intra-class compactness and inter-class separation, and associates with group sparse codes and classifier to obtain the accurate discriminative locality-constrained coefficients and classifier. The extensive results on public databases show the effectiveness of our RFDDL.

In this article, we propose a structured robust adaptive dictionary pair learning (RA-DPL) framework for the discriminative sparse representation (SR) learning. To achieve powerful representation ability of the available samples, the setting of RA-DPL seamlessly integrates the robust projective DPL, locality-adaptive SRs, and discriminative coding coefficients learning into a unified learning framework. Specifically, RA-DPL improves existing projective DPL in four perspectives. First, it applies a sparse l2,1-norm-based metric to encode the reconstruction error to deliver the robust projective dictionary pairs, and the l2,1-norm has the potential to minimize the error. Second, it imposes the robust l2,1-norm clearly on the analysis dictionary to ensure the sparse property of the coding coefficients rather than using the costly l0/l1-norm. As such, the robustness of the data representation and the efficiency of the learning process are jointly considered to guarantee the efficacy of our RA-DPL. Third, RA-DPL conceives a structured reconstruction weight learning paradigm to preserve the local structures of the coding coefficients within each class clearly in an adaptive manner, which encourages to produce the locality preserving representations. Fourth, it also considers improving the discriminating ability of coding coefficients and dictionary by incorporating a discriminating function, which can ensure high intraclass compactness and interclass separation in the code space. Extensive experiments show that our RA-DPL can obtain superior performance over other state of the arts.

We propose a novel structured discriminative block- diagonal dictionary learning method, referred to as scalable Locality-Constrained Projective Dictionary Learning (LC-PDL), for efficient representation and classification. To improve the scalability by saving both training and testing time, our LC-PDL aims at learning a structured discriminative dictionary and a block-diagonal representation without using costly l0/l1-norm. Besides, it avoids extra time-consuming sparse reconstruction process with the well-trained dictionary for new sample as many existing models. More importantly, LC-PDL avoids using the com- plementary data matrix to learn the sub-dictionary over each class. To enhance the performance, we incorporate a locality constraint of atoms into the DL procedures to keep local information and obtain the codes of samples over each class separately. A block-diagonal discriminative approximation term is also derived to learn a discriminative projection to bridge data with their codes by extracting the special block-diagonal features from data, which can ensure the approximate coefficients to associate with its label information clearly. Then, a robust multiclass classifier is trained over extracted block-diagonal codes for accurate label predictions. Experimental results verify the effectiveness of our algorithm.

In this paper, we mainly propose a Robust Adaptive Projective Dictionary Pair Learning (RA-DPL) framework based on the adaptive discriminative representations. Our formulation can seamlessly integrate the robust projective dictionary pair learning and the adaptive sparse representation learning into a unified model. RA-DPL improves the existing DPL algorithm in threefold. First, RA-DPL aims at computing the robust projective dictionary pairs by employing the sparse and robust l2,1-norm to encode the reconstruction error. Second, RA-DPL regularizes the robust l2,1-norm on the analysis dictionary so that the analysis dictionary can extract sparse coefficients from the given samples explicitly. More importantly, the optimization of l2,1-norm is so efficient, that is, the sparse coding step will be time-saving. Third, RA-DPL can clearly preserve the local neighborhood relationship of the sparse coefficients within each class, which can make the learnt representations discriminating and can also improve the discriminating power of learnt dictionary. Extensive simulations on image databases demonstrate that our RA-DPL can obtain the superior performance over other state-of-the-arts.

We mainly propose a robust Embedded Locality-Constrained Label Consistent Dictionary Learning (ELC2DL) framework for discriminative classification. ELC2DL improves the representation and classification performance by performing DL in the noise-removed sparse embedding space, since most real data often contains noise and performing DL over noisy data for reconstruction may decrease performance potentially. To reduce the noise in data, our model computes a sparse projection jointly for noise reduction and then uses the noise-removed data for DL. By incorporating a noise-reduction term with a discriminative locality-constrained label consistent term that associates the label information with each dictionary atom to preserve local structure of training data, a noise-reduction projection, an over-complete dictionary and discriminative sparse codes are obtained jointly. Simulations on several image databases show that our algorithm can deliver enhanced performance over other state-of-the-arts.

In this paper, we propose an analysis mechanism-based structured analysis discriminative dictionary learning analysis discriminative dictionary learning, framework. The ADDL seamlessly integrates ADDL, analysis representation, and analysis classifier training into a unified model. The applied analysis mechanism can make sure that the learned dictionaries, representations, and linear classifiers over different classes are independent and discriminating as much as possible. The dictionary is obtained by minimizing a reconstruction error and an analytical incoherence promoting term that encourages the subdictionaries associated with different classes to be independent. To obtain the representation coefficients, ADDL imposes a sparse -norm constraint on the coding coefficients instead of using or norm, since the - or -norm constraint applied in most existing DL criteria makes the training phase time consuming. The code-extraction projection that bridges data with the sparse codes by extracting special features from the given samples is calculated via minimizing a sparse code approximation term. Then we compute a linear classifier based on the approximated sparse codes by an analysis mechanism to simultaneously consider the classification and representation powers. Thus, the classification approach of our model is very efficient, because it can avoid the extra time-consuming sparse reconstruction process with trained dictionary for each new test data as most existing DL algorithms. Simulations on real image databases demonstrate that our ADDL model can obtain superior performance over other state of the arts.

This paper investigates the salient machine faults representation-based classification issue by dictionary learning. A novel structured latent label consistent dictionary learning (LLC-DL) model is proposed for joint discriminative salient representation and classification. Our LLC-DL deals with the tasks by solving one objective function that aims to minimize the structured reconstruction error, structured discriminative sparse-code error and classification error simultaneously. Also, LLC-DL decomposes given signals into a sparse reconstruction part over structured latent weighted discriminative dictionary, a salient feature extraction part and an error part fitting noise. Specifically, the dictionary is learnt atom by atom, where each dictionary atom is learnt with a latent vector that reduces the disturbance between interclass atoms. The structured coding coefficients are calculated via minimizing the reconstruction error and discriminative sparse code error simultaneously. The salient representations are learnt by embedding signals onto a projection and a robust linear classifier is then trained over the learned salient features directly so that features can be ensured to be optimal for classification, where robust l2,1-norm imposed on the classifier can make the prediction results more accurate. By including a salient feature extraction term, the classification approach of LLC-DL is very efficient, since there is no need to involve an extra time-consuming sparse reconstruction process with the well-trained dictionary for each test signal. Extensive simulations versify the effectiveness of our algorithm.

We propose a new transductive label propagation method, termed Adaptive Neighborhood Propagation (Adaptive-NP) by joint L2,1-norm regularized sparse coding, for semi-supervised classification. To make the predicted soft labels more accurate for predicting the labels of samples and to avoid the tricky process of choosing the optimal neighborhood size or kernel width for graph construction, Adaptive-NP seamlessly integrates sparse coding and neighborhood propagation into a unified framework. That is, the sparse reconstruction error and classification error are combined for joint minimization, which clearly differs from traditional methods that explicitly separate graph construction and label propagation into independent steps, which may result in inaccurate predictions. Note that our Adaptive-NP alternately optimize the sparse codes and soft labels matrices, where the sparse codes are used as adaptive weights for neighborhood propagation at each iteration, so the tricky process of determining neighborhood size or kernel width is avoided. Besides, for enhancing sparse coding, we use the L2,1-norm constraint on the sparse coding coefficients and the reconstruction error at the same time for delivering more accurate and robust representations. Extensive simulations show that our model can deliver state-of-the-art performances on several public datasets for classification.

We propose a new discriminative dictionary learning framework termed Latent Label Consistent K-SVD (LLC-KSVD) for representing and classifying machine faults. Our LLC-KSVD handles the task by minimizing the reconstruction, discriminative sparse-code and classification errors at the same time. To enhance the representation and classification powers, LLC-KSVD aim to decompose given data into a sparse reconstruction part, a salient feature part and an error part. The salient features are learnt by embedding data onto a projection and a classifier is then trained over extracted salient features so that features are ensured to be optimal for classification. Thus, the classification approach of our LLC-KSVD is very efficient, since there is no need to involve a time-consuming sparse reconstruction process with well-trained dictionary for each test signal as existing models. Besides, to make the classifier be robust to noise and outliers, we regularize the l2,¿-norm on classifier so that the predictions are more accurate. Simulations on several machine fault datasets demonstrate the state-of-the-art performance by our LLC-KSVD.

In this paper, we propose a semisupervised label consistent dictionary learning (SSDL) framework for machine fault classification. SSDL is a semisupervised extension of recent fully supervised label consistent dictionary learning approach, since the number of labeled machine data is usually limited in practice. To enable the supervised dictionary learning model to use both labeled and commonly readily available unlabeled data for enhancing performance, we propose to incorporate the merits of label prediction and present a joint label consistent dictionary learning and adaptive label prediction technique. In this setting, we first employ the existing label prediction model to estimate the labels of unlabeled training signals in a transductive fashion for enriching supervised prior. Then, we use predicted labeled data for label consistent dictionary learning. After that, we apply the discriminant sparse codes as the adaptive reconstruction weights for label prediction to update the estimated labels of unlabeled training data and the discriminative sparse codes matrix for label consistent dictionary learning so that classification performance can be enhanced. Thus, an informative dictionary, a sparse-code matrix, and an optimal multiclass classifier can be alternately obtained from one objective function. Besides, the tricky process of choosing optimal kernel width and neighborhood size can also be effectively voided in our scheme due to the adaptive weights. Extensive simulations on several machine fault datasets show that our SSDL method can deliver enhanced performance over other state-of-the-arts for machine fault classification.

In this paper, we propose a L1-Norm driven Semi-Supervised Local Discriminant Projection (S2LDP-L1) for robust dimensionality reduction and image representation. For feature learning, our S2LDP-L1 approach aims at compacting local within-class divergence and separating local betweenclass divergence at the same time in addition to possessing the locality preserving power over all training data. To enable the presented S2LDP-L1 method to be robust against noise in data for feature reduction and representation, the L1-norm that is proven to be robust to noise and outliers is regularized on the constructed scatter matrices for measuring pairwise similarities/ dissimilarities between samples. Thus, the presentation power can be effectively enhanced to improve the subsequent classification task. The derived ratio based model is finally solved by an iterative approach to deliver a discriminating and neighborhood preserving orthogonal projection for extracting features from both training and test samples by embedding data onto it. For classification, an existing label propagation model is used to identify the categories of test data. Extensive results on handwriting digit datasets verified the validity of our S2LDP-L1, compared with other state-of-the-arts.

In this paper, we mainly present a Semi-Supervised Label Consistent KSVD (S2KSVD) algorithm for representing and classifying machine faults. The formulation of our S2KSVD is an improvement to the recent label consistent K-SVD (LC-KSVD), because LC-KSVD is a fully supervised approach, and needs to use supervised class information of all training data to compute a reconstructive & discriminative dictionary. But labeled signals are often expensive to obtain, while in contrast unlabeled signals can be easily captured with low expense from the real world. Thus, the application of LC-KSVD may be constrained in reality. To address this problem, we present S2KSVD through involving a computationally efficient label propagation (LP) process as a preprocessing step. The core idea is to employ the LP process to estimate the labels of unlabeled signals so that supervised prior knowledge that can significantly enhance classification can be increased. Simulation results on several machine fault datasets demonstrate that our algorithm delivers promising performance for machine fault classification.

This paper proposes an enhanced semi-supervised classification approach termed Nonnegative Sparse Neighborhood Propagation (SparseNP) that is an improvement to the existing neighborhood propagation due to the fact that the outputted soft labels of points cannot be ensured to be sufficiently sparse, discriminative, robust to noise and be probabilistic values. Note that the sparse property and strong discriminating ability of predicted labels is important, since ideally the soft label of each sample should have only one or few positive elements (that is, less unfavorable mixed signs are included) deciding its class assignment. To reduce the negative effects of unfavorable mixed signs on the learning performance, we regularize the l2,1-norm on the soft labels during optimization for enhancing the prediction results. The non-negativity and sum-to-one constraints are also included to ensure the outputted labels are probabilistic values. The proposed framework is solved in an alternative manner for delivering a more reliable solution so that the accuracy can be improved. Simulations show that satisfactory results can be obtained by the proposed SparseNP compared with other related approaches.

Traditional label propagation (LP) is shown to be effective for transductive classification. To enable the standard LP to handle outside images, two inductive methods by label reconstruction or by direct embedding have been presented, of which the latter scheme is relatively more efficient, especially for testing. But almost all inductive LP models use 1D vectors of images as inputs, which may destroy the topology structure of image pixels and usually suffer from high complexity due to the high dimension of 1D vectors in reality. To preserve the topology among pixels and address the scalability issue for the embedding based scheme, we propose a simple yet efficient Bilinear Embedding Label Propagation (BELP) by including a bilinear regularization term in terms of tensor representation to correlate the image labels with their bilinear features. BELP performs label prediction over the 2D matrices rather than 1D vectors, since images are essentially matrices. Finally, labels of new images can be easily obtained by embedding them onto a spanned bilinear subspace solved from a joint framework. Simulations verified the efficiency of our approach.