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Recently, rapid deployment on fifth-generation (5G) networks has brought great opportunities for the enabling data-intensive applications, and brings an extending expectation on the developments of 6G. A basic requirement to develop 6G networks is to reach data with low latency, low cost and high coverage in smart IoT. Therefore, this paper proposes a novel machine learning based approach to collect data from multiple sensor devices by cooperation between vehicle and UAV in IoT. Firstly, genetic algorithm is utilized to select vehicular collectors to collect massive data from sensor devices, which aims to maximize coverage ratio and to minimize employment cost. Secondly, we design a novel Deep Reinforcement Learning (DRL)-based route policy to plan collection routes of UAVs with constrain energy, which simplifies the network model, accelerates training speeds and realizes dynamic planning of flight paths. The optimal collection route of an UAV is a series of outputs based on the proposed DRL-based route policy. Finally, our extensive experiments demonstrate that the proposed scheme can comprehensively improve the coverage ratio of massive data collections and reduce collection costs in smart IoT for the future 6G networks.

Knowledge graphs are large graph-structured knowledge bases with incomplete or partial information. Numerous studies have focused on knowledge graph embedding to identify the embedded representation of entities and relations, thereby predicting missing relations between entities. Previous embedding models primarily regard (subject entity, relation, and object entity) triplet as translational distance or semantic matching in vector space. However, these models only learn a few expressive features and hard to handle complex relations, i.e., 1-to-N, N-to-1, and N-to-N, in knowledge graphs. To overcome these issues, we introduce a multi-scale dynamic convolutional network (M-DCN) model for knowledge graph embedding. This model features topnotch performance and an ability to generate richer and more expressive feature embeddings than its counterparts. The subject entity and relation embeddings in M-DCN are composed in an alternating pattern in the input layer, which helps extract additional feature interactions and increase the expressiveness. Multi-scale lters are generated in the convolution layer to learn different characteristics among input embeddings. Specically, the weights of these lters are dynamically related to each relation to model complex relations. The performance of M-DCN on the ve benchmark datasets is tested via experiments. Results show that the model can effectively handle complex relations and achieve state-of-the-art link prediction results on most evaluation metrics.

Nowadays, with the explosive growth of sensor-based devices connected to Internet of Thing (IoT), massive amount of data are generated every day with potential tremendous value. We argue that the value of those data can be extracted through monetize data platform in IoT-Edge-Cloud ecosystems for many parts of the business. In such monetize data platform, the data can be computed and transformed into services in IoT-Edge-Cloud ecosystems and provide Data-As-A-Service (DAAS) for applications. The key to implement such a monetize data platform is to evenly distribute DAAS computing tasks to network devices to maximize the benefits of the system. So, in this paper, we study the Task Type-based Computation Offloading algorithm (TTCO) to implement such platform. We use the "IoT-Edge-Cloud" three-layer multi-hop model, which is closer to the complex scene in monetize data platform. We divide tasks into data-intensive tasks and CPU-intensive tasks, and then combine the cost model of computation offloading with task type to make data-intensive tasks prefer local computing and CPU-intensive tasks prefer offload computing, thereby reducing the monetize data platform transmission volume and improving the overall quality of computation offloading. We then use a hierarchical game model combined with fictitious play to solve the Nash Equilibrium (NE) of the system and obtain the mixed strategies of the devices. Finally, we propose a TTL-constrained flood strategy transmission mechanism to make the algorithm apply to practice. The experimental results prove that our algorithm has a large performance gain in various scenarios, which can be severed as a monetize data platform for IoT-Edge-Cloud ecosystems.

Cryptography chips are often used in some applications, such as smart grids and Internet of Things (IoT) to ensure their security. Cryptographic chips must be strictly tested to guarantee the correctness of the encryption and decryption. Scan-based design-for-testability (DFT) provides high test quality. However, it can also be misused to steal the cipher key of cryptographic chips by hackers. In this article, we present a new scan design methodology that can resist scan-based side-channel attacks by the dynamical obfuscation of scan input data and scan output data. The scan test is managed by a test password, which consists of load password and scan password. When the chip enters into the test mode, it is required to apply the test password via some external input ports. Once the correct load password is delivered, the scan password can be loaded into a special shift register. If the scan password is also correct, the chip testing can proceed normally. In case the load password or the scan password is wrong, the data in scan chains cannot be propagated correctly. Specifically, some elusory bits are sneaked into scan chains dynamically. The advantage of the proposed method is that it has no negative impact on design performance and test flow when powerfully protecting cryptographic chips. The area penalty is also acceptably low compared with other schemes.

Internet of Things (IoT) realizes the interconnection of heterogeneous devices by the technology of wireless and mobile communication. The data of target regions are collected by widely distributed sensing devices and transmitted to the processing center for aggregation and analysis as the basis of IoT. The quality of IoT services usually depends on the accuracy and integrity of data. However, due to the adverse environment or device defects, the collected data will be anomalous. Therefore, the effective method of anomaly detection is the crucial issue for guaranteeing service quality. Deep learning is one of the most concerned technology in recent years which realizes automatic feature extraction from raw data. In this article, the integrated model of the convolutional neural network (CNN) and recurrent autoencoder is proposed for anomaly detection. Simple combination of CNN and autoencoder cannot improve classification performance, especially, for time series. Therefore, we utilize the two-stage sliding window in data preprocessing to learn better representations. Based on the characteristics of the Yahoo Webscope S5 dataset, raw time series with anomalous points are extended to fixed-length sequences with normal or anomaly label via the first-stage sliding window. Then, each sequence is transformed into continuous time-dependent subsequences by another smaller sliding window. The preprocessing of the two-stage sliding window can be considered as low-level temporal feature extraction, and we empirically prove that the preprocessing of the two-stage sliding window will be useful for high-level feature extraction in the integrated model. After data preprocessing, spatial and temporal features are extracted in CNN and recurrent autoencoder for the classification in fully connected networks. Empiric results show that the proposed model has better performances on multiple classification metrics and achieves preferable effect on anomaly detection.

Virtualization technology provides a new way to improve resource utilization and cloud service throughput. However, the randomness of task arrival, tight coupling between resource load imbalance and node heterogeneity, high computing power, and other factors have hindered the energy consumption optimization and cost reduction objectives of the existing technology. Consequently, task scheduling failure cannot be easily eliminated, and cloud computing performance is decreased dramatically. In this study, a strong agile response task scheduling optimization algorithm is proposed on the basis of the peak energy consumption of data centers and the time span of task scheduling. Agile response optimization techniques are also adopted. From the perspective of task failure rate, the proposed algorithm can be used to investigate the strong agile response optimization model, explore the probability density function of the task request queue overflow, and request a timeout to avoid network congestion. Experimental results indicate that the proposed algorithm can achieve the stability and efficiency of task scheduling and effectively improve the throughput of the cloud computing system.

As one of promising distributed multi-robot system, Unmanned Aerial Vehicles (UAVs) can collaborate to offload complex tasks in edge networks. A Smart and Trust Multi-UAV Task Offloading (STMTO) system is established to offload tasks from Internet of Thing (IoT) devices to edge severs through UAVs with a trust style. In STMTO system, first, a group of UAVs is dispatched to relay tasks from devices to edge servers with rich computing resource. A collaborative task collection scheme is proposed to minimize energy consumption and the task processing delay by dividing working area for each UAV and designing the flight trajectory . Secondly, a many-to-many task double auction model is established for devices and edge servers to maximize the offloading utility, where devices act as buyers, edge servers as sellers, and UAVs as auctioneers. Last, to resist attack of malicious edge servers and ensure the task security, a novel trust evaluation method based on the comparison of true utility and expected utility is integrated in auction mechanism. The theoretical analysis and implementation results show that the proposed STMTO system not only achieve the best utility for devices and edge servers simultaneously, but also identify the malicious edge servers and protect task from attacks.

The recommendation systems in the online platforms often suffer from the rating data sparseness and information overload issues. Previous studies on this topic often leverage review information to construct an accurate user/item latent factor. To address this issue, we propose a novel confidence-aware recommender model via review representation learning and historical rating behavior in this article. It is motived that ratings are consistent with reviews in terms of user preferences, and reviews often contain misleading comments (e.g., fake good reviews, fake bad reviews). To this end, the interaction latent factor of user and item in the framework is constructed by exploiting review information interactivity. Then, the confidence matrix, which measures the relationship between the rating outliers and misleading reviews, is employed to further improve the model accuracy and reduce the impact of misleading reviews on the model. Furthermore, the loss function is constructed by maximum a posteriori estimation theory. Finally, the mini-batch gradient descent algorithm is introduced to optimize the loss function. Experiments conducted on four real-world datasets empirically demonstrate that our proposed method outperforms the state-of-the-art methods. The proposed method also further promotes the application in learning resource adaptation. The source Python code will be available upon request.

In the field of network security , it is of great significance to accurately detect various types of Internet of Things (IoT) network intrusion attacks which launched by the attacker-controlled zombie hosts. In this paper, we propose a novel IoT network intrusion detection approach based on Adaptive Particle Swarm Optimization Convolutional Neural Network (APSO-CNN). In particular, the PSO algorithm with change of inertia weight is used to adaptively optimize the structure parameters of one-dimensional CNN. The cross-entropy loss function value of the validation set, which is obtained from the first training of CNN, is taken as the fitness value of PSO. Especially, we define a new evaluation method that considers both the prediction probability assigned to each category and prediction label to compare the proposed APSO-CNN algorithm with CNN set parameters manually (R-CNN). Meanwhile, the comprehensive performance of proposed APSO-CNN and other three well known algorithms are compared in the five traditional evaluation indicators and the accuracy statistical characteristics of 10 times independent experiments. The simulation results show that the multi-type IoT network intrusion attack detection task based on APSO-CNN algorithm is effective and reliable.

Intelligent anomaly detection for key performance indicators (KPIs) is important for keeping services reliable in industrial-based cyber–physical systems (CPS). However, it is common in practice for various KPI sampling strategies to be utilized. We experimentally verify that anomaly detection is highly sensitive to irregular sampling, and accordingly go on to investigate low-cost anomaly detection for large-scale irregular KPIs. Irregular KPIs can be classified into four types: equal interval and unequal quantity (EIUQ) KPIs, unequal interval (UI) KPIs, unequal interval with equal duration (UIED) KPIs, and segmented irregular KPIs. In this article, we propose an anomaly detection framework based on these irregular types. Moreover, to handle the various lengths and phase shifts among EIUQ KPIs, we propose a normalized version of unequal cross-correlation, which slides the KPIs to enable finding the most similar position. To avoid high computational costs, we analyze the low-rank feature of KPIs data and propose a matrix factorization-based alignment algorithm for UIED KPIs; this algorithm treats UIED KPIs as an incomplete matrix and recovers the KPIs to align them before performing anomaly detection. Extensive simulations using three public datasets and two real-world datasets demonstrate that our algorithm can achieve a larger F1-score than Minkowski distance and less time than dynamic time warping distance.

Semi-supervised clustering algorithms have several limitations: 1) the computation complexity of them is very high, because calculating the similarity distances of pairs of examples is time-consuming; 2) traditional semi-supervised clustering methods have not considered how to make full use of must-link and cannot-link constraints. In the clustering, the contribution of a few pairwise constraints to the clustering performance is very limited, and some may negatively affect the outcome; and 3) these methods are not effective to handle high dimensional data, especially for time series data. Up to now, few work touched semi-supervised clustering on time series data. To efficiently cluster large-scale time series data, we first tackle contract time series clustering to produce the most accurate clustering results under a contracted time. We propose a semi-supervised time series clustering framework (STSC), which integrates a fast similarity measure and a constraint propagation approach. Based on the proposed framework, two valid semi-supervised clustering algorithms including fssK-means and fssDBSCAN are designed. Experiments on 11 datasets show that our proposed method is efficient and effective for clustering large-scale time series data.

Billions of sensors and devices are connecting to the Internet of Thing (IoT) and generating massive data which are benefit for smart network systems. However, low-cost, secure, and efficient data collection from billions of IoT devices in smart city is a huge challenge. Recruiting mobile vehicles (MVs) has been proved to be an effective data collection scheme. However, the previous approaches rarely considered the security. In this paper, a novel Baseline Data based Verifiable Trust Evaluation (BD-VTE) scheme is proposed to guarantee security at a low cost. BD-VTE scheme includes Verifiable Trust Evaluation (VTE) mechanism, Effectiveness-based Incentive (EI) mechanism, and Secondary Path Planning (SPP) strategy, which are respectively used for reliable trust evaluation, reasonable reward, and efficient path adjustment. Among them, an active trust verification mechanism is innovatively proposed in the VTE mechanism, which evaluates the trust of MVs by sending UAVs to perceive IoT devices data as baseline data. This is a fundamental change to the previous passive and unverifiable trust mechanism. The simulation results show that BD-VTE scheme reduces the cost by at least 25.12% ∼ 38.03%, improves the collection rate by 0.91% ∼ 9.65% and increases the accuracy by 10.28% on average compared with the previous strategies.

With the evolution of the Internet of Things (IoT), smart cities have become the mainstream of urbanization. IoT networks allow distributed smart devices to collect and process data within smart city infrastructure using an open channel, the Internet. Thus, challenges such as centralization, security, privacy (e.g., performing data poisoning and inference attacks), transparency, scalability, and verifiability limits faster adaptations of smart cities. Motivated by the aforementioned discussions, we present a Privacy-Preserving and Secure Framework (PPSF) for IoT-driven smart cities. The proposed PPSF is based on two key mechanisms: a two-level privacy scheme and an intrusion detection scheme. First, in a two-level privacy scheme, a blockchain module is designed to securely transmit the IoT data and Principal Component Analysis (PCA) technique is applied to transform raw IoT information into a new shape. In the intrusion detection scheme, a Gradient Boosting Anomaly Detector (GBAD) is applied for training and evaluating the proposed two-level privacy scheme based on two IoT network datasets, namely ToN-IoT and BoT-IoT. We also suggest a blockchain-InterPlanetary File System (IPFS) integrated Fog-Cloud architecture to deploy the proposed PPSF framework. Experimental results demonstrate the superiority of the PPSF framework over some recent approaches in blockchain and non-blockchain systems.

Once defined, the access control policies and regulations would never be changed in a running and state transition process. However, it will give attackers the possibility of discovering vulnerabilities in the system, and the control systems lack the ability of dynamic perception of security state and risk, causing the systems to be exposed to risks. In this article, a dynamic Bell–LaPadula (BLP) model is proposed. The conditional random field (CRF) is introduced into the BLP model to optimize the rules. First, the model formalizes the security attributes, states of system, transition rules, and constraint models on the basis of the state transition of CRFs. After the historical system access logs are processed as the original dataset, a feature selection method is proposed to extract the requests and current states as feature vectors. Second, this article presents a rules training algorithm based on L-BFGS to implement the study and training of datasets, and then marks the logs in the test set through Viterbi algorithm automatically. On the base of these, a rule generation algorithm is proposed to dynamically adjust the access control rules based on the current security status and events of the system. Third, the security of CRFs-BLP is proved by theoretical analysis. Finally, the validity and accuracy of the model are verified by estimating the value of the precision, recall, and $F1$ -score. As the system threats are shown to be decreased obviously from these experiments, this dynamic model can decrease the vulnerabilities and risk effectively.

As an important application of the Internet of Things (IoT) devices, sentiment analysis has been paid more attention with the rapid development of artificial intelligence. As a widely used method in artificial intelligence applications, traditional deep learning methods need massive data for training. However, due to the limitations of hardware, IoT devices have deficiencies in processing big data. In the case of insufficient sample size, how to carry out a machine learning method for IoT devices has become a common concern of the industry. In order to perform sentiment analysis on text with few data samples from the IoT devices, we propose FSLM, which is an intelligent few-shot learning model based on Siamese networks. The FSLM model consists of two self-attention models with the same parameters, which are divided into two parts. First, for two input texts, a self-attention model is used to extract sentiment features, and then the Mahalanobis distance is adopted to measure the similarity between two feature vectors to determine whether they belong to the same category. The FSLM is tested on the Amazon Review Sentiment Classification (ARSC) data set. The extensive experimental results on this data set demonstrate that the FSLM model has better accuracy and robustness for text sentiment analysis than other main existing models with a small number of samples.

The issue of impulse noise detection and reduction is a critical problem for image processing application systems. In order to detect impulse noises in corrupted images, a statistic named local consensus index (LCI) is proposed for quantitatively evaluating how noise free a pixel is, and then an impulse noise detection scheme based on LCI is introduced. First, the similarity between arbitrary two pixels in an image is quantified based on both their geometric distance and intensity difference, and the LCI of arbitrary pixel is calculated by summing all the similarity values of pixels in its neighborhood. As a new statistic, the value of LCI indicates the local consensus of the concerned pixel regarding its neighbors and could also tell whether a pixel is noise free or impulsive. Therefore, LCI can be directly used as an efficient indicator of impulse noise. Furthermore, to improve the performance of impulse noise detection, different strategies are applied to the pixels at flat regions and the ones with complex textures, since distributions of LCI value within those regions are totally different. As for impulse noise filtering, a hybrid graph Laplacian regularization (HGLR) method is introduced to restore the intensities of those pixels degraded by impulse noise. We conduct extensive experiments to verify the effectiveness of our impulsive noise detection and reduction method, and the results show that the proposed method outperforms the state-of-the-art techniques in terms of impulse detection and noise removal.

Internet of things (IoT) is one of the main application scenarios of 5th generation mobile networks (5G). Along with the rapid development of 5G, IoT terminal devices will create big data. Generally, IoT terminal devices are lightweight user equipments, for example, wearable devices. In order to take use of these lightweight terminal devices, it is a feasible way to outsource these created big data to the public cloud. When these data are out of the client’s control, it is of crucial importance to ensure the remote data integrity. To solve the weaknesses of the existing remote data integrity checking schemes, we propose the concept of blockchain-based remote data integrity checking (RDIC) scheme for big data. The new concept makes use of blockchain technique which greatly improves the efficiency and security of RDIC. First, the system model and security definition are given for the proposed RDIC scheme by using blockchain. Then, by using efficient modular arithmetic, RSA digital signature, blockchain, etc, we construct a lightweight blockchain-based RDIC scheme. Finally, we analyze its security and performance. The detailed analysis shows that our scheme is provably secure and lightweight.

Copyright infringement adversely affects the interest of copyright holders of images and videos which are uploaded to different websites and peer-to-peer image sharing systems. This paper addresses the problem of detecting copyright infringement so that copyright holders are given due credit for their work. There are several images and videos that are shared every day by millions of users with some amount of modification in images and videos originally uploaded by the copyright holders such as photographers, graphic designers, and video providers. Copyright violators, who are not the original creators of multimedia content modify them using image processing and frame modification techniques such as grayscale conversion, cropping, rotation, frame compression, and frame speed manipulation. Then, upload the tampered images and videos. To address this problem, we propose an IPFS-based (InterPlanetary File System-based) decentralized peer-to-peer image and video sharing platform built on blockchain technology. We use a perceptual hash (pHash) technique to detect copyright violations of multimedia. When multimedia is to be uploaded to the IPFS, the pHash of the same content is determined and checked against existing pHash values in the blockchain network. Similarity with existing pHash values would result in the multimedia being detected as tampered with. Blockchain technology offers the advantage of non-involvement of a third party and consequently the avoidance of a single point of failure.

With billions of sensing devices are deployed in smart cities to monitor regions of interests and collect large sensing data, the Internet-of-Things (IoT) applications are being widely used in various fields and empower the intelligent smart cities. Due to the smart decision made by IoT applications depends on the reliability of data collection, it is pivotal to collect data from the trust sensing devices. However, how to identify the credibility of sensor nodes to ensure the credibility of data collection is a challenge issue. In this article, an active and traceable trust-based data collection (ATTDC) scheme is proposed to collect trust data in Internet of Thing. The main contribution of this article are as follows: 1) an active trust framework is proposed to quickly obtain the trustworthiness of sensor nodes by using unmanned aerial vehicles (UAVs) with a piggybacking method; 2) in order to accurately obtain the trust degree of the sensor nodes, a traceable trust method of obtaining is proposed in which nodes in the network send data packets by digital signature, Tracing suspicious nodes according to data routing paths to obtain active trust, therefore, the acquisition cost of the network can be effectively reduced; and 3) In order to reduce the acquisition cost of UAV, an ant colony algorithm-based flight path algorithm is designed to reduce the flight path of UAV, and obtain the credibility evaluation of as many nodes as possible. The experimental results show that the ATTDC scheme proposed in this article can identify the trust of the sensing nodes faster and more accurately, ensuring the credibility of data collection.

The main issues for the optimization of many-objective evolutionary are about two aspects: the balance between convergence and diversity, and increasing the selection pressure toward the true Pareto-optimal front. To overcome these difficulties, a new Reference Points-based Strengthened dominance relation (RPS-dominance) is proposed and integrated into NSGA-II, named RPS-NSGA-II. It introduces a reference point set and convergence metric Cov to distinguish Pareto-equipment solutions and further stratifies them. The performance of RPS-NSGA-II is evaluated by the WFG and MaF series benchmark problems. Extensive experimental results demonstrate that RPS-NSGA-II has the competitiveness and frequently better results when compared against the main existing algorithm (five recently proposed decomposition-based MOEAs) on 90 commonly-used benchmark problems involving up to 20 objectives.

Because of high mobility, large number of vehicles are utilized to achieve timely and quality-based information in the smart Internet of Things, which has formulated into a dynamic Distributed Networked Systems (DNS). However, designing a vehicular recruitment scheme to enhance a security-based DNS is challenging since it is hard to judge trustworthiness values of vehicular sensors. Therefore, in this paper, a novel vehicular trust evaluation scheme is proposed to analyze and supervise the data collected by vehicular sensors with a trust and low-cost style. To obtain vehicular trusts, the proposed scheme that considers time factor and gap between sensed data and real data is designed to calculate trustworthiness of vehicles. Moreover, sensing data in the vehicle sparse regions has more contributions because of its rareness. Thus, to inspire vehicles to sense data in the vehicle sparse regions, a trustworthiness-based gradient pricing method is designed to pay rewards for the vehicular sensors. Finally, with real vehicular GPS datasets, simulation results demonstrate that the proposed scheme can improve accuracy rate of data sensing by 37.72% and can improve data quality by 76.95%. By incentive pricing method, coverage ratio of data sensing is improved by 13.1%. In general, performances of the proposed scheme can be improved by 19. 39% to 22.32% approximately. Future works focus on improving information security by advanced machine learning methods in the dynamic DNS.

The gap between the peak-hour Internet and the average level is increasing, which inevitably creates a type of temporary cellular weak coverage when there is a surge in data traffic demand, where any cell-edge device will have a low spectrum efficiency (SE). In this article, we propose a novel incentive architecture based on the dynamic radio frequency charging technology to improve the SE and use the Stackelberg game theory to formulate the problem. In such a model, a small base station (SBS) acts as the leader to offer a desired partition of the resource block obtained by a cell-edge device, while some small energy providers (SEPs) and small virtual access points (SVAPs) that are selected from user equipment act as the followers to make their decisions, respectively, to compete for the free part of such a resource block. Following the potential game rules, all the SEPs compete for a specific free resource part allocated by the SBS, and then, all the SVAPs compete for another nonoverlapping part allocated by the SBS on the basis of the results of the SEPs’ potential game. Although our incentive architecture formally has three game stages, it is essentially a two-level Stackelberg game, which is analyzed by using a backward induction method. The theoretical analysis proves the convergence of the above-mentioned game models, and the simulation results demonstrate that the proposed incentive architecture can improve the SE for each cell-edge device.

A P system is a finite, discrete and distributed model with a parallel-layered and net structure. The enzymatic numerical P system (ENPS) has enzyme-like variables that allow each membrane to host more than one production function, and it has been widely used in economics and controllers for autonomous mobile robots. Although the ENPS has shown powerful abilities in numerical calculation, as a decisional process, it had not been able to express its mechanism of evolution, and the existing P system has a limited ability to process the assigned cells in different evolutions. Furthermore, the present decision models have a limited ability to process large-scale decisional tasks. To set up the decisional P system theory and render the existing ENPS more flexible, we present a restriction enzyme to found a conditional enzymatic numerical P system. In this system, we present a series of decisional enzymes and rebuild the structures of cells to achieve a decisional mechanism. Finally, we verify the validation and efficiency of our model by simulating experiments. To the best of our knowledge, we are proposing a decisional P system for the first time, and the results show that this system is very logical and efficiently processes large-scale decisional tasks. Indeed, the conditional enzymatic numerical P system could achieve prior results more quikly by a factor of 188.28, and a decision tree based on DENPS is the 119.85 times faster than the general serial framework.

The blockchain-based management system has been regarded as a novel way to improve the efficiency and safety of Vehicular Ad Hoc Networks (VANETs). A blockchain-based scheme’s performance depends on blockchain nodes’ computing power composed from the road-side unit (RSU). However, the throughput of blockchain-based application in VANETs is limited by the network bandwidth. A single blockchain cannot record large-scale VANETs’ data. In this paper, we design an atomic cross-chain swap-based management system (ACSMS) to boost the scalability of blockchain-based application in VANETs. The blockchain-based public-key encryption with keyword search is further introduced to protect user privacy. The analysis shows that ACSMS achieves cross-chain swap without loss of CAV security privacy. The simulation results show that our method can realize multiple blockchain-based applications in VANETs.

Surrogate-assisted optimization has attracted much attention due to its superiority in solving expensive optimization problems. However, relatively little work has been dedicated to addressing expensive constrained multi-objective discrete optimization problems although there are many such problems in the real world. Hence, a surrogate-assisted evolutionary algorithm is proposed in this paper for this kind of problem. Specifically, random forest models are embedded in the framework of the evolutionary algorithm as surrogates to improve approximate accuracy for discrete optimization problems. To enhance the optimization efficiency, an improved stochastic ranking strategy based on the fitness mechanism and adaptive probability operator is presented, which also takes into account both convergence and diversity to advance the quality of candidate solutions. To validate the proposed algorithm, it is comprehensively compared with several well-known optimization algorithms on several benchmark problems. Numerical experiments are demonstrated that the proposed algorithm is very promising for the expensive constrained multi-objective discrete optimization problems.

The heterogeneous networks which collaborate among Space, Air, Ground, and Sea (SAGS) networks significantly promote the development of the Internet of Things (IoT). Billions of IoT devices in SAGS networks generate massive data to support various applications. We argue that it is crucial to construct a Collaboration Trust Interconnections System (CTIS) to provide the ubiquitous SAGS network accessibility and security. In this paper, a CTIS framework among the Unmanned Aerial Vehicles (UAV), Mobile Vehicles (MVs), and IoT devices is proposed to evaluate trust and select low-cost and high-trust participants to improve data quality. In this framework, MVs record the interaction information to form the verification chains, while the UAV is dispatched to collect baseline data to verify the data reported by MVs, thereby constructing global trust. Then, the hash values of baseline data are delivered to MVs to act as calibration baseline data, which provides a verification certificate for interactions among MVs and constructs local interaction trust. Finally, a greedy-based winner recruitment strategy is proposed to achieve intelligent information control with maximum credibility and cost. The simulation results show that the CTIS framework reduces the cost by 5.62%, reduces the false ratio and packet dropping rate by at least 17.16% and 31.51% compared with previous schemes.

The rapid growth of digital images motivates organizations and individuals to outsource image storage and computation to the cloud. However, the defenseless upload will raise the risk of privacy leakage while the simple encryption would impede the efficient usage of data. In this paper, we propose a privacy-preserving image retrieval scheme, in which the images are encrypted but similar images to a query can be efficiently retrieved from the encrypted images. Specifically, the image content is protected by big-block permutation, $3 \times 3$ block permutation within big-blocks, pixel permutation within $3 \times 3$ blocks, and polyalphabetic cipher. The use of polyalphabetic cipher improves security and causes no degradation in terms of retrieval accuracy as the substitution tables are generated by the order-preserving encryption. In this way, secure Local Binary Pattern (LBP) features can be directly extracted as the local features from the encrypted big-blocks, which is efficient as there is no communication between the cloud server and image owners to do so. The secure local LBP features are used to generate the feature vector for each image by the bag-of-words model. Finally, the similarity among the encrypted images is measured by the Manhattan distance of such feature vectors. The security analysis and experimental results demonstrate that the proposed scheme outperforms the main existing schemes in terms of security and retrieval accuracy.

Adjacent obstacles are difficult to be distinguished, and remote obstacles are detected easily to split. Besides, limited deep learning samples easily result in missed detection of obstacles in urban environment. In view of this, a fast and robust detection method is proposed by fusing Double-Layer Region Growth algorithm and Grid-SECOND detector. At first, SECOND detector is improved by replacing voxel grids with 2D grids and adopting multi-dimensional features to detect obstacles, which can reduce the time consumption and ensure the accurate detection of remote obstacles. Then, the first Region Growing algorithm is used to cluster the undetected and non-empty grids, which can detect obstacles outside the training set. At last, the second Region Growing algorithm is used to refine the detection results of obstacles with larger volume and multi-obstacles grids, and complete the obstacle detection. Through testing in our extracted urban dataset and KITTI dataset, it is verified that the proposed method outperforms state-of-the-art methods and can accurately achieve obstacle detection. The average duration of the entire process is about 50ms.

Accurate estimation of the state of charge (SOC) is critical for the normal use of lithium-ion battery equipment like electric vehicles. However, the SOC of lithium-ion battery is not available by direct measure, but can only indirectly be estimated by measurable variables. According to the nonlinear characteristics between the measured values and SOC during the working period of lithium-ion batteries, we propose a method to estimate the SOC of lithium-ion batteries with Temporal Convolutional Network (TCN). The measured values of voltage, current, and temperature during the use of lithium-ion batteries can be directly mapped to accurate SOC in this method without using a battery model or adaptive filter. The network can self-learning and update parameters by being fed datasets collected under various working conditions and then obtain a model that can correctly estimate SOC under different estimation conditions. In addition, it can also be applied to different types of lithium-ion batteries through transfer learning with only a small amount of battery data. At various ambient temperature conditions, the average MAE estimated by the proposed method is 0.67% for all the tests, which proves that the TCN network is an effective tool to estimate the SOC of lithium-ion batteries.

This paper mainly proposed and researched based on wavelet transform, and then used the X-map denoising technique of value filter. In other words, the value image was filtered in the spatial domain, and the value filtering was used as the standard pulse (salt) noise, also used as in the wavelet domain. After the filtered image was decomposed by biorthogonal double wavelet transform, a wavelet coefficient matrix was generated, and a soft threshold quantisation process was performed on the wavelet coefficients to produce a new wavelet coefficient matrix. In the end, they used a new wavelet coefficient matrix for image reconstruction. The processing resulted that the denoising method proposed in this paper showed that the X image can be denoised, which not only reduced the X-picture-like noise but also preserved the X-picture-like details as much as possible. It also helped to enhance diagnostic accuracy and reduced the difference in reading.

Recent development of Internet of Things (IoT) technologies has triggered a soaring application of intelligent monitoring systems. A sensitive online fault detection for industrial equipment is of utmost significance to improve industrial intelligence based on video sensor network. In this paper, we propose the Appearance and Motion SVM (AMSVM), an online fault detection method fusing multimodal information based on One-Class SVM (OCSVM), to monitor the working conditions of unsupervised equipment in the field. It utilizes multimodal features to detect faults in terms of the appearance and motion patterns of equipment. The motion pattern was generated using the OCSVM-encoded histogram of optical flow orientation (HOFO), and meanwhile we employed Local Binary Pattern Histogram (LBPH) to extract texture features to train OCSVM, depicting appearance patterns. Then, decision level information (i.e., appearance and motion patterns) are combined to produce a more precise characteristic for fault detection. The proposed method herein was validated on several industrial video surveillance data set.

As the industrial sector is becoming ever more flexible in order to improve productivity, legacy interfaces for industrial applications must evolve to enhance efficiency and must adapt to achieve higher elasticity and reliability in harsh manufacturing environments. The localization of machines, sensors and workers inside the industrial premises is one of such interfaces used by many applications. Current localization-based systems are unable to deal with highly variable conditions, meaning that the solutions working well in stationary systems suffer from considerable difficulties in harsh environments, such as factories. As a result, the precision of localization techniques is not satisfactory in most industrial applications. This paper fills in the existing gap between static approaches and dynamic indoor positioning systems, by presenting a solution adapting the system to highly changeable conditions. The proposed solution makes use of a Machine Learning-based feedback loop that learns the variability of the environment. This feedback makes continuous fingerprint calibration feasible even in the presence of different machines and Industrial Internet of Things sensors that introduce variations to the electromagnetic environment. This paper also presents a comprehensive indoor positioning system solution that reduces complexity of hardware, meaning that a multi-standard-transceiver infrastructure may be adopted with reduced capital and operational expenditures. We have developed the system from scratch and have conducted an extensive range of testbed experiments showing that the multi-technology transceiver feature is capable of increasing positioning accuracy, as well as of introducing permanent fingerprints calibration at harsh industrial premises.

In recent years, fingerprint authentication systems have been extensively deployed in various applications, including attendance systems, authentications on smartphones, mobile payment authorizations, as well as various safety certifications. However, similar to the other biometric identification technologies, fingerprint recognition is vulnerable to artificial replicas made from cheap materials, such as silicon, gelatin, etc. Thus, it is especially necessary to distinguish whether a given fingerprint is a live or a spoof one prior to such authentication. In order to solve the problems above, a novel local descriptor named Weber local binary descriptor for fingerprint liveness detection (FLD) has been proposed in this paper. The method consists of two components: the local binary differential excitation component that extracts intensity-variance features and the local binary gradient orientation component that extracts orientation features. The co-occurrence probability of the two components is calculated to construct a discriminative feature vector, which is fed into support vector machine (SVM) classifiers. The effectiveness of the proposed method is intuitively analyzed on the image samples and numerically demonstrated by Mahalanobis distance. Experiments are performed on two public databases from FLD competitions from 2011 and 2013. The results have proved that the proposed method obtains the best detection accuracy among the existing image local descriptors in FLD.

With the gradually opening of energy markets and popularization of Electric Vehicles (EVs), EVs can transmit, dispatch and recharge energy in different markets and domains dynamically. However, in Vehicular Energy Network, EVs may randomly enter and leave a market, it imposes a difficult problem in that how to schedule and distribute energy effectively. Additionally, the location of EV owners usually includes sensitive information such as home addresses, company names, hospital traces, and so on, which may be collected by attackers and may result in the privacy leakage about EV owners. In this article, we propose a decentralized blockchain-enabled energy trading scheme that can trade cross over various domains efficiently, which enables reliable transactions between EVs and energy nodes within short processing delay. It can also preserve the privacy of EV owners, by adopting the k-anonymity method in constructing a united request to hide the location information and creating a clocking area based on undirected graphs. Even though the server is maliciously attacked, the attacker cannot distinguish among EV owners, which breaks the linkage between real locations and identities to preserve EV owners’ privacy. Finally, we conduct a comprehensive experimental evaluation to evaluate the trading performance and location privacy protection performance. The simulation results show that our proposed architecture outperforms over most state-of-the-art schemes in terms of processing delay and location privacy awareness.

With the maturity of the Internet of Things, 5G communication, big data and artificial intelligence technologies, open-pit mine intelligent transportation systems based on unmanned vehicles has become a trend in smart mine construction. Traditional open-pit mine transportation systems rely on human power for command, which often causes vehicle delay and congestion. The operation of unmanned vehicles in an open pit mine relies on many sensors. Using big data from the sensors, we optimize vehicle paths and build an efficient intelligent transportation system. Based on large amounts of data, such as unmanned vehicle GPS data, vehicle equipment information, production plan data, etc., with the goal of reducing vehicle transportation costs, total unmanned vehicle delay time, and ore content fluctuation rate, a multi-objective intelligent scheduling model for open-pit mine unmanned vehicles was established, and it is aligned with actual open pit mine production. Next, we use artificial intelligence algorithms to solve the scheduling problem. To improve the convergence, distribution and diversity of the classical fast non-dominated sorting genetic algorithm (NSGA-II) to solve constrained high-dimensional multi-objective problems, we propose a decomposition-based constrained dominance principle genetic algorithm (DBCDP-NSGA-II), retaining feasible and non-feasible solutions in sparse areas, and compare it with four other commonly-used multi-objective optimization algorithms. Simulation analysis shows our algorithm provides the best overall performance results of the multi-objective models. Furthermore, we apply intelligent scheduling models and optimization algorithms to mining practice and obtain new truck operation routes and schedules, reducing truck operation costs by 18.2%, truck waiting time by 55.5%, and ore content fluctuation by 40.3%. For open-pit mine unmanned transportation, the approach provides a variety of optimized solutions for minimum transportation costs, minimum waiting time, minimum ore content fluctuation rate, and a balance of the three indicators. Through an artificial intelligence algorithm, this study realizes intelligent unmanned vehicle path planning and improves the operation efficiency of open-pit mine intelligent transportation systems.

The correct optimization of the solar cell electrical-model parameters is the key to produce better and more realistic P-V characteristics. This helps prosumers to select solar panels having better comparative efficiency, which in turn increases electricity production. Evolutionary Algorithms have shown comparatively good results in the estimation of these parameters. The Photon-current, Diode dark saturation current, Series resistance, Shunt resistance and Diode ideality factor, constitute the single diode's unknown electrical model parameters. The mathematical-model of the P-V cell is derived in terms of Series-resistance and Diode-ideality factor. These two parameters are then used in a 2-variable single objective function. Using this derived model, Genetic Algorithm and Numerical method, a new parameter estimation technique has been proposed. Making use of machine learning and combination of two algorithms highlights the usefulness of the intended hybrid technique. P-V characteristic and relative maximum power point error of different solar cells, have been compared. The relative analysis disclosed that the proposed method offers more pragmatic P-V characteristics, as compared to the existing methods.

Streaming media has been largely used by millions of users every day. The number of customers and programs, e.g., TV series, movies, and various shows, are still growing fast. However, the demand for video transcoding for various personal terminal devices results in the shortage of computing resources and the prolongation of processing delay in centralized video transcoding systems. To solve this issue, we propose a blockchain, especially, smart contract based scheme that can achieve decentralized and on-demand crowdsourcing for video transcoding, which remarkably mitigates the transcoding overhead. Specifically, our scheme consists of four key components such as employers, workers, task allocation, and payment. An employer initializes the smart contract, releases the task, and initiates the smart contract. Workers bid for the task, and the successful bidder will obtain the task and execute the task. The task allocation mechanism and the payment mechanism can guarantee the profits of both and encourage both as well. Moreover, the smart contract consists of the bidding contract and the task execution contract. The extensive analysis of our proposed scheme justified the feasibility, security for defending against typical threats, applicability in realistic situations, and portability for most multimedia such as videos and audios.

With the arrival of the big data era, data come frequently with increasing volume and high dimensionality, which imposes a considerable challenge on data compression in network representation and analysis. How to learn an effective low-dimensional representation has a dramatic influence on performance of specific network learning tasks. In this paper, we propose an unsupervised embedding learning feature representation scheme by deep Siamese neural networks, aiming to learn an efficient low-dimensional feature subspace. Unsupervised embedding learning is one tough but interesting task in which its searching strategy is performed without the guidance of class label information. Siamese network is a neural network that can learn an efficient feature subspace in a supervised mode. It trains two networks with shared weights simultaneously, and feeds them with random sampling from the same dataset. As a result, the feature space is projected onto a low-dimensional subspace such that the similar samples are with small values close to zero, whereas the dissimilar ones come with big values greater than a predefined margin. We further discuss deep Siamese neural network in an unsupervised mode and its applications to embedding learning. The proposed method can be also used to address semi-supervised feature representation problems. Finally, the learned unsupervised embedding is validated on eight publicly available databases including images, voices, and text documents. Extensive experiments demonstrate the superiority of the proposed method against the compared existing state-of-the-art embedding approaches.

New coronavirus disease (COVID-19) has constituted a global pandemic and has spread to most countries and regions in the world Through understanding the development trend of confirmed cases in a region, the government can control the pandemic by using the corresponding policies However, the common traditional mathematical differential equations and population prediction models have limitations for time series population prediction, and even have large estimation errors To address this issue, we propose an improved method for predicting confirmed cases based on LSTM (Long -Short Term Memory) neural network This work compares the deviation between the experimental results of the improved LSTM prediction model and the digital prediction models (such as Logistic and Hill equations) with the real data as reference Furthermore, this work uses the goodness of fitting to evaluate the fitting effect of the improvement Experiments show that the proposed approach has a smaller prediction deviation and a better fitting effect Compared with the previous forecasting methods, the contributions of our proposed improvement methods are mainly in the following aspects: 1) we have fully considered the spatiotemporal characteristics of the data, rather than single standardized data 2) the improved parameter settings and evaluation indicators are more accurate for fitting and forecasting 3) we consider the impact of the epidemic stage and conduct reasonable data processing for different stage

The application of deep learning methods to diagnose diseases has become a new research topic in the medical field. In the field of medicine, skin disease is one of the most common diseases, and its visual representation is more prominent compared with the other types of diseases. Accordingly, the use of deep learning methods for skin disease image recognition is of great significance and has attracted the attention of researchers. In this study, we review 45 research efforts on the identification of skin disease by using deep learning technology since 2016. We analyze these studies from the aspects of disease type, data set, data processing technology, data augmentation technology, model for skin disease image recognition, deep learning framework, evaluation indicators, and model performance. Moreover, we summarize the traditional and machine learning-based skin disease diagnosis and treatment methods. We also analyze the current progress in this field and predict four directions that may become the research topic in the future. Our results show that the skin disease image recognition method based on deep learning is better than those of dermatologists and other computer-aided treatment methods in skin disease diagnosis, especially the multi deep learning model fusion method has the best recognition effect.

Accurately obtaining road vehicle information is important in intelligent traffic surveillance systems for smart cities. Especially smart vehicle detection is recognized as the critical research issue of intelligent traffic surveillance systems. In this paper, a robust real-time vehicle detection method for the system is proposed. The method combines background subtraction model MOG2(Mixture of Gaussians) with a modified SqueezeNet model (H-SqueezeNet). The MOG2 model is utilized to create scale-insensitive Region of Interest (RoIs) from video frames. H-SqueezeNet is then proposed to accurately identify vehicle category. The effectiveness of the method was verified in CDnet2014 dataset, UA-DETRAC dataset and video data from a traffic intersection in Suzhou, China. The experiment results show that the method can achieves excellent detection accuracy in traffic surveillance systems, and achieve an average detection speed of 39.1 FPS.

As a distributed energy source, open-pit mine solar photothermal-photoelectric membrane distillation can convert solar energy into heat and electrical energy to provide power for a membrane distillation water purification system. In mine sewage treatment, the solar membrane distillation system has the advantages of high desalination rate, good water quality and low cost. However, this system has not been widely promoted and applied because of its high energy consumption and low membrane flux. Different operating parameters have a greater impact on the operating efficiency of the solar membrane distillation system. In this study, a natural cooling film distillation system was built, the response surface method was used to analyze it, and a multi-objective optimization algorithm was used to optimize the operating conditions and improve the energy efficiency of the system. In our experiment, the hot end feed temperature, hot end feed flow rate, cold end cooling water flow rate, and membrane area were used as variables, and the membrane flux, thermal efficiency, and energy consumption values were investigated as target values. We used a support vector machine (SVM) with improved fitting, and substituted the fitting prediction model into the response surface method for the relationship between the variable and the target value. Collaborative analysis was followed by substituting the model into a non-dominated sorting genetic algorithm-II (NSGA-II). After the optimization operation, the optimal working conditions were obtained to improve the operating efficiency of the solar membrane distillation system, which will enable open-pit mine prosumers to realize intelligent management of solar energy generation, storage and consumption simultaneously.

Wireless (smart) sensor networks (WSNs) comprise a myriad of embedded wireless smart sensors. They play a cardinal role in the functioning of many applications, such as the Internet of Things, smart grids, smart production systems, and smart homes, which ultimately render them paramount instruments in the modern age. Recent advances in WSNs have resulted in the rapid development of sensors. However, WSNs will only able to achieve better execution efficiencies if their energy consumption - owing to limited battery life and difficulty of recharging - can be better controlled. Moreover, signal transmission quality determines WSN performance. Hence, two main concerns - energy consumption and signal transmission quality - should be addressed to improve the performance of WSNs. Thus, a new bi-objective simplified swarm optimization algorithm (bSSO) is proposed by employing the concepts of simple routing, SSO, and crowd distance. The performance and applicability of the proposed bSSO using eight different parameter settings are demonstrated through an experiment involving ten WSN benchmarks ranging from 100 to 1000 sensors. The proposed algorithm is then compared with NSGA-II, which is an algorithm widely used to solve multi-objective problems. The results show that the proposed bSSO can successfully achieve the aim of this work.

In the era of Industrial Internet of Things (IIoT), huge amounts of data are generated, and companies are highly motivated to store the data on cloud servers for cost saving and efficient application. However, the IIoT data are always of great value. The direct outsourcing of such data can leak the important information of the companies and cause great business losses. A straightforward solution is to encrypt the data by using standard encryption methods before outsourcing. Nevertheless, this will make data utilization quite inconvenient. This paper focuses on the secure process of image data on cloud servers. Images are stored on cloud servers in encrypted form, and the local binary pattern feature can be directly extracted from the encrypted images for applications. The security analysis and experimental results demonstrate the security and effectiveness of our scheme.

Internet of Things (IoT) applications have been growing significantly in recent years, however, the security issue has not been well studied in the literature for the IoT ecosystem. The sinkhole attack is one of serious destructive attacks for IoT as it is easy to launch the attack and difficult to defend it. In this paper, a Probe Route based Defense Sinkhole Attack (PRDSA) scheme is proposed to resist Sinkhole attack and guarantee security for IoT, which is the first work that can detect, bypass and locate the sinkhole at the same time. The PRDSA scheme proposes a routing mechanism combining the far-sink reverse routing, equal-hop routing, and minimum hop routing, which can effectively circumvent the sinkhole attacks and find a safe route to the real sink, so that the scheme can achieve better sinkhole detection. More importantly, the PRDSA scheme overcomes the limitation of previous schemes that they cannot locate the sinkhole. During the detection of the sinkhole attack, the PRDSA scheme requires the nodes and the sink node to return the signature of the information (e.g., IDs, etc.), so that the location of the sinkhole can be determined. Furthermore, the PRDAS scheme mainly utilizes the characteristics of network energy consumption. The probe route of sinkhole attack mainly occurs in the region where the remaining energy exists. Thus, the PRDSA scheme has little impact on the network lifetime. Theory and experiments show that this scheme can achieve better performance than existing schemes in term of network security and lifetime.

The internal mechanism of the social “earthquakes”- the wars burst in human society, is investigated by nonlinear theory. We explore the scaling behavior in the human activity and the historical evolution of ancient China. Based on the war chronology and history chronology from B.C.2000 to A.D.1840, using statistical analysis together with the detrended fluctuation analysis, we find that the social evolution system shows self-organized critical behavior and there exists self-similar scaling behavior during the evolution system. Based on time-delay reconstructed phase space, a modified radial basis function neural network (modified RBF-NN) is used to predict the number of wars and the transformation of the historical stage. It shows that the prediction result matches quite well with the real value for the following two historical stages. Furthermore, by singular value decomposition and the sparse identification algorithm in time-delay coordinates, the coefficients of the candidate functions in identified system can be solved from a sparse regression problem. We extracted the governing equations of big events burst in social system. The identified system fits well with the real data, and it can capture the same topology with the original system of wars. In this paper, we investigated the wars burst in social system by statistic analysis, neural network as well as sparse identification. It sheds light on the study of wars or conflicts from perspective of data science and dynamical theory.

Cognitive Internet of Things (CIoT) is viewed as the current IoT integrated with cognitive and cooperative mechanisms to promote performance and achieve intelligence. Intelligence sensing has recently become a key research field. We consider the sensing information coverage issues with intelligent technologies, where people contribute data samples for CIoT captured by sensors, typically on smartphones. For CIoT, deficiencies of sensing information coverage result in loss of human life and social unrest. To tackle this challenge, in this paper, we first propose a new metric, called the Quality of Information Coverage (QIC), which characterizes information coverage quality and rewards for data sensing to maximize the QIC. Furthermore, a market-based incentive mechanism is formulated to guarantee the QIC. We analyze the market equilibrium point, and obtain an optimal expression for generating maximum payoffs for CIoT and reporters. Finally, the effects of a market-based incentive mechanism are examined through extensive simulations. The results demonstrate that the intelligent incentive control mechanism reaches the equilibrium point with a higher QIC than other existing schemes. The QIC algorithm proposed in this paper ensures that the standard deviation number of collected data samples for some areas is reduced by 50%–75%, compared to the existing algorithms, thus, these data samples are balanced. Compare to these non-QIC-aware algorithms, the average price of sensing data is reduced by 18.94%.

Pervasive sensing devices make an unprecedented increase in data sensing, collection, and processing in edge network and they form the edge content server system. The edge content server system combines the content-centric network (CCN) to form a huge content propagation system which makes it challenging to achieve efficient content propagation. Different from IP-based, host-oriented Internet architecture, the CCN systems focus on the information that is contained in network and directly accessible, providing more secure and flexible Internet services. This emerging network architecture supports a number of novel applications, such as common interests sharing, mobile data offloading, and information dissemination without Internet access. In this paper, an analytical framework is proposed to address the problem of content propagation among users with the same interests in leveraging location-based social networks, where the check-in patterns of users are recorded. Particularly, we propose a content propagation effectiveness quantitative model that considers the distance between users, users’ interests, and contact rates to formulate the propagation effect and latency. We also apply our framework to two real-world datasets for the evaluation of its effectiveness. Compared with previous studies, our simulated annealing-based algorithm can greatly improve the effects by as much as 25.4%–65.6%, and the contents can be disseminated faster by about 24.6%–57.8%.

Internet of Things (IoT) has become a vital part of our infrastructure to enrich lives and make processes easier. In these people-centric IoT applications, massive personal data are collected in real-time and stored by the resource constrained sensor network. Because any misusage of these personal data might result in the leakage of privacy and economic losses, it is expected that the data requesters can only access to the data what they have purchased, or are entitled to use. How to accurately control data access permissions is one of the prerequisites for IoT data protection. Based on a revised one-way hash chain technique, we proposed a novel Time-based Access Control (TAC) scheme for multi-attribute data in Internet of Things. All the data are partitioned into 2-D subspaces representing generation time and data attribute. Data in each subspace is encrypted with the corresponding sub-key before its transmission to the base station to achieve data privacy and access control. Anyone who wants to read or use data with specified attribute at a particular time must get the corresponding sub-key from the data source node or the owner. TAC can generate a sub-key for data in each subspace in an efficient manner in terms of less sub-key generation time and low memory space usage. We proposed three improved schemes to further reduce sub-key computation time according to different application scenarios. The experimental results show that TAC can be applied to the resource limited WSNs efficiently.

In recent years, with the development of networked Cyber-Physical Systems (CPSs), wireless sensor networks (WSNs), as an important carrier of CPSs, has been applied more and more. In WSNs, many applications require low delay and high reliability for routing the sensing data to sink. Due to the lossy nature of wireless channels, rerouting schemes are often applied to ensure reliable data collection for mission-critical applications. However, rerouting together with multi-hop routing in duty cycle base WSNs will make designing a low delay routing scheme a challenge issue. In this paper, a Pipeline Slot based Fast Rerouting (PSFR) Scheme is proposed to reduce delay in duty cycle based WSNs. The main innovation points of PSFR scheme are as follows: (a) In duty cycle based WSNs, the major delay is caused by the sleep delay when nodes in the route forwarding to next hop node. Therefore, in PSFR scheme, we add a sequential active (SA) slot at the next hop node which is active at the next slot of the active slot of the previous node, which enables the previous node to forward packets to the next hop node in the slot right after receiving packet in active slot and greatly reduces sleep delay. (b) The second, in PSFR scheme, the backup path is designed beforehand in a less stringent pipeline active slot, so the packet can reach the sink with a relatively low delay when rerouting. (c) More importantly, in PSFR scheme, the added SA slots use the residual energy of peripheral nodes, which means they reduce the routing delay without decreasing network lifetime. After sufficient theoretical analysis and experiment, results show that the PSFR scheme can reduce the delay by more than 58.215% in the experiment networks without reducing the network lifetime, and the PSFR scheme can improve the energy utilization of network by more than 27.66%.