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Abstract The Internet of Things (IoT) devices spawn growing diverse social platforms and online data at the network edge, propelling the development of cross-platform applications. To integrate cross-platform data, user identity linkage is envisioned as a promising technique by detecting whether different accounts from multiple social networks belong to the same identity. The profile and social relationship information of IoT users may be inconsistent, which deteriorates the reliability of the effectiveness of identity linkage. To this end, we propose a topic and knowledge-enhanced model for edge-enabled IoT user identity linkage across social networks, named TKM, which conducts feature representation of user generated contents from both post-level and account-level for identity linkage. Specifically, a topic-enhanced method is designed to extract features at the post-level. Meanwhile, we develop an external knowledge-based Siamese neural network for user-generated content alignment at the account-level. Finally, we show the superiority of TKM over existing methods on two real-world datasets. The results demonstrate the improvement in prediction and retrieval performance achieved by utilizing both post-level and account-level representation for identity linkage across social networks.

The cultivation of cashew crops carries numerous economic advantages, and countries worldwide that produce this crop face a high demand. The effects of wind speed and wind direction on crop yield prediction using proficient deep learning algorithms are less emphasized or researched. We propose a combination of advanced deep learning techniques, specifically focusing on long short-term memory (LSTM) and random forest models. We intend to enhance this ensemble model using dynamic time warping (DTW) to assess the spatiotemporal data (wind speed and wind direction) similarities within Jaman North, Jaman South, and Wenchi with their respective production yield. In the Bono region of Ghana, these three areas are crucial for cashew production. The LSTM-DTW-RF model with wind speed and wind direction achieved an R2 score of 0.847 and the LSTM-RF model without these two key features R2 score of (0.74). Both models were evaluated using the augmented Dickey-Fuller (ADF) test, which is commonly used in time series analysis to assess stationarity, where the LSTM-DTW-RF achieved a 90% level of confidence, while LSTM-RF attained an 87.99% level. Among the three municipalities, Jaman South had the highest evaluation scores for the model, with an RMSE of 0.883, an R2 of 0.835, and an MBE of 0.212 when comparing actual and predicted values for Wenchi. In terms of the annual average wind direction, Jaman North recorded (270.5 SW°), Jaman South recorded (274.8 SW°), and Wenchi recorded (272.6 SW°). The DTW similarity distance for the annual average wind speed across these regions fell within specific ranges: Jaman North (±25.72), Jaman South (±25.89), and Wenchi (±26.04). Following the DTW similarity evaluation, Jaman North demonstrated superior performance in wind speed, while Wenchi excelled in wind direction. This underscores the potential efficiency of DTW when incorporated into the analysis of environmental factors affecting crop yields, given its invariant nature. The results obtained can guide further exploration of DTW variations in combination with other machine learning models to predict higher cashew yields. Additionally, these findings emphasize the significance of wind speed and direction in vertical farming, contributing to informed decisions for sustainable agricultural growth and development.

Drought is a serious natural disaster that causes huge losses to various regions of the world. To effectively cope with this disaster, we need to use drought indices to classify and compare the drought conditions of different regions. We can take appropriate measures according to the category of drought to mitigate the impact of drought. Recently, deep learning models have shown promising results in this domain. However, few of these models consider the relationships between different areas, which limits their ability to capture the complex spatio-temporal dynamics of droughts. In this study, we propose a novel multivariate spatio-temporal sensitive network (MSTSN) for drought prediction, which incorporates both geographical and temporal knowledge in the network and improves its predictive power. We obtained the standardized precipitation evapotranspiration index and meteorological data from the climatic research unit dataset, covering the period from 1961 to 2018. This is the first deep learning method that embeds geographical knowledge in drought prediction. We also provide a solid foundation for comparing our method with other deep learning baselines and evaluating their performance. Experiments show that our method consistently outperforms the existing state-of-the-art methods on various metrics, validating the effectiveness of geospatial and temporal information. HIGHLIGHTS A novel multivariate spatio-temporal sensitive network (MSTSN) is proposed for drought prediction.; The MSTSN model captures the relationships between different areas by graph neural network.; The MSTSN model extracts the long-term dependencies of data through a gated recurrent unit and enhances its effect by multi-head self-attention.; The MSTSN model predicts the spatial drought distribution closest to the actual distribution.;

The mobile device has been widely used in many areas. Task offloading is always used to overcome the limitation of processing ability and energy-supply of the mobile devices in the mobile cloud environment. Most researches suppose we know the detail of task (number of instructions) of every sub-task. But most of time, to get the detail of a task is very difficult. This paper uses the grey number to express that uncertain information and propose method to evaluate the performance of offloading tasks. Simulations show that our method selects the solution with the lowest energy consumption.

Long sequence time-series forecasting models pay excessive attention to irrelevant information and noise when predicting air temperature, leading to prediction drift and poor generalization ability. We propose an Air Temperature Forecasting Model Based on RenyiSparse Attention and Adaptive Time Series Decomposition(ATFSAD). The ATFSAD model follows the encoder-decoder structure, embedding RenyiSparse Attention to efficiently capture long-term dependencies from time series. Additionally, to reduce the redundancy mixed into the encoded information, an information distilling technique is designed to filter it out prior to decoding. Lastly, the multi-layer decoder is combined with adaptive time series decomposition to progressively refine the seasonal and trend components of the prediction. The experiments on the Jena meteorological dataset show that ATFSAD outperforms the state-of-the-art models Informer and FEDformer by 9.89% and 5.97% respectively, in predicting air temperature at 10-minute intervals for a full day(144 points), with a mean absolute error(MAE) of 1.73. Therefore, ATFSAD is of practical value for air temperature prediction.

With the rapid development of online social networks, text-communication has become an indispensable part of daily life. Mining the emotion hidden behind the conversation-text is of prime significance and application value when it comes to the government public-opinion supervision, enterprise decision-making, etc. Therefore, in this paper, we propose a text emotion prediction model in a multi-participant text-conversation scenario, which aims to effectively predict the emotion of the text to be posted by target speaker in the future. Specifically, first, an affective space mapping is constructed, which represents the original conversation-text as an n-dimensional affective vector so as to obtain the text representation on different emotion categories. Second, a similar scene search mechanism is adopted to seek several sub-sequences which contain similar tendency on emotion shift to that of the current conversation scene. Finally, the text emotion prediction model is constructed in a two-layer encoder-decoder structure with the emotion fusion and hybrid attention mechanism introduced at the encoder and decoder side respectively. According to the experimental results, our proposed model can achieve an overall best performance on emotion prediction due to the auxiliary features extracted from similar scenes and the adoption of emotion fusion as well as the hybrid attention mechanism. At the same time, the prediction efficiency can still be controlled at an acceptable level.

The typical aim of user matching is to detect the same individuals cross different social networks. The existing efforts in this field usually focus on the users' attributes and network embedding, but these methods often ignore the closeness between the users and their friends. To this end, we present a friend closeness based user matching algorithm (FCUM). It is a semi-supervised and end-to-end cross networks user matching algorithm. Attention mechanism is used to quantify the closeness between users and their friends. We considers both individual similarity and their close friends similarity by jointly optimize them in a single objective function. Quantification of close friends improves the generalization ability of the FCUM. Due to the expensive costs of labeling new match users for training FCUM, we also design a bi-directional matching strategy. Experiments on real datasets illustrate that FCUM outperforms other state-of-the-art methods that only consider the individual similarity.

The combination of Unmanned Aerial Vehicle (UAV) technologies and computer vision makes UAV applications more and more popular. Computer vision tasks based on deep learning usually require a large amount of task-related data to train algorithms for specific tasks. Since the commonly used datasets are not designed for specific scenarios, in order to give UAVs stronger computer vision capabilities, large enough aerial image datasets are needed to be collected to meet the training requirements. In this paper, we take low-altitude aerial image object detection as an example to propose a framework to demonstrate how to construct datasets for specific tasks. Firstly, we introduce the existing low-altitude aerial images datasets and analyze the characteristics of low-altitude aerial images. On this basis, we put forward some suggestions on data collection of low-altitude aerial images. Then, we recommend several commonly used image annotation tools and crowdsourcing platforms for data annotation to generate labeled data for model training. In addition, in order to make up the shortage of data, we introduce data augmentation techniques, including traditional data augmentation and data augmentation based on oversampling and generative adversarial networks.

With the fast proliferation of device sensing and computing, crowed sensing has become the building block of the Internet of things. Consequently, various data collection and incentive mechanisms are investigated for people-centric services. In this paper, we have investigated the problem of privacy-aware people-centric IoT service based on a tailored auction approach. We applied a bi-tier differential privacy methodology on the data collected from crowdsensing IoT devices. A corresponding pricing scheme is also proposed to ensure the property of incentive compatibility, precise service data, and anonymized query results. Comparing to traditional privacy-aware auction schemes which only focus on the cost, our corresponding precise privacy-aware auction scheme provides a tailored IoT service based on the customers’ request. The proposed trial query technique is able to provide a precise assessment of service quality, thus improves the efficiency of the people-centric IoT service. The customer could enjoy the convenience of service evaluation before making a bid, while the actual service data is anonymized to guarantee the service providers’ interests. We evaluate the proposed bi-tier differential privacy schema for auction-based service by conducting extensive simulations. The experimental results show that our proposed method yields higher data utility and accuracy for the IoT service customers with privacy concerns.

The data security of fog computing is a key problem for the Internet of things. Identity-based encryption (IBE) from lattices is extremely suitable for fog computing. It is able to not only simplify certificate management, but also resist quantum attacks. In this paper, firstly, we construct a novel efficient lattice-based IBE scheme with Combined Public Key (CPK) technique by keeping from consumptive trapdoor generation algorithm and preimage sampling algorithm, which is required by the existing lattice-based IBE schemes based on learning with errors (LWE). In addition, its key storage cost is lower and it is IND-ID-CPA secure in the random oracle model. Furthermore, based on this, an enhanced lattice-based IBE scheme with IND-ID-CCA security is developed by employing strong one-time signature. Our schemes only need O(n3/log n) additions of vectors, while the existing schemes need at least O(n3) of additions and multiplications in Setup and Extract phase.

Accurate image segmentation results would show a great significance to computer vision-based manufacturing for complex helical surface. However, the image segmentation for complex helical surface is always a difficult problem because of the uneven gray distribution and non-homogeneous feature patterns of its images. Therefore, a multi-direction evolutionary segmentation model is constructed and a multi-population cooperative evolution algorithm is proposed to solve the new model. According to the characteristics of gray distribution and feature patterns of complex helical surface image, an eigenvector extraction and description strategy is researched by combining gray level co-occurrence matrix algorithm with fractal algorithm, and the complex helical surface image can be described succinctly by gray feature and shape feature. Based on the description algorithm of image features, an image segmentation strategy using cooperative evolution from different eigenvector is discussed, and the helical surface image segmentation is decomposed from a single objective optimization problem to a multi-objective optimization problem to improve the accuracy of segmentation. Meanwhile, a multi-objective particle swarm optimization algorithm based on multi-directional evolution and shared archives is presented. Due to the fact that each eigenvector segmentation corresponds to one evolution direction, the collaboration of local and global segmentation can be realized by information sharing and interaction between evolution directions and the archive set. The comprehensive quality of non-dominated solution can be improved by the selection strategy of local and global optimal solution as well as the archive set maintenance. The practical numerical experiments for complex helical surface image segmentation are carried out to prove the validity of the proposed model and algorithm.

The Industrial Internet of Things (IIoT) plays an important role in the development of smart factories. However, the existing IIoT systems are prone to suffering from single points of failure and unable to provide stable service. Meanwhile, with the increase of node scale and network quantity, the maintenance cost presents to be higher. Such a disadvantage can be effectively compensated by the features such as security, privacy, non-tamperability and distributed deployment supported by the blockchain. In this paper, first, an intelligent manufacturing security model based on blockchain was proposed. Due to the high power consumption and low throughput of the traditional blockchain, IoT devices with limited power consumption can not work independently. Therefore, in this paper, a new Merkle Patricia tree (MPT) was adopted to extend the blockchain structure and provide fast query of node status. Second, since the MPT does not support concurrent operation and the data operation performance deteriorates with high data volume, a lock-free concurrent and cache-based Merkle Patricia tree was proposed (CMPT) to support lock-free concurrent data operation, which can improve the data operation efficiency in multi-core system. The experimental results indicate that, compared with the original MPT, the CMPT proposed in this paper effectively reduced the time complexity of data insertion and data query and improved the speed of block construction and data query.

Most current automatic summarization methods are for English texts. The distinction between words in Chinese text is large, the types of parts of speech are many and complex, and polysemy or ambiguous words appear frequently. Therefore, compared with English text, Chinese text is more difficult to extract useful feature words. Due to the complex syntax of Chinese, there are currently relatively few automatic summarization methods for Chinese text. In the past, only the important sentences in the original text can be selected and simply arranged to obtain a summary with chaotic sentences and insufficient coherence. Meanwhile, because Chinese short text usually contains more redundant information and the sentence structure is not neat, we propose a topic-based automatic summary method for Chinese short text. Firstly, a key sentence selection method is proposed combining topic words and TF-IDF to obtain the score of each text corresponding to the topic in the original text data. Then the sentence with the highest score as the topic sentence of the topic is selected. Considering that the short text of Weibo may contain a lot of irrelevant information and sometimes even lack some important components of topic, three retouching mechanisms are proposed to improve the conciseness, richness and readability of topic sentence extraction results. We validate our approach on natural disaster and social hot event datasets from Sina Weibo. The experimental results show that the polished topic summary not only reflects the exact relationship between topic sentences and natural disasters or social hot events, but also has rich semantic information. More importantly, we can almost grasp the basic elements of natural disaster or social hot event from the topic sentence, so as to help the government guide disaster relief or meet the needs of users for quickly obtaining information of social hot events.

Daily conversations contain rich emotional information, and identifying this emotional information has become a hot task in the field of natural language processing. The traditional dialogue sentiment analysis method studies one-to-one dialogues and cannot be effectively applied to multi-speaker dialogues. This paper focuses on the relationship between participants in a multi-speaker conversation and analyzes the influence of each speaker on the emotion of the whole conversation. We summarize the challenges of emotion recognition work in multi-speaker dialogue, focusing on the context-topic switching problem caused by multi-speaker dialogue due to its free flow of topics. For this challenge, this paper proposes a graph network that combines syntactic structure and topic information. A syntax module is designed to convert sentences into graphs, using edges to represent dependencies between words, solving the colloquial problem of daily conversations. We use graph convolutional networks to extract the implicit meaning of discourse. In addition, we focus on the impact of topic information on sentiment, so we design a topic module to optimize the topic extraction and classification of sentences by VAE. Then, we use the combination of attention mechanism and syntactic structure to strengthen the model’s ability to analyze sentences. In addition, the topic segmentation technology is adopted to solve the long-term dependencies problem, and a heterogeneous graph is used to model the dialogue. The nodes of the graph combine speaker information and utterance information. Aiming at the interaction relationship between the subject and the object of the dialogue, different edge types are used to represent different interaction relationships, and different weights are assigned to them. The experimental results of our work on multiple public datasets show that the new model outperforms several other alternative methods in sentiment label classification results. In the multi-person dialogue dataset, the classification accuracy is increased by more than 4%, which verifies the effectiveness of constructing heterogeneous dialogue graphs.

Existing persona-based dialogue generation models focus on the semantic consistency between personas and responses. However, various influential factors can cause persona inconsistency, such as the speaking style in the context. Existing models perform inflexibly in speaking styles on various-persona-distribution datasets, resulting in persona style inconsistency. In this work, we propose a dialogue generation model with persona selection classifier to solve the complex inconsistency problem. The model generates responses in two steps: original response generation and rewriting responses. For training, we employ two auxiliary tasks: (1) a persona selection task to fuse the adapted persona into the original responses; (2) consistency inference to remove inconsistent persona information in the final responses. In our model, the adapted personas are predicted by an NLI-based classifier. We evaluate our model on the persona dialogue dataset with different persona distributions, i.e., the persona-dense PersonaChat dataset and the persona-spare PersonalDialog dataset. The experimental results show that our model outperforms strong models in response quality, persona consistency, and persona distribution consistency.

It is difficult to accurately identify the winter wheat acreage in the Jianghuai region of China, and the fusion of high-resolution images and medium-resolution image data can improve the image quality and facilitate the identification and acreage extraction of winter wheat. Therefore, the objective of this study is to improve the accuracy of China’s medium-spatial resolution image data (environment and disaster monitoring and forecasting satellite data, HJ-1/CCD) in extracting the large area of winter wheat planted. The fusion and object-oriented classification of the 30 m × 30 m HJ-1/CCD multispectral image and 2 m × 2 m GF-1 panchromatic image (GF-1/PMS) of winter wheat at the jointing stage in the study area were studied. The GF-1/PMS panchromatic images were resampled at 8 m, 16 m and 24 m to produce panchromatic images with four spatial resolutions, including 2 m. They were fused with HJ-1/CCD multispectral images by Gram Schmidt (GS). The quality of the fused images was evaluated to pick adequate scale images for the field pattern of winter wheat cultivation in the study area. The HJ-1/CCD multispectral image was resampled to obtain an image with the same scale as the suitable scale fused image. In the two images, the training samples SFI (samples of fused image) and SRI (samples of resampled image) containing spectral and texture information were selected. The fused image (FI) and resampled image (RI) were used for winter wheat acreage extraction using an object-oriented classification method. The results indicated that the fusion effect of 16 m × 16 m fused image was better than 2 m × 2 m, 8 m × 8 m and 24 m × 24 m fused images, with mean, standard deviation, average gradient and correlation coefficient values of 161.15, 83.01, 4.55 and 0.97, respectively. After object-oriented classification, the overall accuracy of SFI for the classification of resampled image RI16m was 92.22%, and the Kappa coefficient was 0.90. The overall accuracy of SFI for the classification of fused image FI16m was 94.44%, and the Kappa coefficient was 0.93. The overall accuracy of SRI for the classification of resampled image RI16m was 84.44%, and the Kappa coefficient was 0.80. The classification effect of SFI for the fused image FI16m was the best, indicating that the object-oriented classification method combined with the fused image and the extraction samples of the fused image (SFI) could extract the winter wheat planting area with precision. In addition, the object-oriented classification method combining resampled images and the extraction samples of fused images (SFI) could extract the winter wheat planting area more effectively. These results indicated that the combination of medium spatial resolution HJ-1/CCD images and high spatial resolution GF-1 satellite images could effectively extract the planting area information of winter wheat in large regions.

In recent years, the security and privacy issues of face data in video surveillance have become one of the hotspots. How to protect privacy while maintaining the utility of monitored faces is a challenging problem. At present, most of the mainstream methods are suitable for maintaining data utility with respect to pre-defined criteria such as the structure similarity or shape of the face, which bears the criticism of poor versatility and adaptability. This paper proposes a novel generative framework called Quality Maintenance-Variational AutoEncoder (QM-VAE), which takes full advantage of existing privacy protection technologies. We innovatively add the loss of service quality to the loss function to ensure the generation of de-identified face images with guided quality preservation. The proposed model automatically adjusts the generated image according to the different service quality evaluators, so it is generic and efficient in different service scenarios, even some that have nothing to do with simple visual effects. We take facial expression recognition as an example to present experiments on the dataset CelebA to demonstrate the utility-preservation capabilities of QM-VAE. The experimental data show that QM-VAE has the highest quality retention rate of 86%. Compared with the existing method, QM-VAE generates de-identified face images with significantly improved utility and increases the effect by 6.7%.

A new k-anonymous method which is different from traditional k-anonymous was proposed to solve the problem of privacy protection. Specifically, numerical data achieves k-anonymous by adding noises, and categorical data achieves k-anonymous by using randomization. Using the above two methods, the drawback that at least k elements must have the same quasi identifier in the k-anonymous data set has been solved. Since the process of finding anonymous equivalence is very time consuming, a two-step clustering method is used to divide the original data set into equivalence classes. First, the original data set is divided into several different sub-datasets, and then the equivalence classes are formed in the sub-datasets, thus greatly reducing the computational cost of finding anonymous equivalence classes. The experiments are conducted on three different data sets, and the results show that the proposed method is more efficient and the information loss of anonymous dataset is much smaller.

Community detection is a classic and essential task in complex network analysis which aims at understanding their structural properties and functional organization. Community detection based on overlapping nodes has become one of the most popular methods in recent years. But, how to define the overlapping nodes in the real network is an important job. In this paper, we use the speaker-listener label propagation algorithm to find overlapping nodes. Second, we proposed a new metric which is based on local and global attributes to measure the node influence so as to evaluate the overlapping nodes’ importance. Community detection is realized with the $k$ -lowest-influence overlapping nodes deleting. Then, we assign the removed nodes into specific communities by voting to find the final community structure. The voting strategy is based on choosing the specific communities containing most of their neighbors. Finally, the extensive experiments on real-world networks demonstrate that our proposed method improves the quality of community detection methods and show both the effectiveness and efficiency of the method.

With the rapid development of social networks, personal privacy leakage has become more and more serious. A social network is a shared platform. Resources in a social network may be shared by multiple owners. In order to prevent privacy leakage, each owner assigns a corresponding privacy protection strategy. For the same shared contents, integrating the privacy protection strategies of all owners is the key problem for sharing. This paper proposes a rule fusion method of privacy protection for the co-ownership of data shared in social networks. First, the content of the protection is defined according to different privacy requirements. Second, this paper uses predicate logic formulas to abstract the natural language-based description of privacy protection and further provides a logical model of privacy protection rules. Third, this paper gives the definition of privacy protection heterogeneous rules and provides a rule fusion algorithm to ensure no conflict exists among these rules. The experimental results show that the proposed rule-based fusion method of privacy protection strategy performs at a higher level than the privacy protection strategy fusion.

Cache memory plays an important role in improving the performance of web servers, especially for big data transmission, which response time is constrained. It is necessary to use an effective method, such as web cache. Because an outstanding cache replacement algorithm can not only reduce the users access time but also improve the performance of the system. The traditional used weighting replacement policy does not consider the size parameter, hence, it may perform poorly while the datasets are larger. In this paper, we propose a novel, high-performance cache replacement algorithm for the web cache, named weighting size and cost replacement policy (WSCRP) bases on the weighting replacement policy. The algorithm recalculates the objects weight with adding the cost attribute in the cache, then orders the weight. Additionally the influence of various factors on the Web object as frequency, time, and cost value are considered. When the cache space cannot satisfy the new request object, the replacement policy WSCRP replaces the largest weighting and cost object. The experiments show that proposed algorithm has higher

Fireworks algorithm (FWA) is a novel swarm intelligence algorithm recently proposed for solving complex optimization problems. Because of its powerful global optimization ability to solve classification problems, we first present an optimization classification model in this paper. In this model, a linear equation set is constructed according to classification problems. This optimization classification model can be solved by most evolutionary computation techniques. In this paper, a self-adaptive FWA (SaFWA) is developed so that the optimization classification model can be solved efficiently. In SaFWA, four candidate solution generation strategies (CSGSs) are employed to increase the diversity of solutions. In addition, a self-adaptive search mechanism has also been introduced to use the four CSGSs simultaneously. To extensively assess the performance of SaFWA on solving classification problems, eight datasets have been used in the experiments. The experimental results show that it is feasible to solve classification problems through the optimization classification model and SaFWA. Furthermore, SaFWA performs better than FWA, FWA variants with only one CSGS, particle swarm optimization, and differential evolution on most of the training sets and test sets.

Caches are used to improve the performance of the internet, and to reduce the latency of data access time and the low speed of repeated computing processes. Cache replacement is one of the most important issues in a caching system; therefore, it must be coordinated with the caching system to minimize the access latency and maximize the hit rate or byte hit rate. In this paper, we presented a novel caching replacement algorithm named Weighted Greedy Dual Size Frequency (WGDSF) algorithm, which is an improvement on the Greedy Dual Size Frequency (GDSF) algorithm. The WGDSF algorithm mainly adds weighted frequency-based time and weighted document type to GDSF. By increasing the above two weighted parameters, WGDSF performs fairly well at keeping popular objects in the cache and replacing rarely used ones. Our experiment shows that this algorithm has a better hit rate, byte hit rate and access latency than state-of-the-art algorithms, such least Recently Used, least Frequently Used, and GDSF.

With the fast development of social networks, more and more data has been generated. Finding useful information among these data is important. Subgraph matching is the method that can be used in social networks for social search, recommender systems, and so on. In this paper, we choose five typical graph matching methods which are VF2, SPath, TurboISO, BoostIso, and RI to value their performance and scalability in social networks. These methods are verified by three social network data sets whose node’s number vary from thousands to millions. According to the experiments, we can find that VF2 and RI is applicable for rather small graphs while SPath performs better in large graph when average degree of graph is small. When graph with high average degree, TurboISO and BoostIso performs better. What is more, we also compare the efficiency of different matching order when searching. The order choosing strategy proposed by RI is better.

With the popularity of location-aware devices (e.g., smart phones), a large number of trajectory data were collected. The trajectory dataset can be used in many fields including traffic monitoring, market analysis, city management, etc. The collection and release of trajectory data will raise serious privacy concerns for users. If users’ privacy is not protected enough, they will refuse to share their trajectory data. In this paper, a new trajectory privacy protection method based on random sampling differential privacy (TPRSDP), which can provide more security protection, is proposed. Compared with other methods, it takes less time to run this method. Experiments are conducted on two real world datasets to validate the proposed scheme, and the results are compared with others in terms of running time and information loss. The performance of the scheme with different parameter values is verified. The setting of the new scheme parameters is discussed in detail, and some valuable suggestions are given.

The current baseline architectures in the field of the Internet of Things (IoT) strongly recommends the use of edge computing in the design of the solution applications instead of the traditional approach which solely uses the cloud/core for analysis and data storage. This research, therefore, focuses on formulating an edge-centric IoT architecture for smartphones which are very popular electronic devices that are capable of executing complex computational tasks at the network edge. A novel smartphone IoT architecture (SMIoT) is introduced that supports data capture and preprocessing, model (i.e., machine learning models) deployment, model evaluation and model updating tasks. Moreover, a novel model evaluation and updating scheme is provided which ensures model validation in real-time. This ensures a sustainable and reliable model at the network edge that automatically adjusts to changes in the IoT data subspace. Finally, the proposed architecture is tested and evaluated using an IoT use case.

In cloud computing, some large tasks may occupy too many resources and some small tasks may wait for a long time based on First-In-First-Out (FIFO) scheduling algorithm. To reduce tasks’ waiting time, we propose a task scheduling algorithm based on fuzzy clustering algorithms. We construct a task model, resource model, and analyze tasks’ preference, then classify resources with fuzzy clustering algorithms. Based on the parameters of cloud tasks, the algorithm will calculate resource expectation and assign tasks to different resource clusters, so the complexity of resource selection will be decreased. As a result, the algorithm will reduce tasks’ waiting time and improve the resource utilization. The experiment results show that the proposed algorithm shortens the execution time of tasks and increases the resource utilization.

Meteorological technology has evolved rapidly in recent years to provide enormous, accurate and personalized advantages in the public service. Large volumes of observational data are generated gradually by technologies such as geographical remote sensing, meteorological radar satellite, etc. that makes data analysis in weather forecasting more precise but also poses a threat to the traditional method of data storage. In this paper, we present MHBase, (Meteorological data based on HBase (Hadoop Database), a distributed real-time query scheme for meteorological data based on HBase. The calibrated data obtained from terminal devices will be partitioned into HBase and persisted to HDFS (the Hadoop Distributed File System). We propose two algorithms (the Indexed Store and the Indexed Retrieve Algorithms) to implement a secondary index using HBase Coprocessors, which allow MHbase to provide high performance data querying on columns other than rowkey. Experimental results show that the performance of MHBase can satisfy the basic demands of meteorological business services.