A survey on solutions to support developers in privacy-preserving IoT development

Internet-of-Things (IoT) devices are rising in popularity and their usefulness often stems from the amount of data they collect. Data regulations such as the European General Data Protection Regulation (GDPR) require software developers to do their due diligence when it comes to privacy, as they are required to adhere to certain principles such as Privacy-by-Design (PbD). Due to the distributed and heterogeneous nature of IoT applications, privacy-preserving design is even more important in IoT environments. Studies have shown that developers are often not eager to implement privacy and generally do not see it as their duty or concern. However, developers are often left alone when it comes to engineering privacy in the realm of IoT. In this paper, we therefore survey which frameworks and tools have been developed for them, especially in the case of IoT. Our findings indicate that existing solutions are cumbersome to use, only work in certain scenarios, and are not enough to solve the privacy issues inherent IoT development. Based on our analysis, we further propose future research directions.     

A spark-based big data analysis framework for real-time sentiment prediction on streaming data

There are many data sources that produce large volumes of data. The Big Data nature requires new distributed processing approaches to extract the valuable information. Real-time sentiment analysis is one of the most demanding research areas that requires powerful Big Data analytics tools such as Spark. Prior literature survey work has shown that, though there are many conventional sentiment analysis researches, there are only few works realizing sentiment analysis in real time. One major point that affects the quality of real-time sentiment analysis is the confidence of the generated data. In more clear terms, it is a valuable research question to determine whether the owner that generates sentiment is genuine or not. Since data generated by fake personalities may decrease accuracy of the outcome, a smart/intelligent service that can identify the source of data is one of the key points in the analysis. In this context, we include a fake account detection service to the proposed framework. Both sentiment analysis and fake account detection systems are trained and tested using Naïve Bayes model from Apache Spark's machine learning library. The developed system consists of four integrated software components, ie, (i) machine learning and streaming service for sentiment prediction, (ii) a Twitter streaming service to retrieve tweets, (iii) a Twitter fake account detection service to assess the owner of the retrieved tweet, and (iv) a real-time reporting and dashboard component to visualize the results of sentiment analysis. The sentiment classification performances of the system for offline and real-time modes are 86.77% and 80.93%, respectively.     

PF-EdgeCache: Popularity and freshness aware edge caching scheme for NDN/IoT networks

Named Data Networking (NDN) is considered an appropriate architecture for IoT as it naturally supports consumer mobility and provides in-network caching capabilities as leverage to meet IoT requirements. Some caching techniques have been introduced to meet IoT application requirements and enforce the caching at the network edge. However, it remains challenging to design a popularity and freshness aware caching technique that places cached contents at the edge of the network as close to consumers as possible in a natural and simple manner without resorting to cumbersome networking mechanisms and hard-to-insure assumptions. In this paper, we propose PF-EdgeCache, an efficient popularity and freshness aware caching technique that naturally brings requested popular contents to the edge of the network in a manner fully compliant with the NDN standard. Simulations performed using the ndnSIM simulator and a large transit stub topology clearly show the competitiveness of PF-EdgeCache in terms of server hit reduction, eviction rate, and retrieval time compared to some representative work proposed in the literature.     

Deep learning models for cloud,edge, fog,and IoT computing paradigms: Survey,recent advances,and future directions

In recent times, the machine learning (ML) community has recognized the deep learning (DL) computing model as the Gold Standard. DL has gradually become the most widely used computational approach in the field of machine learning, achieving remarkable results in various complex cognitive tasks that are comparable to, or even surpassing human performance. One of the key benefits of DL is its ability to learn from vast amounts of data. In recent years, the DL field has witnessed rapid expansion and has found successful applications in various conventional areas. Significantly, DL has outperformed established ML techniques in multiple domains, such as cloud computing, robotics, cybersecurity, and several others. Nowadays, cloud computing has become crucial owing to the constant growth of the IoT network. It remains the finest approach for putting sophisticated computational applications into use, stressing the huge data processing. Nevertheless, the cloud falls short because of the crucial limitations of cutting-edge IoT applications that produce enormous amounts of data and necessitate a quick reaction time with increased privacy. The latest trend is to adopt a decentralized distributed architecture and transfer processing and storage resources to the network edge. This eliminates the bottleneck of cloud computing as it places data processing and analytics closer to the consumer. Machine learning (ML) is being increasingly utilized at the network edge to strengthen computer programs, specifically by reducing latency and energy consumption while enhancing resource management and security. To achieve optimal outcomes in terms of efficiency, space, reliability, and safety with minimal power usage, intensive research is needed to develop and apply machine learning algorithms. This comprehensive examination of prevalent computing paradigms underscores recent advancements resulting from the integration of machine learning and emerging computing models, while also addressing the underlying open research issues along with potential future directions. Because it is thought to open up new opportunities for both interdisciplinary research and commercial applications, we present a thorough assessment of the most recent works involving the convergence of deep learning with various computing paradigms, including cloud, fog, edge, and IoT, in this contribution. We also draw attention to the main issues and possible future lines of research. We hope this survey will spur additional study and contributions in this exciting area.     

基于产业链型数据的分布式隐私保护商务智能研究

随着经济的快速发展,当前很多企业构成了产业链,通过对其进行分布式的商务智能分析,能够获取很多有价值的信.研究了适用于产业链型数据的大规模分布式隐私保护数据挖掘架构,重点研究基于安全多方计算技术的分布式隐私保护数据挖掘通用算法组件,特别是研究面向产业链型数据的分布式隐私保护数据挖掘算法.该研究不仅将有助于大规模分布式环境下的隐私保护数据挖掘系统的研发,而且能够达到更好地服务经济的目的.     

A framework of pavement management system based on IoT and big data

With the expansion of urban road network, the importance of road maintenance is increasing, which guarantee the operation efficiency of transportation infrastructure. Compare to road construction, road maintenance is more sophisticated. Even though invested a lot of manpower and financial resources, management departments are troubled by insufficient and lagging road maintenance. The development and widely application of technologies including IoT, Big Data and Artificial Intelligence(AI) in various industries offer possible solutions to this issue. However, how to utilize these technologies is a challenge for related administration department because of their weak technical force. To address the problem, a pavement management system(PMC) is developed in this research. Combined with IoT and big data, the PMS provide an overall management structure of road maintenance. Composed of three subsections: Pavement detection and 3D modeling, Data analysis and Decision support, the PMS offer an automated and intelligent solution to related administrative departments and firms. Besides, two road maintenance related firms, one is a road maintenance company and the other is a technical firm that offer smart solutions to road maintenance, are selected as cases to illustrate how PMS are applied to support the daily operations and road maintenance.     

基于理性密码学的分布式隐私保护数据挖掘框架

在数据挖掘的应用中,隐私保护非常重要。在数据中加上噪声可以在一定程度上保护用户的隐私,但会降低数据的准确性,进而影响数据挖掘结果的有效性。提出一种高效的基于理性密码学的分布式隐私保护数据挖掘框架,在此框架中每个参与方都被认为是理性的,而不像在经典密码学中简单地把每个参与方认为是恶意的或诚实的。基于此种假设和一个半可信的第三方,许多数据挖掘函数,如求和、求平均值、求积、比较、和求频繁项等,都可以在本框架下高效地实现。     

A novel edge detection method with application to the fat content prediction in marbled meat

Intramuscular fat is an important quality criterion, notably juiciness, in meat grading. But traditional visual inspectors are time consuming and destructive. However, edge detection techniques characterize meat surface in consistence, rapid, and non-destructive approach. In this paper, novel edge detection method applied on intramuscular fat is presented based on the energy and skewness as two smoothed versions of the image. Parametric analyses were investigated and the method was tested on several images, producing minimum improvements of 6.451%, 1.667% and 7.826% in signal to noise ratio, mean square error and edges localization, respectively, in comparison to Roberts, Prewitt, Sobel, and Canny detectors.     

基于负载均衡的多源流数据实时存储系统

物联网感知流数据多以时序数据为主,具有数据量大、连续到达、多来源等特点。现有的基于HBase的交通流数据存储系统在数据写入并发量大时,仍然存在存储效率低与系统可用性不高的问题。针对该问题,设计并实现了基于负载均衡的多源流数据实时存储系统。该系统将数据代理扩展为集群架构,提出了一种基于负载均衡的任务调度算法,实现了任务与数据代理之间的按序匹配,使数据代理集群负载均衡地处理任务,实现数据并行存储到HBase数据库中。实验对比结果表明:该系统使各数据代理的数据分配比例维持在0.3~0.4,同时以约1.5倍于单数据代理的速度将数据写入HBase数据库。     

LIFT: A new framework of learning from testing data for face recognition

In this paper, a novel learning methodology for face recognition, LearnIng From Testing data (LIFT) framework, is proposed. Considering many face recognition problems featured by the inadequate training examples and availability of the vast testing examples, we aim to explore the useful information from the testing data to facilitate learning. The one-against-all technique is integrated into the learning system to recover the labels of the testing data, and then expand the training population by such recovered data. In this paper, neural networks and support vector machines are used as the base learning models. Furthermore, we integrate two other transductive methods, consistency method and LRGA method into the LIFT framework. Experimental results and various hypothesis testing over five popular face benchmarks illustrate the effectiveness of the proposed framework.     

基于物联网的人工智能图像检测系统设计与实现

物联网络的建立促使人工智能领域取得飞跃性进展。传统图像检测方法利用小波能算法进行背景与边缘噪声划分方式进行图像检测,存在低分辨率图像检测精度低、检测速度慢、缺乏图像深度分析等一系列问题。物联网人工智能发展迅速的环境下,提出基于物联网的人工智能图像检测系统设计。采用智能人工像素点特征采集技术(IAPCCT),对图像进行逐点特征提取,运用物联网丰富数据量资源与处理运算能力对采集图像像素点进行特征分析回馈,回馈信号经人工智能信号图像合成模块(AISIS),对信号做图像转换处理并输出分析结果完成图像检测。通过仿真实验测试证明,基于物联网的人工智能图像检测系统设计具有图像检测率高、识别准确度高、运行稳定、处理高效等优点。     

External integrity verification for outsourced big data in cloud and IoT: A big picture

As cloud computing is being widely adopted for big data processing, data security is becoming one of the major concerns of data owners. Data integrity is an important factor in almost any data and computation related context. It is not only one of the qualities of service, but also an important part of data security and privacy. With the proliferation of cloud computing and the increasing needs in analytics for big data such as data generated by the Internet of Things, verification of data integrity becomes increasingly important, especially on outsourced data. Therefore, research topics on external data integrity verification have attracted tremendous research interest in recent years. Among all the metrics, efficiency and security are two of the most concerned measurements. In this paper, we will bring forth a big picture through providing an analysis on authenticator-based data integrity verification techniques on cloud and Internet of Things data. We will analyze multiple aspects of the research problem. First, we illustrate the research problem by summarizing research motivations and methodologies. Second, we summarize and compare current achievements of several of the representative approaches. Finally, we introduce our view for possible future developments.     

Pipelining the ranking techniques for microarray data classification: A case study

Identification of relevant genes from microarray data is an apparent need in many applications. For such identification different ranking techniques with different evaluation criterion are used, which usually assign different ranks to the same gene. As a result, different techniques identify different gene subsets, which may not be the set of significant genes. To overcome such problems, in this study pipelining the ranking techniques is suggested. In each stage of pipeline, few of the lower ranked features are eliminated and at the end a relatively good subset of feature is preserved. However, the order in which the ranking techniques are used in the pipeline is important to ensure that the significant genes are preserved in the final subset. For this experimental study, twenty four unique pipeline models are generated out of four gene ranking strategies. These pipelines are tested with seven different microarray databases to find the suitable pipeline for such task. Further the gene subset obtained is tested with four classifiers and four performance metrics are evaluated. No single pipeline dominates other pipelines in performance; therefore a grading system is applied to the results of these pipelines to find out a consistent model. The finding of grading system that a pipeline model is significant is also established by Nemenyi post-hoc hypothetical test. Performance of this pipeline model is compared with four ranking techniques, though its performance is not superior always but majority of time it yields better results and can be suggested as a consistent model. However it requires more computational time in comparison to single ranking techniques.     

Ensemble data mining modeling in corrosion of concrete sewer: A comparative study of network-based (MLPNN & RBFNN) and tree-based (RF,CHAID, & CART) models

This research aims to evaluate ensemble learning (bagging, boosting, and modified bagging) potential in predicting microbially induced concrete corrosion in sewer systems from the data mining (DM) perspective. Particular focus is laid on ensemble techniques for network-based DM methods, including multi-layer perceptron neural network (MLPNN) and radial basis function neural network (RBFNN) as well as tree-based DM methods, such as chi-square automatic interaction detector (CHAID), classification and regression tree (CART), and random forests (RF). Hence, an interdisciplinary approach is presented by combining findings from material sciences and hydrochemistry as well as data mining analyses to predict concrete corrosion. The effective factors on concrete corrosion such as time, gas temperature, gas-phase H₂S concentration, relative humidity, pH, and exposure phase are considered as the models’ inputs. All 433 datasets are randomly selected to construct an individual model and twenty component models of boosting, bagging, and modified bagging based on training, validating, and testing for each DM base learners. Considering some model performance indices, (e.g., Root mean square error, RMSE; mean absolute percentage error, MAPE; correlation coefficient, r) the best ensemble predictive models are selected. The results obtained indicate that the prediction ability of the random forests DM model is superior to the other ensemble learners, followed by the ensemble Bag-CHAID method. On average, the ensemble tree-based models acted better than the ensemble network-based models; nevertheless, it was also found that taking the advantages of ensemble learning would enhance the general performance of individual DM models by more than 10%.     

IoTSim-Edge: A simulation framework for modeling the behavior of Internet of Things and edge computing environments

With the proliferation of Internet of Things (IoT) and edge computing paradigms, billions of IoT devices are being networked to support data-driven and real-time decision making across numerous application domains, including smart homes, smart transport, and smart buildings. These ubiquitously distributed IoT devices send the raw data to their respective edge device (eg, IoT gateways) or the cloud directly. The wide spectrum of possible application use cases make the design and networking of IoT and edge computing layers a very tedious process due to the: (i) complexity and heterogeneity of end-point networks (eg, Wi-Fi, 4G, and Bluetooth); (ii) heterogeneity of edge and IoT hardware resources and software stack; (iv) mobility of IoT devices; and (iii) the complex interplay between the IoT and edge layers. Unlike cloud computing, where researchers and developers seeking to test capacity planning, resource selection, network configuration, computation placement, and security management strategies had access to public cloud infrastructure (eg, Amazon and Azure), establishing an IoT and edge computing testbed that offers a high degree of verisimilitude is not only complex, costly, and resource-intensive but also time-intensive. Moreover, testing in real IoT and edge computing environments is not feasible due to the high cost and diverse domain knowledge required in order to reason about their diversity, scalability, and usability. To support performance testing and validation of IoT and edge computing configurations and algorithms at scale, simulation frameworks should be developed. Hence, this article proposes a novel simulator IoTSim-Edge, which captures the behavior of heterogeneous IoT and edge computing infrastructure and allows users to test their infrastructure and framework in an easy and configurable manner. IoTSim-Edge extends the capability of CloudSim to incorporate the different features of edge and IoT devices. The effectiveness of IoTSim-Edge is described using three test cases. Results show the varying capability of IoTSim-Edge in terms of application composition, battery-oriented modeling, heterogeneous protocols modeling, and mobility modeling along with the resources provisioning for IoT applications.     

EVOR-STACK: A label-dependent evolutive stacking on remote sensing data fusion

Land use and land covers (LULC) maps are remote sensing products that are used to classify areas into different landscapes. Data fusion for remote sensing is becoming an important tool to improve classical approaches. In addition, artificial intelligence techniques such as machine learning or evolutive computation are often applied to improve the final LULC classification. In this paper, a hybrid artificial intelligence method based on an ensemble of multiple classifiers to improve LULC map accuracy is shown. The method works in two processing levels: first, an evolutionary algorithm (EA) for label-dependent feature weighting transforms the feature space by assigning different weights to every attribute depending on the class. Then a statistical raster from LIDAR and image data fusion is built following a pixel-oriented and feature-based strategy that uses a support vector machine (SVM) and a weighted k-NN restricted stacking. A classical SVM, the original restricted stacking (R-STACK) and the current improved method (EVOR-STACK) are compared. The results show that the evolutive approach obtains the best results in the context of the real data from a riparian area in southern Spain.     

MRCCA: A novel CCA based method and its application in feature extraction and fusion for matrix data

Multiset features extracted from the same pattern usually represent different characteristics of data, meanwhile, matrices or 2-order tensors are common forms of data in real applications. Hence, how to extract multiset features from matrix data is an important research topic for pattern recognition. In this paper, by analyzing the relationship between CCA and 2D-CCA, a novel feature extraction method called multiple rank canonical correlation analysis (MRCCA) is proposed, which is an extension of 2D-CCA. Different from CCA and 2D-CCA, in MRCCA k pairs left transforms and k pairs right transforms are sought to maximize correlation. Besides, the multiset version of MRCCA termed as multiple rank multiset canonical correlation analysis (MRMCCA) is also developed. Experimental results on five real-world data sets demonstrate the viability of the formulation, they also show that the recognition rate of our method is higher than other methods and the computing time is competitive.     

A review on the computation offloading approaches in mobile edge computing: A game-theoretic perspective

In recent years, novel mobile applications such as augmented reality, virtual reality, and three-dimensional gaming, running on handy mobile devices have been pervasively popular. With rapid developments of such mobile applications, decentralized mobile edge computing (MEC) as an emerging distributed computing paradigm is developed for serving them near the smart devices, usually in one hop, to meet their computation, and delay requirements. In the literature, offloading mechanisms are designed to execute such mobile applications in the MEC environments through transferring resource-intensive tasks to the MEC servers. On the other hand, due to the resource limitations, resource heterogeneity, dynamic nature, and unpredictable behavior of MEC environments, it is necessary to consider the computation offloading issues as the challenging problem in the MEC environment. However, to the best of our knowledge, despite its importance, there is not any systematic, comprehensive, and detailed survey in game theory (GT)-based computation offloading mechanisms in the MEC environment. In this article, we provide a systematic literature review on the GT-based computation offloading approaches in the MEC environment in the form of a classical taxonomy to recognize the state-of-the-art mechanisms on this important topic and to provide open issues as well. The proposed taxonomy is classified into four main fields: classical game mechanisms, auction theory, evolutionary game mechanisms, and hybrid-base game mechanisms. Next, these classes are compared with each other according to the important factors such as performance metrics, case studies, utilized techniques, and evaluation tools, and their advantages and disadvantages are discussed, as well. Finally, open issues and future uncovered or weakly covered research challenges are discussed and the survey is concluded.     

A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method

The position of the inflexion point in the red edge region (680 to 780 nm) of the spectral reflectance signature, termed the red edge position (REP), is affected by biochemical and biophysical parameters and has been used as a means to estimate foliar chlorophyll or nitrogen content. In this paper, we report on a new technique for extracting the REP from hyperspectral data that aims to mitigate the discontinuity in the relationship between the REP and the nitrogen content caused by the existence of a double-peak feature on the derivative spectrum. It is based on a linear extrapolation of straight lines on the far-red (680 to 700 nm) and NIR (725 to 760 nm) flanks of the first derivative reflectance spectrum. The REP is then defined by the wavelength value at the intersection of the two lines. The output is a REP equation, REP = − (c₁ − c₂) / (m₁ − m₂), where c₁ and c₂, and m₁ and m₂ represent the intercepts and slopes of the far-red and NIR lines, respectively. Far-red wavebands at 679.65 and 694.30 nm in combination with NIR wavebands at 732.46 and 760.41 nm or at 723.64 and 760.41 nm were identified as the optimal combinations for calculating nitrogen-sensitive REPs for three spectral data sets (rye canopy, and maize leaf and mixed grass/herb leaf stack spectra). REPs extracted using this new technique (linear extrapolation method) showed high correlations with a wide range of foliar nitrogen concentrations for both narrow and wider bandwidth spectra, being comparable with results obtained using the traditional linear interpolation, polynomial and inverted Gaussian fitting techniques. In addition, the new technique is simple as is the case with the linear interpolation method, but performed better than the latter method in the case of maize leaves at different developmental stages and mixed grass/herb leaves with a low nitrogen concentration.