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Edge computing is a promising alternative to cloud computing for offloading computationally heavy tasks from resource-constrained mobile user devices. Placed at the edge of the network, edge computing is particularly advantageous to delay-limited applications for having a short distance to end-users. However, when a mobile user moves away from the service coverage of the associated edge server, the advantage gradually vanishes, increasing response time. Although service migration has been studied to address this problem focusing on minimizing the service downtime, both zero-downtime and the amount of traffic generated as a result of migration need further study. In this paper, an optimal live migration for containerized edge computing service is studied. This paper presents three zero-downtime migration techniques based on state duplication and state reproduction techniques, and then, proposes an optimal migration technique selection algorithm that jointly minimizes the response time and network traffic during migration. For validation and performance comparison, the proposed migration techniques are implemented on off-the-shelf hardware with Linux operating system. The evaluation results showed that compared with a naive migration, the optimal approach reduced the response time and network load by at least 74.75% and 94.79%, respectively, under considered scenarios.

Multi-choice goal programming (MCGP) has been widely used to find satisfying solutions for multiple criteria/objective decision problems in which the target value of “the more, the better,” or “the less, the better” can easily be obtained. This paper proposes two new models for representing the triangular and trapezoidal membership functions, which improve the efficacy of fuzzy MCGP (FMCGP). Two real-world applications are provided in this study to demonstrate the usefulness of the proposed models. Furthermore, the same problems are resolved by using the proposed nature-inspired optimization method (NIOM) to find the differences between them. While the artificial bee colony (ABC) algorithm is a well-known NIOM technique, studies have shown that it has an excellent performance with high-quality solutions. Thus, this study initially uses the ABC algorithm to find the differences between MCGP and ABC. Finally, some insightful information is obtained from the comparison to contribute to the NIOM and MCGP fields and their respective applications.

Erasable-itemset mining is a valuable method of pattern extraction for helping the manager of a factory analyze production planning. The erasable itemsets derived can be considered important production information regarding how to plan the production of a factory during an economic depression or financial shortage for the manager. After the erasable-itemset mining was proposed in 2009, several efficient mining approaches for finding erasable itemsets have been developed. However, these methods require a considerable amount of execution time when the amount of product data is large. Especially, manufacturing small amounts of versatile products has been a trend today, and it will generate a large product database. This paper adopts a bitmap representation for itemsets in an erasable-itemset mining algorithm to speed up the execution. Unlike the traditional bitmap meaning for frequent itemsets, a bitmap for erasable-itemset mining here denotes the relationship that a product includes at least one material (item) in a specified itemset. Using the bitmap representation can easily find the desired products to check, thus decreasing the scans of a database. Experimental evaluation on synthesized and real datasets was used to compare the proposed approach with the other two under different parameter values. The experimental results show that the proposed approach can make a good trade-off between execution time and memory usage.

In this paper, an effective method for developing a multi-feed multiple-input multiple-output (MIMO) antenna system using the metal rims of a mobile phone without any structural interruption on the long edge frames is proposed. Instead of traditionally designing MIMO antennas with multiple similar antenna structures on the mobile phone frame or its ground plane, the MIMO antennas in this work are realized by reasonably constructing feeding points and grounding points on the entire edge frame. The MIMO antenna system established by such multi-feed technique is capable to achieve much simpler structural configuration, excellent isolation and matching performance while multiple antennas are connected. A planar eight-port MIMO antenna system operating in the 5G frequency band of 3.4~3.6 GHz is investigated initially, and subsequently applied to the curved metal-rims which comply with the industry design of a modern flagship mobile phone. The prototypes are fabricated and measured, evaluation results show that isolations among all ports are better than 15 dB with reflection coefficients below −10 dB within the band of 3.4~3.7 GHz and −6 dB within the band of 3.3~3.8 GHz. In addition, more than 57% total efficiency and envelope correlation coefficient (ECC) of less than 0.3 are obtained. The satisfactory performance reveals the proposed design of multi-feed MIMO antennas can be a promising candidate for 5G metal-rimmed mobile phone applications.

This paper presents a compact dual-band and dual-polarized millimeter-wave patch antenna array with satisfactory performance on element mutual coupling and beam scanning capabilities. Using capacitive feed technique and stacked configuration with extra parasitic strips, the proposed antenna array is able to achieve a wide operating bandwidth in both the low- and high-bands. In order to reduce the array’s footprint, and to enhance the beam scanning performance in both bands, the element spacing is shrunk to less than 0.36 wavelength at 26 GHz. To improve the isolation between array elements due to their small spacings, two effective decoupling approaches are adopted, which result in a 6-dB isolation enhancement. The overall size of the proposed antenna array is only 18.2 mm $\times4.1$ mm $\times1.07$ mm, which is smaller than some industrial mm-Wave antenna modules released recently. Our simulation shows that the antenna array can fully cover the 5G NR bands of n258~n261 simultaneously. The four-element array provides ±60° and ±45° beam scanning performance in the low- and high-bands, respectively. The experimental data of reflection coefficient, mutual coupling, and radiation patterns confirm with the simulated results, rendering the proposed array to be a good candidate for 5G mm-Wave communications.

Mirror therapy (MT) has been proposed as an essential component of upper limb neuro-rehabilitation, using mirror illusion of the unaffected hand movement and superimposing it on the affected hand to improve neuroplasticity for improving motor recovery in stroke patients. In this paper, we developed a virtual reality mirror therapy (VRMT) system, and examined the performance of the proposed VRMT system. The VRMT system consists of a motion-tracking device, a VR goggle, and a VRMT software. Young, healthy participants, and stroke patients with mild-to-severe hemiparesis were recruited in this study, to validate the effectiveness of the proposed VRMT system. Experimental results based on the pinch-holding-up activity (PHUA) test revealed a significant effect on the precision pinch performance of young-healthy participants receiving VRMT, whereas traditional MT could not improve the precision pinch performance in young-healthy participants (Wilcoxon signed-rank test, $Z= -2.693$ , $p=0.007$ ). For patients having suffered a stroke and being treated with VRMT, significant beneficial effects examined by mixed effect model were found on the total score of FMA ( $p=0.033$ ) and hand part of FMA ( $p=0.008$ ). The findings in this study indicate that the VRMT system has a potentially positive effects on the sensorimotor performance of hands in healthy participants. In addition, add-on effect of VR to MT provides beneficial effects on motor function of the upper extremity in chronic stroke patients.

Given the importance of production planning and control in the design of flexible services and manufacturing systems, scheduling problems with interfering jobs are much-needed optimization tools to respond to heterogeneous and fluctuating market demands in a timely fashion. This study contributes to the scheduling literature developing an effective multi-objective (M-O) metaheuristic to solve the Single-machine Scheduling Problems with Interfering Jobs (SSP-IJs). Integrating a local search-based mechanism into the evolutionary search procedure, a Greedy-based non-dominated sorting genetic algorithm III (GNSGA-III) is proposed that effectively explores multi-objective solution environments. Various performance indicators within extensive numerical tests are used to compare the performance of the GNSGA-III with that of the best-performing benchmark algorithm in the literature developed to solve the SSP-IJs. Statistical tests verify that the developed multi-objective optimization algorithm is superior with respect to various performance indicators. Applications of the developed solution approach are worthwhile topics to help advance multi-objective optimization problems.

Scheduling decisions are of certain cachet in production and operations management. Many extensions are developed to help address different scheduling industrial situations, one of which, the Mixed-Blocking Permutation Flowshop Scheduling Problem (MBPFSP), has gained recent recognition due to its wide industrial applications both in manufacturing and service sectors. Given the complexities of MBPFSPs, effective heuristics are needed to help generate dependable solutions for industrial-scale applications. This article proposes an Extended Simulated Annealing (ESA) algorithm to minimize total completion time (TCT) in MBPFSPs, which is the first study in the literature of MBPFSPs considering a measure that helps improve the system's average response time. Through extensive computational experiments, it is shown that the ESA outperforms the best existing solution algorithms proposed for solving the MBPFSPs. Given the freshness of the topic, this research facilitates MBPFSP's wide range of industrial applications to narrow the gap between scheduling theory and practical applications.

Skin cancer is one of the most common cancers in humans in recent years, affecting people of all ages. If skin cancer is treated in time, the curative effect and prognosis are favorable. At present, dermoscopy is an effective way for the early diagnosis of skin cancer. However, manual detection is highly dependent on the clinical experience of doctors, and the complexity of the dermoscopy image itself poses a great challenge to the classification. Therefore, we propose a decision fusion method. Through transfer learning, based on multiple pre-trained convolutional neural networks (CNNs), we use the block to combine multiple CNNs and finally make decisions through multiple blocks. The method of decision fusion can solve the generalization capability of a individual convolutional neural network (CNN) model, and is more robust and stable than the traditional fusion strategy. Based on ISIC 2019 dataset, we use StyleGANs to generate high-quality images to alleviate the problem of less and uneven distribution of the dermoscopy image dataset and improve the classification effect of CNNs. Our proposed method can improve the accuracy of dermoscopy image classification and provide help for dermatologists.

Electronic healthcare (e-health) has gained more and more research attention in recent years, due to its flexibility and convenience. E-health is efficiently enabled by telecare medicine information system (TMIS). TMIS provides seamless transfer and timely sharing of medical information for specific healthcare services. Since communications in TMIS are carried out through unreliable channels, data security and user privacy concerns become prominent. With traditional single-server architecture, users must store massive credentials, which causes inefficient communication and significant overhead. Moreover, user credentials in previously proposed schemes are stored at server side, suffering potential risks. Our work proposes a three-factor user-controlled single sign-on (UCSSO) with fast authentication and privacy protection for TMIS. The contributions of this paper are as follows. Our work integrates three factors including password, smart card and biometrics in authentication procedure, for providing a high-security and privacy-preserved communication. We introduce single sign-on solution that allows users to log in to multiple servers using a single password. User-controlled mechanism is proposed to address insider attacks and the risk that registration center may be compromised. The proposed scheme is designed with fast authentication mechanism that helps to efficiently establishes new session key. Our work is proved secure using BAN logic, ROR model, and AVISPA toolset. The results of performance comparison show that our scheme provides more security properties and bears the least overhead, compared with competitive schemes.

Data mining is a critical technology for extracting valuable knowledge from databases. It has been used in many fields, like retail, finance, biology, etc. In computational intelligence, fuzzy logic has been applied in many intelligent systems widely because it is simple and similar to human inference. Fuzzy utility mining combines utility mining and fuzzy logic for getting linguistic utility knowledge. In this paper, we study a more challenging, complicated, but practical topic called temporal fuzzy utility data mining, which considers the temporal periods in transactions, purchased amounts, item profits, and understandable linguistic terms as important factors. Although an Apriori-based algorithm was proposed previously, its execution was not efficient. We thus use a modified tree structure based on the classical frequent-pattern tree to improve its performance. A tree-based mining algorithm is also proposed to mine temporal fuzzy utility itemsets from quantitative transactional databases. The tree structure is built to keep all temporal fuzzy utility 1-itemsets in a database. All the high temporal fuzzy utility itemsets in a database can be obtained by traversing the tree-based structure. The proposed algorithm gets the final results through two phases. In the first phase, a procedure like FP-Growth is used to find the candidate itemsets. In the second phase, the temporal fuzzy utility database is scanned to decide whether the candidate itemsets are desired. Experimental results show that the proposed algorithm is superior to the existing algorithm for temporal fuzzy utility mining in terms of processing time and used memory.

Surveillance cameras have been widely used in urban environments and are increasingly used in rural ones. Such cameras have mostly been used for security, but they can be applied to the problem of furnishing fine-grained measurements and predictions of precipitation intensity. In this study, we formulated a stacked order-preserving (OP) learning framework to train a network using time-series data. We constructed an OP module, which uses a three-dimensional (3D) convolution operation to extract features with spatial and temporal information and that are associated with ConvLSTM; this feature extraction is used to learn the short-term and OP time-series relationships between features. Furthermore, the OP modules are stacked to form a stacked OP network (SOPNet), which strengthens the relationship between features in long-term time-series image sequences. This SOPNet can be use to obtain fine-grained measurements and predictions of precipitation intensity from images captured by outdoor surveillance cameras. Our main contributions are threefold. First, the SOPNet strengthens the short-term and long-term time-series relationship between features. Second, the SOPNet simultaneously examines spatial and temporal information to measure and predict precipitation intensity. Third, we constructed a precipitation intensity database based on optical images captured by outdoor surveillance cameras. We experimentally evaluated our proposed architecture using our self-collected data set. We found that SOPNet yields better performance and greater accuracy relative to its well-known state-of-the-art counterparts with respect to various metrics.

Production management of perishable goods is highly complex and requires well-informed decisions in corresponding stages. In such production environments, scheduling problems with time constraints are of high relevance to ensure the timely flow of the work-in-process material and goods. This study introduces the no-wait flowshop scheduling problem with release times (NWFSP-RT) to help advance decision support systems in the food production industry. For this purpose, an original mixed-integer linear programming (MILP) formulation is proposed for minimizing makespan. A BS algorithm, and an improved variant, the local search-based Beam Search (BSLS) algorithms are developed to solve the NWFSP-RT problem. Extensive numerical analysis is conducted to analyze the performance of the algorithms in solving this highly intractable extension of the scheduling problems. We showed that BSLS effectively avoids early convergence and local optimality while dismissing non-promising search directions within a partial enumeration solution approach. The statistical analysis confirmed that the improved BS algorithm performs better in terms of solution quality. Applications of the developed heuristic are worthwhile research topics to pursue in solving other complex optimization problems.

High-utility sequential pattern mining (HUSPM) is an emerging topic in data mining, which considers both utility and sequence factors to derive the set of high-utility sequential patterns (HUSPs) from the quantitative databases. Several works have been presented to reduce the computational cost by variants of pruning strategies. In this paper, we present an efficient sequence-utility (SU)-chain structure, which can be used to store more relevant information to improve mining performance. Based on the SU-Chain structure, the existing pruning strategies can also be utilized here to early prune the unpromising candidates and obtain the satisfied HUSPs. Experiments are then compared with the state-of-the-art HUSPM algorithms and the results showed that the SU-Chain-based model can efficiently improve the efficiency performance than the existing HUSPM algorithms in terms of runtime and number of the determined candidates.

Edge computing has played an important role in enabling 5G technology which supports a great number of connected narrow-band IoT devices. In an edge computing architecture enabled with global mobile network, edge or IoT devices are wirelessly connected to the edge of the network. Data acquisition and processing will be handled at or close to the edge of the network in a distributed way. Since edge computing is a heterogeneous distributed interactive system with multiple domains and entities, it might suffer from potential attacks and threats. To provide a trusted edge computing, there must have a robust scheme that allows all participants to mutually authenticate in a secure and privacy-preserved way. With the rapid development of IoT technologies, mobile networks and edge computing architecture, single server has been unable to meet the needs of users. In this paper, we propose a privacy-preserved end-to-end password-based authenticated key exchange protocol for multi-server architecture in edge computing networks. Our protocol allows an end user to use an easy-to-remember password to login to the server, then through foreign agent compute a shared key with another end user for specific use of services. The proposed protocol provides strong user anonymity during communication process. Besides, the proposed protocol is proved to be secure using BAN logic and AVISPA tool. Furthermore, performance analysis shows that the proposed protocol gains stronger security and better computational efficiency. Providing lightweight computation with short key size of ECC, our work is a solution to lower latency and improve efficiency in edge computing networks.

Establishing the conservation reserves is an efficient approach to protect various species, in which maintaining the spatial characteristics of the habit is an important issue. A continuous and compact reserve is vital for long-term survival and reproduction of various species. However, the existing methods for nature reserve design have the problem of the selected sites which are sparse in space. The sparseness refers to the isolated regions which are the unselected (selected) sites and surrounded by the selected (unselected) sites in the designed nature reserve and thus making the management of protected areas and exchange of information between species difficult. In this paper, we propose the concept of a dual-flow mechanism which is called the dual-flow structure ( DFS ) to address the problem of sparseness in sites selected for the reserve network design. The DFS constructs two consecutive arcs: one is for selected sites and the other is for unselected sites, simultaneously. Those two arcs ensure that each site has at least one adjacent site which has the same status (selected/unselected) with the competitive mechanism. The concept of two consecutive arcs can solve the problem of sparseness and achieve spatial continuity. The DFS further utilizes the perimeter in dual-flow structure to achieve the spatial characteristic of compactness. The main contributions of this study are: (1) we design a novel mathematical model which solves the problem of sparseness in the space, including selected and un-selected sites; and (2) the DFS generates the connectivity and compactness of the reserve network with the scientific and reasonable techniques. In the experiments, we take the Daiyun Mountains as a study area to demonstrate the feasibility and effectiveness of the proposed model.

Finding useful portfolios that could be a portfolio of trading strategy or a stock portfolio from financial datasets is always an attractive research topic due to the nature of financial markets. Because investors always want an approach that can continually provide various portfolios, the issue of group stock portfolio optimization (GSPO) has been raised and the algorithms to obtain a group stock portfolio (GSP) have also been described in the past. A GSP divides the whole set of stocks into several stock groups, and the stocks in a group are exchangeable in investment. Thus, when investors are not satisfied with a suggested stock, they can select another stock from the same group to replace the original one. However, the industry diversity of stocks within a group is not regarded in the existing literatures. In this paper, an algorithm for dealing with the diverse group stock portfolio optimization (DGSPO) is proposed to obtain a diverse group stock portfolio (DGSP). The proposed algorithm is based on the group genetic algorithm with the chromosome representation and the fitness function designed for the purpose of finding a good DGSP. Especially, a factor called group diversity is designed to diversify stocks from different industries and is considered in the fitness evaluation. Another factor considers cash dividend is also applied to keep companies with good quality in the portfolio for increasing the profit of a DGSP. Two real financial datasets are used in the experiments to verify the effectiveness of the proposed approach.

Internet of Things (IoT) supports high flexibility and convenience in several applications because the IoT devices continuously transfer, share, and exchange data without human intervention. During shared or exchanged progress of data, security and privacy threats result because the published or shared data mainly corresponds to a raw dataset, and an attacker can easily obtain details on the shared data in an IoT environment. In the paper, we present a sanitization approach by adopting the hierarchical-cluster method to hide confidential information while still discovering useful and meaningful information in the sanitized dataset. The multi-objective particle swarm optimization framework and an algorithm termed as HCMPSO are utilized to balance four side effects, namely, hiding failure, missing cost, artificial cost, and database dissimilarity (Dis), and thereby provide optimized solutions for data sanitization. The experiments are performed to compare the performance of the designed HCMPSO with that of the single-objective cpGA2DT and multi-objective NSGA-II-based approaches. As shown in the results, the designed HCMPSO exhibits good performance in terms of hiding failure, and thus the most confidential information is hidden after the sanitization process. The shared or published data in IoT is secured. Furthermore, the designed sanitization algorithm achieves reasonable results in terms of missing cost, artificial cost, and Dis. Copyright 2019 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission.

This research proposes a new variant of the location-routing problem (LRP) called LRP with Demand Range (LRPDR) by allowing flexibility in the delivery quantity. The goal of the LRPDR is to minimize the objective value calculated by the total cost minus the extra revenue. The total cost consists of the travelling cost of vehicles, the opening cost of the depots, and the activation cost of vehicles. This study proposes a new hybrid algorithm, SAPSO, that combines simulated annealing (SA) and particle swarm algorithm (PSO) for solving the LRPDR. Since this problem has not yet been studied in the literature, a mathematical model is proposed and solved by the Gurobi solver. The results obtained by Gurobi are then compared with those obtained by the proposed SAPSO algorithm. In addition, the performance of the proposed SAPSO algorithm is assessed by solving the LRP benchmark instances, and comparing the results with those of existing state-of-the-art algorithms for LRP. Based on the experimental results, the proposed SAPSO algorithm improves the performance of the basic SA algorithm and outperforms Gurobi. Moreover, the benefits of the LRPDR over LRP are presented in terms of total cost reduction.

This study evaluates various Mixed Integer Programming (MIP) formulations for solving single-machine and parallel-machine scheduling problems, with the objective of minimizing the total completion time and the makespan of jobs. Through extensive numerical study, the MIP formulation, which is suitable for dealing with each specific single-machine or parallel-machine scheduling problem, is identified. Benchmarks are also provided for the development of other algorithms for future research.

Plant classification is a science that is used to assess the quality of forest resources and has been studied extensively. In this paper, we proposed a novel convolutional neural network known as the Fourier Dense Network (FDN) which is a data-driven method to classify the optical aerial images of plants. To efficiently classify plants, the FDN learns and extracts the features of plants in time and frequency domains from the optical images captured using an unmanned aerial vehicle (UAV). In FDN, we designed a fast Fourier dense block (FF-dense block) that describes the features of the plants by using the magnitude and phase information in the frequency domain. Moreover, we used a transition layer to reduce the dimension of feature maps between two FF-dense blocks in the time domain. The primary contributions of this study are as follows: (1) an FF-dense block that considers frequency information and transfers the information into various layers of a block was designed; and (2) the characteristics between the time and frequency domains were repeatedly extracted and combined to more effectively describe the characteristics of tree species. To evaluate our study, we established a novel dataset comprising the UAV-based optical images of plants-vegetational optical aerial image dataset-for conducting plant classification and information retrieval. The dataset contains more than 21863 images of 12 plants. To the best of our knowledge, this is the largest publicly available dataset of the UAV-based optical images of plants. The experimental results demonstrated that the FDN can achieve state-of-the-art performance in terms of plant classification.

Mask data preparation (MDP) is a part of the mask data process for fabricating semiconductors, and its importance has commonly been neglected. This paper proposes an integer linear programming model and two meta-heuristics, a genetic algorithm (GA) and simulated annealing (SA), for solving the MDP scheduling problem (MDPSP). The proposed meta-heuristics are empirically evaluated using 768 simulation instances of MDPSP based on the characteristics of a real technology company and compared with the most commonly used first-come, first-served method. The experimental results reveal that the proposed GA and SA algorithms can critically improve the manufacturing schedule for semiconductor factories.

Outlier detection is an extensive research area, which has been intensively studied in several domains such as biological sciences, medical diagnosis, surveillance, and traffic anomaly detection. This paper explores advances in the outlier detection area by finding anomalies in spatio-temporal urban traffic flow. It proposes a new approach by considering the distribution of the flows in a given time interval. The flow distribution probability (FDP) databases are first constructed from the traffic flows by considering both spatial and temporal information. The outlier detection mechanism is then applied to the coming flow distribution probabilities, the inliers are stored to enrich the FDP databases, while the outliers are excluded from the FDP databases. Moreover, a k-nearest neighbor for distance-based outlier detection is investigated and adopted for FDP outlier detection. To validate the proposed framework, real data from Odense traffic flow case are evaluated at ten locations. The results reveal that the proposed framework is able to detect the real distribution of flow outliers. Another experiment has been carried out on Beijing data, the results show that our approach outperforms the baseline algorithms for high-urban traffic flow.

The widely deployed Internet-of-Things (IoT) devices provide intelligent services with its cognition capability. Since the IoT data are usually transmitted to the server for recognition (e.g., image classification) due to low computational capability and limited power supply, achieving recognition accuracy under limited bandwidth and noisy channel of wireless networks is a crucial but challenging task. In this paper, we propose a deep learning-constructed joint transmission-recognition scheme for the IoT devices to effectively transmit data wirelessly to the server for recognition, jointly considering transmission bandwidth, transmission reliability, complexity, and recognition accuracy. Compared with other schemes that may be deployed on the IoT devices, i.e., a scheme based on JPEG compression and two compressed sensing-based schemes, the proposed deep neural network-based scheme has much higher recognition accuracy under various transmission scenarios at all signal-to-noise ratios (SNRs). In particular, the proposed scheme maintains good performance at the very low SNR. Moreover, the complexity of the proposed scheme is low, making it suitable for IoT applications. Finally, a transfer learning-based training method is proposed to effectively mitigate the computing burden and reduce the overhead of online training.