Forthcoming and Online First Articles

International Journal of Grid and Utility Computing

International Journal of Grid and Utility Computing (IJGUC)

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International Journal of Grid and Utility Computing (69 papers in press)

Special Issue on: AMLDA 2022 Applied Machine Learning and Data Analytics Applications, Challenges, and Future Directions

  • Fuzzy Forests For Feature Selection in High-Dimensional Survey Data: An Application to the 2020 U.S. Presidential Election
    by Sreemanti Dey, R. Michael Alvarez 
    Abstract: An increasingly common methodological issue in the field of social science is high-dimensional and highly correlated datasets that are unamenable to the traditional deductive framework of study. Analysis of candidate choice in the 2020 Presidential Election is one area in which this issue presents itself: in order to test the many theories explaining the outcome of the election, it is necessary to use data such as the 2020 Cooperative Election Study Common Content, with hundreds of highly correlated features. We present the Fuzzy Forests algorithm, a variant of the popular Random Forests ensemble method, as an efficient way to reduce the feature space in such cases with minimal bias, while also maintaining predictive performance on par with common algorithms like Random Forests and logit. Using Fuzzy Forests, we isolate the top correlates of candidate choice and find that partisan polarization was the strongest factor driving the 2020 presidential election.
    Keywords: Fuzzy Forests, Machine Learning, Ensemble Methods, dimensionality reduction, American elections, candidate choice, correlation, partisanship, issue voting, Trump, Biden

  • An Efficient Intrusion Detection System using Unsupervised Learning AutoEncoder
    by ND Patel, BM MEHTRE, Rajeev Wankar 
    Abstract: As attacks on the network environment are rapidly becoming more sophisticated and intelligent in recent years, the limitations of the existing signature-based intrusion detection system are becoming more evident. For new attacks such as Advanced Persistent Threat (APT), the signature pattern has a problem of poor generalization performance. Research on intrusion detection systems based on machine learning is being actively conducted to solve this problem. However, the attack sample is collected less than the normal sample in the actual network environment, so it suffers a class imbalance problem. When a supervised learning-based anomaly detection model is trained with these data, the results are biased toward normal samples. In this paper, AutoEncoder (AE) is used to perform single-class anomaly detection to solve this imbalance problem. The experimental evaluation was conducted using the CIC-IDS2017 dataset, and the performance of the proposed method was compared with supervised models to evaluate the performance
    Keywords: Intrusion Detection System; Advanced Persistent Threat; CICIDS2017; AutoEncoder; Machine Learning; Data Analytics.

Regular Issues

  • Research on modelling analysis and maximum power point tracking strategies for distributed photovoltaic power generation systems based on adaptive control technology   Order a copy of this article
    by Yan Geng, Jianwei Ji, Bo Hu, Yingjun Ju 
    Abstract: As is well-known, the distributed photovoltaic power generation technology has been rapidly developed in recent years. The cost of distributed photovoltaic power generation is much higher than that of traditional power generation modes. Therefore, how to improve the effective use of photovoltaic cells has become a popular research direction. Based on the analysis of the characteristics of photovoltaic cells, this paper presents a mathematical model of photovoltaic cells and a maximum power point tracking algorithm based on hysteresis control and adaptive control technology variable step perturbation observation method. This algorithm can balance the control precision and control speed from the disturbance observation method and improve the tracking results significantly. Finally, the feasibility of the algorithm and the tracking effects are simulated by using Matlab/Simulink software.
    Keywords: distributed photovoltaic; adaptive control technology; maximum power point tracking strategies.

  • Cloud infrastructure planning: models considering an optimisation method, cost and performance requirements   Order a copy of this article
    by Jamilson Dantas, Rubens Matos, Carlos Melo, Paulo Maciel 
    Abstract: Over the years, many companies have employed cloud computing systems as the best choice regarding the infrastructure to support their services, while keeping high availability and performance levels. The assurance of the availability of resources, considering the occurrence of failures and desired performance metrics, is a significant challenge for planning a cloud computing infrastructure. The dynamic behaviour of virtualised resources requires special attention to the effective amount of capacity that is available to users, so the system can be correctly sized. Therefore, planning computational infrastructure is an important activity for cloud infrastructure providers to analyse the cost-benefit trade-off among distinct architectures and deployment sizes. This paper proposes a methodology and models to support planning and the selection of a cloud infrastructure according to availability, COA, performance and cost requirements. An optimisation model based on GRASP meta-heuristic is used to generate a cloud infrastructure with a number of physical machines and Virtual Machines (VM) configurations. Such a system is represented using an SPN model and closed-form equations to estimate cost and dependability metrics. The proposed method is applied in a case study of a video transcoding service hosted in a cloud environment. The case study demonstrates the selection of cloud infrastructures with best performance and dependability metrics, considering the use of VP9, VP8 and H264 video codecs, as well as distinct VM setups. The results show the best configuration choice considering a six user profile. The results also show the computation of the probability of finalising a set of video transcoding jobs by a given time.
    Keywords: cloud computing; performance; availability modelling; GRASP; COA; stochastic Petri nets; cost requirements.

  • SDSAM: a service-oriented approach for descriptive statistical analysis of multidimensional spatio-temporal big data   Order a copy of this article
    by Weilong Ding, Zhuofeng Zhao, Jie Zhou, Han Li 
    Abstract: With the expansion of the Internet of Things, spatio-temporal data has been widely used and generated. The rise of big data in space and time has led to a flood of new applications with statistical analysis characteristics. In addition, applications based on statistical analysis of these data must deal with the large capacity, diversity and frequent changes of data, as well as the query, integration and visualisation of data. Developing such applications is essentially a challenging and time-consuming task. In order to simplify the statistical analysis of spatio-temporal data, a service-oriented method is proposed in this paper. This method defines the model of spatio-temporal data service and functional service. It defines a process-based application of spatio-temporal big data statistics to invoke basic data services and functional services, and proposes an implementation method of spatio-temporal data service and functional service based on Hadoop environment. Taking the highway big data analysis as an example, the validity and applicability of this method are verified. The effectiveness of this method is verified by an example. The validity and applicability of the method are verified by a case study of Expressway large data analysis. An example is given to verify the validity of the method.
    Keywords: spatio-temporal data; RESTful; web service.

  • Research on integrated energy system planning method considering wind power uncertainty   Order a copy of this article
    by Yong Wang, Yongqiang Mu, Jingbo Liu, Yongji Tong, Hongbo Zhu, Mingfeng Chen, Peng Liang 
    Abstract: With the development of energy technology, the planning and operation of integrated energy systems coupled with electricity-gas-heat energy has become an important research topic in the future energy field. In order to solve the influence of wind power uncertainty on the unified planning of integrated energy systems, this paper constructs a wind energy uncertainty quantitative model based on intuitionistic fuzzy sets. Based on this, an integrated energy system planning model with optimal economic cost and environmental cost is established. The model is solved by the harmonic search algorithm. Finally, the proposed method is validated by simulation examples. The effectiveness of the integrated energy system planning method can improve the grid capacity of the wind power and reduce the CO2 of the system. And it has guiding significance for the long-term planning of integrated energy systems
    Keywords: wind power uncertainty; planning method; electricity-gas-heat energy.

  • Finite state transducer based light-weight cryptosystem for data confidentiality in cloud computing   Order a copy of this article
    by Basappa Kodada, Demian Antony D'Mello 
    Abstract: Cloud computing is derived from parallel, cluster, grid and distributed computing and is becoming one of the advanced and growing technologies. With the rapid growth of internet technology and its speed, the number of users for cloud computing is growing enormously, and huge amounts of data are being generated. With the growth of data in cloud, the security and safety of data, such as data confidentiality and privacy, are a paramount issue because data plays a vital role in the current trend. This paper proposes a new type of cryptosystem based on a finite state transducer to provide data confidentiality for cloud computing. The paper presents the protocol communication process and gives an insight into security analysis on the proposed scheme. The scheme proves that it is stronger and more secure than the existing schemes that can be derived from results as proof of concept.
    Keywords: security; confidentiality; encryption; decryption; automata; finite state machine; finite state transducer; cryptography; data safety.

  • Fine-grained access control of files stored in cloud storage with traceable and revocable multi-authority CP-ABE scheme   Order a copy of this article
    by Bharati Mishra, Debasish Jena, Srikanta Patnaik 
    Abstract: Cloud computing is gaining increasing popularity among enterprises, universities, government departments, and end-users. Geographically distributed users can collaborate by sharing files through the cloud. Ciphertext-policy attribute-based (CP-ABE) access control provides an efficient technique to enforce fine-grained access control by the data owner. Single authority CP-ABE schemes create a bottleneck for enterprise applications. Multi-authority CP-ABE systems deal with multiple attribute authorities performing the attribute registration or key distribution. Type I pairing is used in designing the existing multi-authority systems. They are vulnerable to some reported known attacks on them. This paper proposes a multi-authority CP-ABE scheme that supports attribute and policy revocation. Type III pairing is used in designing the scheme, which has higher security, faster group operations, and requires less memory to store the elements. The proposed scheme has been implemented using the Charm framework, which uses the PBC library. The OpenStack cloud platform is used for computing and storage services. It has been proved that the proposed scheme is collusion resistant, traceable, and revocable. AVISPA tool has been used to verify that the proposed scheme is secure against a replay attack and man-in-the-middle attack.
    Keywords: cloud storage; access control; CP-ABE; attribute revocation; blockchain; multi-authority.

  • On generating Pareto optimal set in bi-objective reliable network topology design   Order a copy of this article
    by Basima Elshqeirat, Ahmad Aloqaily, Sieteng Soh, Kwan-Wu Chin, Amitava Datta 
    Abstract: This paper considers the following NP-hard network topology design (NTD) problem called NTD-CB/R: given (i) the location of network nodes, (ii) connecting links, and (iii) each links reliability, cost and bandwidth, design a topology with minimum cost (C) and maximum bandwidth (B) subject to a pre-defined reliability (R) constraint. A key challenge when solving the bi-objective optimisation problem is to simultaneously minimise C while maximising B. Existing solutions aim to obtain one topology with the largest bandwidth cost ratio. To this end, this paper aims to generate the best set of non-dominated feasible topologies, aka the Pareto Optimal Set (POS). It formally defines a dynamic programming (DP) formulation for NTD-CB/R. Then, it proposes two alternative Lagrange relaxations to compute a weight for each link from its reliability, bandwidth, and cost. The paper further proposes a DP approach, called DPCB/R-LP, to generate POS with maximum weight. It also describes a heuristic to enumerate only k?n paths to reduce the computational complexity for a network with n possible paths. Extensive simulations on hundreds of various sized networks that contain up to 299 paths show that DPCB/R-LP can generate 70.4% of the optimal POS while using only up to 984 paths and 27.06 CPU seconds. With respect to a widely used metric, called overall-Pareto-spread (OR), DPCB/R-LP produces 94.4% of POS with OS = 1, measured against the optimal POS. Finally, all generated POS each contains a topology that has the largest bandwidth cost ratio, significantly higher than 88% obtained by existing methods.
    Keywords: bi-objective optimisation; dynamic programming; Lagrange relaxation; Pareto optimal set; network reliability; topology design.

  • HyperGuard: on designing out-VM malware analysis approach to detect intrusions from hypervisor in cloud environment   Order a copy of this article
    by Prithviraj Singh Bisht, Preeti Mishra, Pushpanjali Chauhan, R.C. Joshi 
    Abstract: Cloud computing provides delivery of computing resources as a service on a pay-as-you-go basis. It represents a shift from products being purchased, to products being subscribed as a service, delivered to consumers over the internet from a large scale data centre. The main issue with cloud services is security from attackers who can easily compromise the Virtual Machines (VMs) and applications running over them. In this paper, we present a HyperGuard mechanism to detect malware that hide their presence by sensing the analysing environment or security tools installed in VMs. They may attach themselves with legitimate processes. Hence, HyperGuard is deployed at the hypervisor, outside the monitored VMs to detect such evasive attacks. It employs open source introspection libraries, such as DRAKVUF, LIbVMI etc., to capture the VM behaviour from hypervisor inform of syscall logs. It extracts the features in the form of n-grams. It makes use of Recursive Feature Elimination (RFE) and Support Vector Machine (SVM) to learn and detect the abnormal behaviour of evasive malware. The approach has been validated with a publicly available dataset (Trojan binaries) and a dataset obtained on request from University of new California (evasive malware binaries). The results seem to be promising.
    Keywords: Cloud secuirty,Intrusion detection,virtual machine introspection,system call traces; machine learning ; anaomaly behviour detection; sypder.

  • Dynamic Bayesian network based prediction of performance parameters in cloud computing   Order a copy of this article
    by Priyanka Bharti, Rajeev Ranjan 
    Abstract: Resource prediction is an important task in cloud computing environments. It can become more effective and practical for large Cloud Service Providers (CSPs) with a deeper understanding of their Virtual Machines (VM) workload's key characteristics. Resource prediction is also influenced by several factors including (but not constrained to) data centre resources, types of user application (workloads), network delay and bandwidth. Given the increasing number of users for cloud systems, if these factors can be accurately measured and predicted, improvements in resource prediction could be even greater. Existing prediction models have not explored how to capture the complex and uncertain (dynamic) relationships between these factors owing to the stochastic nature of cloud systems. Further, they are based on score-based Bayesian network (BN) algorithms having limited prediction accuracy when dependency exists between multiple variables. This work considers time-dependent factors in cloud performance prediction. It considers an application of Dynamic Bayesian Network (DBN) as an alternative model for dynamic prediction of cloud performance by extending the static capability of a BN. The developed model is trained using standard datasets from Microsoft Azure and Google Compute Engine. It is found to be effective in predicting the application workloads and its resource requirements with an enhanced accuracy compared with existing models. Further, it leads to better decision making processes with regard to response time and scalability in dynamic situations of the cloud environment.
    Keywords: cloud computing; dynamic Bayesian network; resource prediction; response time; scalability.

  • A privacy-aware and fair self-exchanging self-trading scheme for IoT data based on smart contract   Order a copy of this article
    by Yuling Chen, Hongyan Yin, Yaocheng Zhang, Wei Ren, Yi Ren 
    Abstract: With the development of the era of big data, the demand for data sharing and usage is increasing, especially in the era of Internet of things, thus putting forward a keen demand for data exchanging and data trading. However, the existing data exchanging and trading platforms are usually centralized and usersrnhave to trust platforms. This paper proposes a secure and fair exchanging and trading protocol based on blockchain and smart contract, especially, self-governance without relying centralized trust. By using the protocol, it can guarantee fairness to defend against trade cheating, and security for data confidentiality. It can also guarantee efficiency by transferring data links instead of data between data owners and data buyers. The extensive analysisrnjustified that the proposed scheme can facilitate the self-exchanging and self-trading for big data in a secure, fair and efficient manner.
    Keywords: big data; IoT; fair exchanging; blockchain; smart contract; oblivious protocol; fair trading.

  • Micro-PaaS fog: container based orchestration for IoT applications using SBC   Order a copy of this article
    by Walter D.O. Santo, Rubens De Souza Matos Júnior, Admilson De Ribamar Lima Ribeiro, Danilo Souza Silva, Reneilson Yves Carvalho Santos 
    Abstract: The Internet of Things (IoT) is an emerging technology paradigm in which ubiquitous sensors monitor physical infrastructures, environments, and people in real-time to help in decision making and improve the efficiency and reliability of the systems, adding comfort and life quality to society. In this sense, there are questions concerning the limitation of computational resources, high latency and different QoS requirements related to IoT that move cloud technologies to the fog computing direction, and the adoption of light virtualised solutions, as technologies based in containers to attend to many needs of different domains. This work, therefore, has as its goal to propose and implement a micro-Paas architecture for fog computing, in a cluster of single-board computers (SBC), for orchestration of applications using containers, applied to IoT and that attend to the QoS criteria, e.g. high availability, scalability, load balance, and latency. From this proposed model, the micro-Paas fog was implemented with virtualisation technology in containers using orchestration services in a cluster built with Raspberry Pi to monitor water and energy consumption at a total cost of property equivalent to 23% of a public platform as a service (PaaS).
    Keywords: fog computing; cluster; orchestration; containers; single board computing.

  • Anomaly detection against mimicry attacks based on time decay modelling   Order a copy of this article
    by Akinori Muramatsu, Masayoshi Aritsugi 
    Abstract: Because cyberattackers attempt to cheat anomaly detection systems, it is required to make an anomaly detection system robust against such attempts. We focus on mimicry attacks and propose a system to detect such attacks in this paper. Mimicry attacks make use of ordinary operations in order not to be detected. We take account of time decay in modelling operations to give lower priorities to preceding operations, thereby enabling us to detect mimicry attacks. We empirically evaluate our proposal with varying time decay rates to demonstrate that our proposal can detect mimicry attacks that could not be detected by a state-of-the-art anomaly detection approach.
    Keywords: anomaly detection; mimicry attacks; time decay modelling; stream processing.

  • A cloud-based spatiotemporal data warehouse approach   Order a copy of this article
    by Georgia Garani, Nunziato Cassavia, Ilias Savvas 
    Abstract: The arrival of the big data era introduces new necessities for accommodating data access and analysis by organisations. The evolution of data is three-fold, increase in volume, variety, and complexity. The majority of data nowadays is generated in the cloud. Cloud data warehouses enhance the benefits of the cloud by facilitating the integration of cloud data in the cloud. A data warehouse is developed in this paper, which supports both spatial and temporal dimensions. The research focuses on proposing a general design for spatiobitemporal objects implemented by nested dimension tables using the starnest schema approach. Experimental results reflect that the parallel processing of such data in the cloud can process OLAP queries efficiently. Furthermore, increasing the number of computational nodes significantly reduces the time of query execution. The feasibility, scalability, and utility of the proposed technique for querying spatiotemporal data is demonstrated.
    Keywords: cloud computing; big data; hive; business intelligence; data warehouses; cloud based data warehouses; spatiotemporal data; spatiotemporal objects; starnest schema; OLAP; online analytical processing.

  • Dont hurry, be green: scheduling server shutdowns in grid computing with deep reinforcement learning   Order a copy of this article
    by Mauricio Pillon, Lucas Casagrande, Guilherme Koslovski, Charles Miers, Nelson Gonzales 
    Abstract: Grid computing platforms dissipate massive amounts of energy. Energy efficiency, therefore, is an essential requirement that directly affects their sustainability. Resource management systems deploy rule-based approaches to mitigate this cost. However, these strategies do not consider the patterns of the workloads being executed. In this context, we demonstrate how a solution based on deep reinforcement learning is used to formulate an adaptive power-efficient policy. Specifically, we implement an off-reservation approach to overcome the disadvantages of an aggressive shutdown policy and minimise the frequency of shutdown events. Through simulation, we train the algorithm and evaluate it against commonly used shutdown policies using real traces from GRID5000. Based on the experiments, we observed a reduction of 46% on the averaged energy waste with an equivalent frequency of shutdown events compared with a soft shutdown policy.
    Keywords: deep reinforcement learning; grid computing; energy-aware scheduling; shutdown strategy; Markov decision process; resource management.

  • Authentication and authorisation in service oriented grid architecture   Order a copy of this article
    by Arbër Beshiri, Anastas Mishev 
    Abstract: Applications (services) in nowadays request access to resources that are mostly distributed over the network (wide-area network). These applications usually rely on by mediums such as Grid Computing Infrastructure (GCI) that enable them to be executed. GCI has heterogeneous nature and supports security as an essential part in grid systems. Grid Security Infrastructure (GSI) is a technology standard for grid security. Authentication and even authorisation estimate is a security challenge for grids. This paper discusses the authentication and authorisation infrastructures in the grid, including technologies that cover these two major parts of this domain. Here are surveyed the challenges that security encounters, namely grid authentication mechanisms, grid authorisation mechanisms and models. The combination of the grid authorisation technologies and grid authentication technologies with authorisation infrastructures enables role-based and fine-grained authorisation. Such technologies provide promising solutions for service (resources) oriented grid architecture authentication and authorisation.
    Keywords: grid; service oriented grid architecture; authorisation; authentication; security.

  • Recommendation system based on space-time user similarity
    by Wei Luo, Zhihao Peng, Ansheng Deng 
    Abstract: With the advent of 5G, the way people get information and the means of information transmission have become more and more important. As the main platform of information transmission, social media not only brings convenience to people's lives, but also generates huge amounts of redundant information because of the speed of information updating. In order to meet the personalised needs of users and enable users to find interesting information in a large volume of data, recommendation systems emerged as the times require. Recommendation systems, as an important tool to help users to filter internet information, play an extremely important role in both academia and industry. The traditional recommendation system assumes that all users are independent. In this paper, in order to improve the prediction accuracy, a recommendation system based on space-time user similarity is proposed. The experimental results on Sina Weibo dataset show that, compared with the traditional collaborative filtering recommendation system based on user similarity, the proposed method has better performance in precision, recall and F-measure evaluation value.
    Keywords: time-based user similarity; space-based user similarity; recommendation system; user preference; collaborative filtering.

  • Design and analysis of novel hybrid load-balancing algorithm for cloud data centres   Order a copy of this article
    by Ajay Dubey, Vimal Mishra 
    Abstract: In recent the pandemic scenario there is a paradigm shift, from traditional computing to internet-based computing. Now is the time to store and compute the data in the cloud environment. The Cloud Service Providers (CSPs) establish and maintain a huge shared pool of computing resources that provide scalable and on-demand services around the clock without geographical restrictions. The cloud customers are able to access the services and pay according to the accession of resources. When millions of users across the globe connect to the cloud for their storage and computational needs, there might be issues such as delay in services. This problem is associated with load balancing in cloud computing. Hence, there is a need to develop effective load-balancing algorithms. The Novel Hybrid Load Balancing (NHLB) algorithm proposed in this paper manages the load of the virtual machine in the data centre. This paper is focused on certain problems such as optimisation of performance, maximum throughput, minimisation of makespan, and efficient resource use in load balancing. The NHLB algorithm is more efficient than conventional load-balancing algorithms with reduced completion time (makespan) and response time. This algorithm equally distributes the tasks among the virtual machines on the basis of the current state of the virtual machines and the task time required. The paper compares the result of proposed NHLB algorithm with dynamic load-balancing algorithm and honeybee algorithm. The result shows that the proposed algorithm is better than the dynamic and honeybee algorithms.
    Keywords: cloud computing; data centre; load balancing; virtual machine; makespan; performance optimisation.

  • Virtual traditional craft simulation system in mixed reality environment   Order a copy of this article
    by Rihito Fuchigami, Tomoyuki Ishida 
    Abstract: In a previous study, we implemented a high presence virtual traditional craft system using a head-mounted display (HMD) and a mobile traditional craft presentation system using augmented reality (AR). The high presence virtual traditional craft system realised a simulation experience of traditional crafts in virtual space comprising different cultural styles. However, this system had to construct the different cultural architectures in advance, and the amount of work was enormous. The mobile traditional craft presentation system realised an AR application that allows users to easily arrange 3DCG (Three-Dimensional Computer Graphics) of traditional crafts in real space using mobile terminals. However, this system lacks a sense of presence because users experience traditional crafts on the mobile terminals flat display. Therefore, in this study, we focused on mixed reality (MR) technology and developed an MR virtual traditional craft simulation system using an HMD. With MR technology, we have overcome the work cost and low presence issues relative to the construction of a virtual reality space. As a result, our system provides a simulation experience that realises a high sense of presence and intuitive operation. We conducted a comparative evaluation experiment with 30 subjects to evaluate the constructed MR virtual traditional craft simulation system. We obtained high evaluations of the systems presence and applicability; however, several operability issues were identified.
    Keywords: mixed reality; augmented reality; interior simulation; Japanese traditional crafts; immersive system.

  • The role of smartphone-based social media capabilities in building social capital, trust, and credibility to engage consumers in eWOM: a social presence theory perspective   Order a copy of this article
    by Saqib Mahmood, Ahmad Jusoh, Khalil MD Nor 
    Abstract: Smartphone-based social media has become a well-established channel for users to develop and maintain intimate social relations that enable them to engage in brands-related information exchange, regardless of their time and location, such as eWOM. Nevertheless, little is known about the essential elements of smartphone-based social media that help consumers to develop intimate social relationships and engage them in eWOM. To this end, drawing on the theory of social presence, the current study develops a research model that proposes that interactivity and media richness enhance social presence, giving consumers a sense of psychological proximity. Subsequently, it leads to the development of trust and social capital bonding and bridging. As a result of the bridging and bonding of social capital, consumers' perceived credibility is expected to enable them to engage in eWOM. To empirically investigate the theoretical model, a survey of 407 smartphone-based social media users was conducted in Pakistan. Empirical results reveal that the interactivity and media richness enhance the social presence that proffers consumers' psychological proximity to developing trust and social capital, further enhancing their perceived credibility to engage in eWOM. Discussions, implications, and future directions on the results are described in the final section of the study.
    Keywords: interactivity; media richness; social presence; psychological proximity; social capital; eWOM; trust; smartphone; social media.

  • Cloud infrastructure planning considering the impact of maintenance and self-healing routines over cost and dependability attributes   Order a copy of this article
    by Carlos Melo, Jean Araujo, Jamilson Dantas, Paulo Pereira, Felipe Oliveira, Paulo Maciel 
    Abstract: Cloud computing is the main trend regarding internet service provision. This paradigm, which emerged from distributed computing, gains more adherents every day. For those who provide or aim at providing a service or a private infrastructure, much has to be done, costs related to acquisition and implementation are common, and an alternative to reduce expenses is to outsource maintenance of resources. Outsourcing tends to be a better choice for those who provide small infrastructures than to pay some employees monthly to keep the service life cycle. This paper evaluates infrastructure reliability and the impact of outsourced maintenance over the availability of private infrastructures. Our baseline environments focus on blockchain as a service; however, by modelling both service and maintenance routines, this study can be applied to most cloud services. The maintenance routines evaluated by this paper encompass a set of service level agreements and some particularities related to reactive, preventive, and self-healing methods. The goal is to point out which one has the best cost-benefit for those with small infrastructures, but that still plans to provide services over the internet. Preventive and self-healing repair routines provided a better cost-benefit solution than traditional reactive maintenance routines, but this scenario may change according to the number of available resources that the service provider has.
    Keywords: maintenance; reliability; availability; modelling; cloud Ccmputing; blockchain; container; services; SLA.

  • Toward stance parameter algorithm with aggregate comments for fake news detection   Order a copy of this article
    by YinNan Yao, ChangHao Tang, Kun Ma 
    Abstract: In the detection of fake news, the stance of comments usually contains evidence supporting false news that can be used to corroborate the detected results of the fake news. However, owing to the misleading content of fake news, there is also the possibility of fake comments. By analysing the position of comments and considering the falseness of comments, comments can be used more effectively to detect fake news. In response to this problem, we propose Bipolar Argumentation Frameworks of Reset Comments Stance (BAFs-RCS) and Average Parameter Aggregation of Comments (APAC) to use the stance of comments to correct the prediction results of the RoBERTa model. We use the Fakeddit dataset for experiments. Our macro-F1 results on 2way and 3way are improved by 0.0029 and 0.0038 compared with the baseline RoBERTa model's macro-F1 results at Fakeddit dataset. The results show that our method can effectively use the stance of comments to correct the results of model prediction errors.
    Keywords: fake news detection; BAFs-RCS; APAC; RoBERTa.

  • Enhancing the 5G V2X reliability using turbo coding for short frames   Order a copy of this article
    by Costas Chaikalis, Dimitros Kosmanos, Kostas Anagnostou, Ilias Savvas, Dimitros Bargiotas 
    Abstract: For 5th Generation (5G) Vehicle-to-Everything (V2X) communication it would be desirable to build a dynamically changing reconfigurable system, considering different parameters. Turbo codes had a great impact on the realisation and success of 3G and 4G. Despite their complexity, their use for 5G V2X and short frames represents a challenging issue. Therefore, for the physical layer the choice of decoding iterations and algorithm represent two important parameters to achieve low latency and high performance, increasing the reliability of packet delivery. This is particularly useful for traffic emergency situations under strong interference or radio frequency jamming. For the geometry-based, efficient propagation model (GEMV) for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, our simulation results propose a constant number of three iterations. Subsequently, we investigate the main three turbo decoding algorithms for GEMV and flat Rayleigh fading, and our analysis does not recommend soft output Viterbi algorithm (SOVA) owing to its worse performance. We propose either log-Maximum a Posteriori (MAP) (better performance), or max-log-MAP (lower complexity), in comparison to the far more complex MAP algorithm.
    Keywords: 5G; V2V; turbo codes; GEMV channel.

  • An efficient privacy-preservation algorithm for incremental data publishing   Order a copy of this article
    by Torsak Soontornphand, Mizuho Iwaihara, Juggapong Natwichai 
    Abstract: Data can be continuously collected and grown all the time. Privacy protection designed for static data might not be able to cope with this situation effectively. In this paper, we present an efficient privacy preservation approach based on (k, l)-anonymity for incremental data publishing. We first illustrate the three privacy attacks, i.e., similarity, difference and joint attacks. Then, the three characteristics of incremental data publishing are analysed and exploited to efficiently detect privacy violations. With the studied characteristics, the similarity and join attack detection can be skipped for stable releases. In addition, only a subtype of the similarity attack and the latest previously released dataset need to be detected. From experimental results, the proposed method is highly efficient, with an average execution time eleven times less than a comparable static algorithm. In addition, the proposed method can also maintain better data quality than the compared methods at every setting.
    Keywords: privacy preservation; incremental data publishing; privacy attack; full-domain generalisation.

  • Energy-based cost model of container provisioning on clouds   Order a copy of this article
    by Mauricio Pillon, Aline Moreira, Charles Miers, Guilherme Koslovski, Nelson Gonzalez 
    Abstract: Cloud computing has revolutionised the development and execution of distributed applications by providing on-demand access to virtual resources. Containerisation simplifies management and support of the cloud infrastructure and applications. Clouds typically are consumed in a pay-as-you-go pricing model. However, when applied to containerised environments, such traditional models do not consider resource utilisation values, leading to inaccurate estimates. Moreover, these models do not consider energy consumption, a dominant component of the data centres total cost of ownership. This paper proposes Energy Price Cloud Containers (EPCC), a cost model based on energy consumption that accounts for containers effective resource utilisation. We compare EPCC with AWS Fargate to highlight the benefits of using an energy-based pricing model. Thus, by comparing the cost of an application running using Amazon Web Services (AWS) Fargate with the estimated cost of that application in EPCC, it is possible to identify the benefits of using an energy-based pricing model. The weekly costs estimated when running computational resources at EPCC vary between US$ 2.31 and US$ 10.59. In contrast, when estimating the same amount of resources on AWS Fargate, the costs vary between US$ 2.71 and US$ 29.94. EPCC resulted in a cost reduction of up to 35%.
    Keywords: Pricing Model; Containers; Cloud Computing; Energy Consumption.

  • Jaya-based CAViaR: Hadamard product and key matrix for privacy preservation and data sharing in cloud computing environment   Order a copy of this article
    by Shubangini Patil, Rekha Patil 
    Abstract: Cloud computing system is a powerful computing resource mainly used for data publication and data subscription. Because the cloud environment handles a large volume of information, privacy is a challenging task during data sharing. Hence, an effective method is designed for data privacy preservation using the proposed Jaya-based CAViaR model. The privacy factor is measured using Database Difference Ratio (DBDR) and the utility factor is designed using Tanimoto similarity measure. The data to be shared is protected using key matrix and Hadamard product, such that the optimal matrix is generated using the proposed Jaya-based CAViaR model. The data sharing is accomplished among cloud storage, user and third party verification and validation (TPVV), respectively. Moreover, the protected data is effectively retrieved using key matrix. The proposed model obtained better performance in terms of metrics, such as accuracy, fitness, privacy, and utility with the values of 0.9737, 0.8691, 0.8976, and 0.8407, respectively.
    Keywords: Privacy preservation; data protection; data sharing; Tanimoto similarity measure; Jaya algorithm.

  • Improved identity-based proxy-oriented outsourcing with public auditing for secure cloud storage   Order a copy of this article
    by Yuanyou Cui, Yunxuan Su, Zheng Tu, Jindan Zhang 
    Abstract: With the advent of cloud computing, to verify the integrity of data stored on cloud service providers by proxy while ensuring proxy's credibility has become an important problem. In 2019, Zhang proposed an identity-based proxy-oriented outsourcing with public auditing in cloud-based medical cyberphysical systems that outsourcing medical data to cloud storage, which facilitated the process and access of medical data. Meanwhile, the proxy is added cleverly for improving the practicability and real-time performance of data processing. Unfortunately, some minor flaws are found in their approach in this paper. The signature generated by the agent can be forged to modify the original data and invalidate the original data integrity audit protocol. We have made some adjustments on the basis of the original data transmission protocol to improve the shortcomings of the original scheme. Finally, the security analysis shows that the security of the new protocol is enhanced.
    Keywords: secure cloud storage; data integrity; proxy-oriented.

  • Edge computing and its boundaries to IoT and Industry 4.0: a systematic mapping study   Order a copy of this article
    by Matheus Silva, Vinícius Meyer, Cesar De Rose 
    Abstract: In the last decade, cloud computing transformed the IT industry, allowing companies to execute many services that require on-demand availability of computational resources with more flexible provisioning and cost models, including the processing of already growing data volumes. But in the past few years, other technologies such as internet of things and the digitised industry known as Industry 4.0 have emerged, increasing data generation even more. The large amounts of data produced by user-devices and manufacturing machinery have made both industry and academia search for new approaches to process all this data. Alternatives to the cloud centralised processing model and its inherent high latencies have been studied, and edge computing is being proposed as a solution to these problems. This study presents a preliminary mapping of the edge computing field, focusing on its boundaries to the internet of things and Industry 4.0. We began with 219 studies from different academic databases, and after the classification process, we mapped 90 of them in eight distinct edge computing sub-areas and nine categories based on their main contributions. We present an overview of the studies on the edge computing area, which evidences the main concentration sub-areas. Furthermore, this study intends to clarify the remaining research gaps and the main challenges faced by this field, considering the internet of things and Industry 4.0 demands.
    Keywords: edge computing; internet of things; Industry 4.0; systematic mapping.

  • Cloud workflow scheduling algorithm based on multi-objective particle swarm optimisation   Order a copy of this article
    by Hongfeng Yin, Baomin Xu, Weijing Li 
    Abstract: Owing to the characteristics of market-oriented cloud computing, the objective function of cloud workflow scheduling algorithm should not only consider the running time, but also consider the running costs. The nature of cloud workflow scheduling is to map each task of a workflow instance to appropriate computing resources. Owing to the existence of temporal dependencies and causal dependencies between tasks, the scheduling of cloud workflow instance becomes more complex. The main contribution of this paper is to propose a cloud workflow scheduling algorithm based on multi-objective particle swarm optimisation. The algorithm takes makespan and total cost as two objectives. It provides users with a set of Pareto optimal solutions to select an optimal scheduling scheme according to their own preferences. The performance of our algorithm is compared with state-of-the-art multi-objective meta-heuristics and classical single-objective scheduling algorithms. The simulation results show that our solution delivers better convergence and optimisation capability compared with others. Hence it is applicable to solve multi-objective optimisation problems for scheduling workflows over a cloud platform.
    Keywords: multi-objective optimisation; cloud computing; particle swarm optimisation; workflow scheduling.

  • Data collection in underwater wireless sensor networks: performance evaluation of FBR and epidemic routing protocols for node density and area shape   Order a copy of this article
    by Elis Kulla 
    Abstract: Data collection in Underwater Wireless Sensor Networks (UWSN) is not a trivial problem, because of unpredictable delays and unstable links between underwater devices. Moreover, when nodes are mobile, continuous connectivity is not guaranteed. Therefore, data collection in UWSN Node scarcity and movement patterns create different environments for data collection in underwater communication. In this paper, we investigate the impact of the area shape and node density in UWSN, by comparing Focused Beam Routing (FBR) and Epidemic Routing (ER) protocols. Furthermore, we also analyse the correlation between different performance metrics. From simulation results we found that when using FBR, delay and delivery probability slightly decrease (2.1%) but the overhead ratio decreases noticeably (46.9%). The correlation between performance metrics is stronger for square area shape, and is not noticeable for deep area shape.
    Keywords: underwater wireless sensor networks; focused beam routing; delay tolerant network; area shape; node density; data collection.

  • Joint end-to-end recognition deep network and data augmentation for industrial mould number recognition   Order a copy of this article
    by RuiMing Li, ChaoJun Dong, JiaCong Chen, YiKui Zhai 
    Abstract: With the booming manufacturing industry, the significance of mould management is increasing. At present, manual management is gradually eliminated owing to need for a large amount of labour, while the effect of a radiofrequency identification (RFID) system is not ideal, which is limited by the characteristics of the metal, such as rust and erosion. Fortunately, the rise of convolutional neural networks (CNNs) brings down to the solution of mould management from the perspective of images that management by identifying the digital number on the mould. Yet there is no trace of a public database for mould recognition, and there is no special recognition method in this field. To address this problem, this paper first presents a novel data set aiming to support the CNN training. The images in the database are collected in the real scene and finely manually labelled, which can train an effective recognition model and generalise to the actual scenario. Besides, we combined the mainstream text spotter and the data augmentation specifically designed for the real world, and found that it has a considerable effect on mould recognition.
    Keywords: mould recognition database; text spotter; mould recognition; data augmentation.

  • Intrusion detection and prevention with machine learning algorithms   Order a copy of this article
    by Victor Chang, Sreeja Boddu, Qianwen Ariel Xu, Le Minh Thao Doan 
    Abstract: In recent decades, computer networks have played a key role in modern life and also have escalated the number of new attacks on internet traffic to avoid malicious activities. An intrusion detection system (IDS) is imperative for researching firewalls, anti-viruses, and intrusion (bad connection). Many researchers are working to overcome the challenges of IDS and are focused on getting better accuracy to predict automatically normal data connection and abnormal data. To resolve the above problems, many researchers are focused on traditional machine learning and deep learning algorithms to detect automatically internal and external connections of network protocol. In this paper, creators adopt dissimilar machine learning and deep learning techniques, comparing performance and accuracy with respective times. The dataset KDDcup-1999, which is the most reliable dataset, contains a wide range of network environments.
    Keywords: machine learning; deep learning; security dataset.

  • Privacy-aware trajectory data publishing: an optimal efficient generalisation algorithm   Order a copy of this article
    by Nattapon Harnsamut, Juggapong Natwichai 
    Abstract: With the increasing location-aware technologies that provide positioning services, it is easy to collect users' trajectory data. Such data could often contain sensitive information, i.e., private locations, private trajectory or path, and other sensitive attributes. Therefore, the privacy issue must be addressed properly when data are to be released to another business collaborator for data analytic purposes, especially when an adversary could have partial trajectory information of the target, i.e. the previous locations could be known. LKC-privacy model is a well-known privacy preservation model to protect the privacy attacks of trajectory data publishing. The model required that the data anonymisation is carried on to guarantee privacy preservation. The data generalisation technique, transforming data values to be in more general form, can be developed to preserve privacy. Since its impact on data utility after the anonymisation is usually low, i.e. the anonymised data can be still useful. However, this technique can be time-consuming computing, especially for high-dimensional trajectory data such as the focused scenario. In this paper, we propose an enhanced look-up table brute-force algorithm, Enhanced-LT, to maintain the data utility while preserving privacy based on the LKC-privacy model efficiently. This algorithm can reduce the computing time of the look-up table creation, which is one of the longest computational processes. Our proposed algorithm is evaluated with extensive experiments. From the experiments, the Enhanced-LT algorithm performance in terms of the execution time outperforms the baseline LT algorithm by 48.5% on average. The results show that our proposed algorithm returns not only the optimal solution but also highly efficient computation times.
    Keywords: LKC-privacy; trajectory data publishing; optimal algorithm; generalisation technique.

  • An SMIM algorithm for reduction of energy consumption of virtual machines in a cluster   Order a copy of this article
    by Dilawaer Duolikun, Tomoya Enokido, Makoto Takizawa 
    Abstract: Applications can take advantage of virtual computation services independently of heterogeneity and locations of servers by using virtual machines in clusters. Here, a virtual machine on an energy-efficient host server has to be selected to perform an application process. In this paper, we newly propose an SMI (Simple Monotonically Increasing) estimation algorithm to estimate the energy consumption of a server to perform application processes and the total execution time of processes on a server. We also propose an SMIM (SMI Migration) algorithm to make a virtual machine migrate from a host server to a guest server to reduce the total energy consumption of the servers by estimating the energy consumption in the SMI algorithm. In the evaluation, we show the energy consumption of servers in a cluster can be reduced in the SMIM algorithm compared with other algorithms.
    Keywords: server selection algorithm; migration of virtual machines; green computing systems; SMI algorithm; SMIM algorithm.

  • FIAC: fine-grained access control mechanism for cloud-based IoT framework   Order a copy of this article
    by Bhagwat Prasad Chaudhury, Kasturi Dhal, Srikant Patnaik, Ajit Kumar Nayak 
    Abstract: Cloud computing technology provides various computing resources on demand to the user on pay per use basis. The users use the services without the need for establishment and maintenance costs. The technology fails in terms of its usage owing to confidentiality and privacy issues. Access control mechanisms are the tools to prevent unauthorised access to remotely stored data. CipherText Policy Attribute-based Encryption (CPABE) is a widely used tool for facilitating authorised users to access the remotely stored encrypted data with fine-grained access control. In the proposed model FIAC (Fine-Grained Access Control Mechanism for cloud-based IoT Framework ), the access control mechanism is embedded in the cloud-based application to measure and generate a report on the air quality in a city. The major contribution of this work is the design of three algorithms all of which are attribute based: key generation algorithm, encryption and decryption algorithms. Only authorised users can view it to take appropriate action plans. Carbon dioxide concentrations, dust, temperature, and relative humidity are the parameters that we have considered for air quality. To enhance the security of the cloud-based monitoring system, we have embedded a security scheme, all of which are attribute based. Further, the computation time of the model is found to be encouraging so that it can be used in low power devices. The experimental outcomes establish the usability of our model.
    Keywords: air pollution; carbon dioxide concentrations; dust density; internet of things; FIAC; access control.

  • New principles of finding and removing elements of mathematical model for reducing computational and time complexity   Order a copy of this article
    by Yaroslav Matviychuk, Natalia Kryvinska, Nataliya Shakhovska, Aneta Poniszewska-Maranda 
    Abstract: The original principle of removing elements of a mathematical model based on its parametric identification of neural network is proposed in the paper. The essence of proposed method is to find a functional subset with less variability results and higher accuracy than for the initial functional set of the model. It allows reducing the computational and time complexity of the applications built on the model. Comparison with dropout technique shows the 1,1 decreasing of Root mean squared error. In addition, reducing the complexity allows increasing the accuracy of neural network models. Therefore, reducing the number of parameters is an essential step in data preprocessing used in almost all modern systems. However, known methods of reducing the dimension depend on the problem area, making it impossible to use them in ensemble models.
    Keywords: regularisation; reduction; identification procedure; incorrectness; neural network.

  • A CNN-SVM hybrid model for the classification of thyroid nodules in medical ultrasound images   Order a copy of this article
    by Rajshree Srivastava, Pardeep Kumar 
    Abstract: The thyroid nodule is one of the endocrine issues which is caused by the formation of irregular cells in the thyroid region. The recent success of machine and deep learning techniques in image recognition task leads to solve challenges in diagnostic imaging. An effective convolutional neural network-support vector machine (CNNSVM) hybrid model is proposed using hinge loss function to achieve better results and stable convergence. The efficiency of the proposed model has been evaluated on public and collected datasets having 1180 and 2616 thyroid USG images after data augmentation. The proposed model has achieved an accuracy of 94.57%, specificity of 91.89%, sensitivity of 96.70% and f-measure of 95.64% on dataset-1 and an accuracy of 96%, specificity of 93.93%, sensitivity of 97.80% and f-measure of 98.33%. on dataset-2. It has shown an improvement of 3% to 5% on dataset-1 and 4% to 6% on dataset-2 in comparison with state-of-the-art models.
    Keywords: convolution neural network; thyroid nodules; support vector machines; benign nodules; classification; malignant nodules.
    DOI: 10.1504/IJGUC.2022.10052488
     
  • University ranking approach with bibliometrics and augmented social perception data   Order a copy of this article
    by Kittayaporn Chantaranimi, Rattasit Sukhahuta, Juggapong Natwichai 
    Abstract: Typically, universities aim to achieve a high position in ranking systems for their reputation. However, self-evaluating rankings could be costly because the indicators are not only from bibliometrics, but also the results of over a thousand surveys. In this paper, we propose a novel approach to estimate university rankings based on traditional data, i.e., bibliometrics, and non-traditional data, i.e., Altmetric Attention Score, and Sustainable Development Goals indicators. Our approach estimates subject-areas rankings in Arts & Humanities, Engineering & Technology, Life Sciences & Medicine, Natural Sciences, and Social Sciences & Management. Then, by using Spearman rank-order correlation and overlapping rate, our results are evaluated by comparing with the QS subject ranking. From the result, our approach, particularly the top-10 ranking, performed estimating effectively and then could assist stakeholders in estimating the university's position when the survey is not available.
    Keywords: university ranking; rank similarity; bibliometrics; augmented social perception data; sustainable development goals; Altmetrics.

  • Detection and evolution analysis of TCM patent community based on weighted complex network   Order a copy of this article
    by Na Deng, Tiansi Du, Xu Chen 
    Abstract: In order to provide help and support for the analysis, mining, research, and development of traditional Chinese medicines, and for the discovery of medicine evolution laws, a weighted complex network based medicine community detection algorithm is proposed. Firstly, a weighted network of herbs is constructed according to the correlation degrees between nodes; and then, nodes with the highest PageRank (PR) value are selected iteratively as the initial clustering centres of the community; lastly, the nodes with the highest similarity to the nodes in the community are added to the community one by one, until all the nodes are divided into the corresponding TCM community. We verify the stability and effectiveness of the algorithm on the classic test networks. The algorithm is applied to the community detection and evolution analysis of antihypertensive TCM patents to obtain the core herbs and their evolution laws.
    Keywords: TCM patent; community detection; evolution analysis; weighted complex network; node correlation degree; node importance; node similarity; node direct influence; node common neighbour influence.
    DOI: 10.1504/IJGUC.2022.10052397
     
  • Cost-effective data replication mechanism modelling for cloud storage   Order a copy of this article
    by Khalid Zaman, Altaf Hussain, Muhammad Imran, Muhammad Sohail 
    Abstract: Replication increases data availability and reduces access latency in cloud storage. Solutions prioritise system performance over replica production and storage costs. Under a pay-as-you-go model, cloud users incur rising fees for replica storage and consistency management. Before expanding node copies, we outline a dynamic replication technique. The suggested technique concentrates resources for effective distribution across all nodes. The cost-effectiveness of the proposed model is determined using threshold values; the proposed system reduced cloud replicas, reducing costs. Azure's $6 static solution has three nodes. The adaptive data replication management system reduced copies from three to two, saving memory and reducing the price from $6 to $4. MATLAB and other programming languages are used. Azure is compared with Data Replication and Reproducer. The proposed solution is cheaper than Azure with three replicates.
    Keywords: Azure solution; cloud computing; SaaS; IaaS; PaaS; Data Replication and Reproducer.

Special Issue on: CONIITI 2019 Intelligent Software and Technological Convergence

  • Computational intelligence system applied to plastic microparts manufacturing process   Order a copy of this article
    by Andrés Felipe Rojas Rojas, Miryam Liliana Chaves Acero, Antonio Vizan Idoipe 
    Abstract: In the search for knowledge and technological development, there has been an increase in new analysis and processing techniques closer to human reasoning. With the growth of computational systems, hardware production needs have also increased. Parts with millimetric to micrometric characteristics are required for optimal system performance, so the demand for injection moulding is also increasing. Injection moulding process in a complex manufacturing process because mathematical modelling is not yet established: therefore, to address the selection of correct values of injection variables, computational intelligence can be the solution. This article presents the development of a computational intelligence system integrating fuzzy logic and neural network techniques with CAE modelling system to support injection machine operators, in the selection of optimal machine process parameters to produce good quality microparts using fewer processes. The tests carried out with this computational intelligent system have shown a 30% improvement in the efficiency of the injection process cycles.
    Keywords: computational intelligence; neural networks; fuzzy logic; micro-parts; plastic parts; computer vision; expert systems; injection processes; CAD; computer-aided design systems; CAE; computer-aided engineering.

Special Issue on: Novel Hybrid Artificial Intelligence for Intelligent Cloud Systems

  • QoS-driven hybrid task scheduling algorithm in a cloud computing environment   Order a copy of this article
    by Sirisha Potluri, Sachi Mohanty, Sarita Mohanty 
    Abstract: Cloud computing environment is a growing technology of distributed computing. Typically using cloud computing the services are deployed with individuals or organisations and to allow sharing of resources, services, and information based on the demand of users over the internet. CloudSim is a simulator tool used to simulate cloud scenarios. A QoS-driven hybrid task scheduling architecture and algorithm for dependent and independent tasks in a cloud computing environment is proposed in this paper. The results are compared against the Min-Min task scheduling algorithm, QoS-driven independent task scheduling algorithm, and QoS-driven hybrid task scheduling algorithm. QoS-driven hybrid task scheduling algorithm is compared with time and cost as QoS parameters and it gives a better result for these parameters.
    Keywords: cloud computing; task scheduling; quality of service.

Special Issue on: ICIMMI 2019 Emerging Trends in Multimedia Processing and Analytics

  • Handwritten Odia numeral recognition using combined CNN-RNN   Order a copy of this article
    by Abhishek Das, Mihir Narayan Mohanty 
    Abstract: Detection and recognition is a major task for current research. Almost all the parts of signal processing, including speech and images has the sub-content of it. Data compression mainly uses in multimedia communication, where the recognition is the major challenge. Keeping all these facts in view, the authors have taken an approach for handwritten numbers recognition. To meet the challenge of fake data, a generative adversarial network is used to generate some data and is considered along with original data. The database is collected from IIT, Bhubaneswar, and used in a GAN model to generate a huge amount of data. Further, a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN) are considered for recognition purpose. Though Odia numerals are a little complex, the recognition task was found very interesting. A little work has been done in this direction. However, the application of a deep learning based approach is absent. Long Short Term Memory (LSTM) cells are used as recurrent units in this approach. We have added 1000 images generated through Deep Convolutional Generative Adversarial Network (DCGAN) to the IIT-BBSR dataset. In this method we have used the Adam optimisation algorithm for minimising the error, and to train the network we have used the supervised learning method. The result of this method gives 98.32% accuracy.
    Keywords: character recognition; Odia numerals; deep learning; CNN; RNN; LSTM; DCGAN; Adam optimisation.

  • An optimal channel state information feedback design for improving the spectral efficiency of device-to-device communication   Order a copy of this article
    by Prabakar Dakshinamoorthy, Saminadan Vaitilingam 
    Abstract: This article introduces a regularised zero-forcing (RZF) based channel state information (CSI) feedback design for improving the spectral efficiency of device-to-device (D2D) communication. This proposed method exploits conventional feedback design along with the optimised CSI in regulating the communication flows in the communicating environment. The codebook-dependent precoder design improves the rate of feedback by streamlining time/frequency dependent scheduling. The incoming communication traffic is scheduled across the available channels by pre-estimating their adaptability and capacity across the underlying network. This helps to exchange partial channel information between the communicating devices without the help of base station services. These features reduce the transmission error rates to achieve better sum rate irrespective of the distance and transmit power of the devices.
    Keywords: CSI; D2D; feedback design; precoding; zero-forcing.

Special Issue on: 3PGCIC Cloud and Edge Systems and Applications

  • Quality of service prediction model in cloud computing using adaptive dynamic programming parameter tuner   Order a copy of this article
    by Monika Rd, Om Prakash Sangwan 
    Abstract: With the continuous proliferation of cloud services, the recommendation of optimal cloud service according to user requirement has become an important and critical issue and makes it highly infeasible for a single user, who wants to use the cloud services for some specific application with QoS requirements to try all the cloud services and thus depends on the information collected by other users about the QoS of various cloud services. These collected QoS values are highly nonlinear, complex and uncertain. To deal with the given scenario, there is a specific requirement to develop a recommender system for the prediction of unknown QoS values using some optimization techniques. In this paper, we have developed two models: i) optimised matrix factorisation prediction model ii) optimised fuzzy C-means prediction model. matrix factorisation and fuzzy C-means are some basic traditional techniques used with static model parameters for the prediction of missing values. But these techniques with static parameters are not able to handle the significant changes under the unpredictable internet conditions and sparsity of available historical QoS data. To overcome this problem, we have implied a novel backpropagation based ADP parameter tuning strategy to these two basic prediction techniques where backpropagation is an important mathematical tool of neural network. This is the first time it has been applied with ADP parameter tuner, to the best of our knowledge, for developing the self-adaptive intelligent system and this system provides an automatic parameter tuning capability to our proposed QoS prediction models. To evaluate the proposed approach, we have done a simulation of the approach on a real QOS dataset and experimental results show that our proposed approach yields better prediction accuracy when compared with other traditional approaches.
    Keywords: cloud computing; QoS prediction; ADP parameter tuner; fuzzy C-means clustering; matrix factorisation; backpropagation neural network.

  • Towards a cloud model choice evaluation: comparison between cost/features and ontology-based analysis   Order a copy of this article
    by Pasquale Cantiello, Beniamino Di Martino, Michele Mastroianni, Luigi Colucci Cante, Mariangela Graziano 
    Abstract: In academic institutions, there is frequently the need to provide new services, in a cloud model, to be used in either teaching or research activities. One of the main decisions to be addressed is related to which cloud model to adopt (private, public or hybrid), and which mixing of functionalities to use for the hybrid one. In this paper, two different methodologies (cost/features and semantic-based) are tested in order to identify the best suited cloud model to adopt for a specific problem. The long-term perspective is to build a methodology to serve as a tool to be used as decision support for the ICT manager in order to assist in this decision. The comparison between the two different methodologies may show the strengths and weaknesses of both approaches.
    Keywords: cloud model; decision support system; SWRL; OWL; cloud evaluation; cloud cost analysis.

  • An evaluation environment for high-performance computing combining supercomputing and cloud   Order a copy of this article
    by Yusuke Gotoh, Toshihiro Kotani 
    Abstract: Based on the characteristics of the supercomputer and the cloud system installed in the Hokkaido University Information Initiative Center, Japan, we aim to construct a high-performance computing environment by linking the two types of system. In this paper, we propose a high-performance computing environment for deep reinforcement learning that links the supercomputer and cloud systems. Our proposed system can construct a high-performance computing environment based on the scale of the computing process by the cooperation of the supercomputing and cloud systems with short physical distance and short network distance. In our evaluation of deep reinforcement learning using our proposed system, we confirmed that computer resources can be effectively used by allocating suitable processing for the supercomputer and the cloud according to the usage situations of the CPU, the GPU, and the memory.
    Keywords: cloud service; high-performance computing; processing time; supercomputer.

  • A survey on auto-scaling: how to exploit cloud elasticity   Order a copy of this article
    by Marta Catillo, Massimiliano Rak, Umberto Villano 
    Abstract: Elasticity plays an essential role as far as the wide diffusion of cloud computing is concerned. It enables a cloud application deployment to 'scale' automatically, adapting to workload changes, guaranteeing the performance requirements with minimum infrastructure leasing costs. However, auto-scaling poses challenging problems. This paper gives a detailed overview of the current state of the art on auto-scaling. Firstly the key design points for auto-scaling tools are presented and discussed. Then literature proposals and on-going research are dealt with. Finally existing auto-scaling implementations, including those used by commercial cloud providers, are reviewed.
    Keywords: auto-scaling; elasticity; cloud computing.
    DOI: 10.1504/IJGUC.2022.10049101
     
  • An effort to characterise enhancements I/O of storage environments   Order a copy of this article
    by Laercio Pioli, Victor Ströele, Mario A. R. Dantas 
    Abstract: Data management and storage are becoming challenging nowadays owing to the huge amount of created, processed and stored data. The growing gap between power processing and storage latency increases this performance disparity. Targeting reducing I/O bottleneck in storage environments, researchers are proposing interesting improvements in I/O architectures. High-Performance Computing (HPC) and Data-Intensive Scalable Computing (DISC) applications are types of such systems that are faced with data challenges owing to the need to deal with many parameters when managing data. This study described our characterisation model for classifying research works on I/O performance improvements for storage systems and devices that improves HPC and DISC overall applications performance. This paper presents a set of experiments using a synthetic I/O benchmark performed inside the Grid'5000. We demonstrated that the latency when performing I/O operations can undergo many variations if we take into account the presented factors evaluated in the experiments.
    Keywords: I/O characterisation; I/O performance; I/O improvement; I/O model; HPC; DISC; Big Data; GRID5000; storage system; storage environments.

  • A vision about lifelong learning and its barriers   Order a copy of this article
    by Jordi Conesa 
    Abstract: Around 25 years ago, some researchers argued for moving towards innovative learning models characterized by being more personalized and where the students would have a more active role in deciding what to learn, when to learn and how to learn. Nowadays, there is a need for a flexible, efficient, universal and lifelong education. Lifelong learning is fully integrated into our society and, from the student point of view, it is very different from regular learning. Among these differences there is the maturity of students, the fact that the domains of interest are much broader, the way how learning occurs at different depths, the fact that the topics to study may be related both to work, family and leisure, and that students have little availability due to their necessity to conciliate home, work, leisure and learning. Lifelong learning requires personalized models that adapt to students needs and constraints, but lifelong learners keep suffering from models that are adapted neither to their necessities, nor to the needs of society. This paper reflects on the actual situation of lifelong learning, analyses some of the relevant literature and discusses the challenges to conceptualize, from a transdisciplinary point of view, innovative e-learning models that promote self-determination of students.
    Keywords: lifelong learning; heutagogy; self-determined learning; e-learning.

Special Issue on: Blockchain-based Big-Data Industrial IoT

  • Design of internet of things service system for logistics engineering by using the blockchain technology   Order a copy of this article
    by Aiming Shen 
    Abstract: With the rapid progress of information technology, the application of the Internet of Things (IoT) technology has become more common, which also brings greater challenges to the IoT service system. Most small and medium-sized foreign trade enterprises rely on the third-party cross-border e-commerce platform to carry out retail export business. The logistics service system has the problems of long time-consuming and high transportation and management costs. The centralised management system makes the cooperation and information sharing between IoT devices under different platforms difficult, and the device privacy data are easy to be leaked. In order to solve the above problems and improve the level of logistics warehousing management, an intelligent warehousing management system (WMS) has become a hot topic. Blockchain technology is introduced to build an intelligent WMS model based on the existing technical level of warehouse management.
    Keywords: blockchain technology; logistics engineering; IoT services; warehouse management.

  • Data protection of internet of things for edge computing and deep learning and governance of cyberspace   Order a copy of this article
    by Zhi Li, Yuemeng Ge, Min Jia, Yanrui Xu 
    Abstract: The purpose is to deal with the network security problems in the background of the high information age, ensure the popularization of Internet of things (IoT) technology based on Internet technology, and protect the information, life and property security of countries and individuals. First, the principle and advantages of edge computing and deep learning technology are described. Then, the current laws of IoT data protection are introduced, and the moral and ethical issues involved in IoT data are discussed. Finally, the research results of cyberspace governance in China and Europe are studied. The results show that the respondents who do not know anything about the data protection regulations and do not focus on the regulations are 27% and 18%, respectively. The laws on IoT data protection for edge computing and deep learning technology, such as the General Data Protection Regulations, are used to protect personal privacy by improving the protection ability of technology and giving data rights. Some of them prohibit the application of big data analysis and information disclosure, strengthen the authority of regulatory authorities and increase penalties for data loss. This exploration not just introduces China's legislative direction and specific legislative measures based on the legislative content of the EU bill and conducts transplantation of law. Based on the innovation introduction of the General Data Protection Regulations (GDPR) bill of the European Union (EU), the legislative impact brought by the bill is studied. It reveals the forward trend of data protection legislation in the world. Therefore, as the second-largest economy in the world, China should comply with the legislative situation and connect with international data protection standards.
    Keywords: Internet of things; data protection; legal issues; network security issues; edge computing.

  • Keyword extraction from news corpus by deep learning in the context of internet of things   Order a copy of this article
    by Yan Xiao 
    Abstract: With the rapid development of modern technology and information technology, information generation and dissemination is getting faster and faster. The amount of web text, such as web pages, e-books, news, etc., is exploding. Therefore, it is very important for users to quickly and accurately find out what they are interested in from the large amount of data in the network. Keywords can help users quickly understand the main content of the text and the main idea, improve query efficiency, and save search time. Therefore, in order to solve the problem of increasing information volume, it is more and more important to search more efficiently for the information that people need, to explore new technologies for keyword extraction, and to improve the accuracy of keyword extraction.
    Keywords: internet of things; optical character recognition technology; news corpus; deep learning; Bi-LSTM-CRF.

  • Application of the multi-objective model under the fuzzy differential equation to logistics operation of internet of things   Order a copy of this article
    by Shihui Liu 
    Abstract: This paper explores the application of the multi-objective model (MOM) in the logistics operation of an e-commerce platform. E-commerce is a new business model, and people are studying the use of various technologies to optimise its operation mode. On this basis, the logistics operation on the e-commerce platform is studied by using the method of moments based on deep learning, fuzzy differential equations (FDEs) and the internet of things. On the ImageNet dataset, the parameter initialisation of the network is trained and detected. The initial learning rate of the network is 0.0003, and 45000 iterations are needed to reduce the learning rate. The convergence and stability of FDEs are of great significance for the accuracy of data on e-commerce platforms. The results show that the correlation between web page accessibility and revenue on the e-commerce platform is 0.011, and this data acquisition is based on traditional methods.
    Keywords: internet of things; fuzzy differential equation; multi-objective model; e-commerce; logistics.

  • Design and supply chain management of intelligent logistics system using cloud computing under internet of things   Order a copy of this article
    by Minzhi Wang 
    Abstract: Image recognition is the key to smart logistics systems. Traditional handwriting feature extraction is difficult to meet the requirements of image recognition. Deep learning is used for image recognition. Firstly, convolutional neural network (CNN) and deep Boltzmann machines under deep learning are introduced. Secondly, cellular neural networks are used to perform feature recognition and extraction on images. Finally, a Parzen classifier is used to classify the obtained image features. The novelty is that through the structural design and research of the intelligent logistics system, the CNN is combined to construct a management system of supply chain logistics of image recognition and information processing. Experiments show that the time for the improved algorithm to achieve high recognition accuracy on the Mixed National Institute of Standards and Technology mixed dataset is 198.85 s. When the improved algorithm achieves the same recognition accuracy as the unimproved algorithm, the time is 159.65 s.
    Keywords: intelligent logistics system; internet of things; deep learning; convolutional neural network.

  • Optimising sports marketing strategy by the internet of things and blockchain technology   Order a copy of this article
    by Kun Tang 
    Abstract: The sports industry is commercialing rapidly, transforming sports into special entertainment products on top of the traditional values. More entities engage in sports products and services to meet people's growing sports consumption needs. This exploration aims to meet people's sports consumption needs. First, the sports marketing theory is introduced alongside the concepts of the Internet of Things (IoT) platform and blockchain technology. Next, the sports marketing strategy is established based on the IoT platform + blockchain (Blockchain of Things, BoT). Finally, the influencing factors of sports marketing strategy are analysed, and sports marketing strategy's influence is explored from the technical level. The results show that the current sports market reform has a superior cultural and legal environment. The enthusiasm of the sports users for the sports products is not reduced. The local advantages and development environment can be used to reform and develop sports marketing. At the same time, the obstacles to the development of sports marketing mainly lie in the competitive level, sports technology development, and enterprise management skills. The proposed BoT has a positive role in promoting sports marketing strategy. Specifically, it promotes sports marketing scale, cooperation, and international level. It also specializes in and diversifies sports marketing competitiveness and strategies. Therefore, the finding has reference significance for analysing the impact of the IoT and blockchain on sports marketing. The innovation is using multiple data transmission methods to meet the diversified communication needs of the sports market.
    Keywords: sports industrialisation; sports consumption; sports marketing theory; IoT platform; blockchain technology.

  • Mobile visual search algorithm based on improved VGG-F and hash with application in IOT   Order a copy of this article
    by Shanshan Ji, Jianxin Li, Jie Liu, WenLiang Cao, Bin Li, Fei Jiang, Yang Liu 
    Abstract: This paper explores the use of the deep learning based hash method in IOT to build a more powerful and real-time mobile visual search, and proposes a lightweight, low latency and high-precision mobile visual search algorithm based on the deep hash method and its application in IOT. This paper discusses from two aspects: image semantic feature extraction and fast retrieval. Based on the deep learning method and hash method, an image semantic feature extraction model for Library Digital Humanities is constructed, and the loss function applicable to the field is constructed. The model training and MVS retrieval process experiment are carried out through the Library Data Set, and this method is applied to the Library IOT system. In order to verify the effectiveness of our method, we compared the proposed method with VHB, SSFS, DLBH, SSDH and other methods. Experimental results show that the proposed method is more effective and robust than the existing methods.
    Keywords: IOT; deep learning; hash function; VGG-F; mobile visual search; Library Digital Humanities.

  • Many-objective particle swarm optimisation algorithm based on multi-elite opposition mutation mechanism in the internet of things environment   Order a copy of this article
    by LanLan Kang, Naiwei Liu, Wenliang Cao, Yeh-Cheng Chen 
    Abstract: The multi-objective optimisation problem in the internet of things technology has been widely researched. The family of multi-objective particle swarm optimisation algorithms is among the most representative ones. However, there still exist the shortcomings of overspending and premature convergence. This paper proposes a many-objective particle swarm optimisation algorithm based on opposition-based mutation for elite mechanism. The new algorithm includes three strategies: (1) opposition-based learning population initialisation strategy, which is designed to avoid the blindness and uncertainty of the initial population, and improves the distribution of population and accelerates speed of exploration; (2) multi-elite opposition mutation mechanism, which is proposed to help particles get away from local optimal positions via a targeted exploration in the search space; (3) singularity archive technique, which is established to disturb the global evolution trend and further balance the contradiction of convergence and diversity of the population. The effectiveness of the proposed algorithm is verified by comparing 11 algorithms in the simulation experiments.
    Keywords: internet of things; many-objective optimisation; particle swarm optimisation; opposition-based mutation; singularity archive technique.

  • Security technology for data transmission to the internet of things devices under the application of blockchain technology   Order a copy of this article
    by Guangping Zhou 
    Abstract: The objectives are to improve the security of citizen identity verification, and data privacy in the network environment, to solve the problems of low security in the process of network information transmission, and privacy cannot be reasonably maintained. It aims to further optimise the environment for network information transmission, improve the security and concealment in the process of information transmission, and enhance the expansion and application of blockchain technology in the field of network security, thus providing a strong technical guarantee for information transmission, making it safer and more reliable in practice. An authentication transmission system for general data of the internet of things based on the application of blockchain technology is proposed. In this process, the Gossip consensus algorithm is innovatively integrated into the data transmission process.
    Keywords: blockchain; internet of things; network data transmission; information transmission security.

  • The use mechanism of blockchain and internet of things technology in memorial architecture of a smart city   Order a copy of this article
    by Chili He 
    Abstract: The purpose of this paper is to promote the development of the Internet of Things (IoT) and blockchain technology in the design of the modern memorial architecture of a smart city. First, this work perfects the system structure of the traditional intelligent building, and provides a building intelligent management framework based on IoT technology. Next, blockchain technology is used to establish an intelligent building detection system that integrates the lower-level facilities to the upper-level application information. The basic structure and software of each facility, the nodes of the IoT and the underlying intelligent components are further designed. Then, the calculation process of the system is designed, and the system model is tested. Finally, it is concluded through a calculation that Mae, Mse and Mre are 0.0203, 1.7396 and 0.5892, respectively. The model has good performance and can perform accurate tests. Moreover, the comprehensive evaluation shows that the model realizes the functions of real-time data refresh, data storage, data display of each module and so on. This work is of great significance in promoting intelligent memorial architecture and smart city construction. It can further enhance the rapid development of memorial architecture in the smart city. Meanwhile, it also has important theoretical and practical significance for smart cities' comprehensive development and improvement.
    Keywords: blockchain; IoT technology; memorial architecture; intelligent building; indoor monitoring system.

  • Deep learning for blockchain in medical supply chain risk management   Order a copy of this article
    by F.E.I. Jiang, Chen Xian Jiang, Jian Xin Li 
    Abstract: The COVID-19 pandemics severe shortage of essential medical supplies created significant risks in the medical supply chain operation. Supply chain managers have started focusing on decision-making based on multiple data sources to correctly foresee uncertainty and develop a proactive and predictable intelligent risk management system. These features make using blockchain and deep learning (DL) methods in supply chain risk management (SCRM) feasible but are still in the initial stages. This work provides a comprehensive and detailed literature analysis and emphasises that many blockchains and DL methods are used in various stages of medical SCRM. At the same time, by deploying blockchain and DL and combining them with necessary questionnaires, an effective SCRM model can be used to detect major supply chain risks. By outlining the unresolved challenges that must be solved before the large-scale deployment of DL and blockchain applications. ultimately, research points out the key direction of future research in this field and promotes global supply chain cooperation.
    Keywords: blockchain; deep learning; medical supply chain; risk management; IoT.

  • Application of blockchain technology in copyright protection of digital music information   Order a copy of this article
    by Xinglin Wen 
    Abstract: Currently, a large amount of information is stored in digital form. There have been many information security problems, leading to the emergence of a large number of pirated digital products. Although there have been many protection measures for digital information copyright, the research content on copyright protection for digital music information is still very little. Aiming at the problems of low data storage security, long confirmation period, and vulnerability to attacks in the confirmation system of digital music copyright, combined with blockchain technology (BT), a scheme for credible confirmation of digital music copyright is proposed. Firstly, BT is introduced, and secondly, combined with the Delegate Proof of Stake (DPoS), one of the consensus algorithms, the Practical Byzantine Fault Tolerance (PBFT) is improved, and the roles of nodes in the block are divided. The activity evaluation is added, and the Delegated Practical Byzantine Fault Tolerance (DPBFT) algorithm is proposed. The DPBFT algorithm divides the nodes in the blockchain network into ordinary nodes, consensus nodes, and verification nodes. The system will regularly sort the nodes in the network according to the activity value, and select the node with high activity value to participate in the DPBFT calculation process. Finally, the simulation experiment of the DPBFT algorithm is carried out. The experimental results reveal that the DPBFT algorithm can accommodate up to two faulty nodes, which has a higher fault tolerance rate than the traditional PBFT algorithm. The average throughput can reach 1249, which can meet the needs of the music copyright management system and has practical application value.
    Keywords: blockchain technology; digital music; copyright protection; consensus algorithm.

  • Text complexity analysis of college English textbooks based on blockchain and deep learning algorithms under the internet of things   Order a copy of this article
    by Bingjie Shen 
    Abstract: College English textbooks are of great significance in college English teaching. In the context of the gradual development of the Internet of Things (IoT), this study introduces blockchain and deep learning to build a new college textbooks-oriented complexity analysis model. Three different college English textbooks are analysed from the perspectives of lexical complexity, grammatical difficulty, sentence coherence, and sentence readability, to ensure the comprehensiveness of the experiment. The results are as follows. (1) The coincidence rate of New Horizons College English Corpus (NHCEC) with the college curriculum is the highest, reaching 49.20%. It is followed by New College International Curriculum Corpus (NCICC), with a coincidence rate of 47.18%. The lowest coincidence rate comes to 21st Century College English Corpus (21CCEC). On the other hand, NCICC has the best readability, with a readability index of about 70. NHCEC and 21CCEC have relatively poor text readability, about 65, about 5 lower than NCICC. (2) The accuracy and precision of the text complexity analysis model of English textbooks constructed using neural networks are between 90% and 95%, which is higher than the other two models. In particular, the performance of the ordinal multi-classification regression model is the worst, with accuracy and precision between 70% and 75%, which is about 20% lower than the neural network model. The recall of the neural network model is 91.22%, which is far superior to the other two models. Therefore, compared with the other two models, the neural network model has higher prediction accuracy and more stable performance. This study focuses on the text of the textbook, introduces vocabulary research into the system, and takes it as an indicator to judge the textbook. The results can provide suggestions and improvement directions for the subsequent editing and writing of college English textbooks.
    Keywords: internet of things; deep learning algorithm; blockchain; college English textbook; textual complexity.

Special Issue on: Green Network Communication for Sustainable Smart Grids Current Uses and Future Applications

  • Design and fabrication of dual band AMC-backed monopole antenna for WLAN and WiFi applications   Order a copy of this article
    by B. Madhavi, M. V. Siva Prasad 
    Abstract: This paper presents the design and fabrication of a coplanar waveguide (CPW) fed dual band antenna with a complementary split ring resonator (CSRR) with and without artificial magnetic conductor (AMC). This proposed model exhibits two operational bands with increased gain. The obtained gains at resonances of working bands are 2.6dB and 3.6dB. The antenna with AMC operates at the bands of 2.33GHz-2.57GHz, and 4.85GHz-5.14GHz, respectively. The attained gains at the resonances of working bands for AMC-backed monopole antenna are 5.15dB, and 6.12dB. From the measured results, the bandwidth and gain are increased for AMC-backed design when compared with no AMC condition. In addition, the analysis of both antenna designs also done in HFSS workbench for analysing the return loss, 3D gains, current distributions, and radiation patterns. As per the observed measures of gain values, the proposed antenna is suitable for the applications of WiFi and WLAN.
    Keywords: artificial magnetic conductor; monopole antenna; dual band; gain; coplanar wave guide antenna.

  • Cloud computing data privacy protection method based on blockchain   Order a copy of this article
    by Yingjun He, Wenhui X. Ouyang, Shaolong Li, Lin Wang, Jing Zhou, Wenwei Su, Shenzhang Li, Donghui Mei, Yan Shi, Yanxu Jin, Chenglin Li, Yonghui Ren 
    Abstract: Different from the uniqueness and tamper resistance of traditional data, users are always facing greater risk of leakage in the data storage of cloud computing network. In order to ensure the security of users private data, a cloud computing data privacy protection method based on blockchain is proposed. On the basis of clarifying the infrastructure of blockchain structure and cloud computing storage structure, design cloud computing data privacy protection methods. With the support of blockchain technology, based on the analytic hierarchy process model of cloud computing user privacy protection, based on the definition of blockchain messages and IPFS storage parameter settings. Cloud computing data privacy protection is realized through three parts: cloud computing data privacy disclosure path identification, cloud computing data aggregation algorithm using privacy homomorphism technology and data privacy protection algorithm based on blockchain technology. The experimental results show that the proposed method can enhance the application security of cloud computing privacy data, make it free from the risk of disclosure in the storage process, and thus realize the privacy protection of cloud computing data.
    Keywords: blockchain technology; cloud computing network; user data; privacy data; privacy protection.

  • Performance enhancement Of MIMO-OFDM using hybrid equalisers-based ICI mitigation with channel estimation in time-varying channels   Order a copy of this article
    by Madhavi Latha Pandala, Samanthapudi Swathi, Abdul Hussain Sharief, Suresh Penchala, Ganga Rama Koteswara Rao, Pala Mahesh Kumar 
    Abstract: High spectrum efficiency and resistant to interferences made multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) an exceptionally good choice for the realization of long-term evaluation-advanced (LTE-A) and other advanced mobile communication technologies. Besides the multiple merits, MIMO-OFDM is suffered due to higher inter carrier interference (ICI) and bit error rate (BER) values. In practical scenario, there are many techniques available to evaluate the accuracy of communication system by reducing the ICI and BER further. However, it is necessary to develop several influential hybrid methods for evaluation and mitigation of ICI and BER performance improvement with least undesirable side effects. In addition, it is quite difficult to achieve high spectral efficiency while improving the performance of BER over fast fading channels. Thus, here in this article, both time variant training and time invariant channel estimation (CE) is suggested in MIMO-OFDM. Further, combination of three equalization techniques named as ZF-MMSE-SIC is presented for improving the possessions of BER and ICI in MIMO-OFDM system, where zero forcing (ZF) is utilized for time variant CE, minimum mean square error (MMSE) is employed for time invariant CE schemes and successive interference cancellation (SIC) is utilized to reduce the ICI further to minimum level by enhancing the carrier-to-interference ratio (CIR). Extensive simulations revealed that proposed hybrid methodology performed superior to that of conventional ICI mitigation algorithms over different time varying channels while improving the performance of both BER and spectral efficiency.
    Keywords: MIMO-OFDM; ICI cancellation; zero-forcing; minimum mean square error; carrier-to-interference ratio; successive interference cancelation.

Special Issue on: Cloud and Fog Computing for Corporate Entrepreneurship in the Digital Era

  • Enhanced speculative approach for big data processing using BM-LOA algorithm in cloud environment   Order a copy of this article
    by Hetal A. Joshiara, Chirag S. Thaker, Sanjay M. Shah, Darshan B. Choksi 
    Abstract: In the event that one of the several tasks is being allocated to an undependable or jam-packed machine, a hugely parallel processing job can be delayed considerably. Hence, the majority of the parallel processing methodologies, namely (MR), have espoused diverse strategies to conquer the issue called the straggler problem. Here, the scheme may speculatively introduce extra copies of a similar task if its development is unnaturally slow when an additional idling resource is present. In the strategies-centred processes, the dead node is exhibited. the (RT) along with backup time of the slow task is assessed. The slow task is rerun with the aid of BM-LOA subsequent to the evaluation. In both heterogeneous and homogeneous environments, the proposed approach is performed. Centred on the performance metrics, the proposed research techniques performance is scrutinised in experimental investigation. Thus, when weighed against the other approaches, the proposed technique achieves superior performance.
    Keywords: modified exponentially weighted moving average; speculative execution strategy; Hadoop supreme rate performance; big data processing; rerun.

Special Issue on: ITT 2019 Advances in Next-Generation Communications and Networked Applications

  • Collaborative ambient intelligence based demand variation prediction model   Order a copy of this article
    by Munir Naveed, Yasir Javed, Muhammed Adnan, Israr Ahmed 
    Abstract: Inventory control problem is faced by corporations on a daily basis to optimise the supply chain process and for predicting the optimal pricing for the item sales or for providing services. The problem is heavily dependent on a key factor, i.e. demand variations. Inventories must be aligned according to demand variations to avoid overheads or shortages. This work focuses on exploring various machine learning algorithms to solve demand variation problems in real time. Prediction of demand variations is a complex and non-trivial problem, particularly in the presence of open order. In this work, prediction of demand variation is addressed with the use-cases which are characterised with open orders. This work also presents a novel prediction model that is a hybrid of learning domains as well as domain-specific parameters. It exploits the use of Internet of Things (IoT) to extract domain-specific knowledge, while a reinforcement learning technique is used for predicting the variations in these domain-specific parameters, which depend on demand variations. The new model is explored and compared with state-of-the-art machine learning algorithms using Grupo Bimbo case study. The results show that the new model predicts the demand variations with significantly higher accuracy than other models.
    Keywords: inventory management; reinforcement learning; IoT devices; Grupo Bimbo inventory demand variation.