International Journal of Grid and Utility Computing (71 papers in press)
Public key encryption with equality test for vehicular system based on near-ring
by Muthukumaran Venkatesan, Ezhilmaran Devarasaran
Abstract: In recent years, vehicles have been increasingly integrated with an intelligent transport system (ITS). This has led to the development of Vehicular Ad hoc Networks(VANETs) through which the vehicles communicate with each other in an effective manner. Since VANET assists in both vehicle to vehicle and vehicle to infrastructure communication the matter of security and privacy has become a major concern. In this context, this work presents a public key Encryption with equality test based on DLP with decomposition problems over near-ring The proposed method is highly secure
and it solves the problem of quantum algorithm attacks in VANET systems. Further, the
proposed system prevents the chosen-ciphertext attack in type-I adversary and it is indistinguishable against the random oracle model for the type-II adversary. The proposed scheme is highly secure and the security analysis measures are stronger than existing techniques.
Keywords: near-ring; Diffie-Hellman; vehicular ad hoc networks.
Research on modelling analysis and maximum power point tracking strategies for distributed photovoltaic power generation systems based on adaptive control technology
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
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.
Performance impact of the MVMM algorithm for virtual machine migration in data centres
by Nawel Kortas, Habib Youssef
Abstract: Virtual machine (VM) migration mechanisms and the design of data centres for cloud computing have a significant impact on energy cost and negotiated Service Level Agreement (SLA). The recent work focuses on how to use VM migration to achieve stable physical machine (PM) usage with the objective of reducing energy consumption, under stated SLA constraints. This paper presents and evaluates a new scheduling algorithm called MVMM (Minimisation of Virtual Machine Migration) for VM migration within a data centre environment. MVMM makes use of a DBN (Dynamic Bayesian Network) to decide where and when a particular VM migrates. Indeed, the DBN takes as input the data centre parameters then computes a score for each VM candidate for migration in order to reduce the energy consumption by decreasing the number of future migrations according to the probabilistic dependencies between the data centre parameters. Furthermore, our performance study shows that the choice of a data centre scheduling algorithm and network architecture in cloud computing significantly impacts the energy cost and application performance under resource and service demand variations. To evaluate the proposed algorithm, we integrated the MVMM scheduler into the GreenCloud simulator while taking into consideration key data centre characteristics such as scheduling algorithm, DCN (Data re Network) architecture, link, load and communication between VMs. The performance results show that the use of the MVMM scheduler algorithm within a three-tier debug architecture can reduce energy consumption by over 35% when compared with five well-known schedulers, namely Round Robin, Random, Heros, Green, and Dens.
Keywords: MVMM algorithm; virtual machine; cloud computing; dynamic Bayesian networks; SLA; scheduler algorithm; data centre network architectures; VM migration.
SDSAM: a service-oriented approach for descriptive statistical analysis of multidimensional spatio-temporal big data
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
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
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
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
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
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
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
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
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.
A review on data replication strategies in cloud systems
by Riad Mokadem, Jorge Martinez-Gil, Abdelkader Hameurlain, Joseph Kueng
Abstract: Data replication constitutes an important issue in cloud data management. In this context, a significant number of replication strategies have been proposed for cloud systems. Most of the studies in the literature have classified these strategies into static vs. dynamic or centralised vs. decentralised strategies. In this paper, we propose a new classification of data replication strategies in cloud systems. It takes into account several other criteria, specific to cloud environments: (i) the orientation of the profit, (ii) the considered objective function, (iii) the number of tenant objectives, (iv) the nature of the cloud environment and (v) the consideration of economic costs. Dealing with the last criterion, we focus on the provider's economic profit and the consideration of energy consumption by the provider. Finally, the impact of some important factors is investigated in a simulation study.
Keywords: cloud systems; data replication; data replication strategies; classification; service level agreement; economic profit; performance.
Anomaly detection against mimicry attacks based on time decay modelling
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
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.
A truthful mechanism for crowdsourcing-based tourist spots detection in smart cities
by Anil Bikash Chowdhury, Vikash Kumar Singh, Sajal Mukhopadhyay, Abhishek Kumar, Meghana M. D
Abstract: With the advent of new technologies and the internet around the globe, many cities in different countries are involving the local residents (or city dwellers) for making decisions on various government policies and projects. In this paper, the problem of detecting tourist spots in a city with the help of city dwellers, in strategic setting, is addressed. The city dwellers vote against the different locations that may act as potential candidates for tourist spots. For the purpose of voting, the concept of single-peaked preferences is used, where each city dweller reports a privately held single-peaked value that signifies the location in a city. Given the above discussed scenario, the goal is to determine the location in the city as a tourist spot. For this purpose, we have designed the mechanisms (one of which is truthful). For measuring the efficacy of the proposed mechanisms the simulations are done.
Keywords: tourism; smart cities; crowdsourcing; city dwellers; voting; single-peaked preferences; truthful.
Dont hurry, be green: scheduling server shutdowns in grid computing with deep reinforcement learning
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
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.
IoT service distributed management architecture and service discovery method for edge-cloud federation
by Dongju Yang, Weida Zhang
Abstract: With the continuously increasing number of IoT (Internet of Things) services, the distributed management of IoT services becomes an inevitable trend. Under IoT and edge-cloud federation framework, the primary issues to solve in IoT service management are how to design a suitable distributed management architecture for IoT services, reduce network bandwidth overhead, reduce system latency and support the dynamic awareness of service status and rapid discovery of services. In this paper, the distributed management of IoT services is implemented by constructing layer-ring collaboration architecture on the cloud and edge, the service addressing channel is established on the cloud and edge using master and slave node cluster. The slave node is close to service and is dynamically aware of the service status. At the same time, the master-chord is constructed based on chord protocol among masters to enable the collaborated addressing of multiple master nodes on the cloud. So a strong service routing network on the cloud and edge is established to enable the distributed management of IoT services. This paper focuses on the service registration and discovery methods under this framework, and finally verifies the effectiveness of the method through the highway emergency scenario.
Keywords: IoT service; service discovery; service registration; distributed management architecture; edge-cloud federation.
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.
A graphical front-end interface for React.js considering state-transition diagrams
by Shotaro Naiki, Masaki Kohana, Michitoshi Niibori, Shusuke Okamoto, Yasuhiro Ohtaki, Masaru Kamada
An integrity control model for mass data transmission under big data cloud storage
by Zhengguo Zhu
Abstract: In order to improve the control level of massive data transmission integrity and improve the space utilisation efficiency and data transmission integrity under cloud storage, an integrity control model of mass data transmission under big data cloud storage is constructed. Firstly, the basic principle of big data cloud storage is studied. Secondly, the mass data feature extraction model is established. Thirdly, the massive data integrity control algorithm is designed based on ant algorithm. Finally, simulation analysis is carried out, and the effectiveness of the proposed method is verified.
Keywords: integrity control; mass data transmission; big data cloud storage.
Securing utility computing using enhanced elliptic curve cryptography and attribute-based access control policy
by Saira Varghese, S. Maria Celestin Vigila
Abstract: This paper proposes a data security model for utility computing that integrates securely mapped plain text using Elliptic Curve Cryptography (ECC) without certified keys, attribute-based access control using Reduced Ordered Binary Decision Diagram (ROBDD), cryptographically secure 256-bit pseudorandom numbers and fingerprint security. ECC uses standard elliptic curves of large prime and ROBDD is built with positive and negative attributes of data users and 256-bit pseudorandom secrets along its path. The secret key that is generated is based on the attributes along the valid path of the policy to enhance the security. The proposed model allows secure key exchange by making use of the property of the elliptic curve to convert numbers into points and secure data storage by associating authenticated secrets of end-users with the original secret key, which is beneficial in decentralised architecture. The result reveals that proposed model achieves high security and less space and time complexity for securing cloud data.
Keywords: secure utility computing; ROBDD; reduced ordered binary decision diagram; ECC; elliptic curve cryptography; attribute based access control; cryptographically secure pseudorandom numbers.
Design and analysis of novel hybrid load-balancing algorithm for cloud data centres
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
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
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.
Decentralised priority-based shortest job first queue model for IoT gateways in fog computing
by Jayashree Narayana Swamy, B. Satish Babu, Basavraj Talwar
Abstract: An increased growth in time-critical IoT applications has led to a rise in real time resource requirements. The stringent deadlines on latency have caused IoT applications to move out from far-away cloud servers to distributed fog computing devices infrastructure, which is available locally. In order to meet the touchstones of deadlines and processing times, there is a need to prioritise the job scheduling through the IoT gateways to appropriate fog devices. Studies showed that the queuing models exhibit uncertainties in choosing suitable computing devices, applying priorities to the jobs, deadline achievements, and minimum latency constraints. In this paper, we propose a decentralised priority-based shortest job first queuing model for the IoT gateways for a fog computing infrastructure, which uses the priority-based jobs sorting technique to achieve a better performance and also to overcome most of the uncertainties in queuing.
Keywords: queuing model; IoT; fog computing; M/M/c queues; M/M/c/1 queues.
Cloud infrastructure planning considering the impact of maintenance and self-healing routines over cost and dependability attributes
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
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.
Solving on-shelf availability by a creative inference of data science imputation models
by Ashok Mahapatra, Srikanta Patnaik, Manoranjan Dash, Ananya Mahapatra
Abstract: In retail data science, avenues outside the supply chain, specifically inventory and stocks, are barely explored in the context of on-shelf availability (OSA). In order to explore and propose reliable solutions to estimate OSA implications in retail, particularly the impact of missing sales, we have leveraged our domain experience in retail as well as data science. Consequently, we have presented a holistic perspective to firstly unearth OSA occurrences in a reliable manner and then employ modern techniques in data science to estimate their impact on overall sales. Accordingly, drawing from a wide range of experience in data science and machine learning, we explored and established a correlation between missing value imputations in the realm of data science with the missing sale scenarios in retail.
Keywords: OOS; OSA; out of stock; missing sales; missing value imputation; on-shelf availability; data science framework in retail; data analytics.
Enhancing the 5G V2X reliability using turbo coding for short frames
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
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
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
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
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
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
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
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
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
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
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.
Hybrid VM allocation and placement policy for VM consolidation process in cloud data centres
by Dipak Dabhi, Devendra Thakor
Abstract: The demand for cloud services has increased drastically in recent times. To fulfil the high demand for cloud services requires a higher number of servers of data centres, and the data centres consume high energy. Virtual Machine Consolidation (VMC) is a technique for lowering energy usage in data centres by enhance the use of servers and shutting down underused servers with maintain Service Level Agreements (SLA). Further, the VMC process is divided into four sub-policies: host overload detection, host underload detection, VM selection, and VM placement. This article propose the Hybrid VM Allocation and Placement (HVMAP) strategy, which uses migration control to detect the overload host and effectively locate the destination host. We used the CloudSim simulator with PlanetLab and Bitbrains datasets to evaluate the performance. The evaluation results show that our strategy decreases energy consumption and SLA violation (ESLAV) by 101%, 54%, and 34% for the Planetlab dataset, and 170%, 166%, and 31% for the Bitbrain dataset for three existing policies, respectively.
Keywords: cloud computing; VM consolidation; quality of service; service level agreement; VM selection; overload host detection; VM placement; underload host detection.
GRAPES: semi-automatic approach for forecasting models to predict GameStop prices using cloud computing and machine learning
by Tan Van Vo, Sukhpal Singh Gill
Abstract: Since the Covid-19 pandemic, we have seen a surge of retail investors that now can easily trade anywhere in the world with just a smartphone. Social media groups such as Reddit's WallStreetBets have almost put a few hedge funds close to bankruptcy by driving GameStop share prices to the sky. In this work, we propose a framework called GRAPES which uses cloud computing and machine learning to explore various forecasting techniques in predicting GameStop prices. This work also provides light insight into semi-automating forecasting models using tools such as Google Cloud Platform (GCP), Airflow and Streamlit. Moreover, we monitored the investment funds from Ark Invest to provide additional insight into the market in general. Overall, the paper showed the Autoregressive Moving Average (ARMA) model gives the best accuracy based on the Mean Absolute Percentage Error (MAPE) of 1.12%. This means the predictive model is out by an average of 1.12% from the actual price.
Keywords: Apache Airflow; Google Cloud Platform; Docker; stock; WallStreetBets; Streamlit; forecasting; GameStop; Exchange-Traded Fund.
Modelling energy measurement data with annual duration lines: description of three options for practical use
by Tobias Knayer, Natalia Kryvinska
Abstract: Energy data are necessary for successful planning and potential estimation. To characterise the temporal course, annual duration curves are used. They allow a graphical record and offer to compare the distribution of energy consumption. Often, these data are not available and entail great challenges for planners. Three ways of modelling an annual duration curve are shown, each with a different data basis. Procedure 1 works with measured data, procedure 2 with standard load profiles and monthly or annual consumption values. Procedure 3 shows how an estimate can be made without energy data. Most of the studies deal only with the generation of load profiles, but not of annual duration curves. Moreover, some work with complex mathematical functions and special software considers only private households. There is no comparison with businesses or public buildings. The three options presented here are a simple estimation for a practical use to get a good and fast result.
Keywords: annual duration line; electric load curve; energy estimate; dimensioning of energy solutions; missing measured values; big data; analysis of potential; electric load.
PREFNEG: preference-based price negotiation scheme in fog environment
by Shaifali Malukani, C.K. Bhensdadia
Abstract: Fog computing has emerged as a promising extension of cloud computing. Owing to recent developments, there has been a tremendous increase in the number of fog service consumers and providers who trade for fog resources. Consumers pay rent to the providers for use of the fog resources. A dispute may exist for the announced rent as traders are interested in their financial gain. Price negotiation strategies help in solving this dispute and building long-term solutions. However, owing to the heterogeneous nature of fog computing, QoS attributes significantly vary for a user-fog node association. So, entities may prefer each other based on attributes other than price. They want to get associated with the preferred trading partners at a negotiated price. This work presents a novel preference-based negotiation strategy for many-to-many, bilateral and concurrent negotiations in the fog environment. Results show that PREFNEG significantly increases the average user and fog utilities.
Keywords: fog computing; negotiation; preference; QoS; attributes; price negotiation; trading; resources; consumers; providers.
An SMIM algorithm for reduction of energy consumption of virtual machines in a cluster
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
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
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
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.
University ranking approach with bibliometrics and augmented social perception data
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
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.
Special Issue on: CONIITI 2019 Intelligent Software and Technological Convergence
Computational intelligence system applied to plastic microparts manufacturing process
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
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.
SD-6LN: improved existing internet of things framework by incorporating software defined network approach
by Rohit Das, Arnab Maji, Goutam Saha
Abstract: The Internet of Things (IoT) is a prominent technology in today's world, where its real-time applications are being used in various areas. In spite of the fact that the advances made by IoT are remarkable, the current IoT foundation experiences problems such as availability, reliability, scalability, resiliency, interoperability, security and privacy. Software-Defined Network (SDN) is an approach that can help in resolving many of these IoT limitations. SDN uses a controller that can improve upon the system performance. In this paper, a novel SD-6LN architecture is proposed for existing IPv6 over Low Power Wireless Personal Area Network (6LoWPAN) based IoT infrastructure. The SD-6LN architecture incorporates the network layer of IoT and SDN to address some of the challenges of traditional resource constraint IoT network systems, such as availability, reliability, scalability and resiliency. The experimentation was carried out in simulation. The experimental results indicated improved performance with respect to round trip time, jitter, packet drop, latency and throughput.
Keywords: Architecture; Internet of Things; OpenFlow; Software-Defined Network; 6LoWPAN.
A security analysis of lightweight consensus algorithm for wearable kidney
by Saurabh Jain, Adarsh Kumar
Abstract: Blockchain is a distributed ledger-based technology and provides a solution to many data-centric problems. In recent times, this area has encouraged innovations to handle challenges in many useful applications in which traditional approaches are not found to be successful. A smart healthcare system is one such application where it has been observed that blockchain can play a vital role in terms of combining technologies such as security, data storage, data retrieval, patient-centric approach, and data visualisation. This work proposes a game theory-based approach for consensus building in a distributed network. This approach builds consensus in a trustworthy environment where technologies are explored to provide a problem-centric solution. In this work, the wearable kidney model is analysed to understand the working of the game-theory-based consensus model. This example shows how blockchain technology can be used for consensus building in the healthcare system. The lightweight consensus algorithm consumes fewer resources (suitable for resource constraint devices) and provides an efficient solution to simulate the functionality of a wearable kidney model. The comparative analysis of the result shows that the proposed approach is efficient in fast bit-matching and quick consensus establishment. Results show that kidney blood and urine production are mapped to almost ideal conditions and variations in delay for bit-matching, and algorithm executions are evaluated thereafter. The comparative analysis of the algorithms shows that algorithm 1 outperforms (at least 2.1%) algorithm 2 in delay analysis because of less distributed functionality. Both algorithms are found to be efficient compared with state-of-the-art algorithms for trust establishment.
Keywords: game theory; blockchain; cryptocurrency; lightweightness; hash rate; bit-exchange; challenge-response; attacks.
Special Issue on: ICIMMI 2019 Emerging Trends in Multimedia Processing and Analytics
Handwritten Odia numeral recognition using combined CNN-RNN
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
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: ITT 2019 Advances in Next-Generation Communications and Networked Applications
Comparing the performance of supervised machine learning algorithms when used with a manual feature selection process to detect Zeus malware
by Mohamed Ali Kazi, Steve Woodhead, Diane Gan
Abstract: The Zeus banking malware is one of the most prolific banking malware variants ever to be discovered, and this paper compares and analyses the performance of several supervised machine learning (ML) algorithms when used to detect the Zeus banking malware (Zeus). The key to this paper is that the features that are used for the analysis and detection of Zeus are manually selected, providing the researcher better control over which features can and should be selected. This also helps the researcher to understand the features and the impact that the various feature combinations have on the accuracy of the algorithms when used to detect Zeus. The empirical analysis showed that the decision tree and random forest algorithms produced the best results as they detected all the Zeus samples. The empirical analysis also showed that selecting the feature combinations manually produces varying results, allowing the researchers to understand how the features impact the detection accuracy.
Keywords: Zeus banking malware; machine learning; binary classification algorithms; supervised machine learning; manual feature selection.
Collaborative ambient intelligence based demand variation prediction model
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.
Special Issue on: 3PGCIC Cloud and Edge Systems and Applications
Quality of service prediction model in cloud computing using adaptive dynamic programming parameter tuner
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
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
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
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.
An effort to characterise enhancements I/O of storage environments
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
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: Computational Intelligence Methods for Smart Connectivity in IoT
Machine learning for cloud, fog, edge and serverless computing environments: comparisons, performance evaluation benchmark and future directions
by Parminder Singh, Avinash Kaur, Sukhpal Singh Gill
Abstract: The compute-intensive and latency-sensitive Internet of Things (IoT) applications need to use the services from various computing paradigms, but they are facing many challenges such as large values of latency, energy and network bandwidth. To analyse and understand these challenges, we designed a performance evaluation benchmark that integrates cloud, fog, edge and serverless computing to conduct a comparative study for IoT-based healthcare applications. It gives the platform for the developers to design IoT applications based on user guidelines to run various applications concurrently on different paradigms. Furthermore, we used recent machine learning techniques for the optimisation of resources, energy, cost and overheads to identify the best technique based on important Quality of Service (QoS) parameters. Experimental results show that serverless computing performs better than non-serverless in terms of energy, latency, bandwidth, response time and scalability by 3.8%, 3.2%, 4.3%, 1.5% and 2.7%, respectively. Finally, various promising future directions are highlighted.
Keywords: artificial intelligence; fog computing; edge computing; internet of things; machine learning; serverless computing; cloud computing.
Deep learning based object detection between train and rail transit platform door
by Fen Cheng, Hao Cai
Abstract: This paper proposes a deep learning machine model algorithm and designs a wide-gap outdoor platform foreign object detection system to detect foreign objects between rail transit platform doors and trains. The goal of the system is to monitor the space through video in various environments, and use image detection algorithms to detect and determine whether the space contains foreign objects in the space. The main research ideas surrounding the system are as follows: explore the real environment of the subway, create a virtual experimental environment, design the hardware equipment of the detection system, and obtain video images taken under various weather and light conditions through image analysis and processing. The method designed in this paper can well detect the influence of pedestrians getting on and off the vehicle and the intermediate objects when the train stops on the platform and the detection accuracy of large objects can reach 100%.
Keywords: internet of things; passenger safety; deep learning; machine vision; foreign object detection.
5G555 time base circuit based on intelligent sensor in automotive electrical appliances
by Hejuan Chen
Abstract: With the development of the Internet of Things, more and more attention is paid to the design and integration of sensor circuits in intelligent vehicles. In this paper, the 5G555 time base circuit is used to combine the analogue circuit and logic circuit of the intelligent sensor processing module. MSI (middle-scale integration) and LSI (large-scale integration) are mainly used as circuit function modules. The combined circuit and sequential circuit are added to the function module to achieve the effect of intelligent sensor segment delay and a large amount of data processing. Research shows that, compared with the 5G1555 circuit, the static power consumption of the 5G555 time base circuit is reduced by 25%, and the range of power supply voltage is larger. The intelligent sensor of the 5G555 time base circuit can effectively improve the stability of automobile circuits, improve the safety of automobile driving.
Keywords: intelligent sensor; 5G555 time base circuit; automotive electrical appliances; smart car.
Special Issue on: Blockchain-based Big-Data Industrial IoT
Design of internet of things service system for logistics engineering by using the blockchain technology
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
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.