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  • 1.
    Li, Jianjiang
    et al.
    Department of Computer Science and Technology, University of Science and Technology, Beijing.
    Zhang, Kai
    Department of Computer Science and Technology, University of Science and Technology, Beijing.
    Yang, Xiaolei
    Department of Computer Science and Technology, University of Science and Technology, Beijing.
    Wei, Peng
    Department of Computer Science and Technology, University of Science and Technology, Beijing.
    Wang, Jie
    Department of Computer Science and Technology, University of Science and Technology, Beijing.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Ranjan, Rajiv
    School of Computer Science, China University of Geosciences.
    Category Preferred Canopy-K-means based Collaborative Filtering algorithm2019In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 93, p. 1046-1054Article in journal (Refereed)
    Abstract [en]

    It is the era of information explosion and overload. The recommender systems can help people quickly get the expected information when facing the enormous data flood. Therefore, researchers in both industry and academia are also paying more attention to this area. The Collaborative Filtering Algorithm (CF) is one of the most widely used algorithms in recommender systems. However, it has difficulty in dealing with the problems of sparsity and scalability of data. This paper presents Category Preferred Canopy-K-means based Collaborative Filtering Algorithm (CPCKCF) to solve the challenges of sparsity and scalability of data. In particular, CPCKCF proposes the definition of the User-Item Category Preferred Ratio (UICPR), and use it to compute the UICPR matrix. The results can be applied to cluster the user data and find the nearest users to obtain prediction ratings. Our experimentation results performed using the MovieLens dataset demonstrates that compared with traditional user-based Collaborative Filtering algorithm, the proposed CPCKCF algorithm proposed in this paper improved computational efficiency and recommendation accuracy by 2.81%.

  • 2.
    Li, Zheng
    et al.
    School of Computer Science, Australian National University and NICTA.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Zhang, Miranda
    CSIRO Computational Informatics, Canberra.
    Ranjan, Rajiv
    CSIRO Computational Informatics, Canberra.
    Georgakopoulos, Dimitrios
    CSIRO Computational Informatics, Canberra.
    Zomaya, Albert
    University of Sydney.
    O*Brien, Liam
    Geoscience Australia.
    Sun, Shengtao
    Yanshan University.
    Towards Understanding the Runtime Configuration Management of Do-It-Yourself Content Delivery Network Applications over Public Clouds2014In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 37, p. 297-308Article in journal (Refereed)
    Abstract [en]

    Cloud computing is a new paradigm shift which enables applications and related content (audio, video, text, images, etc.) to be provisioned in an on-demand manner and being accessible to anyone anywhere in the world without the need for owning expensive computing and storage infrastructures. Interactive multimedia content-driven applications in the domains of healthcare, aged-care, and education have emerged as one of the new classes of big data applications. This new generation of applications need to support complex content operations including production, deployment, consumption, personalisation, and distribution. However, to efficiently provision these applications on the Cloud data centres, there is a need to understand their run-time resource configurations. For example: (i) where to store and distribute the content to and from driven by end-user Service Level Agreements (SLAs)? (ii) how many content distribution servers to provision? and (iii) what Cloud VM configuration (number of instances, types, speed, etc.) to provision? In this paper, we present concepts and factors related to engineering such content-driven applications over public Clouds. Based on these concepts and factors, we propose a performance evaluation methodology for quantifying and understanding the runtime configuration these classes of applications. Finally, we conduct several benchmark driven experiments for validating the feasibility of the proposed methodology.

  • 3.
    Liu, Xiao
    et al.
    School of Information Science and Engineering, Central South University, ChangSha .
    Zhao, Shaona
    School of Information Science and Engineering, Central South University, ChangSha .
    Liu, Anfeng
    School of Information Science and Engineering, Central South University, ChangSha .
    Xiong, Naixue
    Department of Mathematics and Computer Science, Northeastern State University, Tahlequah, OK .
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Knowledge-aware Proactive Nodes Selection approach for energy management in Internet of Things2019In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 92, p. 1142-1156Article in journal (Refereed)
    Abstract [en]

    Internet of Things will serve communities across the different domains of life. Tracking mobile targets is one important system engineering application in IOT, and the resource of embedded devices and objects working under IoT implementation are constrained. Thus, building a scheme to make full use of energy is key issue for mobile target tracking applications. To achieve both energy efficiency and high monitoring performance, an effective Knowledge-aware Proactive Nodes Selection (KPNS) system is proposed in this paper. The innovations of KPNS are as follows: 1) the number of proactive nodes are dynamically adjusted based on prediction accuracy of target trajectory. If the prediction accuracy is high, the number of proactive nodes in the non-main predicted area will be decreased. If prediction accuracy of moving trajectory is low, large number of proactive nodes will be selected to enhance monitoring quality. 2) KPNS takes full advantage of energy to further enhance target tracking performance by properly selecting more proactive nodes in the network. We evaluated the efficiency of KPNS with both theory analysis and simulation based experiments. The experimental results demonstrate that compared with Probability-based target Prediction and Sleep Scheduling strategy (PPSS), KPNS scheme improves the energy efficiency by 60%, and can reduce target missing rate and tracking delay to 66%, 75% respectively.

  • 4.
    Osman, Ahmed M. Shahat
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    A novel Big Data Analytics framework for Smart Cities2019In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 91, p. 620-633Article in journal (Refereed)
    Abstract [en]

    The emergence of smart cities aims at mitigating the challenges raised due to the continuous urbanization development and increasing population density in cities. To face these challenges, governments and decision makers undertake smart city projects targeting sustainable economic growth and better quality of life for both inhabitants and visitors. Information and Communication Technology (ICT) is a key enabling technology for city smartening. However, ICT artifacts and applications yield massive volumes of data known as big data. Extracting insights and hidden correlations from big data is a growing trend in information systems to provide better services to citizens and support the decision making processes. However, to extract valuable insights for developing city level smart information services, the generated datasets from various city domains need to be integrated and analyzed. This process usually referred to as big data analytics or big data value chain. Surveying the literature reveals an increasing interest in harnessing big data analytics applications in general and in the area of smart cities in particular. Yet, comprehensive discussions on the essential characteristics of big data analytics frameworks fitting smart cities requirements are still needed. This paper presents a novel big data analytics framework for smart cities called “Smart City Data Analytics Panel – SCDAP”. The design of SCDAP is based on answering the following research questions: what are the characteristics of big data analytics frameworks applied in smart cities in literature and what are the essential design principles that should guide the design of big data analytics frameworks have to serve smart cities purposes? In answering these questions, we adopted a systematic literature review on big data analytics frameworks in smart cities. The proposed framework introduces new functionalities to big data analytics frameworks represented in data model management and aggregation. The validity of the proposed framework is discussed in comparison to traditional approaches through a real use case for bike sharing prediction system.

  • 5.
    Rho, Seungmin
    et al.
    Department of Multimedia, Sungkyul University.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Chen, Weifeng
    Department of Math, Computer Science and Information Systems, California University of Pennsylvania.
    Cyber physical systems technologies and applications2016In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 56, p. 436-437Article in journal (Other academic)
    Abstract [en]

    With the rapid progresses in ICT (Information Communication Technology), multidisciplinary research fields such as Internet of Things (IoT), Cyber-Physical System (CPS), and Social Computing have been widely explored in recent years. These research and application have been speeded up the formation of cyberspace, which will further lead to a subversive change for information science development as well as human production and living (Ma and Yang, 2015 [1]). Cyberspace is being linked to versatile individuals in physical space and social space — Cyber-Physical Society (CPSoc) (Zhuge, 2014 [2]). This special issue on new technologies and research trends for cyber physical systems technologies and application provides high quality contributions addressing related theoretical and practical aspects of CPS technologies and their applications. We have selected five research papers whose topics are strongly related to this special issue.

  • 6.
    Rho, Seungmin
    et al.
    Department of Multimedia, Sungkyul University.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Chen, Weifeng
    Department of Math, Computer Science and Information Systems, California University of Pennsylvania.
    Cyber physical systems technologies and applications: Part II2016In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 61, p. 83-84Article in journal (Other academic)
    Abstract [en]

    The second part of the special issue presents more practical issues on cyber physical systems technologies and application. We have selected five research papers whose topics are strongly related to this special issue. As continued from the part 1, we have selected 5 additional papers

  • 7.
    Sakr, Sherif
    et al.
    University of Taru, Estonia.
    Zomaya, Albert
    University of Sydney, Australia.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Editorial for Special issue of FGCS special issue on “Benchmarking big data systems”2019In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 96, p. 32-34Article in journal (Refereed)
    Abstract [en]

    Even though several big data processing and analytics systems have been introduced with various design architectures, we are still lacking a deeper understanding of the performance characteristics for the various design architectures in addition to lacking comprehensive benchmarks for the various Big Data platforms. There is a crucial need to conduct fundamental research with a more comprehensive performance evaluation for the various Big Data processing systems and architectures. We also lack the availability of validation tools, standard benchmarks, and system performance prediction methods that can help us have a deeper and more solid understanding of the strengths and weaknesses of the various Big Data processing platforms. This special issue is dedicated to original results and achievements by active researchers, designers, and developers working on various issues and challenges related to big data research.

  • 8.
    Sun, Gang
    et al.
    Key Lab of Optical Fiber Sensing and Communications (Ministry of Education), University of Electronic Science and Technology of China, Chengdu, China. Center for Cyber Security, University of Electronic Science and Technology of China, Chengdu, China.
    Li, Yayu
    Key Lab of Optical Fiber Sensing and Communications (Ministry of Education), University of Electronic Science and Technology of China, Chengdu, China.
    Yu, Hongfang
    Key Lab of Optical Fiber Sensing and Communications (Ministry of Education), University of Electronic Science and Technology of China, Chengdu, China. Center for Cyber Security, University of Electronic Science and Technology of China, Chengdu, China.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Du, Xiaojiang
    Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA.
    Guizani, Mohsen
    Department of Electrical and Computer Engineering, University of Idaho, Moscow, ID, USA.
    Energy-efficient and traffic-aware service function chaining orchestration in multi-domain networks2019In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 91, p. 347-360Article in journal (Refereed)
    Abstract [en]

    Service function chaining (SFC) provisioning is helpful not only for saving the capital expenditure (CAPEX) and operational expenditure (OPEX) of a network provider but also for reducing energy consumption in the substrate network. However, to the best of our knowledge, there has been little research on the problem of energy consumption for orchestrating online SFC requests in multi-domain networks. In this paper, we first formulate the problem of an energy-efficient online SFC request that is orchestrated across multiple clouds as an integer linear programming (ILP) model to find an optimal solution. Then, we analyze the complexity of this ILP model and prove that the problem is NP-hard. Additionally, we propose a low-complexity heuristic algorithm named energy-efficient online SFC request orchestration across multiple domains (EE-SFCO-MD) for near-optimally solving the mentioned problem. Finally, we conduct simulation experiments to evaluate the performance of our algorithm. Simulation results show that EE-SFCO-MD consumes less energy than existing approaches while the online SFC’s requirements are met and the privacy of each cloud is effectively guaranteed. The low computational complexity of the heuristic approach makes it applicable for quickly responding to online SFC requests.

  • 9.
    Wazid, Mohammad
    et al.
    Cyber Security and Networks Lab, Innopolis University, Innopolis, Russian Federation.
    Kumar Das, Ashok
    Center for Security, Theory and Algorithmic Research, International Institute of Information Technology, Hyderabad, India.
    Kumar, Neeraj
    Department of Computer Science and Engineering, Thapar University, Patiala, India.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Design of secure key management and user authentication scheme for fog computing services2019In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 91, p. 475-492Article in journal (Refereed)
    Abstract [en]

    Fog computing (fog networking) is known as a decentralized computing infrastructure in which data, applications, compute as well as data storage are scattered in the most logical and efficient place among the data source (i.e., smart devices) and the cloud. It gives better services than cloud computing because it has better performance with reasonably low cost. Since the cloud computing has security and privacy issues, and fog computing is an extension of cloud computing, it is therefore obvious that fog computing will inherit those security and privacy issues from cloud computing. In this paper, we design a new secure key management and user authentication scheme for fog computing environment, called SAKA-FC. SAKA-FC is efficient as it only uses the lightweight operations, such as one-way cryptographic hash function and bitwise exclusive-OR (XOR), for the smart devices as they are resource-constrained in nature. SAKA-FC is shown to be secure with the help of the formal security analysis using the broadly accepted Real-Or-Random (ROR) model, the formal security verification using the widely-used Automated Validation of Internet Security Protocols and Applications (AVISPA) tool and also the informal security analysis. In addition, SAKA-FC is implemented for practical demonstration using the widely-used NS2 simulator.

  • 10.
    Xu, Xiwei
    et al.
    Data61, CSIRO, Sydney, Australia. School of Computer Science and Engineering, UNSW, Sydney, Australia.
    Lu, Qinghua
    Data61, CSIRO, Sydney, Australia. School of Computer Science and Engineering, UNSW, Sydney, Australia.
    Liu, Yue
    College of Computer and Communication Engineering, China. University of Petroleum (East China), Qingdao, China.
    Zhu, Liming
    Data61, CSIRO, Sydney, Australia. School of Computer Science and Engineering, UNSW, Sydney, Australia.
    Yao, Haonan
    College of Computer and Communication Engineering, China. University of Petroleum (East China), Qingdao, China.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Designing blockchain-based applications a case study for imported product traceability2019In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 92, p. 399-406Article in journal (Refereed)
    Abstract [en]

    Blockchain technology enables decentralization as new forms of distributed software architectures, where components can reach agreements on the shared system states without trusting on a central integration point. Since blockchain is an emerging technology which is still at an early stage of development, there is limited experience on applying blockchain to real-world software applications. We applied blockchain application design approaches proposed in software architecture community in a real-world project called originChain, which is a blockchain-based traceability system that restructures the current system by replacing the central database with blockchain. In this paper, we share our experience of building originChain. By using blockchain and designing towards security, originChain provides transparent tamper-proof traceability data with high availability and enables automated regulatory-compliance checking and adaptation in product traceability scenarios. We also demonstrate both qualitative and quantitative analysis of the software architecture of originChain. Based on our experience and analysis, we found that the structural design of smart contracts has large impact on the quality of the system.

  • 11.
    Yan, Zheng
    et al.
    State Key Laboratory on Integrated Services Networks, Xidian University.
    Ding, Wenxiu
    State Key Laboratory of Integrated Services Networks, Xidian University.
    Niemi, Valtteri
    State Key Laboratory of Integrated Services Networks, Xidian University.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Two Schemes of Privacy-Preserving Trust Evaluation2016In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 62, p. 175-189Article in journal (Refereed)
    Abstract [en]

    Trust evaluation computes trust values by collecting and processing trust evidence. It plays an important role in trust management that automatically ensures trust relationships among system entities and enhances system security. But trust evidence collection and process may cause privacy leakage, which makes involved entities reluctant to provide personal evidence that is essential for trust evaluation. Current literature pays little attention to Privacy-Preserving Trust Evaluation (PPTE). Existing work still has many limitations, especially on generality, efficiency and reliability. In this paper, we propose two practical schemes to guard privacy of trust evidence providers based on additive homomorphic encryption in order to support a traditional class of trust evaluation that contains evidence summation. The first scheme achieves better computational efficiency, while the second one provides greater security at the expense of a higher computational cost. Accordingly, two trust evaluation algorithms are further proposed to flexibly support different application cases. Specifically, these algorithms can overcome attacks raised by internal malicious evidence providers to some extent even though the trust evaluation is partially performed in an encrypted form. Extensive analysis and performance evaluation show the security and effectivity of our schemes for potential application prospect and their efficiency to support big data process

  • 12.
    Yan, Zheng
    et al.
    State Key Laboratory of ISN, Xidian University.
    Liu, Jun
    Xi’an Jiaotong University.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Yang, Laurance T.
    Department of Computer Science, St. Francis Xavier University, Antigonish.
    Trustworthy data fusion and mining in Internet of Things2015In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 49, p. 45-46Article in journal (Refereed)
  • 13.
    Yang, Chao-Tung
    et al.
    Department of Computer Science, Tunghai University, Taichung City, Taiwan, ROC.
    Chen, Shuo-Tsung
    Artificial Intelligence Recognition Industry Service Research Center (AIR-IS Research Center), National Yunlin University of Science and Technology, Yunlin, Taiwan, ROC. College of Future, Bachelor Program in Interdisciplinary Studies, National Yunlin University of Science and Technology, Taiwan, ROC.
    Liu, Jung-Chun
    Department of Computer Science, Tunghai University, Taichung City, Taiwan, ROC.
    Yang, Yao-Yu
    Department of Computer Science, Tunghai University, Taichung City, Taiwan, ROC.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Ranjan, Rajiv
    School of Computer, China University of Geosciences, China. School of Computing Science, Newcastle University, United Kingdom.
    Implementation of a real-time network traffic monitoring service with network functions virtualization2019In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 93, p. 687-701Article in journal (Refereed)
    Abstract [en]

    The Network Functions Virtualization (NFV) extends the functionality provided by Software-Defined Networking (SDN). It is a virtualization technology that aims to replace the functionality provided by traditional networking hardware using software solutions. Thereby, enabling cheaper and efficient network deployment and management. The use of NFV and SDN is anticipated to enhance the performance of Infrastructure-as-a-Service (IaaS) clouds. However, due to the presence of a large number of network devices in IaaS clouds offering a plethora of networked services, there is need to develop a traffic monitoring system for the efficient network. This paper proposes and validates an extensible SDN and NFV-enabled network traffic monitoring system. Using extensive experiments, we show that the proposed system can closely match the performance of traditional networks at cheaper costs and by adding more flexibility to network management tasks.

  • 14.
    Yu, Yong
    et al.
    Big Data Research Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu.
    Xue, Liang
    Department of Computing, The Hong Kong Polytechnic University.
    Au, Man Ho
    Department of Computing, The Hong Kong Polytechnic University.
    Susilo, Willy
    Center for Computer and Information Security Research, School of Computing and Information Technology, University of Wollongong.
    Ni, Jianbin
    Big Data Research Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu.
    Zhang, Yafang
    Big Data Research Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Shen, Jian
    School of Computer and Software at Nanjing University of Information Science and Technology, Nanjing.
    Cloud data integrity checking with an identity-based auditing mechanism from RSA2016In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 62, p. 85-91Article in journal (Refereed)
    Abstract [en]

    Cloud data auditing is extremely essential for securing cloud storage since it enables cloud users to verify the integrity of their outsourced data efficiently. The computation overheads on both the cloud server and the verifier can be significantly reduced by making use of data auditing because there is no necessity to retrieve the entire file but rather just use a spot checking technique. A number of cloud data auditing schemes have been proposed recently, but a majority of the proposals are based on Public Key Infrastructure (PKI). There are some drawbacks in these protocols: (1) It is mandatory to verify the validity of public key certificates before using any public key, which makes the verifier incur expensive computation cost. (2) Complex certificate management makes the whole protocol inefficient. To address the key management issues in cloud data auditing, in this paper, we propose ID-CDIC, an identity-based cloud data integrity checking protocol which can eliminate the complex certificate management in traditional cloud data integrity checking protocols. The proposed concrete construction from RSA signature can support variable-sized file blocks and public auditing. In addition, we provide a formal security model for ID-CDIC and prove the security of our construction under the RSA assumption with large public exponents in the random oracle model. We demonstrate the performance of our proposal by developing a prototype of the protocol. Implementation results show that the proposed ID-CDIC protocol is very practical and adoptable in real life.

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