International Journal of Web and Grid Services (9 papers in press)

Regular Issues

• A Comprehensive Survey on Security, Privacy and Authentication in Blockchain  
  by Turki Ali Alghamdi, Nadeem Javaid 
  Abstract: The past few years has witnessed a remarkable outburst of the application of blockchain technology in various fields. This technology serves to tackle the problems associated with the centralized systems. However, in a blockchain based system, major challenges such as security, privacy and authentication are observed. Hence, special attention is required to tackle these challenges. Furthermore, it is vital to have an in-depth understanding of the challenges before designing and developing a new blockchain based system because of its complex nature. In this paper, a comprehensive survey of security, privacy and authentication issues of blockchain is conducted. Additionally, a comparative analysis of existing security mechanisms that are adopted in literature to make the systems secure is presented. A brief survey of closely related survey papers is also provided after critically analyzing their contributions and organization. Besides, a mathematical equation is formulated to compute the recency score of a particular survey paper based on the number of recent papers included in it. Furthermore, critical analysis of the existing techniques and strategies is provided. In addition, the most frequently asked question by the researchers, i.e., when should we use blockchain?, is answered via an extended version of a decision tree. At the end, future research directions are provided to lay a solid research foundation for the upcoming researchers.
  Keywords: Blockchain; Security; Privacy; Authentication; Smart contracts; Consensus; Encryption.
  
  
• A Masking-Based Federated Singular Value Decomposition Method for Anomaly Detection in Industrial Internet of Things  
  by Olena Hordiichuk-Bublivska, Halyna Beshley, Natalia Kryvinska, Mykola Beshley 
  Abstract: The Industrial Internet of Things (IIoT) provides a flexible and scalable manufacturing system that can independently collect and analyze data from sensors based on machine learning, cloud, and edge computing. One of the effective methods of finding patterns in Big Data is recommendation systems. These systems provide an opportunity to find the paths of certain phenomena, and the behavior of industrial devices, to preclude irrelevant data, which significantly reduces the amount of information to be processed. Based on the found regularities in the data, it is possible to predict the most probable future events, such as emergency shutdowns of equipment, the occurrence of emergencies, etc. For recommender systems, the Singular Value Decomposition (SVD) algorithm is most often applied, which is simple and reliable to use . This paper investigates the SVD method for detecting anomalous deviations of data collected from the IIoT system. The Federated Singular Value Decomposition (FedSVD) method is also proposed, which is more suited to large-scale IIoT, as it further protects data and thereby increases its privacy. A comparison of these methods shows greater accuracy and duration of calculations of FedSVD. Based on the study results, it is possible to determine the most optimal algorithm for the operation of recommendation systems and IIoT, depending on the required parameters of speed and reliability of calculations. Then, selecting the optimal method of data processing can be used to automate the processing of the system's self-configuration data when critical parameters are detected. These approaches can improve the efficiency of IIoT systems and enable a new, more efficient solution. A masking-based FedSVD method for anomaly detection and data protection in IIoT is proposed.
  Keywords: IIoT; Big Data; distributed systems; machine learning; recommendation systems; SVD; FedSVD; edge computing; cloud computing.
  
  
• Web Semantics and Ontologies based Framework for Software Component Selection from Online Repositories  
  by Nazia Bibi, Tauseef Ahmed Rana, Ayesha Maqbool, Alina Mirza, Zeshan Iqbal, Muhammad Attique Khan, Majed Alhaisoni, Usman Tariq, Robertas Damasevicius 
  Abstract: Software development deals with larger design and development pressures and requires software system to be developed in a shorter period. A big problem encountered during software development is the inability to find and retrieve the required reusable components. One of the reasons behind this issue is the scarcity of sophisticated techniques and query methods. The primary challenge of effective component retrieval is to bridge the semantic gap between natural language and component description. In this paper, we proposed an approach that offers automatic retrieval of components by employing domain ontologies. It allows users to enter the query in natural language, and a semantic service format is used to treat software components as services. Experimental results show that the proposed approach can retrieve components for a given query accurately and significantly outperforms state-of-the-art approaches. This work also explores statistics for evaluating software components and finally discusses open challenges and future directions.
  Keywords: Ontologies; classification; web semantics; reusability model; component selection; component reuse; quality attributes; recommendation system.
  
  
• PolarisX²: auto-growing context-aware knowledge graph  
  by Yeonsun Ahn, Soyeop Yoo, Okran Jeong 
  Abstract: Artificial intelligence requires advanced technologies in various fields. In particular, natural language processing consists of various tasks because computers need to understand and process human languages. Knowledge graphs represent common sense as a graph, making it easy to understand the relationships between entities. Various studies exist because knowledge graphs could play a crucial role in computers' understanding of natural language. PolarisX is an auto-growing knowledge graph that could especially cope with neologisms. However, existing studies have a limitation in that they rarely correspond to information containing numbers representing a cardinal, ordinal, or quantity and can extract only one relationship from one sentence. We propose the auto-growing context-aware knowledge graph, PolarisX², an entity extraction model capable of responding to numeric information, and a relation extraction model considering type. It also enables multiple knowledge extraction from a single sentence by applying the candidate pair construction model.
  Keywords: auto-expansion; context-aware; knowledge graph; type information; named entity recognition; multiple relation extraction.
  DOI: 10.1504/IJWGS.2023.10056506
   
  
• A systematic review of blockchain adoption in education institutions  
  by Mohrah Saad Alalyan, Naif Alajlan Jaafari, Farookh Khadeer Hussain, Asif Qumer Gill 
  Abstract: There is increasing interest in blockchain technology in relation to its adoption by education institutions. Several studies discuss the adoption of blockchain technology. However, there is a need to systematically synthesise and analyse the diverse body of knowledge to understand the influence of blockchain adoption in education institutions at different levels. Thus, this study applies a systematic literature review (SLR) approach to produce a useful taxonomy of the existing research and organise knowledge related to the adoption of blockchain by education institutions. An analysis of 107 relevant studies revealed the prevalence of review works and conceptual frameworks for blockchain applications as well as a lack of empirical research and theoretical foundations to study blockchain adoption. Based on this, future research directions are suggested to enhance both theoretical and practical knowledge of blockchain adoption in education.
  Keywords: blockchain; education sector; adoption; systematic review.
  DOI: 10.1504/IJWGS.2023.10056509
   
  
• An efficient clustering mechanism towards large scale service composition in IoT  
  by Sugyan Kumar Mishra, Anirban Sarkar 
  Abstract: Internet of things (IoT) applications hinder heterogeneity at different levels, such as device level, usage level, and communication level. In this context, service-oriented architecture (SOA) facilitates limited supports for handling heterogeneity in IoT-based applications. This article proposes a clustered hypergraph colouring (CHC) approach for designing large-scale SOA (LSS). Two noble approaches (service clustering and composition) are discussed in LSS for satisfying the service consumers (SCs) requirements. A service clustering approach is presented for the clustering of homogeneous services. This mechanism enables to reduce the search time for the service composition mechanism. A service composition approach is described with some parameters. Further, the proposed approach is validated through an experimental setup by considering the factors such as the number of services, execution time, and memory size. The execution time of LSS is minimised as compared to normal or medium-scale SOA (NSS) due to the service clustering mechanism. The novelty of this work is to minimise the execution time in the large service domain during service composition.
  Keywords: internet of things; IoT; service-oriented architecture; SOA; service composition; clustered hypergraph colouring approach; service clustering; clinical decision support system; CDSS.
  DOI: 10.1504/IJWGS.2023.10055551
   
  
• A privacy preserving CP-ABE-based access control on data sharing in VANETs  
  by Nan Guo, Jing Hu, Xinyang Deng 
  Abstract: Vehicle ad hoc networks (VANETs) enable fast interconnection between vehicles and roadside infrastructure to ensure stable and continuous network services for vehicles. Data sharing, as the primary service of MANET, not only ensures vehicles can access the required information but also supports vehicles in sharing the required information with other vehicles to provide a better travel experience for drivers and passengers. However, without an effective access control scheme to protect data security, the adversaries can collect data transmitted by other vehicles during data sharing and threaten VANET's security by sending fake data. In order to address the current data-sharing scheme for vehicles that collects information from the environment with low efficiency, high maintenance cost and ineffective access control schemes, a secure and effective data-sharing scheme is proposed in this paper by improving the ciphertext policy attribute-based encryption algorithm (CP-ABE). Performance analysis and simulation experiments show that the scheme can achieve secure and efficient data sharing and enhance the driving experience. When the number of attributes is 20, the computational overhead of our scheme in the encryption phase is 3.18% of the compared scheme, while the computational overhead in the decryption phase is 44% of the compared scheme.
  Keywords: ciphertext policy attribute-based encryption algorithm; CP-ABE; data sharing; access control; vehicular ad hoc networks; VANETs; security; data privacy.
  DOI: 10.1504/IJWGS.2023.10055549
   
  
• A systematic literature review on pharmaceutical supply chain: research gaps and future opportunities  
  by Abeer Mirdad, Farookh Khadeer Hussain, Omar Khadeer Hussain 
  Abstract: Recently, blockchain has increasingly been used in several sectors as the technological underpinning for providing a secure and reliable environment. It has been used in a few sectors for provenance of high-value products. In this paper, we focus on the pharmaceutical sector in which the need for drug provenance is crucial due to counterfeiting and for finding efficient ways to handle drug waste. The focus on this paper is two-fold. The first contribution is to review the existing literature critically and systematically on blockchain use in the pharmaceutical sector. We identify four crucial factors that are critical in the pharmaceutical sector. Subsequently, we systematically review the existing literature on blockchain use in the pharmaceutical sector. The second contribution is based on the analysis of the existing literature, we identify key research gaps on blockchain use in the pharmaceutical sector. Finally, an intelligent platform is proposed to address the identified gaps.
  Keywords: literature review; blockchain; pharmaceutical supply chain research challenges.
  DOI: 10.1504/IJWGS.2023.10056512
   
  

Special Issue on: Security for Cloud Computing

• Searchable Symmetric Encryption Based on the Inner Product for Cloud Storage
  by Jun Yang, Shujuan Li, Xiaodan Yan, Baihui Zhang, Baojiang Cui 
  Abstract: Searchable encryption enables the data owner to store their own data after encrypting them in the cloud. Searchable encryption also allows the client to search over the data without leaking any information about it. In this paper, we rst introduce a searchable symmetric encryption scheme based on the inner product: it is more ecient to compute the inner product of two vectors. In our construction, the parties can be Data Owners, Clients or the Cloud Server. The three parties communicate with each other through the inner product to achieve the goal that the client can search the data in the cloud without leaking any information on the data the owner stored in the cloud. We then perform a security analysis and performance evaluation, which show that our algorithm and construction are secure and ecient.
  Keywords: Searchable Encryption; Searchable Symmetric Encryption; Inner Product; the Cloud Server; Security.