Forthcoming and Online First Articles

International Journal of Web and Grid Services

International Journal of Web and Grid Services (IJWGS)

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International Journal of Web and Grid Services (7 papers in press)

Regular Issues

  • An Energy-Efficient Resource Allocation Algorithm for Managing On-Demand Services in Fog-Enabled Vehicular Ad-Hoc Networks   Order a copy of this article
    by Md Asif Thanedar, Sanjaya Kumar Panda 
    Abstract: Vehicular networks use roadside infrastructures, roadside units (RSUs) and high-power nodes (HPNs), as fog nodes (FNs) in intelligent transport systems (ITSs). However, blending fog computing (FC) in ITS can stimulate the network with latency-sensitive applications, such as self-driving, augmented reality assistance and navigation. FNs assign resource blocks (RBs) to vehicles to furnish the services. However, as the vehicles that reach the network grow, FNs energy consumption increases. Consequently, FNs become ineffective in delivering services. Therefore, to handle this issue, we present an energy-efficient resource allocation (EERA) algorithm to harmonise RB allocation among FNs, such that the energy utilisation of FNs is reduced. EERA algorithm relocates the assigned RBs of vehicles in overlap coverage amid pairs of FNs, such that the allocated RBs of FNs are minimised. The simulation outcomes show that the proposed algorithm minimises the energy consumption of FNs up to 42.5% on average compared to the existing algorithms.
    Keywords: fog computing; resource management; vehicular ad hoc networks; resource block allocation; energy consumption; resource utilisation.
    DOI: 10.1504/IJWGS.2024.10061105
  • Internet of Things-Based Remote Monitoring and Classification of Spinacia Oleracea Leaf Disease Using Deep Learning Approach   Order a copy of this article
    by Swarna Prabha Jena, Sujata Chakravarty, Bijay Paikaray 
    Abstract: Due to the change in the climatical conditions, there is a considerable impact on the plant's growth. Hence, a system with a model has been developed for monitoring Spinacia Oleracea plant which has many health benefits. It will control, monitors and protect it from different disease-causing agents. Here the leafy plant was grown and quality has compared in both fields. The environmental sensors installed in the field continuously capture and stores in the database. The image data in the database are analysed using transfer learning methods, i.e., MobileNetV2, ResNet152V2, InceptionV3, DenseNet201, and VGG16. From experimental results, it has been found that MobileNetV2 has reached the highest accuracy of 95% compared to other models. Finally, web app was developed which will quickly identify and classify the occurrence of the diseases. It has been seen that Spinacia Oleracea is better in growth, nutrient content, and disease-free when grown inside the polyhouse.
    Keywords: spinach; internet of things; growth parameters; polyhouse; edge device; leaf disease.
    DOI: 10.1504/IJWGS.2024.10062142
  • Querying Semantic OpenAPI Descriptions with OASL   Order a copy of this article
    by Chrisa Tsinaraki, Nikolaos Lagogiannis, Nikolaos Mainas, Emmanouil-Georgios Ieronymakis, Euripides Petrakis 
    Abstract: OpenAPI is a standard for describing RESTful services in YAML or JSON that has been actively supported by the industry. The use of semantic web tools (like reasoners) would extend the usage of the semantics captured in OpenAPI descriptions, also allowing for more sophisticated scenarios, like (semi)automatic service composition. To achieve this, OpenAPI descriptions should be mapped to ontologies based on a reference OpenAPI ontology. Querying such ontologies (essentially semantic OpenAPI descriptions) using SPARQL is not an easy task for two reasons: 1) the SPARQL queries are complex, since they comprise many triples; 2) the users should be familiar with the OpenAPI ontology. The OpenAPI SPARQL language (OASL) aims to ease query formulation for semantic OpenAPI descriptions. It is a SPARQL-like RDF query language that allows OpenAPI ontology agnostic users, knowledgeable only of the basics of SPARQL and REST services, to express their queries with a few statements. We have evaluated the performance of OASL on top of a GraphDB database that contains semantic OpenAPI descriptions of real-world services.
    Keywords: query language; OpenAPI SPARQL language; OASL; OpenAPI; ontology; SPARQL-like.
    DOI: 10.1504/IJWGS.2024.10062274
  • Smart and Adaptive Website Navigation Recommendations Based On Reinforcement Learning   Order a copy of this article
    by I-Hsien (Derrick) Ting, Ying-Ling Tang, Kazunori Minetaki 
    Abstract: Improving website structures is the main task of a website designer. In recent years, numerous web engineering researchers have investigated navigation recommendation systems. Page recommendation systems are critical for mobile website navigation. Accordingly, we propose a smart and adaptive navigation recommendation system based on reinforcement learning. In this system, user navigation history is used as the input for reinforcement learning model. The model calculates a surf value for each page of the website; this value is used to rank the pages. On the basis of this ranking, the website structure is modified to shorten the user navigation path length. Experiments were conducted to evaluate the performance of the proposed system. The results revealed that user navigation paths could be decreased by up to 50% with training on 12 months of data, indicating that users could more easily find a target web page with the help of the proposed adaptive navigation recommendation system.
    Keywords: web usage mining; adaptive website; navigation recommendation; reinforcement learning.
    DOI: 10.1504/IJWGS.2024.10062988
  • iBoT: Enhancing Security in Internet of Things Architectures with Blockchain   Order a copy of this article
    by Anastasios Pateritsas, Euripides Petrakis 
    Abstract: The emergence of internet of things (IoT) architectures in recent years has generated the need to apply blockchain to protect data from unauthorised access, malfunctioning devices, or malicious user behaviour. iBoT architecture combines all desirable characteristics of existing blockchain-backed IoT systems and provides a higher level of protection of information stored on the blockchain (i.e., users, data, devices, and applications). iBoT validates and verifies the identity of all entities and applies multi-level authentication by means of decentralised identifiers (DIDs), verifiable credentials (VCs), and smart contracts. iBoT employees Hyperledger Fabric to implement a private blockchain for all system entities. iBoT conforms to the principles of the web of things (WoT) architecture model of W3C that defines a framework for integrating devices into the web. iBoT has been deployed on the Google Cloud Platform (GCP). The experiments have shown that iBoT can handle large workloads in real-time. The response times for 2,000 blockchain read or write requests, of which up to 500 are executed in parallel, are inline with results reported by other researchers.
    Keywords: blockchain; IoT architecture; decentralised credentials; web of things architecture.
    DOI: 10.1504/IJWGS.2024.10062992
  • A Study of Supervised Machine Learning Algorithms for Traffic Prediction in SD-WAN   Order a copy of this article
    by Kashinath Basu, Muhammad Younas, Shaofu Peng 
    Abstract: Modern cloud, web and other emerging distributed services have complex network requirements that cannot be fulfilled via classical networks. This paper presents a novel architecture of a noble Software-Defined Wide Area Network (SD-WAN) that provides the framework for incorporating AI/ML based components for managing different centralised services of the WAN. To leverage the benefit of this framework, a crucial early stage requirement is to accurately identify the traffic category of a flow based on which follow-up actions such as QoS provisioning, resource orchestration, etc. can be implemented. To address this, the research then presents the model of a supervised ML based traffic prediction module and presents a detailed comparison and performance analysis of a shortlisted set of ML models with a variety of traffic categories. The research also takes into account the serialized processes in the models' training and learning phases emphasizing on the sensitivity of the feature selection process in the performance of these algorithms.
    Keywords: supervised machine learning; ML; artificial intelligence; AI; software defined network; SDN; SD-WAN; QoS; QoE; feature selection; naïve bayes; decision tree; nearest neighbor; support vector machine.
    DOI: 10.1504/IJWGS.2024.10063279

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.