International Journal of Networking and Virtual Organisations (8 papers in press)

Regular Issues

• Anomalous Data Detection in Cognitive IoT Sensor Network  
  by Vidyapati Jha, Priyanka Tripathi 
  Abstract: Recent research in the internet of things (IoT) focuses on the insertion of cognition into its system architecture and design, which introduces the new discipline known as cognitive IoT (CIoT). The cognitive internet of things sensor network defines a new paradigm for bridging the gap between the virtual and the real world. Sensors integrated into the CIoT network serve as the primary data collectors. These sensors are used in hazardous or unmanaged a situation, which makes sensor readings prone to errors and abnormalities. Since sensor data are essential to the system's operation, the quality of various data-centric CIoT services will ultimately depend on the accuracy of sensor readings. However, detecting anomalies in sensor data is a complex process because CIoT sensor networks are frequently resource-constrained devices with limited computation, networking, and storage power. To fulfil the objectives, an effective and affordable cognitively-inspired detecting method is required. Therefore, this research proposed a novel technique to identify the anomaly in sensor node data. The experimental evaluation is conducted on the environmental data of 21.25 years, and detection accuracy reveals the efficacy of the proposed method over competing approaches.
  Keywords: anomaly; probability; sensor network; cognitive IoT; CIoT.
  DOI: 10.1504/IJNVO.2024.10061110
   
  
• Retaining remote workers: Factors that affect virtual and hybrid workers' job retention  
  by Vasu Thirasak, Nopadol Rompho 
  Abstract: This study examines factors from Herzberg's motivation-hygiene theory, Deci's self-determination theory, and life-course fit theory to understand their effects on virtual and hybrid workers' job retention. Data were collected from 623 respondents in Thailand, and structural equation modelling and data analysis techniques were used to test the relationships between pay, promotion, supervision, fringe benefits, life-course fit, intrinsic motivation, and extrinsic motivation and job retention for virtual and hybrid workers, as well as the moderating effects of job level and virtual intensity. The results indicate that motivator-hygiene factors pay, promotion, supervision, and fringe benefits do not significantly contribute to the job retention of virtual and hybrid workers. However, the relationships between life-course fit, intrinsic motivation, and extrinsic motivation and job retention were significant. This is one of the very few studies that applies these theories in the context of virtual and hybrid work, which expands the theories boundaries of knowledge.
  Keywords: remote work; virtual work; hybrid work; job retention; motivators; hygiene factors; intrinsic motivation; extrinsic motivation.
  DOI: 10.1504/IJNVO.2024.10062140
   
  
• The Hybrid Augmented Intelligence Open Innovation Platform's Architecture and Scheme, Which Combines Interconnected Virtual and Actual Systems  
  by Yan Zhou, Yong Xia 
  Abstract: The goal of this project is to address the main issues that exist today by creating an open innovation platform for hybrid augmented intelligence (HAI). We describe the fundamental structure of the platform, including the module methods and duties at each level, by putting forth a plan for the platform architecture. We have created protection and reward systems for open innovation to encourage greater participation to foster innovation. Furthermore, we have investigated in detail how blockchain technology is applied to the HAI open innovation platforms data security, which offers a workable way to guarantee information security. This research offers a comprehensive structure and strategy for building the platform. In light of this, the HAI Open Innovation Platforms background welcomes the new era of AI technology development and highlights its importance in fostering innovation, collaboration, and the resolution of challenging issues as well as the acceleration of technological application.
  Keywords: hybrid augmented intelligence; open innovation platform; virtual reality systems; human-computer interaction; digital virtual industrial technology.
  DOI: 10.1504/IJNVO.2024.10064486
   
  
• Employee Hypertension Self-Management Support with Microlearning and Social Learning  
  by Luuk Simons, Bas Gerritsen, Bas Wielaard, Mark A. Neerincx 
  Abstract: A majority of employees over the age of 40 have hypertension, impacting their health and performance. A two-week Self-Management Support (SMS) intervention was tested, with daily feedback and microlearning cycles to improve health self-management competences. On average, participants (n=8) reduced their blood pressure from 145/92 to 126/86 mmHg. User evaluation confirmed the importance of core SMS aspects: information transfer, daily monitoring, enhancing problem solving/decision making, self-treatment using a tailored action plan, coping skills and skilful coach follow-up. Several lessons are drawn on microlearning, peer coaching, health results, intrinsic motivation, and social learning, which appear useful for other health improvement initiatives.
  Keywords: Hypertension; Self-Management Support; microlearning; social learning; eHealth; employee health;.
  DOI: 10.1504/IJNVO.2024.10064738
   
  
• Towards the Creation of a Cluster Theory-Based Accounting System  
  by Oleh Vysochan, Vasyl Hyk, Olha Vysochan 
  Abstract: At the moment, a fairly large number of enterprises carry out their activities by combining into network (cluster) formations with the aim of increasing competitive relations and reducing costs. To ensure the integration of the economic interests of the participants of network formations through the improvement of the coordination and controllability of interactions, an important place is given to the accounting system as the main source of information. The purpose of the article is to research and development of the theoretical and conceptual provisions of the cluster economy paradigm formation in the accounting system. The methodological basis is the fundamental provisions of modern economic institutional theory and scientific works of scientists. As a result of the research, it was possible to analyse the historical aspects of the development of accounting support for managing cluster structures. The areas of development of accounting based on the provisions of economic theory and considering the specific features of cluster structures are identified and substantiated.
  Keywords: cluster; network; economic theory; institutional theory; inter-organizational management; accounting.
  DOI: 10.1504/IJNVO.2024.10065328
   
  
• Cognitively-inspired Intelligent Decision-making Framework in Cognitive IoT Network  
  by Vidyapati Jha, Priyanka Tripathi 
  Abstract: Numerous Internet of Things (IoT) applications require brain-empowered intelligence. This necessity has caused the emergence of a new area called cognitive IoT (CIoT). Reasoning, planning, and selection are typically involved in decision-making within the network bandwidth limit. Consequently, data minimization is needed. Therefore, this research proposes a novel technique to extract conscious data from a massive data set. First, it groups the data using k-means clustering, and the entropy is computed for each cluster. The most prominent cluster is then determined by selecting the cluster with the highest entropy. Subsequently, it transforms each cluster element into an informative element. The most informative data is chosen from the most prominent cluster that represents the whole massive data, which is further used for intelligent decision-making. The experimental evaluation is conducted on the 21.25 years of environmental dataset, revealing that the proposed method is efficient over competing approaches.
  Keywords: IoT; cognitive IoT; Intelligent decision; Data reduction.
  DOI: 10.1504/IJNVO.2024.10065357
   
  
• A Novel Bacterial Colony Optimisation-Based Cluster Head Selection Method for the Internet of Things  
  by Bala Subramanian C, Ramkumar R 
  Abstract: The expanding usage of smart environments has drawn academics’ attention to the internet of things (IoT), which is continually evolving. An optimal cluster head (CH) control how much power is used by the cluster's components, extending the battery life of those components and grouping them into manageable clusters that support network expansion. An optimal CH in the IoT network is selected using bacterial colony optimisation (BCO) to optimise energy usage. The suggested BCO has great search effectiveness and dynamic capabilities, extending the lifetime of sensor nodes (SNs). The number of alive nodes, throughput, and residual energy are used to evaluate the ability of the proposed BCO. The suggested BCO method is compared with several cutting-edge approaches and the outcomes demonstrate the suggested novel BCO approach's superiority to already-existing techniques.
  Keywords: internet of things; IoT; energy efficient; bacterial colony optimisation; BCO; wireless sensor network; cluster head selection.
  DOI: 10.1504/IJNVO.2024.10065407
   
  
• The Design of Intrusion Detection System in MANET using IGWO-ANN Classification Algorithm  
  by Venkatesh. R, K. Sasikala 
  Abstract: Presently, attacks on the internet are maximised with the internets enhancement. Intrusion detection system (IDS) is one of the compassionate layers relevant to information protection. Though researchers have found enormous techniques, there are still issues in detecting new intrusions. So, this framework proposed an effective IDS using IQDFA-based feature selection and IGWO-ANN classification algorithm. Initially, data conversion occurs, where the input data in the form of characters is replaced by the number. Then, to avoid the similar datas training, redundant data is removed. Then, the normalisation occurs, where the feature values are normalised using an average of min and max attribute values. Next, by utilising the IQDFA, the extra features are extracted after the best feature selection. Data classification is conducted using IGWO-ANN. For determining whether the sensor data was attacked or not, the testing of classified data is done. The proposed models performance analysis exhibited enhanced performance than the prevailing methodologies.
  Keywords: Intrusion Detection System (IDS); Numeralization; Grey-Wolf Optimization (GWO); Feature extraction; Artificial Neural Network (ANN); Dragon Fly Algorithm (DFA); Classification.
  DOI: 10.1504/IJNVO.2024.10065425