RESUMEN
Blockchain-based reliable, resilient, and secure communication for Distributed Energy Resources (DERs) is essential in Smart Grid (SG). The Solana blockchain, due to its high stability, scalability, and throughput, along with low latency, is envisioned to enhance the reliability, resilience, and security of DERs in SGs. This paper presents big datasets focusing on SQL Injection, Spoofing, and Man-in-the-Middle (MitM) cyberattacks, which have been collected from Solana blockchain-based Industrial Wireless Sensor Networks (IWSNs) for events monitoring and control in DERs. The datasets provided include both raw (unprocessed) and refined (processed) data, which highlight distinct trends in cyberattacks in DERs. These distinctive patterns demonstrate problems like superfluous mass data generation, transmitting invalid packets, sending deceptive data packets, heavily using network bandwidth, rerouting, causing memory overflow, overheads, and creating high latency. These issues result in ineffective real-time events monitoring and control of DERs in SGs. The thorough nature of these datasets is expected to play a crucial role in identifying and mitigating a wide range of cyberattacks across different smart grid applications.
RESUMEN
The internet of things (IoT) is an emerging paradigm of educational applications and innovative technology in the current era. While capabilities are increasing day by day, there are still many limitations and challenges to utilizing these technologies within E-Learning in higher educational institutes (HEIs). The IoT is well-implemented in the United States of America (USA), United Kingdom (UK), Japan, and China but not in developing countries, including Saudi Arabia, Malaysia, Pakistan, Bangladesh, etc. Few studies have investigated the adoption of IoT in E-Learning within developing countries. Therefore, this research aims to examine the factors influencing IoT adoption for E-Learning to be utilized in HEIs. Further, an adoption model is proposed for IoT-based E-Learning in the contexts of developing countries and provides recommendations for enhancing the IoT adoption for E-Learning in HEIs. The IoT-based E-Learning model categorizes these influencing factors into four groups: individual, organizational, environmental, and technological. Influencing factors are compared along with a detailed description in order to determine which factors should be prioritized for efficient IoT-based E-Learning in HEIs. We identify the privacy (27%), infrastructure readiness (24%), financial constraints (24%), ease of use (20%), support of faculty (18%), interaction (15%), attitude (14%), and network and data security (14%), as the significant E-Learning influencing factors on IoT adoption in HEIs. These findings from the researcher's perspective will show that the national culture has a significant role in the individual, organizational, technological, and environmental behavior toward using new technology in developing countries.
RESUMEN
Cloud computing infrastructure is suitable for meeting computational needs of large task sizes. Optimal scheduling of tasks in cloud computing environment has been proved to be an NP-complete problem, hence the need for the application of heuristic methods. Several heuristic algorithms have been developed and used in addressing this problem, but choosing the appropriate algorithm for solving task assignment problem of a particular nature is difficult since the methods are developed under different assumptions. Therefore, six rule based heuristic algorithms are implemented and used to schedule autonomous tasks in homogeneous and heterogeneous environments with the aim of comparing their performance in terms of cost, degree of imbalance, makespan and throughput. First Come First Serve (FCFS), Minimum Completion Time (MCT), Minimum Execution Time (MET), Max-min, Min-min and Sufferage are the heuristic algorithms considered for the performance comparison and analysis of task scheduling in cloud computing.