Cyberattack patterns in blockchain-based communication networks for distributed renewable energy systems : A study on big datasets

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.© 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)fi=vertaisarvioitu|en=peerReviewed

A Survey of League Championship Algorithm: Prospects and Challenges

The League Championship Algorithm (LCA) is sport-inspired optimization algorithm that was introduced by Ali Husseinzadeh Kashan in the year 2009. It has since drawn enormous interest among the researchers because of its potential efficiency in solving many optimization problems and real-world applications. The LCA has also shown great potentials in solving non-deterministic polynomial time (NP-complete) problems. This survey presents a brief synopsis of the LCA literatures in peer-reviewed journals, conferences and book chapters. These research articles are then categorized according to indexing in the major academic databases (Web of Science, Scopus, IEEE Xplore and the Google Scholar). The analysis was also done to explore the prospects and the challenges of the algorithm and its acceptability among researchers. This systematic categorization can be used as a basis for future studies.Comment: 10 pages, 2 figures, 2 tables, Indian Journal of Science and Technology, 201

Concept of Blockchain technology

Blockchain, Bitcoin's core technology, and spinal cord have received enthusiastic attention since the last couple of decades. The Blockchain serves as a paradigm for distributed and unchangeable computations for bitcoins and cryptocurrencies. The key features behind this technology are to create a reliable, secure, transparent, decentralized, and reliable autonomous ecosystem. It is useful for a variety of applications, especially for legacy devices, resources, and infrastructure. In this article, we presented a technical overview, its application, and the challenges associated with blockchain technology and cryptocurrencies. This study aims to provide a ground-breaking overview and future research direction and promising importance of Blockchain

Enhancing Smart Cities with IoT and Cloud Computing: A Study on Integrating Wireless Ad Hoc Networks for Efficient Communication

شهدت المدن الذكية تطورا جوهريا زاد من امكانياتها بشكل كبير .في الواقع ، لقد أتاحت التطورات الحديثة في انترنت الاشياء (IOT) فرصا جديدة من خلال حل عدد  من المشاكل الحرجة والتي ادت الى ابتكار المدن الذكية بالاضافة الى انشاء و حوسبة الخدمات و التطبيقات المتطورة للعديد من  المجاميع المطورة في المدينة . من اجل تعزيز تنمية المدن الذكية بأتجاه التواصل و المشاركة ،تركز هذه الدراسة على التطور في مجال المعلوماتية في ضوء انترنت الاشياء (IOT) و الحوسبة السحابية (CC) .جمعت بيانات انترنت الاشياء والتي تخص المدن الذكية بشكل متجانس . اصبح انترنت الاشياء الذي يسمح بتواصل الاشخاص مع بعضهم ممكنا باستخدام الذكاء الاصطناعي .بناءا على ذلك ،استخدمنا (ARF) في حسابات الذكاء الاصطناعي .للتبسيط ،ننصح باستخدام تخصيص اصول الالة الافتراضية للحوسبة السحابية التكيفية (ACC-VMRA ) .لتاكيد جدواها ،سنفحص و نضاعف كيفية تطبيق تطورات انترنت الاشياء (IOT ) و الحوسبة السحابية (CC) في المدن الذكية.تظهر نتائج التجربة ان حساب التحسين الموصى به اكثر انتاجية من الطرق الاخرى المستخدمة حاليا.Smart cities have recently undergone a fundamental evolution that has greatly increased their potentials. In reality, recent advances in the Internet of Things (IoT) have created new opportunities by solving a number of critical issues that are allowing innovations for smart cities as well as the creation and computerization of cutting-edge services and applications for the many city partners. In order to further the development of smart cities toward compelling sharing and connection, this study will explore the information innovation in smart cities in light of the Internet of Things (IoT) and cloud computing (CC). IoT data is first collected in the context of smart cities. The data that is gathered is uniform. The Internet of Things, which enables gadgets to connect with one another mostly without human involvement, is made possible by AI. In line with this, The Ad Hoc Routing Function (ARF) AI computation is used for multi-rule simplification, the use of Adaptive Cloud Computing Virtual Machine Asset Allotment Technique (ACC-VMRA) is advised. To confirm its viability, the applied developments of IoT and CC in smart cities is examined and duplicated. The experiment results show that the recommended enhancement calculation is more productive than other currently used methods

Data redundancy reduction for energy-efficiency in wireless sensor networks: a comprehensive review

Wireless Sensor Networks (WSNs) play a significant role in providing an extraordinary infrastructure for monitoring environmental variations such as climate change, volcanoes, and other natural disasters. In a hostile environment, sensors' energy is one of the crucial concerns in collecting and analyzing accurate data. However, various environmental conditions, short-distance adjacent devices, and extreme usage of resources, i.e., battery power in WSNs, lead to a high possibility of redundant data. Accordingly, the reduction in redundant data is required for both resources and accurate information. In this context, this paper presents a comprehensive review of the existing energy-efficient data redundancy reduction schemes with their benefits and limitations for WSNs. The entire concept of data redundancy reduction is classified into three levels, which are node, cluster head, and sink. Additionally, this paper highlights existing key issues and challenges and suggested future work in reducing data redundancy for future research

Reliability aware Resource Scheduling based on Fuzzy Cuckoo Search (FCS) technique for IaaS Cloud

Resource scheduling assigns the precise and accurate tasks to CPU, network, and storage. The aim behind this is the optimum usage of resources. However, well-organized scheduling is needed for both cloud providers and cloud users. Several resource scheduling algorithms have been discussed in the literature, but there are little emphases to reliability aware resource scheduling. In this research article, an innovative technique is proposed that is known as the Fuzzy Cuckoo Search (FCS) technique based on the fuzzy theory and cuckoo search algorithm to solve real-time optimization problematic issues. The FCS technique is used to address the reliability aware resource scheduling problems in IaaS Cloud. An experiment has been carried out on the CloudSim simulator and results of FCS techniques are compared with the Genetic Algorithm (GA), Honey Bee (HB) and Particle Swarm Optimization (PSO) scheduling algorithms. Finally, computational results demonstrate that the FCS technique is produced 39.21% better optimal solutions than the best solutions obtained by the comparison algorithms in terms of failure rate. It specifies that the FCS technique is more appropriate for reliability aware resource scheduling for IaaS Cloud

Fault tolerance aware scheduling technique for cloud computing environment using dynamic clustering algorithm

In cloud computing, resources are dynamically provisioned and delivered to users in a transparent manner automatically on-demand. Task execution failure is no longer accidental but a common characteristic of cloud computing environment. In recent times, a number of intelligent scheduling techniques have been used to address task scheduling issues in cloud without much attention to fault tolerance. In this research article, we proposed a dynamic clustering league championship algorithm (DCLCA) scheduling technique for fault tolerance awareness to address cloud task execution which would reflect on the current available resources and reduce the untimely failure of autonomous tasks. Experimental results show that our proposed technique produces remarkable fault reduction in task failure as measured in terms of failure rate. It also shows that the DCLCA outperformed the MTCT, MAXMIN, ant colony optimization and genetic algorithm-based NSGA-II by producing lower makespan with improvement of 57.8, 53.6, 24.3 and 13.4 % in the first scenario and 60.0, 38.9, 31.5 and 31.2 % in the second scenario, respectively. Considering the experimental results, DCLCA provides better quality fault tolerance aware scheduling that will help to improve the overall performance of the cloud environment

Hybrid gradient descent cuckoo search (HGDCS) algorithm for resource scheduling in IaaS cloud computing environment

Resource scheduling is a procedure for the distribution of resources over time to perform a required task and a decision making process in cloud computing. Optimal resource scheduling is a great challenge and considered to be an NP-hard problem due to the fluctuating demand of cloud users and dynamic nature of resources. In this paper, we formulate a new hybrid gradient descent cuckoo search (HGDCS) algorithm based on gradient descent (GD) approach and cuckoo search (CS) algorithm for optimizing and resolving the problems related to resource scheduling in Infrastructure as a Service (IaaS) cloud computing. This work compares the makespan, throughput, load balancing and performance improvement rate of existing meta-heuristic algorithms with proposed HGDCS algorithm applicable for cloud computing. In comparison with existing meta-heuristic algorithms, proposed HGDCS algorithm performs well for almost in both cases (Case-I and Case-II) with all selected datasets and workload archives. HGDCS algorithm is comparatively and statistically more effective than ACO, ABC, GA, LCA, PSO, SA and original CS algorithms in term of problem solving ability in accordance with results obtained from simulation and statistical analysis

Multi-objective-oriented cuckoo search optimization-based resource scheduling algorithm for clouds

Scheduling problems in cloud computing environment are mostly influenced by multi-objective optimization but frequently deal with using single-objective algorithms. The algorithms need to resolve multi-objective problems which are significantly different from the procedure or techniques used for single-objective optimizations. For this purpose, meta-heuristic algorithms always show their strength to deal with multi-objective optimization problems. In this research article, we present an innovative Multi-objective Cuckoo Search Optimization (MOCSO) algorithm for dealing with the resource scheduling problem in cloud computing. The main objective of resource scheduling problem is to reduce the cloud user cost and enhance the performance by minimizing makespan time, which helps to increase the revenue or profit for cloud providers with maximum resource utilization. Therefore, the proposed MOCSO algorithm is a new method for solving multi-objective resource scheduling problems in IaaS cloud computing environment. Moreover, the effects of the proposed algorithm are analyzed and evaluated by comparison with state-of-the-art multi-objective resource scheduling algorithms using simulation framework. Results obtained from simulation show that the proposed MOSCO algorithm performs better than MOACO, MOGA, MOMM and MOPSO, and balance multiple objectives in terms of expected time to completion and expected cost to completion matrices for resource scheduling in IaaS cloud computing environment