Blockchain technology is transforming industries by enhancing security, transparency, and efficiency. This review explores blockchain's growing relevance and addresses key challenges such as scalability, energy consumption, interoperability, and regulatory compliance, which hinder its widespread adoption. Although prior studies have examined blockchain applications, critical gaps remain. This analysis provides an in-depth review of blockchain in sectors like healthcare, digital identity, AI, and the internet of things (IoT), focusing on unique strengths and limitations. We also examine blockchain's synergy with big data, aiming to uncover new solutions to existing issues. Findings highlight the need for future research on scalability and reliability, outlining a roadmap to address these challenges. Our review seeks to contribute to blockchain's evolution by identifying essential areas for development, ultimately supporting its integration across industries for more efficient, effective solutions.
Crowdsourcing is an emergent model for decentralised and collaborative problem solving. Inventive tasks crowdsourcing processes are in most cases dynamic, they are subject to failure and exceptions arising at runtime. Most of the existing design and implementation notations for crowdsourcing processes do not adequately reflect important features of dynamic crowdsourcing processes. We propose a multi-layered high-level Petri net model (timed-meta ECATNets) to design and control dynamic crowdsourcing processes. In particular, the proposed model makes it possible to adapt crowdsourcing processes in case of task failure. Owing to its use of algebraic Petri nets, the model allows to express compactly: control flow, data flow and time constraints. The proposed model is implemented using Real-Time Maude. The reachability commands and the timed CTL model checker associated to Real-Time Maude could then be used to analyse the correctness of the dynamic and timed behaviours of crowdsourcing processes.
Organisations issue circulars to declare the decision taken by them to the concerned people. However, in recent times, counterfeiting documents to spread fake information is increasing. In addition, such information is shared as scanned copies on the internet. This accelerates the growth of fake circulars at a rapid rate. Hence, it causes dilemma of whether to believe that the circular is genuine or not. To overcome this, this paper proposes the usage of blockchain technology, and smart contracts embed the digital signature into the document. The proposed approach uses the public key to provide a unique digital identity. It enables any user to sign the circulars using the public identifier, which in turn generates the signature parameters. Further, the documents are stored in decentralised peer-to-peer storage to make them highly accessible and tamper-proof. From the experimental results, it is stated that the proposed approach assures the originality of the document in a significant manner.
In the fast-growing sector of cloud computing, users can employ system resources as needed for data storage and computation. Protecting confidential data is the biggest cloud computing challenge. Security challenges in cloud computing are categorised as network, data, software, infrastructure, identity, and access management. Data concerns typically arise with cloud computing: data loss, dependability, availability, breaches, backup, and ownership. Cloud service companies worry about security and privacy when hosting services. Cloud storage data security can be improved in many ways. Cloud storage encrypts data for maximum protection. Layered encryption improves cloud data security. Scrambling and binary logical operation are used in the two-layered encryption method. Data is separated into square matrix shares on the first layer. This layer swaps the square matrix using scrambling. The second layer turns the scrambled matrix to binary, and the XoR logical operation encrypts it. The AWS cloud stores encrypted data. The algorithms provided great security, reduced cipher text size, and shortened encryption and decryption time compared to previous encryption methods.
The aim of this research is to investigate and demonstrate the advantages and limitations of various machine learning techniques for malware classification, specifically focusing on portable executable (PE) files. The study addresses common challenges in machine learning, such as overfitting and underfitting, by employing ensemble methods and pre-processing techniques, including feature selection and hyperparameter tuning. The primary objective is to enhance classifier performance in distinguishing between malicious and benign PE files. Through a comparative analysis of machine learning methodologies such as random forests, decision trees, and gradient boosting, the study highlights the superiority of the random forests algorithm, achieving an impressive accuracy rate of 99%. By thoroughly evaluating the strengths and limitations of each algorithm, the research provides valuable insights into effectively handling diverse malware categories. This paper underscores the significance of ensemble methods, feature engineering, and pre-processing in improving classifier performance for malware classification, specifically in the context of portable executable files.
Conventional analysis of time series signals representing earthquakes does not provide any clue about the vulnerability of such disastrous events. Time series signals contain P and S waves, which can detect earthquake epicentres. Due to the failure of the old method for determining earthquake susceptibility over time, decision-making is needed. This research suggests a multi-criteria decision-making method to determine earthquake signal risk time zones. This study used TOPSIS for this job. TOPSIS ranks greatest and worst resemblance to positive and negative ideal solutions. Alternatives and criteria constitute the decision matrix. Segmenting the earthquake's duration creates alternate time zones, and seismic signal dynamics are used to set criteria. Statistical mean and standard deviation are two criteria among many. Other criteria include Hurst exponent, power spectrum maximum amplitude, and segmented signal anomaly (assumed as alternate). The proposed approach was tested using Indian Meteorological Department Bhuj earthquake data. The paper describes how to evaluate criteria for a time zone alternative. To simplify computation, earthquake incidence is separated into 14 equal-length time segments. Results demonstrate that the proposed method accurately detects earthquake time series signal sensitive time zones.
High performance computing (HPC) systems often require substantial resources, and can take up to several hours or days to execute. Upon a failure, it is important to loose as little computation as possible. In this work we present an algorithm-based fault tolerance (ABFT) strategy for hypercube-based parallel algorithms. The strategy assumes the virtual VCube topology, which has several logarithmic properties that are preserved even as nodes fail. The strategy guarantees that the algorithm does not halt even after up to ( N - 1) nodes crash, in a system of N nodes. We use parallel sorting as a case study, describing how to make a fault-tolerant version of three parallel sorting algorithms: HyperQuickSort, QuickMerge and Bitonic Sort. The algorithms were implemented in MPI using ULMF to handle faults. Experimental results are presented showing the performance and robustness of the solution for sorting up to a billion integers in scenarios with faults.
This paper addresses the escalating risk of electronic health records (EHRs) breaches and unauthorised profiling, emphasising the need for standardised solutions to safeguard patient information. Focusing on privacy and security, the proposed conceptual design introduces Fast IDentity Online Specifications (FIDO2) compatible smart medical cards for healthcare information systems. By leveraging FIDO2, the solution ensures password-less authentication through device attestation, enhancing security in accessing patient information. The cloud computing model adds multiple layers of security, maintaining data confidentiality. Experimental results demonstrate performance comparable to traditional healthcare information systems, with a notable advantage in resource-limited settings. The implementation extends the reach of EHR systems, particularly beneficial in low- and middle-income countries with developing health data exchange infrastructure. The use of FIDO2-based smart cards presents a secure and scalable alternative, addressing critical challenges in EHR privacy and security effectively.
This paper builds on the foundations of computer-based networks in order to assess their dependable and real-time properties. We extend the analysis and modelling of network inaccessibility by using its associated theorem to gauge the origins of dependability issues during the network operation. We show how network transmissions in the presence of errors can be observed and analysed, implying a richer view and understanding of their negative impact to the whole distributed real-time ecosystem. We present results extracted from a simulated scenario of an industrial wireless sensors and actuators network (IWSAN) implemented in C Language, enabling us to conclude that more resilient computer-based networks are needed in such environments, as well as improved modelling and prototyping of distributed real-time systems running on top of them.
