SACM - Australia
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9648
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Item Restricted An AI-Driven, Secure, and Trustworthy Ranking System for Blockchain-Based Wallets(University of Technology Sydney, 2024-07-08) Almadani, Mwaheb; Farookh HussainThe significance of blockchain security has gained considerable interest as blockchain technologies grow in popularity. The spectacular rise in cryptocurrency values has also increased the adoption of blockchain-based wallets(BW/BWs). This tendency emphasizes the need for comprehensive security measures to protect digital assets, maintain transaction integrity and preserve trust in the blockchain networks. The most critical concern surrounding blockchain-based wallets is managing users' private keys, which are essential for authorizing transactions and accessing the digital cryptocurrencies stored in the blockchain network. In recent years, malicious actors have increased efforts to compromise these private keys and take control of the BW's digital assets. Therefore, ensuring the security of private keys through rigorous security protocols is paramount to defend against unauthorized access and potential financial losses. This thesis aims to investigate the integration of hard security, such as authentication techniques and access controls, and soft security measures, such as trust models and ranking systems, in the context of BWs. By incorporating tangible physical defenses (hard security) with intangible procedural strategies (soft security), we present a comprehensive framework for enhancing BW solution security and trustworthiness. This is essential for the widespread adoption and use of blockchain technology in financial transactions and digital asset management. This thesis proposes a secure, intelligent, and trustworthy approach for BW solutions that incorporates 2FA and MFA as hard security measures and an AI-driven ranking system as soft security measures. We have developed a BW website (BWW) with four authentication mechanisms, including different factors such as TOTP and biometrics through facial recognition, allowing BW users to choose their preferred level of security. The BWW remarkably improves the security of BW solutions by defending them against various threats, including sophisticated cyber-attacks, unauthorized access and human-caused weaknesses. Moreover, We introduce a trust-based ranking system (TBW-RAnk) for BW solutions that transparently ranks the BW solutions according to several objective and trusted criteria. TBW-RAnk is built using three AI models, namely the random forest classifier (RFC), the support vector classifier (SVC) and deep neural network (DNN). It has two modes: general and customized for a comprehensive and accurate assessment and recommendation for BW users. Consequently, BW users can make informed decisions and increase their security within the blockchain ecosystem. The proposed approach enhances the security and trustworthiness of BWs and increases their acceptance in the market.41 0Item Restricted Intelligent Blockchain Group-based Methodology for Renewable Energy Trading for Local Communities(Saudi Digital Library, 2023-08-17) Alhazmi, Rania; Hussain, FarookhA new marketplace for the distribution of renewable energy resources (RES) was created to support the integration of RES into energy systems. This market is called the local energy market (LEM) and it helps consumers and prosumers, who generate and consume energy, exchange RES, and balance their generation and consumption locally. Nevertheless, the long-term sustainability of the LEM requires secure, innovative, and decentralised information technology such as blockchain. Although various blockchain energy trading platforms are available, none of them examines how to empower small- scale prosumers and minimise the gap between them and the big players in the market (e.g., power companies, and energy retailers) by adopting sustainable group methods. None of the research discusses how to help a consumer trade energy by recommending the best offer based on his energy needs and preferences. These issues motivate us to conduct this research to develop an innovative methodology for community group renewable energy trading. We use blockchain to secure the trading marketplace and adapt AI approaches to intelligently support continuous group trading and to intelligently support trading decision-making for consumers. In this study, we develop a proof of concept prototype, which is called the Intelligent Blockchain Group Energy Trading platform, to evaluate our proposed research methodology. The main contribution of this research is to introduce a sustainable, decentralised, and intelligent group-based renewable energy marketplace framework for local communities. The prototype was created using React.js and the Hardhat Ethereum blockchain network. The IBGT platform has three major frameworks: the intelligent renewable energy selling group (RESG) framework, the intelligent reputation-based prosumer xiii ABSTRACT assessment framework, and the intelligent renewable energy surplus selection and trading framework. The intelligent RESG framework comprises three systems: an intelligent reputation-based RESG formation system, an intelligent reputation-based RESG membership system, and an intelligent RESG selling offer management system. These systems collaborate to manage and maintain the life cycle of selling group formation. To ensure the market viability of a selling group, we propose the intelligent reputation-based prosumer assessment framework. This framework examines prosumers based on predetermined assessment criteria. Lastly, the intelligent renewable energy surplus selection and trading framework provides a customer with a data-driven intelligent decision support system to assist in the matching phase and the trading process. This study's findings provide vital insight into the significance of reputation value in the energy trading market and the implications of our proposed intelligent blockchain group-based architecture. Based on the results, it is clear that reputation value and blockchain technology can facilitate better energy trading amongst prosumers, leading to more monetary gain and higher overall energy market efficiency. This substantially impacts the development of renewable energy trading systems. It presents a strong case for implementing our proposed formation, assessment, and trading techniques in future energy trading platforms.22 0Item Restricted Investigation on Design and Development Methods for Internet of Things(Saudi Digital Library, 2023-09-06) AlZahrani, Yazeed; Shen, Jun; Yan, JunThe thesis work majorly focuses on the development methodologies of the Internet of Things (IoT). A detailed literature survey is presented for the discussion of various challenges in the development of software and design and deployment of hardware. The thesis work deals with the efficient development methodologies for the deployment of IoT system. Efficient hardware and software development reduces the risk of the system bugs and faults. The optimal placement of the IoT devices is the major challenge for the monitoring application. A Qualitative Spatial Reasoning (QSR) and Qualitative Temporal Reasoning (QTR) methodologies are proposed to build software systems. The proposed hybrid methodology includes the features of QSR, QTR, and traditional data-based methodologies. The hybrid methodology is proposed to build the software systems and direct them to the specific goal of obtaining outputs inherent to the process. The hybrid methodology includes the support of tools and is detailed, integrated, and fits the general proposal. This methodology repeats the structure of Spatio-temporal reasoning goals. The object-oriented IoT device placement is the major goal of the proposed work. Segmentation and object detection is used for the division of the region into sub-regions. The coverage and connectivity are maintained by the optimal placement of the IoT devices using RCC8 and TPCC algorithms. Over the years, IoT has offered different solutions in all kinds of areas and contexts. The diversity of these challenges makes it hard to grasp the underlying principles of the different solutions and to design an appropriate custom implementation on the IoT space. One of the major objective of the proposed thesis work is to study numerous production-ready IoT offerings, extract recurring proven solution principles, and classify them into spatial patterns. The method of refinement of the goals is employed so that complex challenges are solved by breaking them down into simple and achievable sub-goals. The work deals with the major sub-goals e.g. efficient coverage of the field, connectivity of the IoT devices, Spatio-temporal aggregation of the data, and estimation of spatially connected regions of event detection. We have proposed methods to achieve each sub-goal for all different types of spatial patterns. The spatial patterns developed can be used in ongoing and future research on the IoT to understand the principles of the IoT, which will, in turn, promote the better development of existing and new IoT devices. The next objective is to utilize the IoT network for enterprise architecture (EA) based IoT application. EA defines the structure and operation of an organization to determine the most effective way for it to achieve its objectives. Digital transformation of EA is achieved through analysis, planning, design, and implementation, which interprets enterprise goals into an IoT-enabled enterprise design. A blueprint is necessary for the readying of IT resources that support business services and processes. A systematic approach is proposed for the planning and development of EA for IoT-Applications. The Enterprise Interface (EI) layer is proposed to efficiently categorize the data. The data is categorized based on local and global factors. The clustered data is then utilized by the end-users. A novel four-tier structure is proposed for Enterprise Applications. We analyzed the challenges, contextualized them, and offered solutions and recommendations. The last objective of the thesis work is to develop energy-efficient data consistency method. The data consistency is a challenge for designing energy-efficient medium access control protocol used in IoT. The energy-efficient data consistency method makes the protocol suitable for low, medium, and high data rate applications. The idea of energy-efficient data consistency protocol is proposed with data aggregation. The proposed protocol efficiently utilizes the data rate as well as saves energy. The optimal sampling rate selection method is introduced for maintaining the data consistency of continuous and periodic monitoring node in an energy-efficient manner. In the starting phase, the nodes will be classified into event and continuous monitoring nodes. The machine learning based logistic classification method is used for the classification of nodes. The sampling rate of continuous monitoring nodes is optimized during the setup phase by using optimized sampling rate data aggregation algorithm. Furthermore, an energy-efficient time division multiple access (EETDMA) protocol is used for the continuous monitoring on IoT devices, and an energy-efficient bit map assisted (EEBMA) protocol is proposed for the event driven nodes.32 0