Mobile Cloud Computing

Organizational factors, including human resource capabilities, technical skills, and management support, are essential for the successful rollout and customer adoption of electronic banking services. This study examines the impact of organizational factors on e-banking adoption at the Cooperative Bank of Oromia (CBO) in Chiro District, Ethiopia, grounded in the Technology-Organization-Environment (TOE) framework. A cross-sectional survey of 299 customers were conducted using structured questionnaires. Data were used analyzed with descriptive statistics and binary logistic regression. Results indicate that organizational factors have a significant positive effect on e-banking adoption (B = 0.293, p = 0.042, Exp(B) = 1.340). Key barriers identified include shortages of skilled IT personnel, deficiencies in technical and managerial skills, and inadequate staff training. The study recommends that cooperative banks enhance internal capabilities through targeted staff training, recruitment of IT specialists, and improved resource allocation to support digital transformation and financial inclusion in rural Ethiopia.

The need for effective and intelligent intrusion detection systems (IDS) has been highlighted
by the exponential growth of cyber threats. While traditional signature-based systems, such as Snort,
are very accurate at identifying existing assaults, they are unable to detect emerging and zero-day
threats. On the other hand, machine learning (ML)-driven and anomaly-based methods show
flexibility but have significant false positive rates. In-depth examination of Hybrid Intrusion Detection
Systems (HIDS) that integrate ML-driven anomaly analysis with Snort's signature-based detection is
presented in this review paper, which examines the development and convergence of these two
paradigms. A thorough review of recent research from 2018 to 2025 has been done to look at methods,
datasets, algorithms, and assessment metrics.Based on their architectures, detection techniques, and
integration frameworks, the article categorizes hybrid IDS approaches. Important issues including
interoperability, data imbalance, and real-time detection are also covered. Lastly, the article outlines
future possibilities for research on explainable AI, threat intelligence integration, and deep learning
for next-generation intrusion detection systems.

This article examines the personnel challenges faced by the Micro, Small, and Medium Enterprises (MSMEs) sector in Haryana State, India, and provides probable revival remedies. The study includes three components of personnel challenges, such as the attentiveness of employees, training, and recruitment. The study is based on primary data that has been collected from 384 entrepreneurs using a structured questionnaire from six districts in each division, i.e., Yamuna Nagar (39), Faridabad (93), Gurugram (103), Hisar (49), Sonipat (40), and Panipat (60), following the using the Krejcie and Morgan (1970) table. The data have been analysed using SPSS-29 with statistical tools, i.e., frequency, mean, t-test, one-way ANOVA, and percentage. The study found that entrepreneurs in the state faced personnel-related issues, i.e., low cooperation among employees, family problems, strikes, high labour costs, a lack of skilled workers, a lengthy recruitment process, lack of continuous training to employees, and ineffective training, based on various independent variables. Further, the study recommended that welfare services, distinct refresher courses, workshops, and seminars on skill development, as well as competitive salaries, are required to overcome these challenges and reduce staff turnover.

Early detection and diagnosis of basic health condition will result in successful treatment of diseases and reduced fatality
rate in developing countries. It is possible to extract human bio-signals and use it to better understand the bodily health
status and reaction to external factors. This new Vital Smart System (VSS) design has in-built sensors which a user wears
as a wrist band without interfering with his/her daily life activities to monitor his/her major vital signs (BT, BP, HR), of
which current system has limitations to achieve. Using the Dynamic System Design Methodology (DSDM), a more
technically robust, cost-effective and portable real time device was designed. This device contains an IoT interface (BLE)
that connects to smart phones; a GSM interface (SIM900A) relying on AT command framework that accesses defined
medical personnel on the network in cases of emergencies; optical health LEDs that alerts the user; thus, enabling consistent
self-assessment of the individual’s health condition. In this research therefore, we provided a generalized smart three-in
one vital sign system that ensures effective monitoring and early detection of the user’s health status via an optimization
of offline and online cross-interfaces of standalone sensors. 
Keywords: Real-time monitoring, Internet of Things (IoT), Temperature (BT), Vital signs, Wearable health device.

Cloud security is a branch of cyber security that focuses on securing cloud computing systems. It is a method for enterprises to use the internet to access storage and virtual services while saving money on infrastructure, considered as the next-generation architecture since it combines application software and databases into big data centers. However, difficulties with confidentiality, protection, integrity, and compliance may surface when the information is not kept, examined, or computed locally. A wide range of data security-related topics are covered by cloud-based data security. For securing an organization's data across the network, data encryption is a common and effective security strategy that is a great alternative. The most vulnerable data is financial and payment systems, and medical data, which can expose customers or clients critical information. Several academics have also offered a range of approaches to safeguards to maintain data privacy and security while it is being transmitted, but there are still a lot of obstacles to the safety of data that is being stored in the cloud. In this paper, several research papers that have been authored and published in this topic are thoroughly examined and analysed. This study gives a review of the literature on different methods for establishing data security in cloud environment.

With the advent of cloud computing, applications are no longer tied to a single device, but they can be migrated to a high-performance machine located in a distant data center. The key advantage is the enhancement of performance and consequently, the users experience. This activity is commonly referred as computational offloading and it has been strenuously investigated in the past years. The natural candidate for computational offloading is the cloud, but recent results point out the hidden costs of cloud reliance in terms of latency and energy; Cuervo et. al. illustrates the limitations on cloud-based computational offloading based on WANs latency times. The dissertation confirms the results of Cuervo et. al. and illustrates more use cases where the cloud may not be the right choice. This dissertation addresses the following question: is it possible to build a novel approach for offloading the computation that overcomes the limitations of the state-of-the-art? In other words, is it possible to create a computational offloading solution that is able to use local resources when the Cloud is not usable, and remove the strong bond with the local infrastructure? To this extent, I propose a novel paradigm for computation offloading named anyrun computing, whose goal is to use any piece of higher-end hardware (locally or remotely accessible) to offloading a portion of the application. With anyrun computing I removed the boundaries that tie the solution to an infrastructure by adding locally available devices to augment the chances to succeed in offloading. To achieve the goals of the dissertation it is fundamental to have a clear view of all the steps that take part in the offloading process. To this extent, I firstly provided a categorization of such activities combined with their interactions and assessed the impact on the system. The outcome of the analysis is the mapping to the problem to a combinatorial optimization problem that is notoriously known to be NP-Hard. There are a set of well-known approaches to solving such kind of problems, but in this scenario, they cannot be used because they require a global view that can be only vii 6.

With the advent of cloud computing, applications are no longer tied to a single device, but they can be migrated to a high-performance machine located in a distant data center. The key advantage is the enhancement of performance and consequently, the users experience. This activity is commonly referred as computational offloading and it has been strenuously investigated in the past years. The natural candidate for computational offloading is the cloud, but recent results point out the hidden costs of cloud reliance in terms of latency and energy; Cuervo et. al. illustrates the limitations on cloud-based computational offloading based on WANs latency times. The dissertation confirms the results of Cuervo et. al. and illustrates more use cases where the cloud may not be the right choice. This dissertation addresses the following question: is it possible to build a novel approach for offloading the computation that overcomes the limitations of the state-of-the-art? In other words, is it possible to create a computational offloading solution that is able to use local resources when the Cloud is not usable, and remove the strong bond with the local infrastructure? To this extent, I propose a novel paradigm for computation offloading named anyrun computing, whose goal is to use any piece of higher-end hardware (locally or remotely accessible) to offloading a portion of the application. With anyrun computing I removed the boundaries that tie the solution to an infrastructure by adding locally available devices to augment the chances to succeed in offloading. To achieve the goals of the dissertation it is fundamental to have a clear view of all the steps that take part in the offloading process. To this extent, I firstly provided a categorization of such activities combined with their interactions and assessed the impact on the system. The outcome of the analysis is the mapping to the problem to a combinatorial optimization problem that is notoriously known to be NP-Hard. There are a set of well-known approaches to solving such kind of problems, but in this scenario, they cannot be used because they require a global view that can be only vii 6.

The technological advance of mobile devices, networks and cloud technologies progressed and reduced their access costs to the whole human population. Besides that, mobile devices are still limited in the battery capacity, storage, and connectivity. An efficient way to manage their resources is to make the applications self-adaptive and context-aware using the MAPE-K loop model. However, even this method can add a considerable processing cost to their devices. This paper proposes to reduce such costs by applying the computational offloading technique into the classical MAPE-K loop. In this way, we analyzed it based on literature evidence to find a suitable process that allows offloading to remote and cloud servers. The results through experimentation on the proposed model show that there is a substantial performance increasing in the planning activity remote executions compared to the local ones, what is also affected by the distance from the servers.

Encryption methods had been widely used for secure data transmission and communication in both public and private organizations against intruders. Rivest-shamir-adleman (RSA) encryption algorithm is one of the most popular and efficient encryption schemes that has been in used for decades. Due to technological advancement and innovation, there is a threat to this algorithm. It is believed that introduction of quantum computer will break RSA algorithm easily. In view of this, it is pertinent to research into how RSA algorithm could be strengthened against all adversaries. This research aim at protecting client/server communication and file sharing by generating dynamic public and private keys. The proposed method was implemented in visual basic.net 2008. The result shows that dynamic keys do not affect the performance of the system and it is capable of protecting communication and file sharing between client/server. As the key generated keeps changing at an interval, it will difficult for most advance computer to factor any of the keys before another key is generated. This is the basis of the security of the proposed system.

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In 5th Generation (5G) systems, the current Machine-to-Machine (M2M) communications using Wi-Fi or Bluetooth provide a good opportunity to dramatically increase the overall performance. Converged mobile networks can provide M2M communications with significant performance improvement from sharing unlicensed spectrum bands in cellular networks, such as Long Term Evolution-Advanced (LTE-A), by using cognitive radio technology. Thus, the converged mobile network will become one of the most popular future research topics because mobile multimedia content services have been generally accepted among mobile device users. In this article, we provide an overview of converged mobile networks, investigating different types of converged mobile network, different types of convergence, and the current problems and solutions. This survey article also proposes potential research topics in converged mobile networks.

This paper examines how emerging technologies and information technology infrastructure components can improve the service delivery and operational efficiency of the Department of Trade and Industry (DTI). It discusses the role of hardware platforms, operating systems, enterprise software, data management, networking, internet platforms, virtualization, cloud computing, and mobile technologies in supporting government digital transformation. Using a policy recommendation approach, the paper proposes a Phased Integrated IT Infrastructure Modernization Framework to address legacy system limitations, system integration challenges, and the growing demand for faster and more accessible public services. The specific result is a four-phase framework: Phase 1 focuses on infrastructure assessment and governance within 0–3 months to produce a clear roadmap and standardized policies; Phase 2 implements cloud computing, middleware integration, and virtualization within 3–9 months to improve efficiency and interoperability; Phase 3 strengthens personnel training, mobile access, BYOD policies, and cybersecurity within 6–12 months to increase user adoption and secure access; and Phase 4 provides continuous performance monitoring through dashboards, feedback, and infrastructure updates to ensure sustainability and adaptability. The paper concludes that IT modernization in government requires not only technology replacement but also governance, capacity building, cybersecurity, and continuous improvement. Future policy direction should focus on stronger data governance, secure cloud adoption, and regular infrastructure evaluation to support a more efficient, transparent, and competitive digital economy.

This chapter elaborates on energy usage optimization issues by exploiting a resource offloading process based on a social-oriented mobile cloud scheme. The adoption of the proposed scheme enables for increasing the reliability in services provision to the mobile users by guaranteeing sufficient resources for the mobile application execution. More specifically, this chapter describes the process to improve the energy consumption of the mobile devices through the exploitation of a social-oriented model and a cooperative partial process offloading scheme. This research approach exploits social centrality, as the connectivity model for the resource offloading, among the interconnected mobile devices to increase the energy usage efficiency, the mobile nodes availability, as well as the process of execution reliability. The proposed scheme is thoroughly evaluated to define the validity and the efficiency for the energy conservation increase of future mobile computing devices.

Manufacturers of agricultural machinery (Original Equipment Manufacturers or OEMs) connected to the Internet of Things retain technological control of the devices after they are sold to farmers and prevent them from being repaired or modiied independently. This applies, for example, to tractors equipped with hardware, software, and sensors that collect, process, and transmit data relating to the activity carried out in the fields to the network. As in many sectors affected by digital technologies, in precision agriculture we are seeing attempts by OEMs to limit the power of the purchaser-owner-user of the device through intellectual property, contracts, Technological Protection Measures (TPMs), and Digital Rights Management. When a manufacturer of agricultural machinery holds monopoly or oligopoly market power, the obligation not to carry out independent repairs corresponds to an attempt to extend its market power from the production of machinery to repair services and other related markets. This constitutes an abuse of market and contractual power. Among the legal instruments aimed at counteracting the abuse of market and contractual power by OEMs, the limitation of intellectual property and pseudo-intellectual property (the legal protection of TPMs) is of fundamental importance. It would therefore be necessary to introduce into the legal system a right to control the functioning and modify the digital device as a prerogative of owenrship. To be effective, this right must be broad and robust.

The rapid growth of enterprise-scale applications has introduced
significant challenges in designing user interfaces that are both scalable
and intelligent. Traditional UI design approaches often struggle to adapt to
increasing system complexity, diverse user requirements, and dynamic
operational environments. This paper proposes a comprehensive
framework for scalable and intelligent user interface design tailored for
enterprise environments. The framework integrates modular UI
architecture, adaptive design principles, and AI-driven personalization
techniques to enhance usability, performance, and responsiveness. It
emphasizes component-based development, micro-frontend
architectures, and real-time data handling to support large-scale
deployments. Additionally, the framework incorporates machine learning
mechanisms to enable context-aware interactions, predictive assistance,
and user behavior analysis. A layered architecture is presented, combining
presentation, interaction, and intelligence layers to ensure flexibility and
maintainability. The proposed approach is validated through a case study
in an enterprise application scenario, demonstrating improved user
experience, system scalability, and reduced development complexity. The
findings suggest that integrating intelligence with scalable design
principles can significantly improve the effectiveness and adaptability of
user interfaces in modern enterprise systems

High-Performance Computing (HPC) is indispensable for large-scale scientific simulations, but achieving optimal performance on modern supercomputers is increasingly challenging. As HPC systems scale toward exascale, they face escalating complexity in hardware, software, and workloads. Traditional optimization methods (manual tuning and heuristic algorithms) struggle to cope with dynamic workloads and intricate system behaviors. Artificial Intelligence (AI) techniques offer a promising approach to address these challenges. This article provides an overview of AI-powered optimization in HPC, focusing on how machine learning and related AI methods enhance performance, efficiency, and scalability of scientific simulations. We survey key AI techniques applied to HPC optimization including machine learning for performance modeling, deep reinforcement learning for resource management, and AI-driven surrogate models for accelerating simulations and illustrate their impact through case studies in domains such as job scheduling and fluid dynamics. We discuss the practical applications of these techniques, highlighting reported performance gains (e.g., substantial reductions in simulation run time and improved resource utilization). We also examine the challenges in integrating AI with HPC (such as training overhead, data movement, and reliability concerns) and outline future directions for research. The convergence of AI and HPC is poised to produce "smart" simulation workflows that intelligently adapt and optimize in real time, pushing the frontiers of scientific computing.

The mobile cloud computing (MCC) has become more and more present in our life and that is due to the wide availability of mobile devices in the world market (smartphones, tablets, etc.), however unlike the cloud computing has proven a big success and performance in communication technologies, the mobile devices are not able to a fully benefit from this development due to their resource’s limitations such as the CPU, Ram, Battery life, etc. Today, due to heavy applications that are hosted in the cloud, the mobile devices consumes more and more energy, and it contributes greatly to the battery discharge in a very short delays. To resolve this problem, we propose in this paper an implementation of an elastic framework for splitting a customer request between the others devices (best elected devices) for minimizing the processing time, the results have shown that the implementation of this framework contributes significantly to a considerable minimization of the request processing time ...

Computer networks have made the world a small village. However, this sophisticated and ever-growing
communication network has suffered from rapidly increasing attacks (intrusions). Various solutions have low detection rates, high
false positives, high processing time, large trace sizes, and other challenges. This study proposes using machine learning and opensource
security tools to explore host-based intrusion detection systems. The research conducts an in-depth analysis of evasion
attacks, anomaly-based techniques, and signature misuse-based approaches. The Australian Defense Force Academy Linux dataset
for Features is selected from the ADFA-LD dataset using the N-gram feature extraction mechanism. It configures one of the hostbased
intrusion detection tools for open-source, security-signature-based intrusion detection. The experiment shows that the
proposed approach is promising in terms of detection rate, false-positive rate, and processing time. These three machine learning
algorithms are SVM, KNN, and RF for classification, and they achieved better performance in binary classification than in multiclass
classification. The experimental accuracy of SVM is 96.26% with a 5.1% false-positive rate (FPR), KNN is 96.71% with
3.28% FPR, and RF is 96.86% with 3.9% FPR.
Keywords: Host-based intrusion detection, Open-source security, Machine learning algorithms, False alarm rates

We propose a dynamic pruned ensemble framework for zero-day attack detection in cloud-native environments, addressing the limitations of static anomaly detection systems in handling evolving threats and heterogeneous telemetry data. The framework integrates specialized base models-a CNN for network flow analysis, a Transformer for log parsing, and a GBDT ensemble for API metrics-each optimized for distinct cloud observability modalities. A Thompson sampling-based bandit controller dynamically selects and prunes models during inference, balancing detection accuracy with computational efficiency while adapting to real-time cloud context through a GRU-modulated reward function. The system uniquely combines adaptive ensemble pruning with infrastructure-aware feedback, enabling it to respond to autoscaling events and policy changes without manual intervention. Moreover, the framework processes inputs from conventional cloud monitoring tools (e.g., Prometheus, Istio) and fuses anomaly scores into SIEM systems via context-weighted aggregation. Experimental validation demonstrates superior detection performance and resource efficiency compared to monolithic approaches, particularly for previously unseen attack patterns. The proposed method achieves this by continuously optimizing model composition based on operational feedback, hence eliminating redundant computations while maintaining high sensitivity to emerging threats. This work bridges the gap between AIdriven anomaly detection and cloud-native operational constraints, offering a scalable solution for real-time security in dynamic environments.

New information and communication technologies have contributed to the development of the smart city concept. On a physical level, this paradigm is characterized by deploying a substantial number of different devices that can sense their surroundings and generate a large amount of data. The most typical case is image and video acquisition sensors. Recently, these types of sensors are found in abundance in urban spaces and are responsible for producing a large volume of multimedia data. The advanced computer vision methods for this type of multimedia information means that many aspects can be dynamically monitored, which can help implement value-added applications in the city. However, obtaining more elaborate semantic information from these data poses significant challenges related to a large amount of data generated and the processing capabilities required. This paper aims to address these issues by using a combination of cloud computing technologies and mobile computing techniques to design a three-layer distributed architecture for intensive urban computing. The approach consists of distributing the processing tasks among a city's multimedia acquisition devices, a middle computing layer, known as a cloudlet, and a cloud-computing infrastructure. As a result, each part of the architecture can now focus on a small number of tasks for which they are specially designed, and data transmission communication needs are significantly reduced. To this end, the cloud server can hold and centralize the multimedia analysis of the processed results from the lower layers. Finally, a case study on smart lighting is described to illustrate the benefits of using the proposed model in smart city environments. INDEX TERMS Mobile cloud computing, data processing, distributed architectures, smart city, urban computing.

Mastering Cloud Computing: Strategies for the Digital Age
is a comprehensive and practical guide to understanding
the power and potential of cloud technologies in today’s
fast-paced, digitally-driven world. This book explores key
concepts and trends, focusing on the practical aspects of
cloud computing that businesses and individuals must
consider when adopting or optimizing cloud services.The
book begins with an introduction to cloud computing, explaining
foundational concepts such as IaaS, PaaS, and
SaaS, before diving into the complexities of cloud
architecture, security, and deployment models like public,
private, and hybrid clouds. It also covers emerging cloud
technologies like edge computing, serverless architectures,
and artificial intelligence, showcasing how these innovations
are reshaping the way businesses operate and
innovate.With a strong emphasis on real-world applications,
this book equips readers with the knowledge to
make informed decisions about cloud adoption, cost management,
security protocols, and scaling strategies. From
cloud migration to disaster recovery, data management,
and compliance, Mastering Cloud Computing provides
valuable insights that are crucial for any organization
looking to harness the full potential of cloud technologies
for sustainable growth in the digital age. Whether you're a
seasoned IT professional or a business leader, this book
serves as an essential resource for mastering the cloud.

Cloud gaming is a rapidly growing domain that requires low-latency and high-responsiveness architectures to deliver an engaging user experience. This paper presents an innovative Digital Twin (DT)-based framework designed to mitigate the effects of variable network conditions on video streaming and input responsiveness. The proposed architecture integrates Console and User Equipment DTs, which synchronize video streams and user inputs using lightweight RTSP and MQTT-based communication, ensuring real-time interaction even in challenging network scenarios. To validate the framework, we developed a custom cloud gaming application simulating a shark-and-fish pursuit game. This use case allows for precise evaluation of system performance under varying latency and delay conditions. Experimental results demonstrate the architecture's ability to maintain game responsiveness , revealing critical delay thresholds and highlighting the impact of delay variability on user performance. These findings illustrate the potential of DT-based architectures to enhance the robust-ness and fairness of cloud gaming systems.

This study examined the organization and storage of staff records in the Archive of Ahmadu Bello
University, Zaria. The study aimed at identifying the types of staff records available, the
procedures for organizing staff records, the storage facilities used, and the challenges faced in
organizing and storing staff records. The research utilized a qualitative methodology, with
interviews used as the primary data collection instrument. The findings indicated that the available
staff records include staff appointment letters, retirement letters, sack letters, queries, and birth
certificates. The organization of staff records is conducted using various methods, including
alpha-numeric, subject-based, geographic, and serial organization, among others. The study
concludes that different types of staff records are available, organized, and stored in Ahmadu
Bello University's Archive using multiple methods. The methods utilized a combination of
alphabetic and numeric codes, subject-based organization, location, and record order using
closed and open storage, among others. The study recommends the inclusion of other types of staff
records, such as appraisal reports and disciplinary actions. The study suggests the utilization of
organizational methods such as alphabetical, numerical, and chronological for effective
organization of staff records in Ahmadu Bello University's Archive, among other
recommendations.
Keywords: Staff records, Organization Storage, Archive, Ahmadu Bello University

Insurance contact centers operate under a persistent structural tension between the administrative uniformity that generalist routing assumes and the benefit-plan heterogeneity that employer-sponsored insurance produces. Conventional inquiry-type routing architectures classify incoming member contacts by call category without reference to employer group identity or plan configuration, generating plan-context mismatches that surface as coverage guidance errors, eligibility verification failures, and first-contact resolution breakdowns. This article examines Group-ID-Based Intelligent Routing as a precision-routing architecture that resolves this tension by embedding employer group identifiers directly into routing decisioning logic at the point of member contact. Drawing on peer-reviewed and recognized academic research spanning insurance digitization, intelligent routing systems, healthcare data integrity, contact center automation, and AI-driven service operations, the article addresses the architectural principles of identifier resolution, dynamic queue assignment, infrastructure integration, operational impact, and the strategic pathway toward AI-enhanced routing. The article foregrounds conceptual reasoning, design trade-offs, and boundary conditions across the routing design space, with particular attention to the enrollment data synchronization and legacy system integration constraints that determine real-world implementation viability.

Abstract─With evolution of internet, security becomes a major concern. Number of malicious programs called malware, travels through network into systems. They have many advanced properties like self-hiding, selfhealing and stealth mode execution, which are hard to detect. Therefore, the major challenge for researchers today is to detect and mitigate such programs. Since there is a new virus implemented every minute no detection mechanism can be designed which gives 100% protection but by keeping the anti-virus database up to date we can escape many attacks. In this paper, an effort has been made to explain the design of a system program which can scan the vulnerable files on the system, generate logs and this can later be used to design antivirus software and stop virus execution. This program aims to scan system files and target the files which are vulnerable present on the system based on their file extensions. It generates logs after the system scan is complete which can be studied and used for anti-virus creation.

Artificial intelligence algorithms, which work by taking human intelligence as an example, can be applied to every field in the "Smart Society" era. Artificial intelligence using big data is also included in autonomous applications in the media field. Artificial intelligence applications, which have recently come to the fore with personalized television examples, can be used at almost every stage of television broadcasting, including pre-production, production and post-production stages. Television broadcasting consists of multiple business components. In this context, examples of artificial intelligence appear in areas such as script writing, actor selection, set design, camera use, sound editing, music production, subtitle production, graphic design, editing processes, news production, artificial intelligence announcers, and determination of the target audience. For example, even the television content of some large broadcasting companies is designed by artificial intelligence supported by big data. This situation calls into question the future of television

Blockchain technology provides a decentralized and tamper-resistant method for storing
digital records. Legal documents require high levels of security, authenticity, and integrity
because they represent important agreements, ownership records, and official
certifications. Traditional centralized document management systems often suffer from
security risks such as data breaches, unauthorized modification, and system failures. This
research proposes a Blockchain-Based eVault system for secure storage and verification
of legal documents. The system integrates blockchain technology with cryptographic
hashing and decentralized storage systems to ensure document authenticity and
long-term protection. Blockchain technology provides a decentralized and tamper-resistant
method for storing digital records. Legal documents require high levels of security,
authenticity, and integrity because they represent important agreements, ownership
records, and official certifications. Traditional centralized document management systems
often suffer from security risks such as data breaches, unauthorized modification, and
system failures. This research proposes a Blockchain-Based eVault system for secure
storage and verification of legal documents. The system integrates blockchain technology
with cryptographic hashing and decentralized storage systems to ensure document
authenticity and long-term protection.

The rapid proliferation of Internet of Things (IoT) devices and smart sensors has resulted in an unprecedented growth
of distributed data, challenging traditional cloud-centric analytics frameworks. Centralized processing models often introduce
latency, bandwidth congestion, and privacy vulnerabilities, particularly in time-sensitive and mission-critical applications.

In recent years, mobile devices such as smart phones, tablets empowered with tremendous technological advancements. Augmenting the computing capability to the distant cloud help us to envision a new computing era named as mobile cloud computing (MCC). However, distant cloud has several limitations such as communication delay and bandwidth which brings the idea of proximate cloud of cloudlet. Cloudlet has distinct advantages and is free from several limitations of distant cloud. However, limited resources of cloudlet negatively impact the cloudlet performance with the increasing number of substantial users. Hence, cloudlet is a viable solution to augment the mobile device task to the nearest small scale cloud known as cloudlet. However, this cloudlet resource is finite which in some point appear as resource scarcity problem. In this paper, we analyse the cloudlet resource scarcity problem on overall performance in the cloudlet for mobile cloud computing. In addition, for empirical analysis, we make some definitions, assumptions and research boundaries. Moreover, we experimentally examine the finite resource impact on cloudlet overall performance. By, empirical analysis, we explicitly establish the research gap and present cloudlet finite resource problem in mobile cloud computing. In this paper, we propose a Performance Enhancement Framework of Cloudlet (PEFC) which enhances the finite resource cloudlet performance. Our aim is to increase the cloudlet performance with this limited cloudlet resource and make the better user experience for the cloudlet user in mobile cloud computing.

Multiplayer network games attract more and more interest nowadays. In order to make the system scalable, the multi-server architecture is introduced. In such systems, as the dynamic behavior of the users in the virtual world may result in significant workload imbalance between servers, a dynamic load sharing and transferring mechanism is crucial to achieve both quick response time and high throughput. In this paper, we present a multi-server model based on the emerging grid technology for supporting multiplayer network games. The core of this model is based on a new abstraction "gamelet", which represents the execution logic within a partitioned game world and is featured by its high mobility for supporting dynamic load sharing. A prototype of a 3D multiplayer game has been implemented based on gamelet concept. Preliminary results show that our approach can help make a more dynamic and cost effective multi-server network game system.

The transition from fifth-generation (5G) to sixth-generation (6G) mobile networks represents a fundamental shift in wireless communication paradigms, driven by the need for ultra-low latency, extreme data rates, native intelligence, and support for mission-critical and immersive applications. This paper presents the Rexhep Network Optimization Framework, a layered and AI-native architectural model designed to enable a smooth, efficient, and scalable evolution from 5G to 6G systems. The proposed framework integrates physical and spectrum intelligence, intelligent radio access networks (RAN) with edge computing, virtualized core networks with network slicing, and AI-driven optimization and control mechanisms. It further incorporates advanced service layers supporting extended reality (XR), digital twins, AI-based security, and mission-critical services. The framework explicitly addresses the coexistence of 5G and 6G technologies through phased deployment, hybrid optimization, and dynamic spectrum management, ensuring backward compatibility while enabling 6G-dominant capabilities. By positioning artificial intelligence as a cross-layer enabler rather than an auxiliary function, the proposed framework provides a systematic approach for network automation, resilience, and performance optimization in next-generation communication ecosystems. The presented model offers a conceptual foundation for future research, standardization, and practical deployment strategies toward 6G networks.

cloud is an extreme data center for outsourced data. The quality of video service gets poor when there occurs the long buffering and sporadic disturbance. While requirement of video traffic across mobile networks has become difficult, the wireless link ability cannot put up with the traffic demand. The Cloud is a well-known tool because of its functional architectural tools. Sharing the data is another common functional property for the cloud users. These data sharing include video data in social networks, this was done frequently through mobile devices like smart phones, tablets, laptops etc. video traffic demand across mobile networks has become a difficult task, and the quality of service get reduced as there is a gap between the traffic demand and link ability. Mobile network has limited bandwidth and long buffering time. To solve these issues adaptive mobile video streaming and efficient public video sharing has been proposed. AMoV and ESoV build a private agent to give video streaming services for each mobile user. These two approaches show scalable results in social network environment. With this framework, the overloading buffering time and interference can be avoided Index Terms -secure processing, video base, sub video base, and secrete keys, ________________________________________________________________________________________________________

The integration of predictive analytics in financial governance has fundamentally transformed risk management and compliance monitoring capabilities across the financial sector. Cloud-based platforms have emerged as essential infrastructure components, enabling real-time data processing, advanced threat detection, and automated compliance monitoring. The evolution of autonomous systems, powered by artificial intelligence and machine learning algorithms, has enhanced the accuracy of risk predictions while significantly reducing operational costs. Implementation challenges, including data quality management and security concerns, have been addressed through comprehensive governance frameworks and advanced encryption protocols. The convergence of quantum computing, blockchain technology, and edge computing is shaping the future of financial governance, promising enhanced processing capabilities and improved security measures. The adoption of these advanced technologies has demonstrated substantial improvements in operational efficiency, risk detection accuracy, and compliance monitoring effectiveness, establishing new standards in financial governance and risk management.

Development of future generation computer architectures requires fast and accurate simulation tools that allow to test, verify, and analyze the behavior of the given architecture along with the intended workload. We present a simulation framework based on a structural architecture description language that uses the open source compiler infrastructure LLVM to dynamically translate instruction sequences of the simulated architecture into machine instructions of the host machine. We show that the optimizations in the simulator and the LLVM compiler lead to an outstanding runtime performance: A 5-stage MIPS core is simulated at a peak performance of up to 800 MHz.

In the era of time-critical applications such as healthcare, Edge Computing (EC) has emerged as a pivotal paradigm for delivering low-latency, responsive services by processing data closer to end users. However, the inherent resource constraints of edge nodes

This directed work describes the development and implementation of a digital platform to optimize the preferential cash advance request process, a financial service associated with credit cards at Banco de Crédito de Bolivia S.A. (BCP). The project arises as a response to the bank's identified need to modernize and digitize a process that was previously carried out manually and in-person, requiring customers to physically visit branches. The methodology used was Agile Scrum, with two-week sprints that allowed incremental deliveries and adaptation to changes during development. The project was structured in clearly defined phases: requirements analysis, architectural modeling, development, testing, and production deployment. The developed solution integrates multiple Core APIs from the bank, including internet banking credential validation, OTP generation and authentication for two-factor authentication, financial product queries, digital document signing, transaction management (credits and debits), and digital information safeguarding. The system architecture was designed following bank standards and was validated by the institutional Architecture Committee.
The main impact of the project was the significant reduction in request processing time, going from 24 hours including travel to branch and waiting) to less than 5 minutes completely online. This represents a substantial improvement in user experience and optimization of the bank's operational resources. The application was developed using .NET Core technologies (version 6), with ASP.NET Core Web API for the backend and ASP.NET Core MVC for the frontend, integrated with SQL Server for data storage. Continuous integration and deployment pipelines were implemented in Azure DevOps for development, certification, and production environments. The results obtained demonstrate successful fulfillment of the proposed objectives: functional and non-functional requirements were analyzed and documented, system architecture was modeled through C4 diagrams and use cases, functionality was verified with the quality control team, and the solution was implemented meeting the bank's security and quality standards. Although the application was temporarily disabled in production due to the national economic situation related to dollar scarcity, during its active period it demonstrated correct functionality and met business expectations. The project establishes the foundation for future reactivation when market conditions are more favorable.

Protecting banking data is crucial in today's data security and privacy environment. Due to the rise of online commerce, cryptography is crucial to protecting Internet communication and transaction secrecy. To limit access and safeguard email, phone conversations, and financial accounts from hackers, cable and satellite networks utilize encryption. For secrecy, data integrity, user authentication, and non-repudiation, cryptography is essential . Cryptography encrypts text. In this effort, encryption is crucial. The decryption process demonstrates the reciprocal nature of encryption by restoring encrypted text . Cryptography draws from diverse fields and is extensively reliant on discrete mathematics. Specifically, modern cryptography places substantial reliance on the number theory . Amidst various encryption techniques, the Hill cipher (HC) stands out for its mathematical complexity and resilience against cryptanalysis. Despite seeming straightforward, it uses linear matrix transformations for symmetric encryption, masking the letter frequencies in plaintext and guaranteeing high throughput. As a polygraphic substitution cipher, the Hill Cipher uses digraphs and trigraphs, or groupings of three letters, and allows for different block sizes. Its encryption method, which uses linear algebra, requires a basic comprehension of matrices. Similar to the Affine cipher, the Hill cipher employs modulo algebra. The decryption process involves computing the inverse of the fundamental matrix. However, the primary drawback of this is the requirement for an invertible full matrix . In addition, traditional methods of implementation of this cipher limit its potential, particularly when confronted with complex numbers or the need for heightened security. Further, this technique is vulnerable to plaintext-ciphertext attacks . This article delves into matrix extensions of the Hill cipher specifically tailored for Gaussian integers. Gaussian integers, denoted by the integers 'a' and 'b', represent complex numbers and were initially introduced in 1831 by the eminent German mathematician Carl Friedrich Gauss. The method of implementation described in this study leverages NumPy and Pandas, Python packages renowned for their advanced numerical, and data manipulation capabilities.

Due to tremendous use of smartphones the concern of cloud computing in mobile devices emerges, which is known as Mobile Cloud Computing (MCC). It involves the usage of mobile devices and cloud computing to perform resource intensive tasks using the internet with minimum impact on cellular resources. Nowadays, people are relying on mobile devices due to their small size and user friendly interface but due to its limited storage capacity, people can no more rely on internal RAM. Therefore, this promotes a drastic need for technology to make it possible for anyone to access their data anywhere anytime. As a result, Mobile Cloud Computing facilitates mobile users with its enticing technology by providing its on-demand and scalable services. But privacy and security are the main concern for a mobile user in the modern era. Thus, issues regarding security can be divided into cloud security and mobile network user's security, respectively. However, the primary focus of this study is to analyze how to secure the user's data in a mobile cloud. Leading to objectives, the current study presents a comprehensive analysis of existing techniques that can be considered for securing data in MCC efficiently. Moreover, this work will contribute a state-of-the-art roadmap to research and development communities for the right selection of proposed approach.

With the explosive growth of mobile applications and extensive praxis of cloud computing, mobile cloud computing has been introduced to be a potential technology for mobile services. But privacy is the main concern for a mobile user in the modern era. In the current study, we address the privacy challenges faced by mobile users while outsourcing their data to the service provider for storage and processing. However, a secure mobile user is required to protect these fundamental privacy factors such as their personal data, real identity, current location and the actual query sent to the cloud vendor server while availing different cloud services. Under these privacy metrics, we evaluated the existing approaches that are counting privacy challenge in mobile cloud computing. The primary focus of this study is to presents a critical survey of recent privacy protection techniques. Leading to objective, the current study conduct a comparative analysis of these state of the art methods with their strong points, privacy level and scalability. After analysis, this paper suggests the pseudo-random permutation method could be a promising solution that can be taken into consideration for preserving user personal information and data query privacy in MCC more efficiently. Primarily, the purpose of the survey was to focus on further advancements of the suggested method. Furthermore, we present the future research directions in the mobile cloud computing paradigms.

With the proliferation of Internet of Things (IoT) ecosystems, traditional resource orchestration mechanisms, executed on fog devices, encounter significant scalability, reliability and security challenges. To tackle these challenges, recent decentralized algorithms in Fog-IoT use Distributed Ledger Technologies to orchestrate resources and payments between peers. However, while distributed ledgers provide many desirable properties, their consensus mechanism introduces a performance bottleneck. This paper introduces Light-HIDRA, a consensus-less and decentralized resource orchestration system for Fog-IoT environments. At its core, Light-HIDRA uses Byzantine Reliable Broadcast (BRB) to coordinate actions without centralized control, therefore drastically reducing communication overhead and latency compared to consensus-based solutions. Light-HIDRA coordinates the scheduling and execution of workloads, and securely manages the payments that peers receive for dedicating resources to workloads. Light-HIDRA further increases performance and reduces overhead by grouping peers into distinct domains. We conduct an in-depth analysis of the protocol's security properties, investigating its efficiency and robustness in diverse situations. We evaluate the performance of Light-HIDRA, highlighting its performance over HIDRA, a state-of-the-art baseline that uses smart contracts. Our experiments demonstrate that Light-HIDRA reduces the bandwidth usage by up to 57x, the latency of workload offloading by up to 142x, and shows superior throughput compared to HIDRA.

In the last decade, Cloud technology has emerged as, one of the biggest trends in technology. Cloud is a platform, which makes data and software available online at anytime, anywhere, and from any device connected to Internet. In today's conditions, the business world is constantly changing, and becoming more competitive and more sophisticated by the development of cloud technology. Accounting, like other business sectors, adopts and applies cloud computing solutions to provide relevant information, as well as real-time business reviews to all stakeholders. This paper has made an attempt to provide a theoretical overview of cloud accounting covering its concept, benefits, shortcomings, comparison with the traditional one and some other important aspects that may shape the accounting profession in the coming years. The aim of the paper is to bring together the several facets regarding cloud accounting that could enable the readers to understand the importance of cloud accounting in a business context and to show a comparative view with the conventional accounting

Function-as-a-Service (FaaS) revolutionizes cloud-native application deployment by providing stateless execution environments. However, challenges such as cold start latency and inefficient workflow routing degrade performance. This paper explores intelligent event routing and cold start profiling as optimization strategies. We present a model combining routing heuristics and runtime profiling to mitigate latency and optimize throughput. Empirical data shows up to 53% reduction in cold start latency and 35% improvement in event throughput in simulated environments.

With the increasing number of mobile devices and IoT devices across a wide range of real-life applications, our mobile cloud computing devices will not cope with this growing number of audiences soon, which implies and demands the need to shift to fog computing. Task scheduling is one of the most demanding scopes after the trust computation inside the trustable nodes. The mobile devices and IoT devices transfer the resource-intensive tasks towards mobile cloud computing. Some tasks are resource-intensive and not trustable to allocate to the mobile cloud computing resources. This consequently gives rise to trust evaluation and data sync-up of devices joining and leaving the network. The resources are more intensive for cloud computing and mobile cloud computing. Time, energy, and resources are wasted due to the nontrustable nodes. This research article proposes a multilevel trust enhancement approach for efficient task scheduling in mobile cloud environments. We first calculate the t...

We proposed to utilize a centralized broker-node to perform task scheduling for the resource augmentation of a large number of mobile devices. The task scheduler model focused on energy optimization was proposed for the centralized task scheduling problem. In this paper, the model extends the optimization process by including an economic element to it. Thus, we propose an energy and monetary cost-aware mathematical task scheduler model. Compared to the previous model, this model, can allow mobile devices to offload multiple tasks to cloud resources. The results in this paper are more thorough and more aspects of task offloading have been analysed. For instance, the model is evaluated under two different resource augmentation environments for mobile cloud computing: a local private cloud and public clouds. More precisely, the task scheduling problem is optimally solved to minimize: (i) the total energy consumption when applied to a local private cloud, and (ii) the total energy consu...

Offline and Online Bank Data Synchronization System is used by credit societies to save the money of people with small businesses, or e.g. shopkeepers, vegetable vendors. These people deposit money at the credit society. The cash collector is the person who is working under the credit society. The cash collector collects the money from these people.Cash collector enters the transaction data on his tablet. We are storing our whole data on local storage at first and it will upload the data to Microsoft’s cloud, Azure, as soon as the system gets online. Azure services through a global network of Microsoft-managed datacenters [2].