Limited Processing Capacity Research Articles

Evaluating Link Lifetime Prediction to Support Computational Offloading Decision in VANETs.

In urban mobility, Vehicular Ad Hoc Networks (VANETs) provide a variety of intelligent applications. By enhancing automobile traffic management, these technologies enable advancements in safety and help decrease the frequency of accidents. The transportation system can now follow the development and growth of cities without sacrificing the quality and organisation of its services thanks to safety apps that include collision alerts, real-time traffic information, and safe driving applications, among others. Applications can occasionally demand a lot of computing power, making their processing impractical for cars with limited onboard processing capacity. Offloading of computation is encouraged by such a restriction. However, because vehicle mobility operations are dynamic, communication times (also known as link lifetimes) between nodes are frequently short. VANET applications and processes are impacted by such communication delays (e.g., the offloading decision when using the Computational Offloading technique). Making an accurate prediction of the link lifespan between vehicles is therefore challenging. The effectiveness of the communication time estimation is currently constrained by the link lifespan prediction methods used in the computational offloading process. This work investigates five machine learning (ML) algorithms to predict the link lifetime between nodes in VANETs in different scenarios. We propose the procedures required to carry out the link lifetime prediction method using existing ML techniques. The tactic creates datasets with the features the models need to learn and be trained. The SVR and XGBoost algorithms that were selected as part of the assessment process were trained. To make the prediction using the trained models, we modified the lifespan prediction function from an offloading approach. To determine the viability of applying link lifespan predictions from the models trained in the road and urban scenarios, we conducted a performance study. The findings indicate that compared to the conventional prediction strategy described in the literature, the suggested link lifetime prediction via regression approaches decreases prediction error rates. An offloading method from the literature is extended by the selected SVR. The task loss and recovery rates might be significantly reduced using the SVR. XGBoost outperformed its ML competitors in task recovery or drop rate by 70% to 80% in an assessed hypothesis compared to an offloading choice technique in the literature. With greater offloading rates from an application on the VANET, this effort is intended to give better efficiency in estimating this data using machine learning in various vehicular settings.

A stacking-based short-term wind power forecasting method by CBLSTM and ensemble learning

Aiming at the problem that the traditional wind power forecasting is difficult to deal with a large amount of strong volatility data and limited processing capacity of time series, a wind power forecasting method based on multi-model combination under stacking framework was proposed. First, the wind turbine data are cleaned by density-based spatial clustering of applications with the noise clustering method. Considering the differences of data observation and training principles, the proposed stacking method embedded multiple machine learning algorithms to utilize their diversified strength. The stacking base-learner includes the CBLSTM model, which has the advantages of deep architecture feature extraction, and takes into account data timing and nonlinear relationship as well as XGBoost and other tree ensemble learning models that were suitable for complex data modeling. The feasibility of the algorithm was verified by using the actual wind power data of two wind farms in Northeast and Western China. Experimental results show that the stacking ensemble learning method proposed has better forecasting performance and stability than other single forecasting models, which is of great significance to guide wind power dispatching operation and improve wind power consumption capacity.

Effects of spatial attention on spatial and temporal acuity: A computational account.

In our daily lives, the visual system receives a plethora of visual information that competes for the brain's limited processing capacity. Nevertheless, not all visual information is useful for our cognitive, emotional, social, and ultimately survival purposes. Therefore, the brain employs mechanisms to select critical information and thereby optimizes its limited resources. Attention is the selective process that serves such a function. In particular, covert spatial attention - attending to a particular location in the visual field without eye movements - improves spatial resolution and paradoxically deteriorates temporal resolution. The neural correlates underlying these attentional effects still remainelusive. In this work, we tested a neural model's predictions that explain these phenomena based on interactions between channels with different spatiotemporal sensitivities - namely, the magnocellular (transient) and parvocellular (sustained) channels. More specifically, our model postulates that spatial attention enhances activities in the parvocellular pathway, thereby producing improved performance in spatial resolution tasks. However, the enhancement of parvocellular activities leads to decreased magnocellular activities due to parvo-magno inhibitory interactions. As a result, spatial attention hampers temporal resolution. We compared the predictions of the model to psychophysical data, and show that our model can account qualitatively and quantitatively for the effects of spatial attention on spatial and temporal acuity.

Novel Security Models for IoT–Fog–Cloud Architectures in a Real-World Environment

With the rise of the Internet of Things (IoT), there is a demand for computation at network edges because of the limited processing capacity of IoT devices. Fog computing is a middle layer that has appeared to address the latency issues between the Internet of things (IoT) and the cloud. Fog computing is becoming more important as companies face increasing challenges in collecting and sending data from IoT devices to the cloud. However, this has led to new security and privacy issues as a result of the large number of sensors in IoT environments as well as the massive amount of data that must be analyzed in real time. To overcome the security challenges between the IoT layer and fog layer and, thus, meet the security requirements, this paper proposes a fine-grained data access control model based on the attribute-based encryption of the IoT–Fog–Cloud architecture to limit the access to sensor data and meet the authorization requirements. In addition, this paper proposes a blockchain-based certificate model for the IoT–Fog–Cloud architecture to authenticate IoT devices to fog devices and meet the authentication requirements. We evaluated the performance of the two proposed security models to determine their efficiency in real-life experiments of the IoT–Fog–Cloud architecture. The results demonstrate that the performance of the IoT–Fog–Cloud architecture with and without the blockchain-based certificate model was the same when using one, two, or three IoT devices. However, the performance of the IoT–Fog–Cloud architecture without the access control model was slightly better than that of the architecture with the model when using one, two, or three IoT devices.

Effects of Working Memory Training on Children’s Memory and Academic Performance: the Role of Training Task Features and Trainee’s Characteristics

Learning and academic performance are explained mainly by basic limited-capacity processes, most notably by working memory (WM). Consequently, training WM has been considered a promising approach to fostering these abilities. However, school-based investigations are rare. This study examined the effects of training task features and trainees’ characteristics on transfer to cognitive and academic measures. Eighty-six typically developing 8–12-year-old children completed 6 weeks of either WM training with n-back and complex span tasks or a control training with perceptual-matching tasks in a regular school setting. The study also assessed some personal variables of the children, such as neuroticism, conscientiousness, joy of learning, and power of endurance. The WM training group showed increased WM and math performance compared to the control group. Also, there was a trend toward some improvements in vocabulary after WM training, and overall improvements after both trainings were observed in fluid intelligence and reading. Analyses of individual differences in the WM training group indicated increased training performance in relation to emotional stability, conscientiousness, power of endurance, as well as teacher-reported joy of learning and social integration of participants. Thus, the results indicate the potential of WM training to improve WM capacity and mathematical skills and reveal the impact of regulative, motivational, and social factors on cognitive training performance.

Weighted utility aware computational overhead minimization of wireless power mobile edge cloud

Fifth-generation (5G) wireless networks are projected to support a large number of low-power devices, which are an essential component of Internet of Things (IoT) technologies. The existing network design is expected to face a number of challenges in serving such massive additional devices. Furthermore, these devices are bound by limited power processing and storage capacity, despite the fact that they are supposed to analyze massive volumes of data. In the last decade, the concepts of cloud and edge computing have received significant attention in order to increase network performance. Furthermore, wireless power transfer (WPT) is integrated with the cloud for energy transmission to low-power devices. This work presents a mathematical model for scheduling tasks that are being executed at low-power devices and concurrently on the cloud edge. The proposed mathematical model takes into account practical constraints and is intended to distribute resources among devices optimally while minimizing network overhead. To deal with the conflicting constraints, the model is further transformed into an unconstrained one. To solve the proposed model, evolutionary methods are being explored. Finally, a Monte Carlo simulation is used to validate the model. The simulation results show that partial offloading outperforms the edge-only computation scheme, with the partial offloading scheme demonstrating a 20% improvement in network overhead.

Offloading Decisions in a Mobile Edge Computing Node with Time and Energy Constraints

This article describes a simulated annealing based offloading decision with processing time, energy consumption and resource constraints in a Mobile Edge Computing Node. Edge computing mostly deals with mobile devices subject to constraints. Especially because of their limited processing capacity and the availability of their battery, these devices have to offload some of their heavy tasks, which require a lot of calculations. We consider a single mobile device with a list of heavy tasks that can be offloadable. The formulated optimization problem takes into account both the dedicated energy capacity and the total execution time. We proposed a heuristic solution schema. To evaluate our solution, we performed a set of simulation experiments. The results obtained in terms of processing time and energy consumption are very encouraging.

Wiener and Kalman Detection Methods for Molecular Communications.

Diffusion-based molecular communication system (DBMC) is a system in which information-carrying molecules are sent from the transmitter and passively transported to the receiver in a fluid environment. Nanomachines, which are the main part of this system, have limited processing capacity. Besides, at the receiver, high inter-symbol interference (ISI) occurs due to free movement of molecules and the variance of the observation noise is signal dependent. Hence, it is important to design high-performance and low complexity receiver detection methods. In this paper, finite impulse response (FIR) Wiener filter is introduced for the first time, which has considerably less computational complexity compared to the minimum mean square error (MMSE) algorithm proposed in the literature. Moreover, extended Kalman filter is introduced for the first time to DBMC as a receiver detection method. Finally, Viterbi algorithm is modified and used as a benchmark for performance evaluation.

On the integration of diverging material flows into resource‐constrained project scheduling

This study deals with an extension of the resource-constrained project scheduling problem (RCPSP) by constraints on material flows released during the execution of project activities. These constraints arise from limited processing capacities for materials and maximum inventories of intermediate storage facilities. Production scheduling problems with converging material flows have been studied extensively. However, this is the first project scheduling problem integrating diverging material flows typically observed in dismantling projects, e.g., building deconstruction, power plant decommissioning, or battery/car decommissioning. Diverging material flows do not directly impact the project planning but only impose delays in the case of congestion. We model material flows by using operations that represent the processing of materials, and cumulative resources that represent storage facilities. As a method for efficiently generating starting solutions, we propose a schedule generation scheme tailored to the particular precedence structure of such problems. Furthermore, we extensively study the schedule generation scheme’s performance on generated test instances and compare it to the constraint programming solver IBM ILOG CP Optimizer. It turns out that the solution quality strongly depends on the employed model and that neither of the two solution methods is generally superior.

A Multi-Commodity Mathematical Modelling Approach—Hazardous Waste Treatment Infrastructure Planning in the Czech Republic

This paper presents an analysis of infrastructure for the processing of Czech hazardous waste and pays attention to predictions of waste management development in the upcoming years. For this purpose, a unique complex approach to modelling future waste management changes is applied. The method uses a multi-commodity network flow model with reverse flows between treatment facilities to consider complete waste management of hazardous waste. The future outlook (2030) for the forecasted generation of different types of hazardous waste in the Czech Republic requires decisions on waste treatment facility infrastructure. The uniqueness lies in using real data for such a wide scope of a task, further enhanced by concurrent analysis of more types of waste interconnected through limited processing capacities. The results indicate the insufficiency in hazardous waste thermal treatment and stabilization. A suggestion is to extend the incineration capacity because it influences the stabilization units, which must process the remaining waste. The recommended increase is 100% with different proportions in individual regions.

Modeling subsea gas-liquid separation using proxy for phase separation for pre-salt reservoirs with high GOR

Brazilian pre-salt oil fields include ultra-deep water reservoirs with high CO2 content and high gas/oil ratio (GOR). For these cases, large volumes of CO2-rich gas reach the topside facilities that present limited gas processing capacity. In this context, the production of the oil field is restricted to the maximum gas production capacity of these facilities. The use of a subsea gas-liquid separation (SGLS) and reinjection system may be a solution to boost oil production, as this strategy allows reinjecting part of produced gas directly from the seabed. In this study, we developed a methodology for modeling the subsea separation and reinjection process, which is integrated with a compositional reservoir simulator. A proxy based on an equation of state (EOS) was used for volumetric phase separation in the subsea separator. This approach eliminates the necessity of flash calculation at the SGLS conditions. The methodology makes it possible to assess the potential impact of implementing this technology from a reservoir management perspective. The modeling methodology is applied to a benchmark case (synthetic simulation model representative of a pre-salt reservoir) and evaluated the impact of the SGLS over total production. The results show that the SGLS technology can successfully boost oil production in the context of platform gas production restriction. As soon as the SGLS was implemented, an extended oil production plateau was achieved due to increased gas production capacity. We observed a significant GOR increment in produced fluids during the simulation as a result of gas recycling strategy. This increasing GOR caused the field production to be restricted by maximum gas production constraint most of the time, despite the SGLS implementation. A higher GOR also affects the SGLS performance, increasing the proportion of gas that becomes available for separation at subsea separator conditions. An economic analysis is presented, showing that the SGLS has the potential to increase the financial return of the project. The introduced methodology will enable the inclusion of the subsea separation process in future analyses, paving the way for feasibility assessments and optimization processes for cases in which the SGLS may be an alternative.

Response-code conflict in dual-task interference and its modulation by age

Difficulties in performing two tasks at once can arise from several sources and usually increase in advanced age. Tasks with concurrent bimodal (e.g., manual and oculomotor) responding to single stimuli consistently revealed crosstalk between conflicting response codes as a relevant source. However, how this finding translates to unimodal (i.e., manual only) response settings and how it is affected by age remains open. To address this issue, we had young and older adults respond to high- or low-pitched tones with one (single task) or both hands concurrently (dual task). Responses were either compatible or incompatible with the pitch. When responses with the same level of compatibility were combined in dual-task conditions, their response codes were congruent to each other, whereas combining a compatible and an incompatible response created mutually incongruent (i.e., conflicting) response codes, potentially inducing detrimental crosstalk. Across age groups, dual-task costs indeed were overall highest with response-code incongruency. In these trials, compatible responses exhibited higher costs than incompatible ones, even after removing trials with strongly synchronized responses. This underadditive cost asymmetry argues against mutual crosstalk as the sole source of interference and corroborates notions of strategic prioritization of limited processing capacity based on mapping-selection difficulty. As expected, the effects of incongruent response codes were found to be especially deleterious in older adults, supporting assumptions of age-related deficits in multiple-action control at the level of task-shielding. Overall, our results suggest that aging is linked to higher response confusability and less efficient flexibility for capacity sharing in dual-task settings.

A Hybrid Zeolite Membrane-Based Breakthrough for Simultaneous CO2 Capture and CH4 Upgrading from Biogas.

Biogas is an environmentally friendly and sustainable energy resource that can substitute or complement conventional fossil fuels. For practical uses, biogas upgrading, mainly through the effective separation of CO2 (0.33 nm) and CH4 (0.38 nm), is required to meet the approximately 90-95% purity of CH4, while CO2 should be concomitantly purified. In this study, a high CO2 perm-selective zeolite membrane was synthesized by heteroepitaxially growing a chabazite (CHA) zeolite seed layer with a synthetic precursor that allowed the formation of all-silica deca-dodecasil 3 rhombohedral (DDR) zeolite (with a pore size of 0.36 × 0.44 nm2). The resulting hydrophobic DDR@CHA hybrid membrane on an asymmetric α-Al2O3 tube was thin (ca. 2 μm) and continuous, thus providing both high flux and permselectivity for CO2 irrespective of the presence or absence of water vapor (the third largest component in the biogas streams). To the best of our knowledge, the CO2 permeance of (2.9 ± 0.3) × 10-7 mol m-2 s-1 Pa-1 and CO2/CH4 separation factor of ca. 274 ± 73 at a saturated water vapor partial pressure of ca. 12 kPa at 50 °C have the highest CO2/CH4 separation performance yet achieved. Furthermore, we explored the membrane module properties of the hybrid membrane in terms of the recovery and purity of both CO2 and CH4 under dry and wet conditions. Despite the high intrinsic membrane properties of the current hybrid membrane, reflected by the high permeance and SF, the corresponding module properties indicated that high-performance separation of CO2 and CH4 for the desired biogas upgrading was achieved at a limited processing capacity. This supports the importance of understanding the correlation between the membrane and module properties, as this will provide guidance for the optimal operating conditions.

A Communication Interface for Multilayer Cloud Computing Architecture for Low Cost Underwater Vehicles*

To enable high computational loads for low cost underwater drones, a cloud based architecture is proposed to take advantage of recent development in machine learning and computer vision. The processing power made available will benefit vehicles with limited onboard processing capacity. The rapid development of cloud computing services have made servers with significant computational resources easier to access. In this paper, a communication interface for cloud based multilayer architecture is proposed to enable real time performance by distributing the workload to networked processing devices. It adopts a publish-subscribe model for efficient communication between the layers. The latency and workload distribution are evaluated to assess the efficiency of the proposed method. An application to semantic segmentation of under-water scenes is also tested to measure the framework capabilities for real-time operation using more resource-demanding tools. The conducted experiments resulted in time and performance gains through offloading the underwater vehicle, and forwarding the computations to the cloud based layer.

Efficient VNF Placement for Poisson Arrived Traffic

The emergence of Network Function Virtualization (NFV) and Software Defined Network (SDN) has greatly reformed the network. It is important to reduce the queuing delay spent/observed in NFV servers for the placement of Virtual Network Functions (VNFs). In this work, we mainly focus on the placement of VNFs with Poisson Arrived Traffic (VNFPPAT) to tackle the queuing delay problem. Both the Poisson distribution of traffic flows and various resource-sharing VNFs lead to the prolonged queuing delay in NFV servers with limited processing capacities. Considering the end-to-end delay as our optimization objective, we formulate this problem as a 0-1 quadratic fractional programming problem. This formulation is linearized to obtain the optimal solution for small scale networks. After proving VNFPPAT is NP-hard, we propose heuristic algorithms to obtain sub-optimal placement schemes. Through extensive simulations, we have shown that our proposed algorithms outperform the related state-of-the-art Improve Service Chaining Performance (ISCP) by 72% in terms of the end-to-end delay.

Automated object detection on aerial images for limited capacity embedded device using a lightweight CNN model

With the growing demand for geospatial data, challenging aerial images with high spatial, spectral, and temporal resolution achieve excellent development. Currently, deep Convolutional Neural Network (CNN) structures are applied widely for object detection. Nevertheless, existing deep CNN-based models consist of complex network structures and require immense amounts of graphics processing unit (GPU) computation power with high energy consumption. Thus, achieving efficient real-time object detection for limited memory and processing capacity embedded device is a major challenge. This paper proposes a feasible and lightweight object detection model based on deep CNN where a mobile inverted bottleneck module is adopted in the backbone structure. Moreover, an enhanced spatial pyramid pooling is adopted to increase the receptive field in the network by concatenating the multi-scale local region features. The experimental results demonstrated that the proposed model achieved higher average precision and required the smallest memory storage compared to previous works. Moreover, the proposed model offers the best trade-offs in terms of detection accuracy, model size, and detection time which has excellent potential to be deployed on limited capacity embedded device.

A Multi Agent Based Dynamic Resource Allocation in Fog-Cloud Computing Environment

Cloud high latency limitation has necessitated the introduction of Fog computing paradigm that extends computing infrastructures in the cloud data centers to the edge network. Extended cloud resources provide processing, storage and network services to time sensitive request associated to the Internet of Things (IoT) services in network edge. The rapid increase in adoption of IoT devices, variations in user requirements, limited processing and storage capacity of fog resources and problem of fog resources over saturation has made provisioning and allotment of computing resources in fog environment a formidable task. Satisfying application and request deadline is the most substantial challenge compared to other dynamic variations in parameters of client requirements. To curtail these issues, the integrated fog-cloud computing environment and efficient resource selection method is highly required. This paper proposed an agent based dynamic resource allocation that employs the use of host agent to analyze the QoSrequirements of application and request and select a suitable execution layer. The host agent forwards the application request to a layer agent which is responsible for the allocation of best resource that satisfies the requirement of the application request. Host agent and layers agents maintains resource information tables for matching of task and computing resources. CloudSim toolkit functionalities were extended to simulate a realistic fog environment where the proposed method is evaluated. The experimental results proved that the proposed method performs better in terms of processing time, latency and percentage QoS delivery.
 HIGHLIGHTS
 
 The distance between the cloud infrastructure and the edge IoT devices makes the cloud not too competent for some IoT applications, especially the sensitive ones
 To minimize the latency in the cloud and ensure prompt response to user requests, Fog computing, which extends the cloud services to edge network was introduced
 The proliferation in adoption of IoT devices and fog resource limitations has made resource scheduling in fog computing a tedious one
 
 GRAPHICAL ABSTRACT

Detection of DIS Flooding Attacks in IoT Networks Using Machine Learning Methods

In today, Internet of Things (IoT) has a wide usage area and makes easier our lives with smart objects that can communicate with each other without human intervention. However, as with Wireless Sensor Networks, IoT networks bring new risks. These risks reaching worrying levels cause some significant issues such as security, privacy, and energy in the network topology. The IPv6 Routing Protocol for Low-Power and Lossy Network (RPL) is a routing protocol for resource-constrained devices in IoT networks. When it transmits packets between nodes, the nodes can be exposed to a series of attacks. DODAG Information Solicitation (DIS) Flooding attack is one of the most effective types of attacks against this protocol and negatively affects the energy level of the node and its limited processing capacities. Although many intrusion detection methods are used to detect attacks in IoT security, innovative and energy-saving methods are needed. DIS Flooding attacks detection and prevention methods have not been adequately presented in the literature. To address the mentioned need, this study provides high-performance detection of DIS Flooding attacks by applying Logical Regression (LR) and Support Vector Machine machine learning methods. The experiments are implemented by using the Contiki-Cooja simulation environment and the experimental results have been evaluated using various performance metrics. It can be concluded that LR achieves higher attack detection in terms of accuracy.

카메라 기반 강화학습을 이용한 드론 장애물 회피 알고리즘

Among drone autonomous flight technologies, obstacle avoidance is a very important technology that can prevent damage to drones or surrounding environments and prevent danger. Although the LiDAR sensor-based obstacle avoidance method shows relatively high accuracy and is widely used in recent studies, it has disadvantages of high unit price and limited processing capacity for visual information. Therefore, this paper proposes an obstacle avoidance algorithm for drones using camera-based PPO(Proximal Policy Optimization) reinforcement learning, which is relatively inexpensive and highly scalable using visual information. Drone, obstacles, target points, etc. are randomly located in a learning environment in the three-dimensional space, stereo images are obtained using a Unity camera, and then YOLov4Tiny object detection is performed. Next, the distance between the drone and the detected object is measured through triangulation of the stereo camera. Based on this distance, the presence or absence of obstacles is determined. Penalties are set if they are obstacles and rewards are given if they are target points. The experimennt of this method shows that a camera-based obstacle avoidance algorithm can be a sufficiently similar level of accuracy and average target point arrival time compared to a LiDAR-based obstacle avoidance algorithm, so it is highly likely to be used.

DESIGN FINANCIAL ACCOUNTING USING BLOCKCHAIN APPROACH IN EDUCATION

Indonesia memasuki revolusi 4.0 yang membawa banyak sekali perubahan di segala bidang termasuk bidang teknologi. Teknologi keterbaharuan terbesar bersifat terdesentralisasi dengan ciri transparan, nyaman, permanen serta tidak bisa diganti yang biasa dikenal dengan Blockchain. Blockchain sudah diterapkan di banyak bidang semacam cryptocurrency, akuntansi serta tata kelola pendidikan. Tetapi, teknologi Blockchain saat ini sedang dalam sesi percobaan serta mempunyai sebagian permasalahan yang wajib dituntaskan tercantum kapasitas pemrosesan informasi yang terbatas, kerahasiaan data, serta kesusahan regulasi. Artikel ini menerangkan tentang kemampuan pelaksanaan teknologi Blockchain dalam akuntansi keuangan pendidikan pendidikan. Maka dari itu, penelitian ini mengusulkan desain framework bagi finansial pendidikan tinggi agar keamanan buku besar dapat ditingkatkan. Dalam jangka panjang, platform ini secara efisien bisa kurangi kesalahan dalam pengungkapan serta manajemen laba, tingkatkan mutu data akuntansi pendidikan serta kurangi asimetri data. Metode analisis dengan perhitungan skor SUS terhadap sistem Blockchain akuntansi menunjukkan 85, sehingga framework ini termasuk dalam kategori Net Promoter. Hasil akhir penelitian ini mampu mengoptimalkan finansial keuangan pada pendidikan tinggi dengan menggunakan Blockchain. Indonesia is entering the 4.0 revolution, which brings many changes in all fields, including technology. The most extensive new technology is decentralized with transparent, convenient, permanent, and irreplaceable characteristics, commonly known as Blockchain. Blockchain has been applied in all fields, especially in the field of technology. But Blockchain technology is currently still in the pilot stage which has some issues and needs to be resolved. These include regulatory difficulties, limited information processing capacity, and data confidentiality. This article describes the ability to implement Blockchain technology in educational, financial accounting. Therefore, this study proposes a framework design for higher education finance to improve the security of the ledger. In the long term, this platform can efficiently reduce disclosure and earnings management errors, improve the quality of educational accounting data, and reduce data asymmetry. The analysis method by calculating the SUS score against the accounting Blockchain system shows 85, so this framework is included in the Net Promoter category. The final result of this research can optimize financial finance in higher education by using Blockchain.