987 research outputs found
Class Based Multi Stage Encryption for Efficient Data Security in Cloud Environment Using Profile Data
The security issues in the cloud have been well studied. The data security has much importance in point of data owner. There are number of approaches presented earlier towards performance in data security in cloud. To overcome the issues, a class based multi stage encryption algorithm is presented in this paper. The method classifies the data into number of classes and different encryption scheme is used for different classes in different levels. Similarly, the user has been authenticated for their access and they have been classified into different categories. According to the user profile, the method restricts the access of user and based on the same, the method defines security measures. A system defined encryption methodology is used for encrypting the data. Moreover, the user has been returned with other encryption methods which can be decrypted by the user using their own key provided by the system. The proposed algorithm improves the performance of security and improves the data security
Social Factors in P2P Energy Trading Using Hedonic Games
Lately, the energy communities have gained a lot of attention as they have
the potential to significantly contribute to the resilience and flexibility of
the energy system, facilitating widespread integration of intermittent
renewable energy sources. Within these communities the prosumers can engage in
peer-to-peer trading, fostering local collaborations and increasing awareness
about energy usage and flexible consumption. However, even under these
favorable conditions, prosumer engagement levels remain low, requiring trading
mechanisms that are aligned with their social values and expectations. In this
paper, we introduce an innovative hedonic game coordination and cooperation
model for P2P energy trading among prosumers which considers the social
relationships within an energy community to create energy coalitions and
facilitate energy transactions among them. We defined a heuristic that
optimizes the prosumers coalitions, considering their social and energy price
preferences and balancing the energy demand and supply within the community. We
integrated the proposed hedonic game model into a state-of-the-art
blockchain-based P2P energy flexibility market and evaluated its performance
within an energy community of prosumers. The evaluation results on a
blockchain-based P2P energy flexibility market show the effectiveness in
considering social factors when creating coalitions, increasing the total
amount of energy transacted in a market session by 5% compared with other game
theory-based solutions. Finally, it shows the importance of the social
dimensions of P2P energy transactions, the positive social dynamics in the
energy community increasing the amount of energy transacted by more than 10%
while contributing to a more balanced energy demand and supply within the
community.Comment: to be submitted to journa
Novel Charging and Discharging Schemes for Electric Vehicles in Smart Grids
PhD ThesisThis thesis presents smart Charging and Discharging (C&D) schemes in the smart grid that enable a decentralised scheduling with large volumes of Electric Vehicles (EV) participation. The proposed C&D schemes use di erent strategies to atten the power consumption pro le by manipulating the charging or discharging electricity quantity. The novelty of this thesis lies in: 1. A user-behaviour based smart EV charging scheme that lowers the overall peak demand with an optimised EV charging schedule. It achieves the minimal impacts on users' daily routine while satisfying EV charging demands. 2. A decentralised EV electricity exchange process matches the power demand with an adaptive blockchain-enabled C&D scheme and iceberg order execution algorithm. It demonstrates improved performance in terms of charging costs and power consumption pro le. 3. The Peer-to-Peer (P2P) electricity C&D scheme that stimulates the trading depth and energy market pro le with the best price guide. It also increases the EV users' autonomy and achieved maximal bene ts for the network peers while protecting against potential attacks. 4. A novel consensus-mechanism driven EV C&D scheme for the blockchain-based system that accommodates high volume EV scenarios and substantially reduces the power uctuation level. The theoretical and comprehensive simulations prove that the penetration of EV with the proposed schemes minimises the power uctuation level in an urban area, and also increases the resilience of the smart grid system
Incentives and Two-Sided Matching - Engineering Coordination Mechanisms for Social Clouds
The Social Cloud framework leverages existing relationships between members of a social network for the exchange of resources. This thesis focuses on the design of coordination mechanisms to address two challenges in this scenario. In the first part, user participation incentives are studied. In the second part, heuristics for two-sided matching-based resource allocation are designed and evaluated
Pando: Personal Volunteer Computing in Browsers
The large penetration and continued growth in ownership of personal
electronic devices represents a freely available and largely untapped source of
computing power. To leverage those, we present Pando, a new volunteer computing
tool based on a declarative concurrent programming model and implemented using
JavaScript, WebRTC, and WebSockets. This tool enables a dynamically varying
number of failure-prone personal devices contributed by volunteers to
parallelize the application of a function on a stream of values, by using the
devices' browsers. We show that Pando can provide throughput improvements
compared to a single personal device, on a variety of compute-bound
applications including animation rendering and image processing. We also show
the flexibility of our approach by deploying Pando on personal devices
connected over a local network, on Grid5000, a French-wide computing grid in a
virtual private network, and seven PlanetLab nodes distributed in a wide area
network over Europe.Comment: 14 pages, 12 figures, 2 table
Loan Default Prediction: A Complete Revision of LendingClub
Predicción del default: Una revisión completa de LendingClub El objetivo del estudio es determinar un modelo de predicción de default crediticio usando la base de datos de LendingClub. La metodología consiste en estimar las variables que influyen en el proceso de predicción de préstamos pagados y no pagados utilizando el algoritmo Random Forest. El algoritmo define los factores con mayor influencia sobre el pago o el impago, generando un modelo reducido a nueve predictores relacionados con el historial crediticio del prestatario y el historial de pagos dentro de la plataforma. La medición del desempeño del modelo genera un resultado F1 Macro Score con una precisión mayor al 90% de la muestra de evaluación. Las contribuciones de este estudio incluyen, el haber utilizado la base de datos completa de toda la operación de LendingClub disponible, para obtener variables trascendentales para la tarea de clasificación y predicción, que pueden ser útiles para estimar la morosidad en el mercado de préstamos de persona a persona. Podemos sacar dos conclusiones importantes, primero confirmamos la capacidad del algoritmo Random Forest para predecir problemas de clasificación binaria en base a métricas de rendimiento obtenidas y segundo, denotamos la influencia de las variables tradicionales de puntuación de crédito en los problemas de predicción por defecto.The study aims to determine a credit default prediction model using data from LendingClub. The model estimates the effect of the influential variables on the prediction process of paid and unpaid loans. We implemented the random forest algorithm to identify the variables with the most significant influence on payment or default, addressing nine predictors related to the borrower's credit and payment background. Results confirm that the model’s performance generates a F1 Macro Score that accomplishes 90% in accuracy for the evaluation sample. Contributions of this study include using the complete dataset of the entire operation of LendingClub available, to obtain transcendental variables for the classification and prediction task, which can be helpful to estimate the default in the person-to-person loan market. We can draw two important conclusions, first we confirm the Random Forest algorithm's capacity to predict binary classification problems based on performance metrics obtained and second, we denote the influence of traditional credit scoring variables on default prediction problems
Research on trust model in container-based cloud service
Container virtual technology aims to provide program independence and resource sharing. The container enables flexible cloud service. Compared with traditional virtualization, traditional virtual machines have difficulty in resource and expense requirements. The container technology has the advantages of smaller size, faster migration, lower resource overhead, and higher utilization. Within container-based cloud environment, services can adopt multi-target nodes. This paper reports research results to improve the traditional trust model with consideration of cooperation effects. Cooperation trust means that in a container-based cloud environment, services can be divided into multiple containers for different container nodes. When multiple target nodes work for one service at the same time, these nodes are in a cooperation state. When multi-target nodes cooperate to complete the service, the target nodes evaluate each other. The calculation of cooperation trust evaluation is used to update the degree of comprehensive trust. Experimental simulation results show that the cooperation trust evaluation can help solving the trust problem in the container-based cloud environment and can improve the success rate of following cooperation
Evolution through reputation: noise-resistant selection in evolutionary multi-agent systems
Little attention has been paid, in depth, to the relationship between fitness evaluation
in evolutionary algorithms and reputation mechanisms in multi-agent systems, but if
these could be related it opens the way for implementation of distributed evolutionary
systems via multi-agent architectures. Our investigation concentrates on the effectiveness
with which social selection, in the form of reputation, can replace direct
fitness observation as the selection bias in an evolutionary multi-agent system. We do
this in two stages: In the first, we implement a peer-to-peer, adaptive Genetic Algorithm
(GA), in which agents act as individual GAs that, in turn, evolve dynamically
themselves in real-time, using the traditional evolutionary operators of fitness-based
selection, crossover and mutation. In the second stage, we replace the fitness-based
selection operator with a reputation-based one, in which agents choose their mates
based on the collective past experiences of themselves and their peers. Our investigation
shows that this simple model of distributed reputation can be successful as the
evolutionary drive in such a system, exhibiting practically identical performance and
scalability to direct fitness observation. Further, we discuss the effect of noise (in the
form of “defective” agents) in both models. We show that the reputation-based model
is significantly better at identifying the defective agents, thus showing an increased
level of resistance to noise
- …