A cloud-aided privacy-preserving multi-dimensional data comparison protocol

A Quality-of-Service-Aware Service Composition Method in the Internet of Things Using a Multi-Objective Fuzzy-Based Hybrid Algorithm

The Internet of Things (IoT) represents a cutting-edge technical domain, encompassing billions of intelligent objects capable of bridging the physical and virtual worlds across various locations. IoT services are responsible for delivering essential functionalities. In this dynamic and interconnected IoT landscape, providing high-quality services is paramount to enhancing user experiences and optimizing system efficiency. Service composition techniques come into play to address user requests in IoT applications, allowing various IoT services to collaborate seamlessly. Considering the resource limitations of IoT devices, they often leverage cloud infrastructures to overcome technological constraints, benefiting from unlimited resources and capabilities. Moreover, the emergence of fog computing has gained prominence, facilitating IoT application processing in edge networks closer to IoT sensors and effectively reducing delays inherent in cloud data centers. In this context, our study proposes a cloud-/fog-based service composition for IoT, introducing a novel fuzzy-based hybrid algorithm. This algorithm ingeniously combines Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC) optimization algorithms, taking into account energy consumption and Quality of Service (QoS) factors during the service selection process. By leveraging this fuzzy-based hybrid algorithm, our approach aims to revolutionize service composition in IoT environments by empowering intelligent decision-making capabilities and ensuring optimal user satisfaction. Our experimental results demonstrate the effectiveness of the proposed strategy in successfully fulfilling service composition requests by identifying suitable services. When compared to recently introduced methods, our hybrid approach yields significant benefits. On average, it reduces energy consumption by 17.11%, enhances availability and reliability by 8.27% and 4.52%, respectively, and improves the average cost by 21.56%.

Facilitating big-data management in modern business and organizations using cloud computing: a comprehensive study

Modern digital life has produced big data in modern businesses and organizations. To derive information for decision-making from these enormous data sets, a lot of work is required at several levels. The storage, transmission, processing, mining, and serving of big data create problems for digital domains. Despite several efforts to implement big data in businesses, basic issues with big data remain (particularly big-data management (BDM)). Cloud computing, for example, provides companies with well-suited, cost-effective, and consistent on-demand services for big data and analytics. This paper introduces the modern systems for organizational BDM. This article analyzes the latest research to manage organization-generated data using cloud computing. The findings revealed several benefits in integrating big data and cloud computing, the most notable of which is increased company efficiency and improved international trade. This study also highlighted some hazards in the sophisticated computing environment. Cloud computing has the potential to improve corporate management and accountants' jobs significantly. This article's major contribution is to discuss the demands, advantages, and problems of using big data and cloud computing in contemporary businesses and institutions.

Data Feature Extraction Method of Wearable Sensor Based on Convolutional Neural Network

With the rapid development of society and science technology, human health issues have attracted much attention due to wearable devices' ability to provide high-quality sports, health, and activity monitoring services. This paper proposes a method for feature extraction of wearable sensor data based on a convolutional neural network (CNN). First, it uses the Kalman filter to fuse the data to obtain a preliminary state estimation, and then it uses CNN to recognize human behavior, thereby obtaining the corresponding behavior set. Moreover, this paper conducts experiments on 5 datasets. The experimental results show that the method in this paper extracts data features at multiple scales while fully maintaining data independence, can effectively extract corresponding feature data, and has strong generalization ability, which can adapt to different learning tasks.

Blockchain-based federated learning methodologies in smart environments

Blockchain technology is an undeniable ledger technology that stores transactions in high-security chains of blocks. Blockchain can solve security and privacy issues in a variety of domains. With the rapid development of smart environments and complicated contracts between users and intelligent devices, federated learning (FL) is a new paradigm to improve accuracy and precision factors of data mining by supporting information privacy and security. Much sensitive information such as patient health records, safety industrial information, and banking personal information in various domains of the Internet of Things (IoT) including smart city, smart healthcare, and smart industry should be collected and gathered to train and test with high potential privacy and secured manner. Using blockchain technology to the adaption of intelligent learning can influence maintaining and sustaining information security and privacy. Finally, blockchain-based FL mechanisms are very hot topics and cut of scientific edge in data science and artificial intelligence. This research proposes a systematic study on the discussion of privacy and security in the field of blockchain-based FL methodologies on the scientific databases to provide an objective road map of the status of this issue. According to the analytical results of this research, blockchain-based FL has been grown significantly during these 5 years and blockchain technology has been used more to solve problems related to patient healthcare records, image retrieval, cancer datasets, industrial equipment, and economical information in the field of IoT applications and smart environments.

Forecasting Daily Electricity Price by Hybrid Model of Fractional Wavelet Transform, Feature Selection, Support Vector Machine and Optimization Algorithm

This paper proposes a novel hybrid forecasting model with three main parts to accurately forecast daily electricity prices. In the first part, where data are divided into high- and low-frequency data using the fractional wavelet transform, the best data with the highest relevancy are selected, using a feature selection algorithm. The second part is based on a nonlinear support vector network and auto-regressive integrated moving average (ARIMA) method for better training the previous values of electricity prices. The third part optimally adjusts the proposed support vector machine parameters with an error-base objective function, using the improved grey wolf and particle swarm optimization. The proposed method is applied to forecast electricity markets, and the results obtained are analyzed with the help of the criteria based on the forecast errors. The results demonstrate the high accuracy in the MAPE index of forecasting the electricity price, which is about 91% as compared to other forecasting methods.