Social Anchor: Privacy-Friendly Attribute Aggregation From Social Networks

In the last decade or so, we have experienced a tremendous proliferation and popularity of different Social Networks (SNs), resulting more and more user attributes being stored in such SNs. These attributes represent a valuable asset and many innovative online services are offered in exchange of such attributes. This particular phenomenon has allured these social networks to act as Identity Providers (IdPs). However, the current setting unnecessarily imposes a restriction: a user can only release attributes from one single IdP in a single session, thereby, limiting the user to aggregate attributes from multiple IdPs within the same session. In addition, our analysis suggests that the manner by which attributes are released from these SNs is extremely privacy-invasive and a user has very limited control to exercise her privacy during this process. In this article, we present Social Anchor, a system for attribute aggregation from social networks in a privacy-friendly fashion. Our proposed Social Anchor system effectively addresses both of these serious issues. Apart from the proposal, we have implemented Social Anchor following a set of security and privacy requirements. We have also examined the associated trust issues using a formal trust analysis model. Besides, we have presented a formal analysis of its protocols using a state-of-the-art formal analysis tool called AVISPA to ensure the security of Social Anchor. Finally, we have provided a performance analysis of Social Anchor

Smart Relay Selection Scheme Based on Fuzzy Logic with Optimal Power Allocation and Adaptive Data Rate Assignment

In this paper fuzzy logic-based algorithm with improved process of relay selection is presented which not only allocate optimal power for transmission but also help in choosing adaptive data rate. This algorithm utilizes channel gain, cooperative gain and signal to noise ratio with two cases considered in this paper: In case-I nodes do not have their geographical location information while in case-II nodes are having their geographical location information. From Monte Carlo simulations, it can be observed that both cases improve the selection process along with data rate assignment and power allocation, but case-II is the most reliable with almost zero probability of error at the cost of computational complexity which is 10 times more than case-I

Machine Learning for Predictive Analytics in Social Media Data

Machine Learning (ML) has become a potent predictive analytics tool in several fields, including the study of social media data. Social media sites have developed into massive repositories of user-generated information, providing insightful data about user trends, interests, and behavior. This abstract emphasizes the use of machine learning methods for predictive analytics in social media data and examines the potential and problems unique to this field. Utilizing the capabilities of machine learning algorithms to identify significant trends and forecast user behavior from social media data is the goal of this study. The study makes use of a sizable dataset made up of user profiles, blog posts, comments, and engagement metrics gathered from well-known social networking sites. Predictive models are created using a variety of machine learning algorithms, such as ensemble methods, neural networks, decision trees, and support vector machines. As a result, this study emphasizes how important machine learning is for doing predictive analytics on social media data. The employment of diverse algorithms and preprocessing methods yields insightful information about user behavior and enables precise prediction of user behaviors. To improve the prediction powers of machine learning in this area, future research should concentrate on tackling the obstacles related to social media data, such as privacy concerns and data quality issues

Dichotomy model based on the finite element differential equation in the educational informatisation teaching reform model

The dichotomy model of education informatisation is essential, which means the measurement of education informatisation construction and development. Finite element differential equations play an essential role in signal and information teaching. To improve teaching information, the paper applies the dichotomy model of finite element differential equations to the reform of physics education information teaching. This article fully introduces the basic principles of the dichotomy model in finite element differential equations and introduces several analysis methods of the inverse Laplace transform of differential equations. At last, the method is applied to the informatisation of physics education to improve the quality of teaching

An efficient resource optimization scheme for D2D communication

With the rapid development of wireless technologies, wireless access networks have entered their Fifth-Generation (5G) system phase. The heterogeneous and complex nature of a 5G system, with its numerous technological scenarios, poses significant challenges to wireless resource management, making radio resource optimization an important aspect of Device-to-Device (D2D) communication in such systems. Cellular D2D communication can improve spectrum efficiency, increase system capacity, and reduce base station communication burdens by sharing authorized cell resources; however, can also cause serious interference. Therefore, research focusing on reducing this interference by optimizing the configuration of shared cellular resources has also grown in importance. This paper proposes a novel algorithm to address the problems of co-channel interference and energy efficiency optimization in a long-term evolution network. The proposed algorithm uses the fuzzy clustering method, which employs minimum outage probability to divide D2D users into several groups in order to improve system throughput and reduce interference between users. An efficient power control algorithm based on game theory is also proposed to optimize user transmission power within each group and thereby improve user energy efficiency. Simulation results show that these proposed algorithms can effectively improve system throughput, reduce co-channel interference, and enhance energy efficiency

Enhancing IoT Security: A Few-Shot Learning Approach for Intrusion Detection

Recently, the number of Internet of Things (IoT)-connected devices has increased daily. Consequently, cybersecurity challenges have increased due to the natural diversity of the IoT, limited hardware resources, and limited security capabilities. Intrusion detection systems (IDSs) play a substantial role in securing IoT networks. Several researchers have focused on machine learning (ML) and deep learning (DL) to develop intrusion detection techniques. Although ML is good for classification, other methods perform better in feature transformation. However, at the level of accuracy, both learning techniques have their own certain compromises. Although IDSs based on ML and DL methods can achieve a high detection rate, the performance depends on the training dataset size. Incidentally, collecting a large amount of data is one of the main drawbacks that limits performance when training datasets are lacking, and such methods can fail to detect novel attacks. Few-shot learning (FSL) is an emerging approach that is employed in different domains because of its proven ability to learn from a few training samples. Although numerous studies have addressed the issues of IDSs and improved IDS performance, the literature on FSL-based IDSs is scarce. Therefore, an investigation is required to explore the performance of FSL in IoT IDSs. This work proposes an IoT intrusion detection model based on a convolutional neural network as a feature extractor and a prototypical network as an FSL classifier. The empirical results were analyzed and compared with those of recent intrusion detection approaches. The accuracy results reached 99.44%, which shows a promising direction for involving FSL in IoT IDSs

Constructing Artistic Surface Modeling Design Based on Nonlinear Over-limit Interpolation Equation

The digital and physical methods of establishing minimal curved surfaces are the basis for realizing the design of the minimal curved surface modeling structure. Based on this research background, the paper showed an artistic surface modeling method based on nonlinear over-limit difference equations. The article combines parameter optimization and 3D modeling methods to model the constructed surface modeling. The research found that the nonlinear out-of-limit difference equation proposed in the paper is more accurate than the standard fractional differential equation algorithm. For this reason, the method can be extended and applied to the design of artistic surface modeling

An energy-efficient fog-to-cloud Internet of Medical Things architecture

In order to increase the reliability, accuracy, and efficiency in the eHealth, Internet of Medical Things is playing a vital role. Current development in telemedicine and the Internet of Things have delivered efficient and low-cost medical devices. The Internet of Medical Things architectures being developed do not completely recognize the potential of Internet of Things. The Internet of Medical Things sensor devices have limited computation power; in case if a patient is using implanted medical devices, it is not easy to recharge or replace the devices immediately. Biosensors are small devices with limited energy if these devices do not wisely utilize the energy may drain sharply and devices become inactive. The current medical solutions place the bulk of data on cloud-based systems that ultimately creates a bottleneck. In this article, an energy-efficient fog-to-cloud Internet of Medical Things architecture is proposed to optimize energy consumption. In the proposed architecture, Bluetooth enabled biosensors are used, because Bluetooth technology is an energy efficient and also helps to enable the sleep and awake modes. The proposed fog-to-cloud Internet of Medical Things works in three different modes periodic, sleep–awake, and continue to optimize the energy consumption. The proposed technique enabled the sensing modes that gathers the patients’ data efficiently based on their health conditions. The sensed data are transmitted to the relevant fog and cloud devices for further processing. The performance of fog-to-cloud Internet of Medical Things is evaluated through simulation; the results are compared with the results of existing techniques in terms of an end-to-end delay, throughput, and energy consumption. It is analyzed that the proposed technique reduces the energy consumption between 30% and 40%