Distributed Authentication Model for Secure Network Connectivity in Network Separation Technology

Considering the increasing scale and severity of damage from recent cybersecurity incidents, the need for fundamental solutions to external security threats has increased. Hence, network separation technology has been designed to stop the leakage of information by separating business computing networks from the Internet. However, security accidents have been continuously occurring, owing to the degradation of data transmission latency performance between the networks, decreasing the convenience and usability of the work environment. In a conventional centralized network connection concept, a problem occurs because if either usability or security is strengthened, the other is weakened. In this study, we proposed a distributed authentication mechanism for secure network connectivity (DAM4SNC) technology in a distributed network environment that requires security and latency performance simultaneously to overcome the trade-off limitations of existing technology. By communicating with separated networks based on the authentication between distributed nodes, the inefficiency of conventional centralized network connection solutions is overcome. Moreover, the security is enhanced through periodic authentication of the distributed nodes and differentiation of the certification levels. As a result of the experiment, the relative efficiency of the proposed scheme (REP) was about 420% or more in all cases.

Design of Resources Allocation in 6G Cybertwin Technology Using the Fuzzy Neuro Model in Healthcare Systems

In 6G edge communication networks, the machine learning models play a major role in enabling intelligent decision-making in case of optimal resource allocation in case of the healthcare system. However, it causes a bottleneck, in the form of sophisticated memory calculations, between the hidden layers and the cost of communication between the edge devices/edge nodes and the cloud centres, while transmitting the data from the healthcare management system to the cloud centre via edge nodes. In order to reduce these hurdles, it is important to share workloads to further eliminate the problems related to complicated memory calculations and transmission costs. The effort aims mainly to reduce storage costs and cloud computing associated with neural networks as the complexity of the computations increases with increasing numbers of hidden layers. This study modifies federated teaching to function with distributed assignment resource settings as a distributed deep learning model. It improves the capacity to learn from the data and assigns an ideal workload depending on the limited available resources, slow network connection, and more edge devices. Current network status can be sent to the cloud centre by the edge devices and edge nodes autonomously using cybertwin, meaning that local data are often updated to calculate global data. The simulation shows how effective resource management and allocation is better than standard approaches. It is seen from the results that the proposed method achieves higher resource utilization and success rate than existing methods. Index Terms are fuzzy, healthcare, bioinformatics, 6G wireless communication, cybertwin, machine learning, neural network, and edge.

A Connection Access Mechanism of Distributed Network based on Block Chain

Cross-border payment optimization technology based on block chain has become a hot spot in the industry. The traditional method mainly includes the block feature detection method, the fuzzy access method, the adaptive scheduling method, which perform related feature extraction and quantitative regression analysis on the collected distributed network connection access data, and combine the fuzzy clustering method to optimize the data access design, and realize the group detection and identification of data in the block chain. However, the traditional method has a large computational overhead for distributed network connection access, and the packet detection capability is not good. This paper constructs a statistical sequence model of adaptive connection access data to extract the descriptive statistical features of the distributed network block chain adaptive connection access data similarity. The performance of the strategy retrieval efficiency in the experiment is tested based on the strategy management method. The experiment performs matching query tests on the test sets of different query sizes. The different parameters for error rate and search delay test are set to evaluate the impact of different parameters on retrieval performance. The calculation method of single delay is the total delay or the total number of matches. The optimization effect is mainly measured by the retrieval delay of the strategy in the strategy management contract; the smaller the delay, the higher the execution efficiency, and the better the retrieval optimization effect.

Image and pattern reconstruction using differentiable plasticity

The brain is a substantial boon to humankind that adapts nature accordingly. The brain can learn and unlearn based on the situation. This singularity of human learning led to the research creating models using Artificial Intelligence (AI) to incorporate the brain’s behavior. The investigation opened up many new approaches to study AI with neural networks by adding new techniques to imitate the human brain’s functionalities. Many models can learn from experience like Recurrent Neural Network(RNN’s), Long Short Term Memory (LSTM) with the fixed network size. This paper describes the simple method of creating the model which will behave similar to the biological brain and recreates its differentiable plasticity to adopt the features of neural network connection. It also shows that applying plasticity and the Hebbian plastic connection rule can result in optimization in RNN. This new approach of reconstruction of images based on plastic neural network experiments shows that the above novel approach gives more optimized results than the traditionally used RNN techniques. In this paper, a proposal is made where models can memorize and reconstruct unseen sets of images by solving recurrent networks using plasticity rules.

An Efficient Authentication Scheme for Internet of Things

The Internet of Things (IoT) is increasingly affecting human lives in multiple profound ways. “Things” have the ability to communicate, generate, transmit and store data over the network connection. During each communication between “Things”, the data transmitted is potentially vulnerable to malicious attacks, loss, distortions and interruption which impair functionality, system efficiency and user satisfaction. Additionally, inappropriate user controls can cause problems in IoT services, such as granting anonymous users access to personal resources and enable legitimate users to access resources in an illegal manner or preventing legitimate users to access resources in an authorized manner. Therefore, communications between things need to be authenticated, authorized, secured and ensured to have high privacy by applying a strong authentication protocol. The aim of this research is to enhance the authentication protocol, starting by reducing the heavy use of storage in “Things”, and eliminating unnecessary messages during authentication steps, taking into consideration the network security analysis. This research represents a security performance analysis and enhancement authentication for the IoT. The results indicate that the enhanced protocol has a positive effect on minimizing packet length and time performance in authenticating users having once obtained access to the visited location area compared with the other two protocols used for comparative purposes, with 33% increased the proposed protocol performance.

A Smart Shelf Design for Retail Store Real Time Inventory Management Automation

Conventional retail store inventory management systems rely on stockroom actions. However, especially in big scale retail stores, a certain amount of goods is placed on the display shelves. The items placed on the display shelves are not counted until their tags are identified by a cash register and marked as sold in the inventory management system. In this study, we propose a smart shelf that is capable of counting the specific items placed on it by detecting the location and the weight of the items. Our approach assumes that specific items in a retail store are placed in a specified location on each shelf, which is a widely preferred approach. The identified product information is then transferred to the inventory management system through the local network connection, and products on the display shelves can be counted in real time. The results show that the location and weight of the items can be identified with remarkable accuracy.

Knowledge search, knowledge integration and enterprise breakthrough innovation under the characteristics of innovation ecosystem network: The empirical evidence from enterprises in Beijing-Tianjin-Hebei region

Enterprises acquire heterogeneous knowledge through external knowledge search and adapt to the change of external environment, which is of great significance to enterprise breakthrough innovation. This paper takes the innovation ecosystem as the boundary of the research paradigm. Based on innovation ecosystem theory, knowledge management theory and enterprise innovation theory, this paper constructs a moderated mediation model of the enterprise knowledge search, knowledge integration and breakthrough innovation under the characteristics of innovation ecosystem network. This model is tested on the survey data of 344 technology enterprise and manufacturing industries including R&D departments in the Beijing-Tianjin-Hebei region. The research results show that: knowledge integration plays part of the intermediary role between knowledge search and enterprise breakthrough innovation; the larger the network scale, the stronger the network connection, the stronger the intermediary role of knowledge integration on the relationship between knowledge search and enterprise breakthrough innovation. The research results reveal the important role of the innovation ecosystem in enterprise breakthrough innovation. At the same time, the research on knowledge search and enterprise breakthrough innovation should consider the network characteristics of innovation ecosystem and the ability of enterprise knowledge integration.

A Web-Based Decision Support Systems Framework for Ill-Structured Decision Situations

Decisions and actions in an ill-structured situation often include high-time constraints, lack of information, and poor cognitive efforts. Obtaining the necessary information through an information systems tool is supposed to be the best solution in such situations. To expose the decision situation, this study has taken the fire and civil defense service as the field of study. In exploring the required information resources, elements of the system architecture, and suitability of the proposed system in the current field, this study has resorted to the qualitative approach. To assess the dependability and performance of the systems, this study has used the RAS metrics and a black-box test. The result showed that the reliability stood within 62.70–70.00%, and its availability stood at 99.00% with a downtime of 3.65 days/year from a three-month study. As per the black-box test with standard 4G network connection, the system takes an average loading time of 1.00s for alphanumeric contents, 3.50s for images and graphics, and 5.50s for loading maps and navigations. This research evidenced that, the local emergency response and rescue units in developing countries like Bangladesh might want to use a well-designed response support system for improved acquisition, dissemination, and utilization of response information.

Social incentive factors in interventions promoting sustainable behaviors: A meta-analysis

Based on a meta-analysis, this paper highlights the strength and relevance of several social incentive factors concerning pro-environmental behaviors, including social influence, network factors (like network size, network connection and leadership), trust in others, and trust in institutions. Firstly, our results suggest that social influence is necessary for the emergence of pro-environmental behaviors. More specifically, an internal social influence (i.e., motivating people to change their perceptions and attitudes) is essential to promote pro-environmental behaviors. Secondly, network connection encourages pro-environmental behaviors, meaning that the effectiveness of a conservation policy can be improved if connections among individuals are increased. Finally, trust in institutions can dictate individual behaviors to shape policy design and generate desired policy outcomes.