Integration Of Heterogeneous Networks Research Articles

Advancing emotion recognition in social media: A novel integration of heterogeneous neural networks with fine-tuned language models

Social media platforms have emerged as crucial sources for emotion analysis, but the issue of non-compliance in labeling by fine-tuned large language models (LLMs) can significantly impact the accuracy of emotion classification. This study addresses this challenge by introducing a novel compliance-driven training set that systematically harmonizes label discrepancies across multiple LLMs, thereby enhancing classification accuracy by over 5% on the non-compliance set. Integrating this compliance set with a Heterogeneous Neural Network (HNN) architecture, we propose a robust framework for emotion classification. Our approach is validated on three diverse datasets, GoEmotion, Friends, and TEC, demonstrating substantial improvements in accuracy, F1 score, and recall over baseline models. These results confirm the effectiveness of our compliance-driven strategy and establish a new benchmark for emotion recognition in social media content. The proposed framework offers a versatile and scalable solution applicable across various languages and platforms, ensuring broad utility in advanced emotion classification tasks.

Study on Traffic Anomaly Detection of Wireless Communication Network Based on Fuzzy Relation Equation

As the core technology of intrusion detection system, network abnormal traffic detection has always been an important research direction in academia and industry. Related studies show that the failure to find the abnormal situation in the network in time will cause incalculable damage to the computer system and even the whole network. With the emergence of large-scale lightweight terminal nodes, whose characteristics of low computing power and continuous data collection, a distributed abnormal traffic detection technology has emerged. As a kind of distributed structure with decentralized data, federated learning can not only protect local data privacy, reduce communication overhead, but also achieve the effect of centralized training, However, in the era of the Internet of Things with heterogeneous network integration and regular access of massive terminals, the network traffic distribution of different devices is differentiated due to the different security needs of diversified terminals. This will lead to the traditional federated learning-based network anomaly traffic detection facing two major challenges, the uneven data distribution leads to the model training cannot be optimized, and the distributed training global model is not suitable for local network anomaly traffic detection. The scheme of this paper showed significant advantages in the performance evaluation of backbone and mission UAVs, achieving an accuracy of 92.47% and 93.01%, respectively. In contrast, the accuracy of the traditional federated learning method is 89.87% and 89.11%, respectively, which is slightly lower than the present scheme. We propose a framework and algorithm for network anomalous traffic detection based on cluster federated learning. Taking the Internet of Vehicles as the background, the security requirements of the devices connected to the Internet of Vehicles are analyzed, and a set of federated learning data sets meeting the distribution of network traffic in practical applications are constructed with the field recognized data set KDDCup99. This paper verifies the excellent performance of the network anomaly traffic detection mechanism based on cluster federated learning in the case of heterogeneous data distribution.

An Effective Multi-Agent Ant Colony Optimization Algorithm for QoS-Aware Cloud Service Composition

In order to improve the efficiency of wireless network virtualization resource processing, this paper combines the dynamic resource allocation algorithm to construct a wireless network virtualization resource sharing model. This paper proposes a task-oriented resource management model and uses the task-oriented TRS model to describe resources and service processes, reducing the complexity of formulating resource allocation strategies. Moreover, this paper comprehensively considers factors such as centralized coordination control cost and limited domain topology visibility to improve the dynamic resource allocation algorithm. Through the abstraction and unified representation of network resources, resource sharing, and efficient reuse, the coexistence and integration of heterogeneous wireless networks can be realized. Wireless network virtualization can decouple complex and diverse network management and control functions from hardware and extract them to the upper layer for unified coordination and management, thereby reducing network management costs and improving network management and control efficiency

Bezbjednost u sajber fizičkim i IoT sistemima

The security of Cyber-Physical Systems (CPS) and the Internet of Things (IoT) is a significant issue due to the increasing integration of these systems into critical infrastructure, production and other industrial environments, as well as the growing use of IoT devices in various areas of life. It is therefore important to consider the security challenges in these systems and work on their resolution in order to maintain the stability and reliability of these systems. This paper discusses the security of CPS and IoT systems, including the architecture of such systems, security requirements and standards, as well as specific security issues that arise in both types of systems. In particular, CPS security is considered through the physical interface, control system, availability and time constraints, while IoT security is considered through trust management, secure routing protocols, integration of heterogeneous networks and privacy protection.

A Sustainable Business Model for a Neutral Host Supporting 5G and beyond (5GB) Ultra-Dense Networks: Challenges, Directions, and Architecture.

With the deployment of the fifth generation (5G) mobile network systems and the envisioned heterogeneous ultra-dense networks (UDNs), both small cell (SmC) and distributed antenna system (DAS) technologies are required by mobile network operators (MNOs) and venue owners to support multiple spectrum bands, multiple radio access technologies (RATs), multiple optical central offices (COs), and multiple MNOs. As a result, the neutral host business model representing a third party responsible for managing the network enterprise on behalf of multiple MNOs has emerged as a potential solution, mainly influenced by the desire to provide a high user experience without significantly increasing the total cost of ownership (TCO). However, designing a sustainable business model for a neutral host is a nontrivial task, especially when considered in the context of 5G and beyond (5GB) UDNs. In this paper, under an integrated optical wireless network infrastructure, we review how SmC and DAS technologies are evolving towards the adoption of the neutral host business model and identify key challenges and requirements for 5GB support. Thus, we explore recent candidate advancements in heterogeneous network integration technologies for the realization of an efficient 5GB neutral host business model design capable of accommodating both SmC and DAS. Furthermore, we propose a novel design architecture that relies on virtual radio access network (vRAN) to enable real-time dynamic resource allocation and radio over Ethernet (RoE) for flexible and reconfigurable fronthaul. The results from our simulations using MATLAB over two real-life deployment scenarios validate the feasibility of utilizing switched RoE considering end-to-end delay requirements of 5GB under different switching schemes, as long as the queuing delay is kept to a minimum. Finally, the results show that incorporating RoE and vRAN technologies into the neutral host design results in substantial TCO reduction by about 81% in an indoor scenario and 73% in an outdoor scenario.

Generative Adversarial Learning for Intelligent Trust Management in 6G Wireless Networks

The emerging sixth generation (6G) is the integration of heterogeneous wireless networks, which can seamlessly support anywhere and anytime networking. But high quality of trust should be offered by 6G to meet mobile user expectations. Artificial intelligence (AI) is considered as one of the most important components in 6G. AI-based trust management is a promising paradigm to provide trusted and reliable services. In this article, a generative-adversarial-learning-en-abled trust management method is presented for 6G wireless networks. Some typical AI-based trust management schemes are first reviewed, and then a potential heterogeneous and intelligent 6G architecture is introduced. Next, the integration of AI and trust management is developed to optimize intelligence and security. Finally, the presented AI-based trust management method is applied to secure clustering to achieve reliable and real-time communications. Simulation results have demonstrated its excellent performance in guaranteeing network security and service quality.

100 Gbps Dynamic Extensible Protocol Parser Based on an FPGA

In order to facilitate the transition between networks and the integration of heterogeneous networks, the underlying link design of the current mainstream Information-Centric Networking (ICN) still considers the characteristics of the general network and extends the customized ICN protocol on this basis. This requires that the network transmission equipment can not only distinguish general network packets but also support the identification of ICN-specific protocols. However, traditional network protocol parsers are designed for specific network application scenarios, and it is difficult to flexibly expand new protocol parsing rules for different ICN network architectures. For this reason, we propose a general dynamic extensible protocol parser deployed on FPGA, which supports the real-time update of network protocol parsing rules by configuring extended protocol descriptors. At the same time, the multi-queue protocol management mechanism is adopted to realize the grouping management and rapid parsing of the extended protocol. The results demonstrate that the method can effectively support the protocol parsing of 100 Gbps high-speed network data packets and can dynamically update the protocol parsing rules under ultra-low latency. Compared with the current commercial programmable network equipment, this solution improves the protocol update efficiency by several orders of magnitude and better supports the online updating of network equipment.

IeHDDP: An Integrated Solution for Topology Discovery and Automatic In-Band Control Channel Establishment for Hybrid SDN Environments

In-Band enhanced Hybrid Domain Discovery Protocol (ieHDDP) is a novel integral approach for hybrid Software-Defined Networking (SDN) environments that simultaneously provides a topology discovery service and an autonomous control channel configuration in the band. This contribution is particularly relevant since, to the best of our knowledge, it is the first all-in-one proposal for SDN capable of collecting the entire topology information (type of devices, links, etc.) and establishing in-band control channels at once in hybrid SDN environments (composed by SDN/no-SDN, wired/wireless devices), even with isolated SDN devices. ieHDDP facilitates the integration of heterogeneous networks, for example, in 5G/6G scenarios, and the deployment of SDN devices by using a simple exploration mechanism to gather all the required topological information and learn the necessary routes between the control and data planes at the same time. ieHDDP has been implemented in a well-known SDN software switch and evaluated in a comprehensive set of randomized topologies, acknowledging that ieHDDP is scalable in representative scenarios.

Special issue on aerial access networks for 6G

The current development of 5G networks represents a breakthrough in the design of communication networks, for its ability to provide a single platform enabling a wide variety of data services. With these significant enhancements enabled by 5G, it is already possible to envision the needs towards 6G. Providing "connectivity from the sky" is one innovative trend for upcoming 6G communication systems. Satellites, high and low altitude platforms (HAP and LAP), drones, aircrafts, and airships are being considered as candidates for deploying wireless communications complementing terrestrial communications. Combining these network elements, Aerial Access Networks (AANs) have attracted significant attention from both academia and industry, and are recognized as a candidate solution for 6G communications. AANs are heterogeneous networks that are engineered to utilize satellites, HAPs, and LAPs to build network access platforms. Unlike terrestrial wireless networks, AANs are characterized by dynamic, thus frequently changed, network topologies and more vulnerable communication connections. However, AANs are not intended to replace the existing technologies, but instead to work with them in a complementary and integrated fashion. Therefore, AANs are accompanied with the demand for seamless integration of heterogeneous networks, such that the network Quality-of-Service (QoS) can be improved. Therefore, it is essential to develop new communications, signal processing, and optimization approaches to accommodate diverse services and applications with different QoS requirements in various scenarios.

Multiple Optimizations-Based ESRFBN Super-Resolution Network Algorithm for MR Images

Magnetic resonance (MR) images can detect small pathological tissue with the size of 3–5 image pixels at an early stage, which is of great significance in the localization of pathological lesions and the diagnosis of disease. High-resolution MR images can provide clearer structural details and help doctors to analyze and diagnose the disease correctly. In this paper, MR super-resolution based on the multiple optimizations-based Enhanced Super Resolution Feed Back Network (ESRFBN) is proposed. The method realizes network optimization from the three perspectives of network structure, data characteristics and heterogeneous network integration. Firstly, a super-resolution network structure based on multi-slice input optimization is proposed to make full use of the structural similarity between samples. Secondly, aiming at the problem that the L1 or L2 loss function is based on a per-pixel comparison of differences, without considering human visual perception, the optimization method of multiple loss function cascade is proposed, which combines the L1 loss function to retain the color and brightness characteristics and the MS-SSIM loss function to retain the contrast characteristics of the high-frequency region better, so that the depth model has better characterization performance; thirdly, in view of the problem that large deep learning networks are difficult to balance model complexity and training difficulty, a heterogeneous network fusion method is proposed. For multiple independent deep super-resolution networks, the output of a single network is integrated through an additional fusion layer, which broadens the width of the network, and can effectively improve the mapping and characterization capabilities of high- and low-resolution features. The experimental results on two super-resolution scales and on MR images datasets of four human body parts show that the proposed large-sample space learning super-resolution method effectively improves the super-resolution performance.

AI-Assisted Service Virtualization and Flow Management Framework for 6G-Enabled Cloud-Software-Defined Network-Based IoT

The sixth-generation (6G) communication technology provides a high level of interoperability through terahertz data transfer and latency-less service sharing. Due to its interoperable nature, the integration of heterogeneous networks, such as the Internet of Things (IoT) and cloud radio access networks (CRANs), is performed at ease. This integration is managed using software-defined networks (SDNs) for managing the Quality of Service (QoS) experience of the users, irrespective of the application. This manuscript proposes the service virtualization and flow management framework (SVFMF) for the reliable utilization of resources in the 6G-cloud environment. The imbalance in a service request and response due to overloaded and idle virtual resources is addressed in this framework. For this purpose, this framework endorses service virtualization and user allocation modules for mitigating the drawbacks of imbalanced service allocations. Linear decision making of the service virtualization process helps to reduce the computation and service discovery by identifying overloaded services and performing a reallocation. The purpose of user allocation is to distribute the service requests to the idle service providers to reduce the prolonged wait time of the increasing user requests. The performance of the proposed framework is verified using experimental analyses, for the metrics service discovery and computation time, service failure ratio, and flows. The reliability of SVFMF is proved by varying the density of users, virtual machines, service requests, and user allocation per virtual machine, respectively.

Edge-Intelligence-Empowered, Unified Authentication and Trust Evaluation for Heterogeneous Beyond 5G Systems

Integrating artificial intelligence (AI) with mobile edge computing, edge intelligence (EI) has emerged as a new paradigm for 5G and beyond 5G (B5G) systems. The integration of EI and heterogeneous networks (e.g., mobile and wireless local area networks) also raises new concerns about security and privacy. This article examines two important security aspects of EI-empowered, heterogeneous, B5G networks, that is, authentication and trust-evaluation-based compromised user equipment (UE) detection. Technical challenges are discussed. A new edge-computing-enabled, unified authentication framework is developed, which authenticates UEs consistently via heterogeneous networks with UEs' privacy preserved. A new trust-evaluation-based compromised UE detection method is developed based on reinforcement learning to prevent compromised UEs from launching internal attacks. Case studies show that the new frameworks improve the authentication efficiency and detection rate of compromised UEs, as compared to technologies specified by current 5G standards. The frameworks have great potential to secure B5G in future heterogeneous network environments.

Endogenous Trusted DRL-Based Service Function Chain Orchestration for IoT

With the development of the Internet of Things, trust has become a limited factor in the integration of heterogeneous IoT networks. In this regard, we use the combination of blockchain technology and SDN/NFV to build a heterogeneous IoT network resource management model based on the consortium chain. In order to solve the efficiency problem caused by the full amount of data on the chain, we deploy light nodes and full nodes for the consortium chain. At the same time, we use the idea of identification to realize the separation of identification and resource information, build the application mode of on-chain identification and off-chain information, and realize resources endogenous trust management. We also propose a practical Byzantine fault-tolerant consensus mechanism based on reputation value to save consensus costs and improve efficiency. Combined with artificial intelligence technology, we introduce deep reinforcement learning for service function chain orchestration, and design a service function chain orchestration algorithm based on Asynchronous Advantage Actor-Critic to optimize orchestration costs. The final simulation results show that the consensus algorithm and service function chain orchestration algorithm we designed have good performance in terms of cost saving and efficiency improvement.

A hierarchical game-based power allocation algorithm in 5G heterogeneous integrated networks

In this paper, we proposed a novel power allocation algorithm based on hierarchical game in 5G heterogeneous integrated networks. The radio network controller is the first layer, the base stations are the second layer, and the base station users are the third layer. The stackelberg game model is used between the first and second layers, in which the radio network controller is the leader and the base stations are the followers. The radio network controller sets the interference prices for the base stations and the users in the third layer can update their transmission power by non-cooperative game based on the setting interference price. According to the proposed scheme, the users power allocation can reach the Nash equilibrium through by non-cooperative game and obtain the optimal transmission power. Moreover, it is proved that the equilibrium solution is composed of the optimal price and the optimal transmission power, and the convergence of this algorithm is also verified. The simulation results show that the proposed algorithm can decrease the interference and improve the system capacity.

Integrating Mobile Ad Hoc Networks with the Internet Based on OLSR

Although a standalone and isolated mobile ad hoc network (MANET) is practical in many scenarios, integration with the Internet is much more advantageous. The integration of a MANET with the Internet provides MANET users with Internet access and hence increases the scope of the MANET application. In addition, the Internet can benefit from this integration by an extension of the network coverage area. However, the integration of heterogeneous networks raises many issues. To overcome the incompatibilities between different architectures, gateways are used. This paper proposes a lightweight integration scheme for a MANET and the wider Internet, based on the optimized link state routing (OLSR). OLSR routing messages are reengineered and optimized to meet the needs of integration without involving additional routing messages for gateway discovery. The compulsory registration of a MANET node with a gateway node in a traditional integration is not required in the proposed approach, meaning that nodes can move freely within the local MANET with no disruption in the connection to another node in the external network. The proposed system is evaluated using the OMNET++ network simulator and is compared to another existing system. The simulation results demonstrate the validity of the proposed approach.

Multi-criteria Handover Decision for Heterogeneous Networks: Carrier Aggregation Deployment Scenario

Carrier aggregation and integration of heterogeneous networks allow the mobile user equipment to benefit from wider bandwidth and radio coverage of different access technologies. However, these technologies have increased handoff scenario probability through user equipment’s mobility, leading to high outage probability and low throughput. Handover is an important aspect of mobility management which allows users to migrate from one cell to another without losing connections. But no lone access technology meets the requirements of providing seamless communication without loss and delay. Therefore, high-quality service continuity and reliable user equipment access to network anywhere and at any time require the design of an appropriate handover decision algorithm. In this paper, a multi-criteria based handover decision-making algorithm is proposed to evade loss of communication and provide better performance to the system. It adaptively makes handover decisions based on different decision criteria (load, availability of resources, and the handover scenario type) in addition to signal interference to noise ratio. The multicriteria handover decision making algorithm-based method chooses among the cells that satisfy the requirements for the handover. As compared to some existing handover decision algorithms, simulation result shows that this algorithm improves system performance in terms of handover failure by 93%, 72%, and 58%; radio link failure reduction by 77%, 43%, and 22%; and handover ping–pong by 81%, 59%, and 36% over the conventional received signal strength, received wireless transmission line, and multiinfluence factor hand over decisions respectively.

Research on Energy Internet Power Line Communication Technology Based on Block Chain

With the continuous development of energy Internet technology, its requirements for communication systems are getting higher and higher. As for the energy Internet, it is based on the concept of the Internet and the existing smart grid. Its communication system also needs to rely on the existing power communication system. In the energy Internet, the use of block chain and intelligent contract technology to meet credible and automatic point-to-point energy transactions can improve the overall operating efficiency of the system, reduce management costs, and make the energy Internet a self-organizing and self-regulating structure. In essence, energy Internet and block chain technology must be built on the Internet of Things, a common intelligent device. This paper takes the power line communication technology of the Internet in the current power system as the research object, analyzes the main challenges and problems in the construction of energy Internet based on blockchain, and then provides the corresponding research basis for the future heterogeneous network integration.

Computational Enhancements of Wearable Healthcare Devices on Pervasive Computing System

The diverse user demands in the system supported with the internet of things are often managed efficiently, using the computing system that is pervasive. Pervasive computing system in an integration of heterogeneous distributed network and communication technologies and other referred as the ubiquitous computing. All though it handles the user requirement properly. The ingenuousness in the conveyance of the information, in the standard of handling and extending the heterogeneity assistance for the dispersed clients are still under construction in the as it is very challenging in the pervasive computing system. In order to provide proper and a steadfast communication for the users using an IOT based wearable health care device the paper introduces the new dispersed and elastic computing model (DECM). The developed system utilizes the recurrent-learning for the examining the allocation of resources according to the requirements as well as the allotment aspects. Based on the determined requirements of the resources, the pervasive computing system provide services to the user in the end with minimized delay and enhanced rate of communication for the health care wearable devices. The developed system emphasis also on managing the mobility, apart from allocation of resources and distribution for proper data conveyance over the wearable health care device. The working of the laid out system is determined by the experimental analysis. The constancy of the model developed is demonstrated utilizing the metrics such as the failure of request, time of response, managed and backlogged requests, bandwidth as well as storage used. The developed model heightens the number of request managed properly (handled) along with the bandwidth and storage and minimizes the failure in requests, backlogs and the time taken for response.

SINR and Fuzzy Approach based Enhanced Handoff Decision Making Algorithm

There has been unprecedented growth in the wireless networks and range of services provided by them in recent times, there has also been continuous evolution in network technologies aimed at achieving higher speeds, security, reliability and quality of service. The arrival of 3G and 4G networks have revolutionized the wireless data and multimedia services and work is being done at rapid pace to develop the fifth generation (5G) networks to further enhance the Quality of Service. Integration of heterogeneous networks is a must in order to achieve the objective of providing the best quality of services to a mobile user without delays or disruptions. In order to achieve seamless mobility in heterogeneous networks environment, Vertical Handoff (VHO) is the key issue that needs to be addressed effectively. For a long time, the researchers have considered RSS based strategies to be most reliable and effective for VHO decisions. The authors of the paper have discussed various studies which show that SINR based strategies can offer better and more efficient VHO decisions. The paper also focuses on studies of Fuzzy based approach for VHO algorithms.

Heterogeneous networks integration for disease-gene prioritization with node kernels.

The identification of disease-gene associations is a task of fundamental importance in human health research. A typical approach consists in first encoding large gene/protein relational datasets as networks due to the natural and intuitive property of graphs for representing objects' relationships and then utilizing graph-based techniques to prioritize genes for successive low-throughput validation assays. Since different types of interactions between genes yield distinct gene networks, there is the need to integrate different heterogeneous sources to improve the reliability of prioritization systems. We propose an approach based on three phases: first, we merge all sources in a single network, then we partition the integrated network according to edge density introducing a notion of edge type to distinguish the parts and finally, we employ a novel node kernel suitable for graphs with typed edges. We show how the node kernel can generate a large number of discriminative features that can be efficiently processed by linear regularized machine learning classifiers. We report state-of-the-art results on 12 disease-gene associations and on a time-stamped benchmark containing 42 newly discovered associations. Source code: https://github.com/dinhinfotech/DiGI.git. Supplementary data are available at Bioinformatics online.