Heterogeneous Network Selection Research Articles

Heterogeneous wireless network selection using feed forward double hierarchy linguistic neural network

Network selection in heterogeneous wireless networks (HWNs) is a complex issue that requires a thorough understanding of service features and user preferences. This is because the various wireless access technologies have varying capabilities and limitations, and the best network for a voice, video, and data service depends on a variety of factors. For selecting the optimal network in HWNs, varying factors such as the user’s position, accessible network resources, service quality requirements, and user preferences must be considered. The classical decision making procedure is very difficult and uncertain to select the desirable HWNs for voice, video, and data. Therefore, we develop a novel decision making model based on feed-forward neural networks under the double hierarchy linguistic information for the selection of the best HWNs for voice, video, and data. In this article, we introduce a novel feed-forward double hierarchy linguistic neural network using the Hamacher t-norm and t-conorm. Further, the feed-forward double hierarchy linguistic neural network applies to the decision making model for the selection of the best HWNs for voice, video, and data. In this novel decision making model, we first take the given data about HWNs and use the converting function to convert the given data into a double hierarchy linguistic term set. We calculate the hidden layer and output layer information by using Hamacher aggregation operations. Finally, we use the sigmoid activation function on the output layer information to decide on the best HWNs for voice, video, and data according to ranking. The proposed approach is compared with other existing models of decision making and the results of the comparison show that the proposed technique is applicable and reliable for the decision support model.

A Hybrid Approach Combining SAW and TOPSIS for Network Interface Selection in Heterogeneous Wireless Networks

A Hybrid Approach Combining SAW and TOPSIS for Network Interface Selection in Heterogeneous Wireless Networks

Correction to: Network selection in heterogeneous dense networks based on user clustering

Correction to: Network selection in heterogeneous dense networks based on user clustering

Energy-aware Joint Route Selection and Resource Allocation in Heterogeneous Satellite Networks

Energy-aware Joint Route Selection and Resource Allocation in Heterogeneous Satellite Networks

Network selection in heterogeneous dense networks based on user clustering

Network selection in heterogeneous dense networks based on user clustering

Energy Efficiency Based Multi Service Heterogeneous Access Network Selection Algorithm

Energy Efficiency Based Multi Service Heterogeneous Access Network Selection Algorithm

Enhanced Pelican Optimization Algorithm for Cluster Head Selection in Heterogeneous Wireless Sensor Networks.

In the research of heterogeneous wireless sensor networks, clustering is one of the most commonly used energy-saving methods. However, existing clustering methods face challenges when applied to heterogeneous wireless sensor networks, such as energy balance, node heterogeneity, algorithm efficiency, and more. Among these challenges, a well-designed clustering approach can lead to extended node lifetimes. Efficient selection of cluster heads is crucial for achieving optimal clustering. In this paper, we propose an Enhanced Pelican Optimization Algorithm for Cluster Head Selection (EPOA-CHS) to address these issues and enhance cluster head selection for optimal clustering. This method combines the Levy flight process with the traditional POA algorithm, which not only improves the optimization level of the algorithm, but also ensures the selection of the optimal cluster head. The logistic-sine chaotic mapping method is used in the population initialization, and the appropriate cluster head is selected through the new fitness function. Finally, we utilized MATLAB to simulate 100 sensor nodes within a configured area of 100 × 100 m2. These nodes were categorized into four heterogeneous scenarios: m=0,α=0, m=0.1,α=2, m=0.2,α=3, and m=0.3,α=1.5. We conducted verification for four aspects: total residual energy, network survival time, number of surviving nodes, and network throughput, across all protocols. Extensive experimental research ultimately indicates that the EPOA-CHS method outperforms the SEP, DEEC, Z-SEP, and PSO-ECSM protocols in these aspects.

Retracted: Load Balancing Selection Method and Simulation in Network Communication Based on AHP-DS Heterogeneous Network Selection Algorithm

Retracted: Load Balancing Selection Method and Simulation in Network Communication Based on AHP-DS Heterogeneous Network Selection Algorithm

Access selection in heterogeneous wireless networks based on user preferences

Access selection is an important key in heterogeneous networks, and the design of a new algorithm for decision is not a trivial task. Different aspects must be taken into consideration while designing a new decision algorithm, including both users' requirements (in terms of resources, QoS, user's preferences), and operator policies that aim to maximise the utilisation of its network capacity and to deliver services with acceptable QoS levels for the largest number of customers. Thus, in this paper, we propose a new selection algorithm based on user's preferences. The comparison between the proposed scenarios is given based on several performance indicators. The results show the improvement achieved by increasing the resource utilisation and therefore the overall system capacity.

Access selection in heterogeneous wireless networks based on user preferences

Access selection in heterogeneous wireless networks based on user preferences

A novel bi-directional heterogeneous network selection method for disease and microbial association prediction

Microorganisms in the human body have a great impact on human health. Therefore, mastering the potential relationship between microorganisms and diseases is helpful to understand the pathogenesis of diseases and is of great significance to the prevention, diagnosis, and treatment of diseases. In order to predict the potential microbial disease relationship, we propose a new computational model. Firstly, a bi-directional heterogeneous microbial disease network is constructed by integrating multiple similarities, including Gaussian kernel similarity, microbial function similarity, disease semantic similarity, and disease symptom similarity. Secondly, the neighbor information of the network is learned by random walk; Finally, the selection model is used for information aggregation, and the microbial disease node pair is analyzed. Our method is superior to the existing methods in leave-one-out cross-validation and five-fold cross-validation. Moreover, in case studies of different diseases, our method was proven to be effective.

Digital Twin Empowered Heterogeneous Network Selection in Vehicular Networks With Knowledge Transfer

Digital twin (DT) has emerged as a promising technology to create digital replicas of physical vehicles to facilitate driving in Internet of Vehicles (IoV). However, due to the uneven distribution of vehicles in the networks and the dynamics of heterogeneous wireless links, how to optimize the connection along the vehicle's trip between the vehicle and its DT to achieve the lowest cost ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , delay and energy consumption) is a fundamental challenge. In this paper, we investigate the joint network selection and power level allocation problem in the data synchronization issue between vehicles and DTs in the heterogeneous access networks. In order to better use the information shared by DT to facilitate the data synchronization process, we propose a learning-based heterogeneous network selection scheme in DT empowered IoV. First, we consider the data divided into blocks and model the data synchronization problem as a Markov Decision Process (MDP) to minimize the long-term cost in terms of the delay and energy consumption. Based on the information obtained from the digital twin communications, we develop an actor-critic learning approach to solve the MDP problem with the block-level state method, and obtain the optimal network selection strategy for the vehicles. In addition, we explore the capability of digital twins by applying transfer learning. The vehicles are divided into two types, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , learner vehicles and expert vehicles, so that the knowledge can be transferred from the expert vehicles to the learner vehicles to effectively improve the learning efficiency and decrease the data synchronization latency. Extensive experiments demonstrate that our scheme can effectively find the optimal network selection policy and achieve the fast convergence and better performance compared with existing related approaches.

An Autonomous Adaptive Enhancement Method Based on Learning to Optimize Heterogeneous Network Selection

Aims and Background: Mobile workstations are frequently used in challenging environments of heterogeneous networks. Users must move between various networks for a myriad of purposes, including vertical handover. At this time, it is critical for the mobile station to quickly pick the most appropriate networks from all identified alternative connections with the decision outcome, avoiding the ping-pong effect to the greatest extent feasible. Objectives and Methodology: Based on a combination of network characteristics as well as user choice, this study offers a heterogeneous network selection method. This technique integrates three common Multi-Attribute Decision-Making (MADM) techniques, notably the Fuzzy Analytic Hierarchy Process (FAHP), Entropy, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), to take into consideration user preferences for every prospective network as well as the real scenario of heterogeneous networks. For different traffic classes, FAHP is first utilized to determine the weights of network parameters and the utility numbers of total options available. Next, entropies and TOPSIS are utilized to obtain only the unbiased weights of network factors and utility principles of totally different options. Results: The most suitable networks, whose utility number is the greatest and larger than that of the equivalent number of present networks of the phone station, are chosen to provide accessibility based on the utility numbers of each prospective system as a limit. The suggested method not only eliminates a particular algorithm's one-sided character but also dynamically changes the percentage of each method in the desired outcome based on real needs. Conclusion: The proposed model was compared to the three existing hybrid methods. The results showed that it could precisely choose the optimized network connectivity and significantly reduce the value of vertical handoffs. It also provides the requisite Quality of Service (QoS) and Quality of Everything (QoE) in terms of the quantitative benefits of vertical handovers.

QoS-Balancing Algorithm for Optimal Relay Selection in Heterogeneous Vehicular Networks

Intelligent Transportation System (ITS) could facilitate communications among various road entities to improve the driver’s safety and driving experience. These communications are called Vehicle-to-Everything (V2X) communications that can be supported by LTE-V2X protocols. Due to frequent changes of network topology in V2X, the source node (e.g., a vehicle) may have to choose a Device-to-Device(D2D) relay node to forward its packet to the destination node. In this paper, we propose a new method for choosing an optimal D2D relay node. The proposed method considers Quality of Service (QoS) requirements for selecting D2D relay nodes. It employs an Analytic Hierarchy Process (AHP) for making decisions. The decision criteria are linked with channel capacity, link stability and end-to-end delay. A number of simulations were performed considering various network scenarios to evaluate the performance of the proposed method. Simulation results show that the proposed method improves Packet Dropping Rate (PDR) by 30% and delivery ratio by 23% in comparison with the existing methods.

Modality of Multi-Attribute Decision Making for Network Selection in Heterogeneous Wireless Networks

Modality of Multi-Attribute Decision Making for Network Selection in Heterogeneous Wireless Networks

Multi-Agent Reinforcement Learning for Network Selection and Resource Allocation in Heterogeneous Multi-RAT Networks

The rapid production of mobile devices along with the wireless applications boom is continuing to evolve daily. This motivates the exploitation of wireless spectrum using multiple Radio Access Technologies (multi-RAT) and developing innovative network selection techniques to cope with such intensive demand while improving Quality of Service (QoS). Thus, we propose a distributed framework for dynamic network selection at the edge level, and resource allocation at the Radio Access Network (RAN) level, while taking into consideration diverse applications’ characteristics. In particular, our framework employs a deep Multi-Agent Reinforcement Learning (DMARL) algorithm, that aims to maximize the edge nodes’ quality of experience while extending the battery lifetime of the nodes and leveraging adaptive compression schemes. Indeed, our framework enables data transfer from the network’s edge nodes, with multi-RAT capabilities, to the cloud in a cost and energy-efficient manner, while maintaining QoS requirements of different supported applications. Our results depict that our solution outperforms state-of-the-art techniques of network selection in terms of energy consumption, latency, and cost.

Satellite Integration into 5G: Deep Reinforcement Learning for Network Selection

This paper proposes a deep-Q-network (DQN) controller for network selection and adaptive resource allocation in heterogeneous networks, developed on the ground of a Markov decision process (MDP) model of the problem. Network selection is an enabling technology for multi-connectivity, one of the core functionalities of 5G. For this reason, the present work considers a realistic network model that takes into account path-loss models and intra-RAT (radio access technology) interference. Numerical simulations validate the proposed approach and show the improvements achieved in terms of connection acceptance, resource allocation, and load balancing. In particular, the DQN algorithm has been tested against classic reinforcement learning one and other baseline approaches.

Correction to: A Fused Heterogeneous Deep Neural Network and Robust Feature Selection Framework for Human Actions Recognition

A Correction to this paper has been published: 10.1007/s13369-021-05881-4

RETRACTED ARTICLE: A Fused Heterogeneous Deep Neural Network and Robust Feature Selection Framework for Human Actions Recognition

RETRACTED ARTICLE: A Fused Heterogeneous Deep Neural Network and Robust Feature Selection Framework for Human Actions Recognition

An intelligent handoff optimization algorithm for network selection in heterogeneous networks

An intelligent handoff optimization algorithm for network selection in heterogeneous networks