Vertical Handover Decision Research Articles

Multicriteria and Quality of Service- Aware Vertical Handover Solution for Vehicle-to-Infrastructure Communication in Multitier Heterogeneous Networks.

The growing integration of vehicle-to-infrastructure (V2I) communication within multitier heterogeneous networks (HetNets) introduces challenges in maintaining seamless connectivity and Quality of Service (QoS) for vehicular users. This paper introduces a modified Multicriteria and QoS-aware Vertical Handover Decision Algorithm (mV2I-MHA) tailored to address these challenges. By considering multiple criteria - such as packet loss ratio, cost, available bandwidth and packet latency - the proposed solution intelligently manages vertical handovers between different network tiers. Numerous Multi-Criteria Decision-Making (MCDM) techniques have been proposed to address the right target network selection aiming to reduce unnecessary handovers, incorrect network selections and the associated processing time. However, many of these solutions overlook the significance of varying criteria weights in vertical handover decisions made by their selection algorithms. Through comprehensive simulations and comparisons with existing techniques, the effectiveness of the proposed – in this paper - solution is demonstrated in enhancing handover success rate, reducing handover failure rate, minimizing latency, and overall elevating the QoS for V2I communication in multitier HetNets. MATLAB R2020a was utilized to simulate the work. Using packet latency and handover failure as the metrics for performance, the results were contrasted with those of an existing works in terms of handover failure rate and packet delay. The developed mV2I-MHA showed considerable percentage reductions of 10.5% and 23.9% in packet latency and handover failure rate, respectively.

An Optimized Vertical Handover Decision Model for the Heterogeneous DSRC/LTE Vehicular Networks

An Optimized Vertical Handover Decision Model for the Heterogeneous DSRC/LTE Vehicular Networks

An adaptive context-aware vertical handover decision algorithm for heterogeneous networks

In a heterogeneous network, vertical handovers occur frequently between different access technologies to meet the requirements of mobile users. The selection of an access technology for a mobile user for handover largely depends on the contextual information of the user and network at that point of time. In this work, we have proposed an adaptive, context-aware vertical handover decision algorithm for heterogeneous networks. Both static and dynamic contextual information about the user and the network are given as inputs to our proposed algorithm. They are provided proactively by the networks to the mobile nodes on a timely basis. An optimal network scanning procedure is introduced as part of the handover process to minimize the energy consumption of the nodes. We also propose a utility function-based cell load balancing mechanism to improve overall resource utilization. In addition, a delay-sensitive handover traffic-based hybrid channel allocation mechanism is proposed. The simulation results clearly suggest that the proposed work significantly enhanced the overall performance of the system as compared to some existing work in terms of call blocking probability, channel utilization rate, consumed energy, and handover delays.

Reinforcement learning and neuro‐fuzzy GNN‐based vertical handover decision on internet of vehicles

SummaryThe Internet of Vehicles (IoV) has become a lot of concern for scientific society because of its rising importance in creating an intelligent transportation system. However, the existing systems addressed handover and network selection difficulties, but they did not solve the necessity for frequent handover problems. Hence, in this research, handover decision difficulties are removed by using a novel state‐action–reward–state‐action reinforcement learning method in which it learns the current policy action to determine whether a handover decision is required in the fast‐moving vehicle therefore the handover failure problem is reduced. Moreover, the existing fuzzy rules do not consider non‐line‐of‐sight and the learning capabilities of the neural networks to select the suitable network therefore it creates network selection difficulty. Hence, the network selection difficulties are removed by using a novel fuzzy‐based graph neural network in which the fuzzy logic is connected to the graph hierarchical structure to select the right network therefore the efficient and suitable network is selected on the internet of vehicle communication. Moreover, the existing process is to select the routes completely established on the ancient broadcast history of the vehicles and their channel metrics which causes the frequent handover and poor performance of the network. Hence, the routing selection problem is eliminated by using a novel cluster‐based routing algorithm in which the vehicle was divided into clusters to select the efficient route of the internet of the vehicle therefore the best routing path is carried out. As a result, the proposed system is improving the quality of the service and reducing the handover failure and delay of the system.

I-MEREC-T: Improved MEREC-TOPSIS scheme for optimal network selection in 5G heterogeneous network for IoT

The Internet of Things (IoT) requires seamless mobility and services with low power consumption, low packet loss ratio, ultra-low latency, and high reliability. Ultra-dense deployment of small coverage radio access networks improves network throughput, latency, and packet loss ratio but increases the frequency of vertical handovers. Optimal network selection to ensure ultra-reliable, low-latency, seamless connectivity to provide massive machine-type communication to support a wide range of services for the emergence of IoT and Industry 5.0 through the next generation heterogeneous networks is one of the most challenging fields of research. The best network selection is one of the most crucial phases of the vertical handover decision algorithm in an ultra-dense heterogeneous network. Inadequate network selection can lead to failed handovers and ping pong handovers, which drastically affect the quality of services and the quality of experience. In this article, we have developed a new improved method based on improved removal effects of criteria- technique for order performance by similarity to ideal solution (I-MEREC-TOPSIS) network selection scheme. It is based on the removal effect of alternatives’ attributes. It is an intelligent integration of the I-MEREC weight method of alternative criteria and the TOPSIS alternative ranking multi-attribute decision-making technique. In the proposed scheme, optimal network selection is not only dependent on the best performance score of candidate networks but also on the statistically defined threshold value of servicing and target networks. In comparison to CRiteria Importance Through Intercriteria Correlation (CRITIC) TOPSIS, Entropy-TOPSIS, Statistical Variance Procedure(SVP) TOPSIS, and MEREC-TOPSIS, the proposed scheme has reduced ping-pong handovers and handover failures.

Network selection based on chi-square distance and reputation for internet of things

The internet of things (IoT) has become one of the most important technologies of the 21<sup>st</sup> century. The IoT environment is composed of heterogeneous IoT communication networks. These technologies are complementary and need to be integrated to meet the requirements of different types of IoT applications that require the mobility of the IoT device under different IoT communication networks. In this paper, the vertical handover decision method is considered to select the appropriate network among different IoT technologies. So, IoT devices, equipped with several radio technologies, can select the most suitable network based on several criteria like quality of service (QoS), cost, power, and security. In this work, a multi-attribute decision-making algorithm (MADM) based on techniques for order preference by similarity to an ideal solution (TOPSIS) that uses chi-square distance instead of Euclidean distance is proposed. The network reputation is added to reduce the average number of handoffs. The proposed algorithm was implemented to select the best technology depending on the requirements of the different IoT traffic classes. The obtained results showed that our proposition outperforms the traditional MADM algorithms.

Soft Computing-Based Schemes for Handover Management in Future Networks

The progression to next generation networks is replete with abundant co-existing technologies. To comply with the always best connected paradigm, several vertical handover decision approaches have been proposed in literature, using advanced techniques and tools. This paper discusses the application of soft computing techniques in the vertical handover decision-making process with emphasis on the state-of-the-art techniques. For a comprehensive evaluation, the algorithms are classified into three sets based on the soft computing technique used, namely fuzzy logic, machine learning, and evolutionary algorithms, and representative handover algorithms in each group are discussed. These papers are categorized in a well-defined structure to bring out their contribution, to underline the pretermitted notions, and to bring forth the emerging issues for future research. This paper summarizes the soft computing concepts and reviews its applications in candidate network selection, QoE enhancement, and reducing the unnecessary handovers.

Algorithm for vertical handover decision using geometric mean and MADM techniques

Algorithm for vertical handover decision using geometric mean and MADM techniques

Vertical Handover Decision Making Algorithms for 5G Heterogeneous Networks: A Review

The wireless networks have been rapidly used in our daily lives, especially mobile wireless networks. An environment consisting of several networks such as WLAN, 4G-LTE, and 5G, is known as a heterogeneous network. Wireless networks are widely used to support various data services such as live video streaming, upload data, download data and cloud service depending upon their capacity and nature. When the mobile terminal is moving, the network availability will be changed. Therefore, the mobile terminal must perform handover from one network to another network in heterogeneous networks to maintain the call and quality of service (QoS). This process is known as Vertical Handover (VHO). A seamless handover is important to provide uninterrupted high quality service while performing the handover. To achieve this, various vertical handover decision making algorithms have been proposed and studied using advanced tools and proven concepts. This paper gives an overview of current research on handover decision making in fifth generation (5G) heterogeneous wireless networks. Keywords—Vertical Handover Decision Making, Heterogeneous Networks, Quality of Service, 5G, Seamless Handover

Multi-Criteria Fuzzy-Based Decision Making Algorithm to Optimize the VHO Performance in Hetnets

Despite the seemingly exponential growth of mobile and wireless communication, this same technology aims to offer uninterrupted access to different wireless systems like Radio Communication, Bluetooth, and Wi-Fi to achieve better network connection which in turn gives the best quality of service (QoS). Many analysts have established many handover decision systems (HDS) to enable assured continuous mobility between various radio access technologies. Unbroken mobility is one of the most significant problems considered in wireless communication networks. Each application needs a distinct QoS, so the network choice may shift appropriately. To achieve this objective and to choose the finest networks, it is important to select a best decision making algorithm that chooses the most effective network for every application that the user requires, dependent on QoS measures. Therefore, the main goal of the proposed system is to provide an enhanced vertical handover (VHO) decision making program by using a Multi-Criteria Fuzzy-Based algorithm to choose the best network. Enhanced Multi-Criteria algorithms and a Fuzzy-Based algorithm is implemented successfully for optimal network selection and also to minimize the probability of false handover. Furthermore, a double packet buffer is utilized to decrease the packet loss by 1.5% and to reduce the number of handovers up to 50% compared to the existing systems. In addition, the network setup has an optimized mobility management system to supervise the movement of the mobile nodes.

Neuro-fuzzy Architecture for Handover Decision Making in Emerging Heterogeneous Networks

In emerging heterogeneous networks, seamless vertical handover is a critical issue. There must be a trade-off between the handover decision delay and accuracy. This paper’s concern is to contribute to reliable vertical handover decision making that makes a trade-off between complexity and effectiveness. So, the paper proposes a neuro-fuzzy architecture that joints the capacity of learning of the artificial neural networks with the power of linguistic interpretation of the fuzzy logic. The architecture can learn from experience how executing a handover to a particular access network affects the quality of service. Simulation results reveal that this architecture is fast, enhances the overall performance and reliability better than the fuzzy logic-based approach.

SAPIENT: Enabling Real-Time Monitoring and Control in the Future Communication Infrastructure of Air Traffic Management

This paper describes the SAPIENT system, a real-time monitoring and control infrastructure for Air Traffic Management. Within the latter, aircrafts constantly measure the state and quality of their datalinks, and report these measurements to a ground entity, tagging them with a time/space reference. The ground entity, then, builds a map of the monitored portion of the sky, and can feed back information to the aircrafts themselves regarding conditions that they would not be able to measure otherwise. This allows optimal vertical handover decisions to be made, increasing service continuity and improving communication performance. We show that the SAPIENT system can be implemented using existing technologies, without the need for expensive hardware. Moreover, we show via simulation that a small, negligible increase in the communication overhead due to SAPIENT reporting brings about considerable benefits.

Development of energy-efficient model in long term evolution (LTE) network using optimum network engineering tools

The concept of the heterogeneous network was introduced to meet the demands of the mobile network’s traffic flow capacity and data rate. It consists of dissimilar networks with diverse wireless access technologies. By convention, a Mobile Subscriber (MS) may travel and attain the vertical handover by a single criterion vertical handover decision, but, conversely, this may cause ineffective handover, unbalanced system load, and service disruption. This Thesis proposed an enhanced vertical handover optimization using multi-attribute parameters in the heterogeneous network environment consisting of 2G, 3G, Long Term Evolution-Advanced (LTE-A in 4G), and Passive Optical Network (PON). The results for vertical handover such as signal strength, service quality, handover delay, monetary price, user mobility pattern, bandwidth availability and security are linked with this project. These Key Performance Indicators (KPIs) conform with the ITU index to assess the quality of the service of mobile networks (QoS). Examples include network coverage, the integrity of service, retainability of service, availability of network and accessibility of networks. The work only includes 2g, 3g and 4g. It also expands to coverage and capacity, but, for consecutive vertical delivery services request because of time constraints, it does not cover the execution of the Algorithm.

Algorithm for Vertical Handover Decision in Vehicular Environment

In the present scenario, co-existence of various wireless networks necessitates efficient vertical handover decision algorithms to maintain cost effective seamless connectivity across various networks, as the user is moving from one place to another. In this paper, the authors have presented a vertical handover decision algorithm which calculates a fitness value for each available network based on received signal strength, channel capacity, cost, battery life, velocity, and quality of service (QoS). In addition, the travelling speed of the user and the vehicular density at that time is used to capture the handover track of the user for network management. An alternate model using fuzzy inference system is also presented.

Dynamic Q-learning and Fuzzy CNN Based Vertical Handover Decision for Integration of DSRC, mmWave 5G and LTE in Internet of Vehicles (IoV)

Internet of vehicles commonly known as IOV is a newly emerged area which with the help of internet assisted communication provides the support to the vehicles. Due to the access of more than one radio access network, 5G makes the connectivity ubiquitous. Vehicle mobility demands for handover in such heterogeneous networks. Instead of using better technology for long ranges and other types of traffic, the vehicles are using devoted short range communications at short ranges. Commonly, networks for handovers were used to be selected directly or with the available radio access it used to connect automatically. With the help of this, the hand over occurrence now takes places frequently. This paper is based on the incorporation of DSRC, LTE as well as mm Wave on Internet of vehicles which is integrated with the Handover decision making algorithm, Network Selection and Routing. The decision of the handovers is to ensure that if there is any requirement of the vertical handovers using dynamic Q-learning algorithms in which entropy function is used to predict the threshold according to the characteristics of the environment. The network selection process is done using Fuzzy Convolution Neural Network commonly known as FCNN which makes the fuzzy rules by considering the parameters such as strength of its signal, its distance, the density of the vehicle, the type of its data as well the Line of Sight (LoS). V2V chain routing is presented in such a manner that V2V pairs are also selected with the help of jellyfish optimization algorithm considering three metrics – Vehicle metrics, Channel metrics and Vehicle performance metrics. OMNET++ simulator is the software in which system is developed. The performance evaluation is done according to its Handover Success Probability, Handover Failure, Redundant Handover, Mean Throughput, delay and Packet Loss.

Vertical handover algorithm based on multi-attribute and neural network in heterogeneous integrated network

A novel vertical handover algorithm based on multi-attribute and neural network for heterogeneous integrated network is proposed in this paper. The whole frame of the algorithm is constructed by setting the network environment in which we use the network resources by switching between UMTS, GPRS, WLAN, 4G, and 5G. Each network build their own three-layer BP (Back Propagation, BP) neural network model and then the maximum transmission rate, minimum delay, SINR (signal to interference and noise ratio, SINR), bit error rate, user moving speed, and packet loss rate which can affect the overall performance of the wireless network are employed as reference objects to participate in the setting of BP neural network input layer neurons and the training and learning process of subsequent neural network data. Finally, the network download rate is adopted as prediction target to evaluate performance on the five wireless networks and then the vertical handover algorithm will select the right wireless network to perform vertical handover decision. The simulation results on MATLAB platform show that the vertical handover algorithm designed in this paper has a handover success rate up to 90% and realizes efficient handover and seamless connectivity between multi-heterogeneous networks.

Fuzzy Logic-Based Handover Requirement Analysis and Access Network Selection for Device-to-Device Communication

In order to address the exponential growth of data traffic, the concept of device-to-device (D2D) communication has been introduced in fifth generation (5G). The seamless handover to the finest available access network is very essential in furnishing high quality of experience (QoE) to the devices involved in D2D communication. An intelligent, fast and reliable vertical handover (VHO) decision scheme is required for D2D to achieve seamless mobility and uninterrupted service. To address these issues, a two-stage fuzzy logic-based VHO scheme is proposed in this work. Here, fuzzy logic-based handover requirement analysis (HRA) is carried out in stage 1 based on received signal strength (RSS), data rate (DR) and latency (LA). This stage minimizes the unnecessary handovers. In stage 2, fuzzy-analytic hierarchy process (Fuzzy-AHP), fuzzy-technique for order preference by similarity to ideal solutions (Fuzzy-TOPSIS) are used in ranking and selecting the target access network. Through simulations, it has been proved that the proposed scheme offers improved performance than the classical and fuzzy-based multi-attribute decision-making (MADM) schemes. The proposed scheme offers 3.45[Formula: see text], 7.33[Formula: see text], 13.51[Formula: see text], 40[Formula: see text] minimum improvement in data rate, handover decision delay, handover blocking probability and number of handovers executed over the classical MADM schemes considered.

Asynchronous gateway reallocation communication in heterogeneous 5G networks

SummaryBackgroundSmart vision of the world is the latest challenge believing that 5G networks could feed all of our internet device needs. The exchange of information (data) needs not to go through backhaul because lots of capabilities will be available close to where it is needed. Presently, the majority of communications are based on single‐mode transmission. Communications will be more responsive and interactive due to 5G technology. The implementation of 5G faces various problems. It is necessary to improve the network capacity by densification, increasing spectrum, and measuring the spectral efficiency. And it also needs to reduce the latency, and it must be energy efficient.PurposeIt is fit for inter‐access service network (ASN) (asynchronous) communications due to the incapability to maintain vertical handoff communications. The need for vertical handovers hinges on various factors that need to be analyzed concurrently with the signal strength in heterogeneous wireless environment.MethodologyThus, it proposed to utilize a vertical handover decision algorithm centered on the priority and speed of the subscriber. Implementation confirms the claim of the proposed method to achieve better channel utilization and maintains the QoS requirements of the mobile users.FindingsTime latency calculation, evaluation of energy utilization between different nodes, and performance analysis packet loss are evaluated.ConclusionBetter efficiency is attained for data transmission among the users that is one mobile node to other with commercial event management and appropriate processing in each node.

Data-Driven Link Assignment With QoS Guarantee in Mobile RF-Optical HetNet of Things

This article investigates a heterogeneous network (HetNet) consisting of an overlapped coverage of radio frequency (RF) and optical wireless communication (OWC) to support human connectivity to the Internet of Things (IoT) in the fifth generation and beyond (5G+) mobile networks. Such a HetNet-based IoT benefits from the high throughput of the OWC and the high reliability of the RF communications. To ensure a reliable link with Quality-of-Service (QoS) guarantee in terms of network delay and throughput, vertical handovers are triggered within the HetNet. A cross-layer data-driven approach is adopted to reach optimal handover decisions and tackle the challenges associated with the mobility and reliability of IoT. As mobile time-varying wireless channel gains in optical links are not publicly available, we first present a realistic model for the mobile optical channel that reflects the nature of human mobility, and hence, a useful model that features the spatial–temporal patterns of indoor mobile channels is proposed. Using the created data set, we then present a data-driven algorithm that predicts abrupt outages in Line-of-Sight (LOS) optical links and evaluates the optical channel quality through deep learning. Given the resulting LOS link outage prediction in OWC, a reinforcement-learning-based approach is proposed to implement optimal vertical handover decisions with the QoS guarantee. The proposed handover decision algorithm learns to make a tradeoff between the outage risk and the cost of excessive handovers. The numerical results demonstrate considerable improvement in overall latency and handover rate under indoor mobility for bidirectional links.

QUALITY OF SERVICE PROVISIONING USING MULTICRITERIA HANDOVER STRATEGY IN OVERLAID NETWORKS

The poor coverage problem inside the buildings and the existence of disparate wireless technologies have led to the development of integrated macro femtocellular networks. Correct and efficient vertical handover decision is of utmost importance in such integrated macro femtocellular networks. Initially, Received Signal Strength (RSS) used to be the sole criterion for taking a decision for vertical handover. Such decision leads to inaccurate results and therefore unnecessary handovers. In this paper, a multicriteria vertical handover strategy based on received signal strength, user velocity, number of users in a cell and data rate of the cell is proposed. This proposed strategy and the traditional algorithm are simulated and the results are analyzed quantitatively for different QoS parameters namely ping pong rate, packet loss ratio, throughput, packet delay, jitter and signalling cost. The results show significant improvement in the QoS parameters when the proposed strategy is used as compared to traditional algorithm.