Heterogeneous Wireless Environment Research Articles

A machine learning access network selection in a heterogeneous wireless environment

AbstractIn the context of Heterogeneous Wireless Networks (HWN), achieving the best connectivity is crucial for users. With the availability of multiple Radio Access Technologies (RATs), users are constantly looking to be “Always Best Connected” (ABC) by connecting to the best possible RAT. This challenge is also important in Next‐Generation Networks (NGN), where researchers are proposing models and algorithms to make the ABC paradigm a reality. One such challenge is the network selection problem, which involves the automatic and transparent selection of the best RAT at a given time. Many papers in this field have used Multi‐Attribute Decision‐Making (MADM) methods, which have been implemented with various objectives such as increasing quality of service (QoS), reducing energy consumption and costs etc. This paper proposes a modified machine learning algorithm, specifically an adapted K‐means algorithm, to address this challenge. The results show that this proposal outperforms legacy MADM methods in terms of latency, jitter, packet loss, and data rate. So, the paper presents a new approach to network selection in HWN and NGN, and the results suggest that it could be a promising solution to this important challenge.

Resource-Constraint Network Selection for IoT Under the Unknown and Dynamic Heterogeneous Wireless Environment

We investigate the problem of network selection for the Internet of Things (IoT) to maximize the quality of experience (QoE) in a heterogeneous wireless environment. Different from the traditional network access approaches with the assumption that the network state information (NSI) is static and known a priori, a scenario where the NSI of networks is unknown and dynamic to IoT devices is considered. Due to hardware limitations in IoT, the device has a limited resource budget and consumes resources, e.g., the energy, during the process of network access. To maximize the cumulative QoE before resource exhausts, the device should learn and estimate the NSI of networks, and make appropriate decisions to balance the network selection and resource consumption. To address this issue, we formulate the problem as a combination of multi-armed bandit and optimization problems and propose two algorithms NCSA and NSA. Moreover, we consider the fact that the device has various traffic types and propose an algorithm MT-NSA. Theoretical analysis shows that the regret of all algorithms has a sub-linear relationship with the resource budget. The effectiveness is validated by simulations.

Deep Learning with Game Theory Assisted Vertical Handover Optimization in a Heterogeneous Network

Problem: In next-generation networks, users can optimize or tune their preferences with a seamless transfer of diverse access methodologies for maximizing the Quality of Service (QoS) and cost savings. In these heterogeneous wireless environments, users are prepared with several multimode wireless devices for maximizing media services through several access networks. Such networks may vary regarding energy usage, available bandwidth, technology, coverage range, monetary cost, etc. In recent days, vertical handover has attained higher performance owing to the improvements in mobility models through adopting the Fourth Generation (4G) technologies. On the other hand, these improvements are restricted to some cases, so, it does not offer support for generic mobility. Consequently, diverse strategies were implemented by considering these mobility models. However, it suffers from improper network selection, late and too-early handovers, repeated handovers, high packet loss, etc. Aim: This paper tackles the problem of vertical handover problem in the heterogeneous network using deep learning with game theory. Methods: The proposed model develops a non-cooperative game approach, in which all base stations compete selfishly to transmit at higher power. The overall performance in terms of throughput, handover, energy consumption, and load balancing is attained by optimizing the transmission power by the game theory. For performing this model, the required data like path loss, SINR, data rate, load, etc are generated by the deep learning called Recurrent Neural Network (RNN). Results: From the simulation findings, the handoff probability of the recommended RNN+Game Theory is correspondingly secured at 6.9%, 22.6%, and 8.2% superior to TOPSIS, ABC-PSO, and game theory when taking the time like 5 secs for user velocity as 30 km/h. Conclusion: Results show that the proposed game theoretical approach with deep learning provides a throughput enhancement while reducing the power consumption, in addition, to minimizing the unnecessary handover and balancing the load between base stations.

Interface Selection and Optimization of Weights using Artificial Neural Network in Heterogeneous Wireless Environment

Abstract Given the variety of networks, interfaces, mediums, and other resources accessible in a wireless heterogeneous location today, mobile communication is in a healthy competitive environment. When clients or users have access to numerous interfaces at once, a problem occurs. Users therefore require a clever or intelligent system to connect them to the finest services based on their needs and choices. Interface management controls the interfaces that are accessible and links the user to the best. The intelligent usage of various radio accesses/interfaces is made possible in this research by interface management with artificial neural networks (ANNs). Various parameters of different networks are used to make the choice. In this paper, a back propagation neural network (BPNN) method for switching between 3G, WLAN, 4G, and 5G networks is suggested. By allocating appropriate weights, the various network properties are employed as selection parameters. Fuzzy Analytic Hierarchy Process (FAHP) is used to initialize the weights, and the BPNN is used to optimize them. In order to obtain the best value, the goal value and the original value are compared, and the difference between them is used as an adjusting value for the weights. The network is trained using backpropagation techniques. The comparison between the proposed algorithm and the current algorithm demonstrates the new method's flexibility and the network's optimal connectivity with a high rate of successful handovers.

Network Selection in Wireless Heterogeneous Environment Based on Available Bandwidth Estimation

Background: With the availability of multiple options in wireless network simultaneously, Always Best Connected (ABC) requires dynamic selection of the best network and access technologies. Objective: In this paper, a novel dynamic access network selection algorithm based on the real time is proposed. The Available BandWidth (ABW) of each network is required to be estimated to solve the network selection problem. Methods: Proposed algorithm estimates available bandwidth by taking averages, peaks, low points and bootstrap approximation for network selection. It monitors real-time internet connection and resolves the selection issue in internet connection. The proposed algorithm is capable of adapting to prevailing network conditions in heterogeneous environment of 2G, 3G and WLAN networks without user intervention. It is implemented in temporal and spatial domains to check its robustness. Estimation error, overhead, estimation time with the varying size of traffic and reliability are used as the performance metrics. Results: Through numerical results, it is shown that the proposed algorithm’s ABW estimation based on bootstrap approximation gives improved performance in terms of estimation error (less than 20%), overhead (varies from 0.03% to 83%) and reliability (approx. 99%) with respect to existing techniques. Conclusion: Our proposed methodology of network selection criterion estimates the available bandwidth by taking averages, peaks, and low points and bootstrap approximation method (standard deviation) for the selection of network in the wireless heterogeneous environment. It monitors realtime internet connection and resolves internet connections selection issue. All the real-time usage and test results demonstrate the productivity and adequacy of available bandwidth estimation with bootstrap approximation as a practical solution for consistent correspondence among heterogeneous wireless networks by precise network selection for multimedia services.

Enhanced Handoff Decision Making for Application-Aware Environment by Using Blended Approach

Seamless integration of the new and existing networks is required to create next generation wireless networks capable of providing wide range of services coupled with uninterrupted connectivity and high speeds offering unprecedented quality of service to users. Smooth and proficient handoff techniques are important to achieve optimal network performance in heterogeneous wireless environments. This paper proposes Multiple Criteria Decision Making (MCDM) techniques of both non-fuzzy (Technique for Order of Performance by Similarity to Ideal Solution (TOPSIS)) and fuzzy (Fuzzy TOPSIS (FTOPSIS)) type for network ranking. The proposed algorithm uses priority ranking to assign weights to six parameters by using Fuzzy Analytic Hierarchy Process (FAHP). Further, TOPSIS and FTOPSIS techniques are compared for various traffic classes and used for network selection in the prevalent network conditions. The results show that contingent on the traffic classes, the FTOPSIS selects the ideal network with diminished ping-pong rate and call blocking probability as compared to TOPSIS while accounting for the uncertain environment.

Heterogeneous wireless network selection by combining GRA and VIKOR under a fuzzy environment

In a heterogeneous wireless environment, network selection is a strategic issue and has a significant impact on providing the best quality of service (QoS) to the users. The selection of an apt network among various alternatives is a kind of multi-criteria decision-making (MCDM) problem. This paper proposes a model based on VlseKriterijumskaOptimizacija I KompromisnoResenje (VIKOR) under fuzzy environment and grey relational analysis (GRA) for the selection of suitable network in a heterogeneous wireless environment. Here, triangular fuzzy linguistic variables are used to handle the vagueness and subjectivity of the decision-making process. This study focuses on four alternatives such as universal mobile telecommunications system (UMTS), long term evolution (LTE), worldwide interoperability for microwave access (WiMAX) and wireless local area network (WLAN). A six evaluation criteria such as throughput, delay, jitter, packet loss, cost, security are considered for choosing the suitable network in heterogeneous wireless environment.

BBR-Based Congestion Control and Packet Scheduling for Bottleneck Fairness Considered Multipath TCP in Heterogeneous Wireless Networks

Aiming at improving the performance of Multipath TCP (MPTCP) in heterogeneous wireless network environments, in this paper, by taking the advantages Bottleneck Bandwidth and Round-trip propagation time (BBR) and considering bottleneck fairness, we propose BBR-based Congestion Control and Packet Scheduling scheme, called BCCPS. The proposed BCCPS first considers a BBR-based congestion control algorithm, MPTCP-BBR, for MPTCP by adaptively adjusting the sending rate of each subflow according to the real probing rate rather than the loss information to enhance the goodput while keeping bottleneck fairness with regular TCP. Then, considering that the different sending rates of the subflows in heterogeneous wireless networks will result in the problem of out-of-order (OFO) delivery, a fine-grained packet scheduling scheme is proposed to keep in-order delivery and so as to reduce the application layer completion time. The performance of the proposed scheme is evaluated via both NS-3 and real network scenarios. Experimental results show that our proposed scheme far outperforms existing MPTCP schemes in heterogeneous wireless environment.

Factors Affecting 5G Network Architecture with Integration of Higher-Level Protocol Using Radio Access Technology

In this study, we suggest a revolutionary network architectural design strategy for future 5G mobile networks, which we believe would be beneficial. The proposed design is based on a mobile environment that is centered on the user and incorporates a range of wireless and mobile technologies to achieve this. Because it is impossible to make changes to any wireless technology, new or old, in a heterogeneous wireless environment, each solution for next-generation mobile and wireless networks should be implemented in the service stratum, whereas radio access technologies should be implemented in the transport stratum in the Next-Generation Networks approach. The user terminal in the proposed design has the option of altering the Radio Access Technology - RAT based on the parameters that have been supplied. This paper introduces the Policy-Router, which is a node in the core network that constructs IP tunnels to the mobile terminal across many available RATs to the terminal, while enabling the mobile terminal to change RATs in an unobtrusive manner. After learning about the RAT via the mobile terminal's performance measurements, the mobile terminal selects the RAT by executing a multi-criteria decision-making process using the specified user agent. In this paper, we describe the QoSPRO methodology, which allows control information to be shared between the mobile terminal and the Policy Router throughout the performance evaluation procedure.

MIMODSA:Non-Pre-emptive Scheduling in Cognitive Radio Ad-Hoc Networks (CRAHNs)

Resource allocation and proficient practice of spectral frequencies to the cognitive consumers is remaining as a major apprehension in cognitive radio networks. Dynamic resource allocation can resolve the numerous research trials in CRAHNs with respect to spectrum access, allocation, and sharing. In the view of aforementioned research issue, dynamic resource allocation using non-pre-emptive scheduling is proposed by a combination of spectrum assignment and prioritization scheduling of secondary users in a cognitive wireless environment. This research work is proposing an algorithm for multiple input multiple output dynamic spectrum allocation using non-pre-emptive scheduling (MIMO-DSANPS) in CRAHNs is proposed. The simulation is performed on heterogeneous wireless environment and comparing our proposed algorithm with existing Dynamic Resource Allocation (DRA-CRN) algorithm and comparing the quality of service parameters, throughput, interference with sub-channels, packet drop and delay evaluation. As per the simulation study, the existing algorithm is improved with our proposed MIMO-DSANPS by 17% efficient in accessing the spectrum.

Integration of Fuzzy Logic and ABC Algorithm for Optimized Network Selection in Heterogeneous Wireless Environment

Background & Objective: Various Radio Access Technologies are integrated in the next generation of Heterogeneous Wireless Networks. The coexistence of various kinds of wireless access networks ensures high service quality (QoS) for the users. Seamless vertical handover plays a significant role in providing ubiquitous access to users. The ability to select the optimal access network out of available access networks decides the comprehensive performance of the system. A novel scheme: Handoff Urgency Estimator and Target Access Network Selector using Artificial Bee Colony algorithm (HUETANSABC) for deciding the necessity of handover and selection of the best network is proposed in this paper. The objective of the proposed work is to choose the most promising access network out of available coexisting networks for enhancing user experience. Fuzzy logic provides reliable results even when the input parameters are random in nature and can not be defined precisely. Artificial Bee Colony is an effective method for searching and optimization. Proposed system combines best of fuzzy logic and ABC algorithm for timely initiation of vertical handover. To gather the required information for handover, services provided by IEEE 802.21 standard are utilized. Proposed integration of fuzzy logic and the ABC algorithm has resulted in a decreasing number of unnecessary handovers. Methods: The effect of varying context parameters is analyzed using Fuzzy Inference System to estimate the urgency of handover. The optimization of a target access network selection process is achieved using meta-heuristic method. Results: Simulation results on MATLAB indicate that the proposed system performs better than ABC and Multiple Attribute Decision Making (MADM) techniques.

A Use of Fuzzy TOPSIS to Improve the Network Selection in Wireless Multiaccess Environments

Constantly faster, mobile terminals are developing, as well as wireless networks, to satisfy the growth of “Always Best Connected” demand. Users nowadays want to access the best available wireless network, either from 3GPP or IEEE group technologies, wherever they are, without losing their sessions. Consequently, mobile terminals must seamlessly transfer the communications to another access technology (vertical handover) if needed, as they often move into heterogeneous wireless environments. This work aims to optimize the network selection step in the vertical handover process. Multiattribute Decision-Making methods naturally fit this context. Nevertheless, they make wrong handover decisions sometimes, due to imprecise data collected from the metrics. This manuscript presents the use of a hybrid method, combining the fuzzy technique for order preference by similarity to the ideal situation and fuzzy analytic network process, in the network selection, to improve the quality of service and avoid, as much as possible, unnecessary handovers. The results demonstrate that this combination is the best, compared to the other methods of the same type in the network selection context.

Mobility Aware Network Selection in a Heterogeneous Wireless Environment

Abstract Different Radio Access Technologies (RATs) coexist in the same area has encouraged the researchers to get profit from the available networks by selecting of the best RAT at each moment of the call session to satisfy the user requirements. In this paper, we address a real-world problem which is the frequent mobility of the users in heterogeneous networks. We present in this paper a framework that allows users to select the best networks for the whole call session especially form a mobility perspective. The framework consists of several steps, starting by the path prediction which is performed using a Markov model order 2. The second step is to make the network selection on the zones of each predicted path, while in the third step; we get the best RAT’s configuration for each predicted path. Finally, we use another function to select one of the best configurations to be used for all the possible used paths. The results show that our proposal performs very well by eliminating the unnecessary vertical handover while maintaining a good Quality of Service (QoS).

A novel modeling approach for vertical handover based on dynamic k-partite graph in heterogeneous networks

The future network world will be embedded with different generations of wireless technologies, such as 3G, 4G and 5G. At the same time, the development of new devices equipped with multiple interfaces is growing rapidly in recent years. As a consequence, the vertical handover protocol is developed in order to provide ubiquitous connectivity in the heterogeneous wireless environment. Indeed, by using this protocol, the users have opportunities to be connected to the Internet through a variety of wireless technologies at any time and anywhere. The main challenge of this protocol is how to select the best access network in terms of Quality of Service (QoS) for users. For that, many algorithms have been proposed and developed to deal with the issue in recent studies. However, all existing algorithms permit only the selection of one access network from the available networks during the vertical handover process. To cope with this problem, in this paper we propose a new approach based on k-partite graph. Firstly, we introduce k-partite graph theory to model the vertical handover problem. Secondly, the selection of the best path is performed by a robust and lightweight mechanism based on cost function and Dijkstra’s algorithm. The experimental results show that the proposed approach can achieve better performance of QoS than the existing algorithms for FTP traffic and video streaming.

Flow/Interface Association for multi-connectivity in heterogeneous wireless networks: e-Health case

A heterogeneous wireless environment is composed of different radio access technologies (RATs) including IEEE standards (e.g., Wi-Fi) and 3GPP (e.g., 2G, 3G, 4G, 5G), each with different coverage and different capacity to cater for diverse service requirements. In such an environment, multiple access networks could be available for a mobile user. Each user can run multiple applications that have particular Quality of Service (QoS) requirements. Hence, it would be beneficial for a multi-interface terminal to simultaneously use two or more interfaces to gain in performance. However, using multiple networks simultaneously may consume more energy than using only one interface. Therefore, energy consumption should be considered as a criterion in the Flow/Interface Association (FIA). This paper presents a novel method called Smart Tabu Search (STS) that takes into account network conditions, the monetary cost of the network, QoS requirements of the applications, user preferences, and energy consumption of the mobile device to select the optimal FIA that achieves the best trade-off among all the considered criteria. We validate our proposal using both simulations and testbed experiments.

An optimized multi‐attribute vertical handoff approach for heterogeneous wireless networks

SummaryWireless networking is becoming an increasingly important and popular way of providing global information access to users on mobility. Considering the accessibility of different advances, it is hard to choose the best system that guarantees client fulfilment. The best network selection in heterogeneous condition is a primary research issue. In Dynamic Network Selection Function (DNSF) method, which employs physical layer metrics as criteria for network selection, computation of functions‐based relative dynamic weight optimization is proposed. This method is implemented through simulation in a typical heterogeneous wireless environment of Universal Mobile Telecommunications Service (UMTS) and Wireless Local Area Network (WLAN). The number of unnecessary handover occasions with the Handoff failure rate of the client between accessible systems is utilized as the execution measurements, and constantly best‐associated system is chosen in view of the cost function. It is inferred that DNSF based approach yields ideal system determination in heterogeneous condition.

Energy efficient resource matching algorithm for multi-homing services in dynamic wireless environment

This paper investigates the matching problem between radio resources and multi-homing services in a dynamic heterogeneous wireless environment. A mobile terminal with a multi-homing service simultaneously transmits data to multiple radio access networks using multiple air interfaces. This paper proposes an energy-efficient resource matching algorithm to statistically guarantee the service rate requirements of each user with the lowest power consumption. First, according to the concept of resource matching and the dynamic characteristics of wireless channels, this paper defines two matching probability metrics and subsequently selects the most appropriate metric to build a resource matching model to realize the maximum energy efficiency of each user while meeting the statistical guarantee constraints. In particular, the Rayleigh fading channel model is introduced in this paper to accurately reflect the dynamics of the wireless channel, and a complete expression of the matching probability metric is deduced. Second, according to the optimal solution characteristics of the optimization model, the original problem is decomposed into the inner power minimization problem and the outer matching probability maximization problem, and the convex optimization and the equality constrained optimization are separately used to solve the problem. Hence, the two-layer distributed solution algorithm is proposed. In particular, this paper conducts rigorous mathematical analyses on the convergence, optimality and complexity of the algorithm and constructs an improved resource matching model by relaxing the statistical guarantee parameters in the case of no solution to the model. Finally, the simulation results show that this algorithm has better performance than the three existing resource matching algorithms, can achieve lower user power consumption, and can realize the goal of the best match between the network resources and service requirements in a dynamic environment.

МЕТОД ПІДВИЩЕННЯ НАДІЙНОСТІ ПЕРЕДАЧІ ДАНИХ ПІД ЧАС ВИКОРИСТАННЯ ПРОТОКОЛУ TCP FREEZE ШЛЯХОМ ПІДВИЩЕННЯ ПРОПУСКНОЇ СПРОМОЖНОСТІ

У даній статті розглянуто протокол TCP, розглянуті його недоліки, а, зокрема, розриви TCP з'єднань. Розглянуто існуючі модифікації, які дозволяють усунути такі недоліки. Виявлено недоліки існуючих методів. Запропоновано метод управління передачею даних, в протоколі TCP Freeze, що дозволяє підвищити надійність передачі даних. Такий метод заснований на динамічній зміні, в залежності від умов, параметрів протоколу, які в своїй незміненій версії задаються статично. Висновки. Ефективність методу управління передачею даних, що використовується в протоколі TCP Freeze, може бути поліпшена в бездротовому гетерогенному середовищі за допомогою варіювання параметрів. Виявлено, що за умови рівності параметрів і їх значенні близькому до 20 , пропускні спроможності протоколів TCP Freeze і TCP Reno будуть практично рівними. При більш низьких значеннях параметрів протокол TCP Reno показує кращу ефективність, ніж TCP Freeze, а при великих значеннях параметрів пропускна здатність протоколу TCP Freeze буде перевищувати таку у TCP Reno. Використання запропонованого методу дозволяє зменшити час передачі даних в мережі внаслідок реалізації алгоритму управління розміром плаваючого вікна протоколу, що згладжує флуктуації останнього і приводить до меншого числа повторних передач пакетів даних, а також обробку події хендовера.

RETRACTED ARTICLE: Maximizing quality of multimedia streaming services in heterogeneous wireless networks using optimal multi-server technique

User expectations pertaining to multimedia data is huge. Issues in streaming like slow start and buffering hinder the quality of received content. Many authors have addressed the buffering issues. Their solutions in order to avoid buffering focussed on reducing the received multimedia quality in an attempt to avoid buffering. In this proposed work, quality issues in multimedia streaming have been focussed upon in WLAN-4G networks. A modified cat swarm optimization algorithm for optimal server selection (MCSO-SS) has been proposed to maximize the multimedia quality in terms of quality of service (QoS) and quality of experience (QoE). The proposed technique has been tested in a heterogeneous wireless environment that includes WLAN and 4G cellular networks. Experimental results prove that our proposed MCSO-SS based streaming technique increases the received multimedia content quality compared to existing techniques in terms of QoS (delay, throughput, service failure rate), and QoE (PSNR, SSIM).

Network Selection in Wireless Heterogeneous Environment by C-P-F Hybrid Algorithm

Numerous wireless communication technologies have been employed to manage mobile users anywhere, any time and anyhow. Additionally, users are more and more fascinated by multimedia applications such as voice, audio and video, which require Quality of Service (QoS) support. To retain the user with Always Best Connected network in such restrictions is a challenging issue. A contemporary approach for efficient network selection in wireless heterogeneous networks is conferred. The approach composed of two criteria: the first is the cost function comprising of received signal strength, available bit rate, signal to noise ratio, throughput and bit error rate metrics. The metrics’ respective weights are being optimized by Particle Swarm Optimization (PSO). The second criterion consists of fuzzy logic system fed with similar metrics as inputs and targeted towards same output. The final decision of network selection is taken by the blend of these two criteria. Simulation results indicated that the proposed scheme based on Cost function, PSO and Fuzzy system (C-P-F) provided better performance in terms of minimizing the unnecessary handoffs (network selection rate), utility degree and load balancing. The proposed algorithm (C-P-F) significantly reduces the network selection rate by 50% as compared to existing algorithm based on cost function and PSO. This reduction indicated higher probability of guaranteed session continuity and good quality of the currently running service, which resulted in high user satisfaction levels. It enhances user satisfaction 55% and reduced network selection rate upto 75% in comparison of existing network selection techniques.