An algorithm for heterogeneous wireless network connections for user preferences and services

Next-generation wireless networks offer Internet connection through various technologies anytime and anywhere. The selection of an optimal technology from these available technologies is essential to guarantee user mobility and service continuity in a heterogeneous wireless environment. This paper proposes a new network selection model which is based on the integration of simple additive weighting (SAW) method in the framework of trading market non-cooperative game theory and the analytic hierarchy process (AHP) method was utilized to estimate the weights of the parameters that affect the network selection process. The proposed solution enables the mobile user to negotiate with competing networks by providing the user preference to be considered for the network selection process. The proposed solution is analyzed and tested through simulations. The results show the efficiency of proposed method which is able to optimize the user’s satisfaction.

Heterogeneous wireless networks (HWNs) are essential in modern communication systems, as they seamlessly integrate various radio access technologies (RATs). In this context, network selection (NS) emerges as a pivotal element, responsible for selecting the most appropriate network for user equipment (UE) during transitions between RATs. Conventional NS mechanisms, such as the multi-attribute decision-making (MADM) methods, are commonly employed for their fast ranking of RATs, real-time support, and flexibility. However, they suffer from three primary limitations; the rank reversal problem (RRP), overlooking specific user/service requirements while favouring the highest-ranking RAT, and the associated frequent handovers. To address these limitations, in this paper, we first employ one of the most recent and effective MADM approaches, known as the measurement of alternatives and ranking according to the compromise solution (MARCOS), to model and solve the NS problem (MARCOS-NS) for the first time in the literature. We then propose novel sigmoid utility functions to assess the quality of each RAT attribute within the HWNs environment, taking into account user/application requirements. Further, we enhance MARCOS-NS by replacing its original normalization technique with the proposed sigmoid utility functions to overcome its limitation, creating a new MADM approach called MARCOS-Utility. Our results demonstrate the superiority of MARCOS-Utility over conventional MADM approaches as it completely eliminates the RRP, reduces vertical handover occurrences by an average of 33.1%, and achieves a balance between data rate and packet loss ratio for the streaming traffic class.

Along with the continuous development of wireless communication technology and popularization of multi- mode terminal, the seamless fusion of various heterogeneous wireless networks has gradually become the trend of net- work development. The diversified forms of network access enable the multi-mode terminal to dynamically select the ac- cess network with the highest utility value to obtain network service according to specific standard. The study selects a typical heterogeneous network scenario, abstracts users' network selection process to be group game model, adopts the evolutionary methods, and then studies user's network selection process through replicator dynamics. On the basis of it, it puts forwards a kind of network selection algorithm in the view of replicator dynamics and verifies its algorithm perfor- mance through simulation results, and then analyzes the impact of different factors on evolutionary equilibrium in detail.

With the increasing popularity of Electric Vehicles (EVs), the demand for electric vehicle charging stations (EVCS) has grown significantly. Selecting an appropriate charging station based on user preferences is essential for a satisfactory charging experience. Existing EVCS selection solutions often focus solely on distance or cost, neglecting other crucial factors such as charging time, waiting time, and available facilities. To address these challenges, we propose the Personalized Charging Station Selection Scheme (PC3S), which integrates Fuzzy Analytical Hierarchical Process (FAHP) and Multi-attribute Decision Making (MADM) theory. FAHP is used to handle imprecise and uncertain user preferences, interdependencies among criteria, and to determine the weights of each selection criterion. MADM, specifically TOPSIS, ranks alternative charging stations with respect to each criterion and calculates their weighted priorities. The charging stations are then ranked in ascending order by priority. We developed a mathematical model of our proposed scheme and implemented a numerical example based on a real dataset from the US Department of Energy. MATLAB was used to evaluate PC3S effectiveness using three different scenarios: travel distance-based, price-based, and a random scenario. The performance analysis of PC3S showed that it outperformed other approaches like ELECTRE, PROMETHEE, VIKOR, SAW, and BlockEv in terms of accuracy (over 97%, 95%, and 95.5%) and ranking abnormality (less than 5%, 8%, and 9%) across all three scenarios. By considering user preferences and properly ranking alternative charging stations, our scheme enhances user experience and facilitates a more customized and efficient charging station selection process, potentially leading to wider adoption of EVs.

In a heterogeneous wireless network, multiple networks are available to provide seamless connectivity. In this environment, the terminal has the ability to connect to most efficient network as per users requirements. This is known as network selection. This selection is done by assigning weightage to network attributes on the basis of users preference. In this paper, selection of network is done using Evolutionary and Bankruptcy Game Model. This simulation is done for conversational and background traffic class. Simulation results of score obtained in case of available WIMAX (Worldwide Interoperability for Microwave Access), WLAN (Wireless Local Area Network), and UMTS (Universal Mobile Telecommunications Service) environment is used for the selection of a network. The selection using Evolutionary Game and Bankruptcy Game Model is done at user terminal side and network operator side respectively. Therefore, Game theory proves to be useful for network selection in fifth generation of wireless network.

In order to provide ubiquitous access for the users, future generation network integrate a multitude of radio access technologies (RAT’S) which can interoperate between them. However, the most challenging problem is the selection of an optimal radio access network, in terms of quality of service anywhere at anytime. This paper proposes a novel ranking algorithm, which combines multi attribute decision making (MADM) and Mahalanobis distance. Firstly, a classification method is applied to build a classes which having the homogeneous criteria. Afterwards, the Fuzzy AHP, MADM method is applied to determine weights of inter-classes and intraclasses. Finally, Mahalanobis distance is used to rank the alternatives. The simulation results show that the proposed algorithm can effectively reduce the ranking abnormality and the number of handoffs.

Variety of vertical handoff algorithms (VHA) based on multi attribute decision making (MADM) methods have been proposed to help the user to select dynamically the best access network in terms of quality of service. However, there is no study that examines the validity of MADM methods which led to inefficient network selection due to inconsistent ranking outcomes. To address this issue, this paper proposes a new validation approach based on group MADM methods. This approach takes into account the weighting algorithms and allows to select the most valid ranking algorithm which can be used in specific traffic classes for network selection decision. Simulation results are presented to illustrate the effectiveness of our new validation approach for the network selection algorithm.

Compensatory multiattribute decision-making (MADM) methods are founded on the trade-offs between attributes, allowing an alternative to compensate for its weakness in an attribute with its strength in another attribute. We call them heterogeneous MADM methods because they generally consider the unlimited trade-off between attributes. In other words, they even allow that very poor performance of an attribute to be compensated by the strong performance of another attribute. However, this may not be acceptable to decision makers (DMs). They may accept the limited trade-offs between attributes, making them more homogeneous. In these situations, MADM methods should be modified to consider the limited trade-offs between attributes. This modification comes with some conceptual and technical difficulties. This study presents some examples to show the concept of limited trade-offs clearly and presents a modified version of the simple additive weighting (SAW) method, H-SAW, considering the limited trade-offs between attributes. We also integrate H-SAW and fuzzy analytic hierarchy process (FAHP) methods to supplier selection and illustrate the real application of H-SAW method.

Next generation wireless networks will consist of different types of radio access technologies. Emerging diverse applications have attracted a lot of users. Therefore, having the best connectivity anytime anywhere has become an important issue. Users would like to have continuous high quality network connection even when they handover from one network type to the other. Each access technology has its own features and service quality. In this regard, when there is a handover, it is important to choose the most suitable network based on user needs. Different algorithms have been proposed for network selection. Multi Attribute Decision Making (MADM) methods are widely used due to their precision and low computational complexity. In this paper, usual normalization methods used in network selection and their drawbacks are presented and a new method, by taking user preferences and application needs into account, is proposed. Based on the proposed normalization method, a new decision process is suggested which is based on ELECTRE II algorithm. Our proposed method considers the dynamic nature of wireless networks and the uncertainty in the collected network performance metrics. Simulation results show that using the proposed handover decision process, the probability of ranking abnormality, which is a common drawback of MADM methods, is reduced significantly compared to other methods such as SAW, GRA and TOPSIS and the decision algorithm is more stable in terms of sensitivity to change in the amounts of decision criteria.

Providing service continuity to the end users with best quality is a very important issue in the next generation wireless communications. With the evolution of the mobile devices towards a multimode architecture and the coexistence of multitude of radio access technologies (RAT's), the users are able to benefit simultaneously from these RAT's. However, the major issue in heterogeneous wireless communications is how to choose the most suitable access network for mobile's user which can be used as long as possible for communication. To achieve this issue, this paper proposes an intelligent network selection strategy which combines two multi attribute decision making (MADM) methods such as analytic network process (ANP) and the technique for order preference by similarity to an ideal solution (TOPSIS) method. The ANP method is used to find the differentiate weights of available networks by considering each criterion and the TOPSIS method is applied to rank the alternatives. Our new strategy for network selection can dealing with the limitations of MADM methods which are the ranking abnormality and the ping-ponf effect.

Heterogeneous wireless network in the future will provide users with more service selections. The user can se- lect the network with the best effect based on the specific demand and individual preference, and random selection will not only cause disorder of the network and low load efficiency but also impact the user's experience. The previous studies believe that the attribute of the network only has monotonous impact on the utility of user's use, namely, the larger the ef- ficiency indicator the better, and the smaller the cost indicator the better. However, in reality, it changes with the condi- tions. The study divides the users into two types of quality preference and cost preference based on the model of multi- attribute decision-making and constructs the network selection model in the environment of heterogeneous wireless net- work targeted at the voice business, video business and data business by taking into account the decision attributes of network bandwidth, delay, response speed, safety, cost to use, etc., thus realizing the objective of increasing the network utility of the user under the circumstance of adding no load to the network.

SummaryNetwork selection mechanisms have a significant role in guaranteeing the QoS for users in a heterogeneous wireless networks environment. These mechanisms allow the selection of an optimal wireless network to satisfy the needs of users. Users are provided with the opportunity to select from multiple connectivity opportunities available all over various wireless networks. Furthermore, the network operators themselves can execute active selection strategies that facilitate proper decision making, in which user preferences are considered. This study proposes a new noncooperative competing game‐theoretic model and strategy space based on user preference. This model can solve network selection problems and capture the inter‐linkages of decisions taken by various networks. A generalized simple additive weighting method is incorporated into the framework of noncooperative game theory. In addition, the utility function is employed to assess the usefulness of the system. Simulation results and analysis illustrate the efficacy of the suggested model in attaining optimum network utility for heterogeneous wireless networks while optimizing user satisfaction. Copyright © 2014 John Wiley & Sons, Ltd.

Achieving reliable connectivity in heterogeneous vehicular networks is a challenging task, owing to rapid topological changes and unpredictable vehicle speeds. As vehicular communication demands continue to evolve, multipath connectivity is emerging as an important tool, which promises to enhance network interoperability and reliability. Given the limited coverage area of serving access technologies, frequent disconnections are to be expected as the vehicle moves. To ensure seamless communication in dynamic vehicular environments, an intelligent path management algorithm for Multipath TCP (MPTCP) has been proposed. The algorithm utilizes a network selection mechanism based on Fuzzy Analytic Hierarchy Process (FAHP), which dynamically assigns the most appropriate underlying network for each running application. The selection process takes into account multiple factors, such as path quality, vehicle mobility, and service characteristics. In contrast to existing solutions, our proposed method offers a dynamic and comprehensive approach to network selection that is tailored to the specific needs of each service to ensure that it is always paired with the optimal access technology. The results of the evaluation demonstrate that the proposed method is highly effective in maintaining service continuity during vertical handover. By tailoring the network selection to the specific needs of each application, our path manager is able to ensure optimal connectivity and performance, even in challenging vehicular environments, delivering a better user experience, with more reliable connections, and smoother data transfers.

Previous researches outlined the advantages of the Analytical Hierarchy Process (AHP) and Analytic Network Process (ANP) methods in solving Multi-Attribute Decision Making (MADM) problems. The advancement of the above methods was continually developed as an effort to cover up various weaknesses. Mainly related to the consistency and linguistic variables in translating the expert opinions. Thus, it initialized the emergence of Fuzzy AHP (F-AHP) and Fuzzy ANP (F-ANP). Due to the restricted operation of these algorithms in smartphone selection, this research attempted to investigate the effectiveness of both methods in providing the analysis of criteria weight, the final recommendation weight, the product recommendation weight, and the execution time in DSS-SmartPhoneRec application development. A survey of one hundred respondents of University students identified the dominant criteria in selecting the smartphone, namely price, Random Access Memory (RAM), processor, internal memory, and camera. Hence, five alternative products were then chosen as the appropriate smartphones’ recommendations based on the respondent’s preferences. As an automatic tool, a DSS-SmartPhoneRec application was built to analyze and compare between F-AHP and F-ANP methods in resolving the smartphone selection cases. It revealed that the level of consistency of criteria weight, the final weight of recommendation, and the weight that the product-based F-ANP was 40% greater than F-AHP. In terms of execution time, F-AHP had a shorter time than F-ANP. Meanwhile, the comparison of products recommendation from DSS-SmartPhoneRec and a manual test showed that F-ANP was 16% more in line with the respondents’ predilection. In a nutshell, the DSS-SmartPhoneRec administered the devote smartphone recommendations based on the user’s expectation. The comparison analysis furnished a learning outcome for the users in determining the appropriate MADM method tailored to the type of cases.

The wireless technologies in a heterogeneous wireless network usually differ in terms of, but not limited to, their offered bandwidths, operating frequencies and costs, coverage areas, and latencies. Currently, no single wireless technology claims to provide cost-effective services, which offers high bandwidths and low latencies to all mobile users in a large coverage area. This is where the need for well-organized vertical handoffs (VHOs) between heterogeneous wireless technologies become evident. This paper presents a new VHO algorithm which takes into account a complete set of system attributes to fulfill two tasks. The first task is to perform the VHO Necessity Estimation (VHONE) utilizing several parallel Fuzzy Logic Controllers (FLCs) with reduced rule sets to estimate the VHO necessity. The second task is the selection of the best network as the target for VHO, where a ranking algorithm is developed based on TOPSIS. Priority Weights for the different system attributes are calculated based on a Fuzzy Analytical Hierarchy Process (FAHP). Later, simulations based on four traffic classes (conversational, streaming, background, and interactive) in the presence of three wireless networks (WLAN, WMAN, and WWAN), are carried out using a comprehensive wireless simulation test-bed that contains all the necessary Radio Resource Management (RRM) modules.