Quality Of Service Requirements Research Articles

Machine Learning-Based Resource Allocation Algorithm to Mitigate Interference in D2D-Enabled Cellular Networks

Mobile communications have experienced exponential growth both in connectivity and multimedia traffic in recent years. To support this tremendous growth, device-to-device (D2D) communications play a significant role in 5G and beyond 5G networks. However, enabling D2D communications in an underlay, heterogeneous cellular network poses two major challenges. First, interference management between D2D and cellular users directly affects a system’s performance. Second, achieving an acceptable level of link quality for both D2D and cellular networks is necessary. An optimum resource allocation is required to mitigate the interference and improve a system’s performance. In this paper, we provide a solution to interference management with an acceptable quality of services (QoS). To this end, we propose a machine learning-based resource allocation method to maximize throughput and achieve minimum QoS requirements for all active D2D pairs and cellular users. We first solve a resource optimization problem by allocating spectrum resources and controlling power transmission on demand. As resource optimization is an integer nonlinear programming problem, we address this problem by proposing a deep Q-network-based reinforcement learning algorithm (DRL) to optimize the resource allocation issue. The proposed DRL algorithm is trained with a decision-making policy to obtain the best solution in terms of spectrum efficiency, computational time, and throughput. The system performance is validated by simulation. The results show that the proposed method outperforms the existing ones.

End-to-end latency upper bounds and service chain deployment algorithm based on industrial internet network

The diverse service requests in industrial Internet networks require flexible and efficient service chain deployment to ensure the quality of service (QoS). However, current deployment algorithms for service chains are primarily designed to guarantee only low end-to-end latency; they often overlook the amount of service chains that can be accommodated by the network and could lead to severe network load imbalances, significantly reducing service efficiency and causing serious network congestion issues. To address the above issues, we develop a mathematical model of the network topology and service request chains by integrating Network Function Virtualization (NFV) and Software Defined Networking (SDN). Utilizing network calculus theory, we derive the upper bound of end-to-end delay for service chain routing and analyzed the relationship between the upper bound of service chain routing delay and the resource allocation of Virtual Network Function (VNF) nodes. Based on the aforementioned model, we propose a novel service chain deployment algorithm named the Delay-Aware Load-Balanced Routing Algorithm (DLBRA). DLBRA comprehensively considers network traffic load balancing and end-to-end latency of service chains, rationally allocating VNF node resources to complete the determined service chain routing deployment. Experimental results indicate that, compared to the shortest path and load balancing algorithms, DLBRA not only ensures that the end-to-end delay of the service chain meets its QoS requirements, but also effectively reduces network load imbalance, significantly increasing the number of service chain requests that the network can accommodate. Additionally, DLBRA provides tailored deployment guidance for different types of service chains, such as latency-sensitive and data-intensive service chains, ensuring optimal utilization of network resources. This algorithm enhances the efficiency of service chain deployment in industrial internet scenarios and possesses broad application potential in other network environments where delay optimization and load balancing are critical, such as intelligent transportation, cloud computing, and 5G networks.

A multi-queue-based ECN marking strategy for multi-class QoS guarantee in programmable networks

Currently, network applications are experiencing explosive growth, and various types of network applications are showing a trend of varied demands for quality of network service. However, the existing Explicit Congestion Notification (ECN) marking methods have not taken into account the diversified Quality of Service (QoS) requirements of network applications. This article introduces a multi-queue ECN marking strategy targeting multiple QoS guarantees. The strategy utilizes virtual queues and dynamic weighted round-robin scheduling to achieve traffic partitioning in a programmable data plane. It constructs a multi-queue, multi-class QoS queuing model based on the QoS requirements of different traffic and network conditions. The model is solved by real-time to obtain the ECN marking thresholds and round-robin weights for different queues, in order to achieve dynamic QoS requirements of different network applications. We implemented this strategy in Mininet and BMv2, and compared it with DCQCN, P4QCN, and TCN. The experimental results indicate that this policy demonstrates good performance in terms of queue length, RTT, and throughput, while also ensuring fairness between traffics. Results of the experiment indicate that the proposed approach is superior to DCQCN and P4QCN in the field of performance fluctuation and rapid feedback, and it exhibits notable advantages over TCN, and also ensures the fairness of traffic.

Design and implementation of an integrated OWC and RF network slicing-based architecture over hybrid LiFi and 5G networks

AbstractRadio frequency (RF) systems tend to become congested and overused due to the increasing number of users, devices and the multiple technologies involved in their deployment. This leads to the downgrading of quality of service (QoS) further caused by interference with different signals. Optical Wireless communications (OWC) are emerging as a feasible alternative as they offer unlicensed, interference-free spectrum by using the frequency range located in the visible and invisible light spectrum. Its applications can be found in various fields such as healthcare, education, finance and industry 4.0. Moreover, it enhances the security and privacy of communications. Nevertheless, the limited spectrum in OWC also requires optimised resource allocation to support the QoS of different applications or users whilst lacking established infrastructure to manage this. To address these challenges, this paper proposes a novel 5G-LiFi framework able to ensure QoS requirements by introducing network slicing in Light Fidelity (LiFi) networks integrated with 5G infrastructure. This paper has developed and deployed a 5G-LiFi architecture capable of providing network slicing capabilities over the LiFi segment of the hybrid network. It allows a full control over the network traffic and tailored, improved QoS capabilities. The proposed solution has been empirically validated and evaluated in a realistic testbed employing real-world LiFi and 5G network equipment, and yielded promising results in terms of bandwidth, delay, jitter and packet loss. This work concludes that the use of heterogeneous networks integrating OWC with RF is a suitable solution and it can lead to a better use and exploitation of the different spectrums, improving the QoS offered to end-users.

Resource optimization of multi-UAV assisted communication system based on user scheduling

In order to improve the transmission rate of the multi-user mobile communication downlink wireless transmission system, a resource optimization method for multi-unmanned aerial vehicle (UAV) assisted communication system based on user scheduling and trajectory optimization is proposed. The proposed method establishes an optimization problem with the maximization of the total multi-user throughput as the criterion under the constraints of user scheduling, total energy consumption of UAVs and user service quality requirements. In order to solve this non-convex problem, the original non-convex problem is decomposed into three easy-to-handle non-convex sub-problems by using the block coordinate descent method (BCD). The sub-problems are transformed and solved by introducing slack variables, first-order Taylor expressions, continuous convex approximation (SCA) and other methods, and then alternately iteratively optimized to obtain an approximate suboptimal solution to the original non-convex problem. Simulation results show that the proposed algorithm can effectively improve the total system throughput and has good convergence in both single and multi-UAV communication systems.

Quality of Service and Congestion Control in Software-Defined Networking Using Policy-Based Routing

Managing queuing delays is crucial for maintaining Quality of Service (QoS) in real-time media communications. Customizing traditional routing protocols to meet specific QoS requirements—particularly in terms of minimizing delay and jitter for real-time media—can be both complex and time-intensive. Furthermore, these protocols often encounter challenges when adapted for vendor-specific hardware implementations. To address these issues, this paper leverages the programmable features of Software-Defined Networking (SDN) to simplify the process of achieving user-defined QoS, bypassing the limitations of traditional routing protocols. In this work, we propose a policy-based routing module that integrates with traditional routing protocols to ensure QoS for real-time media flows. QoS is achieved by rerouting the flow along a new low-latency path calculated by the proposed module when the queuing delay exceeds a certain threshold. The experimental results demonstrate that the proposed solution significantly enhances the performance of traditional routing protocols within an SDN framework, effectively reducing the average end-to-end delay by 80% and total packet loss by 73%, while also improving jitter and alleviating network congestion.

A bandwidth allocation scheme based on GRU traffic prediction in passive optical networks

With the advancement of information technology, network slicing technology has emerged as a viable solution for ensuring Quality of Service (QoS) in optical access networks. Current research is increasingly focusing on the integration of optical access networks with network slicing technologies. This paper proposes a bandwidth allocation scheme based on traffic prediction, specifically designed for resource management in optical network slicing scenarios. The scheme employs a Gated Recurrent Unit (GRU) neural network model to forecast future traffic, and combines bandwidth and latency factors to allocate bandwidth to each slice based on predicted values, thereby meeting the QoS requirements of various services. Simulation results indicate that, compared to the baseline algorithm, the proposed scheme achieved a 35.42% increase in Explaining Variance Score (EVS) and a 38.16% improvement in R2 score for factory slicing prediction. Similarly, for data center slicing prediction, the EVS score increased by 32.29% and the R2 score improved by 41.96%. In terms of performance metrics such as latency, packet loss rate, and throughput, the proposed algorithm outperforms both traditional prediction algorithms and the baseline algorithm.

A Resource Allocation Algorithm for Cloud-Network Collaborative Satellite Networks with Differentiated QoS Requirements

With the continuous advancement of cloud computing and satellite communication technology, the cloud-network-integrated satellite network has emerged as a novel network architecture. This architecture harnesses the benefits of cloud computing and satellite communication to achieve global coverage, high reliability, and flexible information services. However, as business types and user demands grow, addressing differentiated Quality of Service (QoS) requirements has become a crucial challenge for cloud-network-integrated satellite networks. Effective resource allocation algorithms are essential to meet these differentiated QoS requirements. Currently, research on resource allocation algorithms for differentiated QoS requirements in cloud-network-integrated satellite networks is still in its early stages. While some research results have been achieved, there persist issues such as high algorithm complexity, limited practicality, and a lack of effective evaluation and adjustment mechanisms. The first part of this study examines the state of research on network virtual mapping methods that are currently in use. A reinforcement-learning-based virtual network mapping approach that considers quality of service is then suggested. This algorithm aims to improve user QoS and request acceptance ratio by introducing QoS satisfaction parameters. With the same computational complexity, QoS is significantly improved. Additionally, there has been a noticeable improvement in the request acceptance ratio and resource utilization efficiency. The proposed algorithm solves existing challenges and takes a step towards more practical and efficient resource allocation in cloud-network-integrated satellite networks. Experiments have proven the practicality of the proposed virtual network embedding algorithm of Satellite Network (SN-VNE) based on Reinforcement Learning (RL) in meeting QoS and improving utilization of limited heterogeneous resources. We contrast the performance of the SN-VNE algorithm with DDRL-VNE, CDRL, and DSCD-VNE. Our algorithm improve the acceptance ratio of VNEs, long-term average revenue and delay by an average of 7.9%, 15.87%, and 63.21%, respectively.

Quality of Service-Aware Multi-Objective Enhanced Differential Evolution Optimization for Time Slotted Channel Hopping Scheduling in Heterogeneous Internet of Things Sensor Networks.

The emergence of the Internet of Things (IoT) has attracted significant attention in industrial environments. These applications necessitate meeting stringent latency and reliability standards. To address this, the IEEE 802.15.4e standard introduces a novel Medium Access Control (MAC) protocol called Time Slotted Channel Hopping (TSCH). Designing a centralized scheduling system that simultaneously achieves the required Quality of Service (QoS) is challenging due to the multi-objective optimization nature of the problem. This paper introduces a novel optimization algorithm, QoS-aware Multi-objective enhanced Differential Evolution optimization (QMDE), designed to handle the QoS metrics, such as delay and packet loss, across multiple services in heterogeneous networks while also achieving the anticipated service throughput. Through co-simulation between TSCH-SIM and Matlab, R2023a we conducted multiple simulations across diverse sensor network topologies and industrial QoS scenarios. The evaluation results illustrate that an optimal schedule generated by QMDE can effectively fulfill the QoS requirements of closed-loop supervisory control and condition monitoring industrial services in sensor networks from 16 to 100 nodes. Through extensive simulations and comparative evaluations against the Traffic-Aware Scheduling Algorithm (TASA), this study reveals the superior performance of QMDE, achieving significant enhancements in both Packet Delivery Ratio (PDR) and delay metrics.

Case Study of Reverse Merger - A Case Study on the Performance of SF Holding's Acquisition of Dingtai New Materials

SF Express continues to lead the Courier industry in China with its standardization, high quality and high effectiveness advantages. However, as customers' requirements for service quality continue to increase, the cost of express delivery industry continues to increase, and in the case of numerous express delivery industries and fierce competition, SF Holding's traditional business model and existing capital are difficult to stabilize its leading position in the express delivery industry. In order to ensure the expansion of its own capital and seize a favorable position in the market, SF Holding chose to go public in 2016 through the backdoor of Dingtai New Materials. This paper will explain in detail the principle, practical operation, strategy and practice behind the success of SF Express backdoor listing, and study the financial performance of SF Holding by analyzing four indicators of debt repayment, operation, development and profitability according to the financial index method, sum up the strategic experience of the enterprise, and provide effective reference for the development and transformation of similar enterprises.

Service recommendation in JointCloud environments: An efficient regret theory-based Qos-aware approach

With the proliferation of data-intensive applications, there arises an urgent demand for a substantial amount of cloud services to meet their requirements for data analysis. This globalized yet cooperative business landscape necessitates new cooperative models across the world. JointCloud, as a novel cross-cloud cooperation computing model, takes the first step towards establishing an evolving cloud ecosystem where all cloud service providers could collaboratively serve globalized computation needs. The collaboration among various cloud service providers enhances both the availability and Quality of Services(QoS) of cloud services, enabling a cloud service provider to concurrently serve users with differentiated QoS requirements. This unique characteristic further complicates the problems of QoS-aware service recommendations, rendering conventional approaches obsolete and inefficient. Thus, there is an urgent need to improve the efficiency and effectiveness of the service recommendation method, which is of vital importance for the JointCloud environment. In this paper, we present a two-stage efficient regret theory-based service recommendation method for the JointCloud environment. In the first stage of our proposed method, we cluster the cloud service providers to reduce the choice space to improve the efficiency of cloud service recommendations. In the second stage, we meticulously identify the most appropriate services within one cluster. To enhance the overall rationality of service recommendation, we introduce a subjective and objective combined weighting method and a regret theory based ranking method. Extensive experimental results demonstrate that our approach can facilitate fast and accurate service recommendations.

Reliability and Safety Testing of a Ventilator Remote Control System Against Communication Failures and Network Disruptions.

The need for remote ventilator control has been highlighted by the COVID-19 Public Health Emergency. Remote ventilator control from outside a patient's room can improve response time to patient needs, protect health care workers, and reduce personal protective equipment (PPE) consumption. Extending remote control to distant locations can expand the capabilities of frontline health care workers by delivering specialized clinical expertise to the point of care, which is much needed in diverse health care settings, such as tele-critical care and military medicine. However, the safety and effectiveness of remote ventilator control can be affected by many risk factors, including communication failures and network disruptions. Consensus safety requirements and test methods are needed to assess the resilience and safety of remote ventilator control under communication failures and network disruptions. We designed two test methods to assess the robustness, usability, and safety of a remote ventilator control prototype system jointly developed by Nihon Kohden OrangeMed, Inc. and DocBox, Inc. ("the NK-DocBox system") to control the operation of an NKV-550 critical care ventilator under communication failures and network disruptions. First, the robustness of the NKV-550 ventilator was tested using a remote-control application developed on OpenICE - an open-source medical device interoperability platform - to transmit customized high-frequency and erroneous remote-control commands that could be caused by communication failures in a real-world environment. The second method utilized a network emulator to create different types and severity of network quality of service (QoS) degradation, including bandwidth throttling, network delay and jitter, packet drop and reordering, and bit errors, in the NKV-DocBox system to quantitatively assess the impact on system usability and safety. The NKV-550 ventilator operated as expected when remote-control commands arrived as fast as once per second. It ignored erroneous commands attempting to adjust invalid ventilation parameters. When facing commands that set the ventilation mode and parameters to invalid values, it reset the ventilation mode or parameters to default values, the safety implication of which may merit further evaluation. When any network QoS attribute (except for packet reordering) started to degrade, the NK-DocBox System experienced interference to its remote-control function, such as delays in the transmission of ventilator data and remote-control commands within the system. When the network QoS was worse than 500 ms network delay, 100 ms network jitter, 1% data drop rate, 12Mbps minimal bandwidth, or 1e-6 bit error rate, the system became unsafe to use. For example, ventilator waveforms visualized on the remote-control application demonstrated freezes, out-of-synchronization, and moving backwards; and the connection between the ventilator and the remote-control application became unstable. The presented test methods confirmed the robustness of the NKV-550 ventilator against high-frequency and erroneous remote control, quantified the impact of network disruptions on the usability, reliability, and safety of the NK-DocBox system and identified the minimum network QoS requirements for it to function safely. These generalizable test methods can be customized to evaluate other remote ventilator control technologies and remote control of other types of medical devices against communication failures and network disruptions.

Weight assignment in cloud service selection based on FAHP and rough sets

Cloud computing is becoming the most widely used technology as the next-generation platform for conducting business. Many cloud providers offer the same functionalities, but have different quality of service (QoS) attributes. Thus the selection of cloud services, which meets the user-desired QoS requirements, has become increasingly complex. In this paper, we present a method that integrates both subjective and objective weights for evaluating the cloud services. This method is based on rough set theory and a proposed extended version of Fuzzy Analytic Hierarchy Process (FAHP) and takes into consideration the trust level associated with the experts, whose evaluations are considered in the calculation of subjective weights, and the preferences of the end user.

A novel iterative double auction design and simulation platform for packetized energy trading of prosumers in a residential microgrid

AbstractPacketized energy encapsulates energy into modulated, routable, and trackable energy packets, enhancing the flexibility of managing distributed energy resources and expediting prosumers’ participation in transactive energy markets. In the context of packetized energy trading (PET), energy prosumers are naturally deemed as self‐interested agents seeking to obtain their own benefits. To align with prosumers’ demand, supply, quality of service (QoS), and system‐level social welfare, it is necessary to explore the design of prosumers’ bidding strategies and the market clearing methods, considering prosumers’ utility and the best demand response to markets. This study addresses challenges arising from prosumers’ selfishness and asymmetric preferences by proposing a PET‐oriented iterative double auction (IDA‐PET) design, where prosumers are allowed to iteratively change the bids before the auctioneer clears the market. Moreover, IDA‐PET accommodates system capacity constraints, energy balance, and economic constraints, providing cooperative strategies for both prosumers and the auctioneer. To validate the effectiveness of IDA‐PET, a novel and dedicated co‐simulation platform based on the hierarchical engine for large‐scale infrastructure co‐simulation platform is developed and case studies are conducted within a residential microgrid. The simulation results demonstrate that IDA‐PET can efficiently enhance the revenue of the auction market while meeting prosumers’ QoS requirements.

Keeping Verticals’ Sovereignty During Application Migration in Continuum

Competing service providers in the cloud environment ensure services are delivered under the promised security requirements. It is crucial for mobile services where user’s movement results in the service’s migration between edge servers or clouds in the Continuum. Maintaining service sovereignty before, during, and after the migration is a real challenge, especially when the service provider has committed to ensuring its quality following the Service Level Agreement. In this paper, we present the main challenges mobile service providers face in a cloud environment to guarantee the required level of security and digital sovereignty as described in the Security Service Level Agreement, with emphasis on challenges resulting from the service migration between the old and new locations. We present the security and sovereignty context intended for migration and the steps of the migration algorithm. We also analyze three specific service migration cases for three vertical industries with different service quality requirements.

Improving the Quality of Hospital Services Through the Integration of Servqual, Kano, and Two Iterations of Quality Function Deployment (QFD)

Purpose – This research focuses on the problem of decreasing the number of private hospital visits, one of the causes of which is patient dissatisfaction with the healthcare services provided. This research aims to design recommendations for improving the quality of a hospital's health services based on the perceived needs of patients so that it is hoped that patient satisfaction can be achieved well. Methodology/approach – The literature review defines service quality and patient requirements in this research. Servqual and Kano questionnaires are used in this research, with 150 patients as respondents. After the quantitative data has been obtained and analyzed, the next step is to collect qualitative data by conducting interviews with health service management experts. By using QFD analysis, the relations between patient requirements and service characteristics are obtained. Targeted health service criteria will be chosen to be used as recommendations to improve hospital services. Findings – Some patient requirements are not optimally fulfilled by the hospital. Service characteristics and service critical parts are prioritized for the recommendation in improving the hospital health services. Novelty/value – The research combines scientific disciplines marketing, namely Service Quality (Servqual) and scientific disciplines operations management, namely Kano and Quality Function Deployment (QFD) first iteration (House of Quality) and second iteration (Part Deployment), with the hospital’s health service as the research object. The integration can be used in other service companies, especially in health services.

Optimization of resource allocation in 5G networks: A network slicing approach with hybrid NOMA for enhanced uRLLC and eMBB coexistence

SummaryTraditional Orthogonal Multiple Access (OMA) and spectrum sharing methods struggle to provide the diverse quality of service (QoS) demands for enhanced mobile broadband (eMBB), ultra‐reliable low latency communications (uRLLC), and massive machine type communications (mMTC) leading to suboptimal performance and service quality degradation. Single‐carrier‐non‐orthogonal multiple access (SC‐NOMA) appears to be a more optimized solution. It can serve multiple users simultaneously on the same time‐frequency resources. This approach offers both enhanced spectrum efficiency and meets the QoS requirements for the coexistence of eMBB, uRLLC, and mMTC. However, SC‐NOMA has some drawbacks. Decoding a user's signal involves a complex successive interference cancellation (SIC) process that gets harder with more users causing delays and errors. Additionally, strong user signals can interfere with weaker ones, limiting the number of users per channel. In order to overcome the drawbacks associated with OMA and SC‐NOMA, this paper introduces a new method called user‐paired NOMA (hybrid NOMA). Hybrid NOMA adopts a strategic approach, employing two user pairing techniques: near‐far/far‐near (NF‐FN) and near‐near/far‐far (NN‐FF). NF‐FN pairing prioritizes users with similar signal strengths but different distances from the base station. This minimizes interference for the weaker user during SIC. NN‐FF pairing, on the other hand, groups users with similar signal strengths and proximity. This approach further simplifies SIC and minimizes potential interference altogether. The simulation results demonstrate trade‐offs between eMBB and uRLLC performance. OMA suffers with dedicated resource allocation, while SC‐NOMA balances performance but experiences interference. NN‐FF prioritizes eMBB and offers best latency, while NF‐FN prioritizes uRLLC with high spectral efficiency but suffers from higher latency. Finally, by providing a thorough grasp of how hybrid NOMA resource allocation works to improve the performance of various use cases, this research makes a significant contribution to the field of 5G spectrum optimization.

Priority-based subcarrier allocation algorithm for maximal network connectivity in 5G networks

With the widespread adoption of wireless technology, there has been a significant surge in the number of devices seeking wireless connectivity over the past decade. To meet the extensive demand for high-data-rate wireless connectivity, the fifth-generation (5G) cellular network plays a pivotal role. 5G cellular network aims to support a large number of applications with ultra-high data rates by maximizing device connectivity while satisfying quality of service (QoS) requirements. In this paper, we present an innovative priority-based subcarrier allocation (PSA) algorithm to address the challenge of maximizing connectivity in 5G new radio (5G NR) networks. Initially, we formulate the connectivity maximization problem as a subcarrier allocation problem by considering three key parameters: bandwidth requirement, waiting time, and energy level of user devices. The objective of the formulated problem is to optimally allocate subcarriers to multiple users in order to maximize connectivity while maintaining QoS requirements. To address the problem, we propose the PSA algorithm that prioritizes bandwidth, waiting time, and energy parameters using the R-method. To accommodate the network scenarios, we develop three variants of the PSA algorithm—PSA-1, PSA-2, and PSA-3. These variants allocate subcarriers based on the priority-based score of user. We carried out a simulation-based study to illustrate the effectiveness of our proposed algorithm in comparison to traditional methods. The simulation results reveal that our proposed algorithms outperform first come first serve (FCFS) and longest remaining time first (LRTF), and achieves comparable or superior results compared to priority and fairness-based resource allocation with 5G new radio numerology (PFRA-0N) in terms of the number of user allocations, average user allocation ratio, user drop ratios and average connectivity rate. Compared to the shortest job first (SJF) technique, our proposed PSA algorithm performance is slightly inferior in terms of the number of user allocations, average allocation ratio and average connectivity rate; however, it shows superior performance in drop ratios. Further, the proposed algorithms show significant improvements in execution time compared to the optimal exhaustive search solution.

Cost-aware workflow offloading in edge-cloud computing using a genetic algorithm

The edge-cloud computing continuum effectively uses fog and cloud servers to meet the quality of service (QoS) requirements of tasks when edge devices cannot meet those requirements. This paper focuses on the workflow offloading problem in edge-cloud computing and formulates this problem as a nonlinear mathematical programming model. The objective function is to minimize the monetary cost of executing a workflow while satisfying constraints related to data dependency among tasks and QoS requirements, including security and deadlines. Additionally, it presents a genetic algorithm for the workflow offloading problem to find near-optimal solutions with the cost minimization objective. The performance of the proposed mathematical model and genetic algorithm is evaluated on several real-world workflows. Experimental results demonstrate that the proposed genetic algorithm can find admissible solutions comparable to the mathematical model and outperforms particle swarm optimization, bee life algorithm, and a hybrid heuristic-genetic algorithm in terms of workflow execution costs.

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.