Acceptable Quality Of Service Research Articles

Factors Influencing Customer Switching Intentions in Online Food Delivery: A Perspective of The Push-Pull-Mooring Model

The online food delivery applications born in the digital era have transformed the habits of users in meeting their needs through these platforms. With the convenience of ordering various cuisines from the comfort of their homes, customers experience significant time and energy savings. In this context, service providers must deliver acceptable service quality; otherwise, customers will swiftly switch to competitors offering better value. This research aims to identify the driving, pulling, and anchoring effects on the intention of customers to switch between online food delivery services, utilizing the Push-Pull-Mooring framework to evaluate the relationships between variables. The study employed Structural Equation Modeling of the Partial Least Squares (SEM-PLS) type. The research findings indicate that push effects and pull effects have a positive influence on switching intention, while mooring effects do not positively impact switching intention. Furthermore, the study reveals that mooring effects do not moderate the relationship between push effects, pull effects, and switching intention. These evaluation results can serve as guidance for online food delivery companies, assisting them in enhancing their services to build strong relationships with existing customers and attract new ones.

A flexible approach for demand-responsive public transport in rural areas

Rural mobility research has been left aside in favor of urban transportation. Rural areas? low demand, the distance among settlements, and an older population on average make conventional public transportation inefficient and costly. This paper assesses the contribution that on-demand mobility has the potential to make to rural areas. First, demand-responsive transportation is described, and the related literature is reviewed to gather existing system configurations. Next, we describe and implement a proposal and test it on a simulation basis. The results show a clear potential of the demand-responsive mobility paradigm to serve rural demand at an acceptable quality of service. Finally, the results are discussed, and the issues of adoption rate and input data scarcity are addressed.

Security Enhancement of SIP Protocol in VoIP Communication

In Voice over IP (VoIP) systems, calls are started, maintained, and ended using the Session Initiation Protocol (SIP). One of the SIP flaws is the lack of enough data to validate users, and its contents can be changed to fake the Caller Identification (CID). Fake CID can be used by hackers to trick receivers and obtain valuable materials. Existing solutions have a number of faults, including the use of more network resources, the use of insufficient data to identify fake CID, and high call setup delay for caller and callee during the validation process. This work presents an algorithm to enhance SIP protocol security to mitigate the flaws of the previous solutions that are used to address CID spoofing. By using the Media Access Control (MAC) address, the algorithm can validate the CID and warn the callee for the fake CID. The algorithm was developed and tested by using Mininet, Python based open source OpenFlow (POX) controller, SIPp simulator, Linphone softphone, and freePBX. All faked CID were detected and receivers were alerted on the linphone screen. Session setup delay obtained are within the 15.1 ms to 27.3 ms, which are within the acceptable Quality of Service (QoS) ranges.

Neoliberalism, Compulsory Education and Compulsion to Pay: Magnitude, Patterns and Determinants of Household Expenditure in Indian Punjab

Based on a recent primary survey, this study aims to examine the magnitude, patterns and determinants of household expenditure on compulsory schooling in Punjab. Our findings suggest that the increasing presence of private schools in the education marketplace, particularly in the contemporary era of neoliberalism, has commodified the education in the state, and these education providers offer compulsory schooling in diverse forms, with varying costs depending on the demand of their student clientele. Parents who enroll their children in elite private schools pay approximately seven times more than those choosing low-fee private schools. Private schools exert undue financial burden on their parent clientele (particularly those from low- and middle-income groups). This situation highlights the concept of ‘compulsion to pay’, especially in the absence of an acceptable quality of service provided by state-owned schools. Our regression analysis results on determinants of household expenditure reveal that households from socially and economically marginalised sections (for example, scheduled castes and the poor) spend significantly less than their more affluent counterparts. We recommend that the state take a more stringent approach to regulating these non-state education providers, particularly in terms of exorbitant fees, which would prevent further widening of the existing socio-economic inequality in the state.

Deep reinforcement learning assisted co-optimization of Volt-VAR grid service in distribution networks

With the increasing penetration of distributed energy resources in distribution networks, Volt-VAR control and optimization (VVC/VVO) have become very important to ensure an acceptable quality of service to all customers. System operators can rely on slow-responding utility devices, including capacitor banks and on-load tap changing transformers, along with fast-responding battery and photovoltaic (PV) inverters for the VVC/VVO implementation. Because of variations in response time of these two classes of devices, and different control actions (discrete versus continuous), coordinated and optimal scheduling and operation have become of utmost importance. This paper develops a look-ahead deep reinforcement learning (DRL)-based multi-objective VVO technique to improve the voltage profile of active distribution networks, decrease network and inverter power loss, and save the operational cost of the grid. It proposes a deep deterministic policy gradient (DDPG)-based approach to schedule the optimal reactive and/or active power set-points of fast-responding inverters, and a deep Q-network (DQN)-based DRL agent to schedule the discrete decisions variables of slow-responding assets. The reactive power output of PV and battery smart inverters are scheduled at 30-minute intervals and the capacitors’ commitment status is scheduled with several hour intervals. The proposed framework is validated on the modified IEEE 34-bus and 123-bus test cases with embedded PV and PV-plus-storage. To validate the efficacy of the proposed VVO, it is compared with several scenarios, including the base case without VVO, localized droop control of DERs, DDPG-only, and twin delayed DDPG (TD3) agent-based DRL techniques. The results justify the superior performance of the proposed method to improve the voltage profile, reduce network power loss, and minimize the look-ahead grid operational cost while minimizing the undesirable power losses in inverters as a result of power factor adjustments.

QoS Conditions for VoIP and VoD

This paper concerns quality evaluation of the telecommunication services: VoIP (representing the RT interactive class) and VoD (representing the MM streaming class). Subjective and objective methods and tools for perceived quality measurement are analyzed and compared. Subjective tests are performed for selected video sequences using the Double-Stimulus Impairment Scale (DSIS) method. Thus the objective algorithms (VQM and VQmon) are calibrated. Speech quality is measured using the objective methods: PESQ and POLQA. Threshold values for network parameters (packet loss rate, delay jitter) are set, that guarantee the acceptable service quality.

Optimal locations and sizes of layover charging stations for electric buses

Public transit agencies across the world are rapidly converting their bus fleets from diesel or hybrid powertrains to battery-electric propulsion systems. To realize the benefits of the transition to battery-electric buses (BEBs) while retaining acceptable quality of service and limiting capital costs, agencies must intelligently decide where to locate recharging infrastructure. While most agencies electrifying their fleets plan to install chargers at bases where buses are kept overnight, a question faced by many fleet operators is where to install layover chargers that provide additional energy while buses are in operation during the day. To address this challenge, this work presents a mixed-integer linear programming model, referred to as BEB-OCL (BEB Optimal Charger Location), that optimizes the tradeoff between upfront charging infrastructure costs and operational performance. The key decision variables include the locations at which to install chargers, the number of chargers installed at each chosen location, and the location, duration, and sequence of charger visits for each bus. We also introduce a second optimization model, referred to as BEB-BRP (BEB Block Revision Problem), that revises vehicle schedules by dispatching backup buses to serve some trips so that buses do not run out of battery and all passenger trips are still completed as scheduled. The models are applied to a case study of the highest-ridership bus routes in King County, WA, USA, where an electric bus deployment is currently underway.

Digital twin-assisted and mobility-aware service migration in Mobile Edge Computing

Mobile Edge Computing (MEC) is emerging as one of the key technologies to process massive amount of data at the edge of the network for upcoming 6G networks. In the paradigm of MEC, users can offload the computation-intensive and delay-sensitive tasks to the edge servers, since the capabilities of User Equipment (UE) cannot be not sufficient to meet different quality of service (QoS) requirements. However, due to the limited coverage of edge servers, users can move between multiple edge servers. In such cases, service migration is complicated which arises the challenge of where and when to migrate the service dynamically to maintain acceptable QoS. In addition, high uncertainty of the user mobility and different QoS requirements of the services are challenging. Instead of assigning each user to the nearest edge server, we propose a Digital Twin (DT)-assisted service migration approach to minimize the task completion time. The contributions in this paper are threefold: First, a DT edge network architecture is presented that uses the real-time and historical data to determine service migration strategy. Second, the DT classifies the computing tasks as delay-sensitive task and delay-insensitive tasks to meet the QoS requirements. Third, the DT predicts the mobility of users in the future based on the Hidden Markov Model (HMM) and studies the impact of reducing the cost of service migration. Thus, we propose an optimization algorithm to find optimal resource allocation at each edge server. The simulation results shows that our DT-assisted service migration model can achieve less delay and service migration rate compared with the traditional methods.

A comprehensive survey on QoS for video transmission in heterogeneous mobile ad hoc network

AbstractMajor challenging issue in H‐MANETs (Heterogeneous Mobile Ad hoc Networks) is video transmission because it gets affected due to various factors like dynamic change in topology, interference, node mobility, collusion, and so on. Periodic connection is caused by the topology dynamic change, which leads to significant packet loss. Advancements provide the technical basis for video streaming over H‐MANETs in wireless networking and mobile devices. And efforts to achieve video transmission via H‐MANETs have run into some barriers, which have been overcome in various ways. Nonetheless, H‐MANETs are essential to provide video transmission certainty and quality provisioning. Due to the limited resources, the unreliability of wireless links, node mobility, and heterogeneity, establishing video streaming or enabling real‐time multimedia applications over H‐MANET with acceptable QoS (quality of service) poses several issues. The article's main goal is to survey QoS‐based video transmission in H‐MANETs. This study discusses the video transmission method in H‐MANET and the importance of the QoS that may be used for video transmission in H‐MANET. Various routing protocols and existing methodologies to improve QoS for video transmission in H‐MANET are discussed. In addition, the impact of delay in video transmission over H‐MANET has been discussed. Several obstacles and issues have been highlighted in providing QoS for video transmission in H‐MANET. From 2013 to 2021, a systematic literature review (SLR) is done for QoS‐based video transmission in the H‐MANET. This survey systematically gives the technical direction for research on QoS‐based video transmission and discusses potential future aspects.

QoS-aware secure transmission design for VLC with semi-grant-free NOMA scheme.

This work aims to enhance the physical layer security (PLS) of non-orthogonal multiple access (NOMA) aided indoor visible light communication (VLC) system with semi-grant-free (SGF) transmission scheme, in which a grant-free (GF) user shares the same resource block with a grant-based (GB) user whose quality of service (QoS) must be strictly guaranteed. Besides, the GF user is also provided with an acceptable QoS experience, which is closely aligned with the practical application. Both active and passive eavesdropping attacks are discussed in this work, where users' random distributions are taken into account. Specifically, to maximize the secrecy rate of the GB user in the presence of an active eavesdropper, the optimal power allocation policy is obtained in exact closed-form and the user fairness is then assessed by Jain's fairness index. Moreover, the secrecy outage performance of the GB user is analyzed in the presence of the passive eavesdropping attack. Both exact and asymptotic theoretical expressions for the secrecy outage probability (SOP) of the GB user are derived, respectively. Furthermore, the effective secrecy throughput (EST) is investigated on the basis of the derived SOP expression. Through simulations, it is found that the PLS of this VLC system can be significantly improved by the proposed optimal power allocation scheme. The radius of the protected zone, the outage target rate for the GF user, and the secrecy target rate for the GB user would have pronounced impacts on the PLS and user fairness performance of this SGF-NOMA assisted indoor VLC system. The maximum EST will increase with the increasing transmit power and it is hardly influenced by the target rate for the GF user. This work will benefit the design of indoor VLC system.

Model-based cloud service deployment optimisation method for minimisation of application service operational cost

Many currently existing cloud cost optimisation solutions are aimed at cloud infrastructure providers, and they often deal only with specific types of application services. Unlike infrastructure providers, the providers of cloud applications are often left without a suitable cost optimisation solution, especially concerning the wide range of different application types. This paper presents an approach that aims to provide an optimisation solution for the providers of applications hosted in the cloud environments, applicable at the early phase of a cloud application lifecycle and for a wide range of application services. The focus of this research is the development of the method for identifying optimised service deployment option in available cloud environments based on the model of the service and its context, intending to minimise the operational cost of the cloud service while fulfilling the requirements defined by the service level agreement. A cloud application context metamodel is proposed that includes parameters related to both the application service and the cloud infrastructure relevant for the cost and quality of service. By using the proposed optimisation method, knowledge is gained about the effects of the cloud application context parameters on the service cost and quality of service, which is then used to determine the optimal service deployment option. The service models are validated using cloud applications deployed in laboratory conditions, and the optimisation method is validated using the simulations based on the proposed cloud application context metamodel. The experimental results based on two cloud application services demonstrate the ability of the proposed approach to provide relevant information about the impact of cloud application context parameters on service cost and quality of service and use this information for reducing service operational cost while preserving the acceptable service quality level. The results indicate the applicability and relevance of the proposed approach for cloud applications in the early service lifecycle phase since application providers can gain valuable insights regarding service deployment decision without acquiring extensive datasets for the analysis.

LPP2KL: Online Location Privacy Protection Against Knowing-and-Learning Attacks for LBSs

When user-centric location privacy protections are used by the public (e.g., published as a mobile application to Mac app store), they are found to be vulnerable to a novel kind of potential inference attacks. These attacks can utilize the open-use protection’s input–output pairs to closely study the protection’s structure and mechanism. We name this kind of attacks as knowing-and-learning (KL) attacks. Targeted at such potential but threatening deep learning-based KL attacks in local search services, we propose a novel location privacy protection framework LPP2KL. LPP2KL aims to generate an obfuscated location, which is robust to the aforementioned attacks, so that the user’s current location (real) can be replaced by the generated location (pseudo) when submitted to the untrusted search server. To achieve this, we first depict a feasible net structure to implement the deep KL attack and verify its power with extensive experiments. Second, to simulate the attack-and-defense process, we develop a novel network structure with an adversary net and a protection net. These nets play a mini–max game toward privacy inference in an adversarial manner until an equilibrium is reached. Meanwhile, the protection net also achieves the balance between preserved privacy and quality of service through utility-constrained privacy optimization. We introduce a novel penalty function to handle the utility-constrained privacy optimization problem. Extensive experiments are conducted on Yelp and FoursSwarm datasets, which validate the generality of our proposed LPP2KL in protection handling and illustrate its effectiveness in utility control under the acceptable quality of service.

UAV-Enabled Mobile Edge Computing for Resource Allocation Using Cooperative Evolutionary Computation

Edge computing is a viable paradigm for supporting the Industrial Internet of Things deployment by shifting computationally demanding tasks from resource-constrained devices to powerful edge servers. In this study, mobile edge computing (MEC) services are provided for multiple ground mobile nodes (MNs) through a time-division multiple access protocol using the unmanned aerial vehicle (UAV)-enabled edge servers. Remotely controlled UAVs can serve as MEC servers due to their adaptability and flexibility. However, the current MEC approaches have proven ineffective in situations where the number of MNs rapidly increases, or network resources are sparsely distributed. Furthermore, suitable accessibility across wireless networks via MNs with an acceptable quality of service is a fundamental problem for conventional UAV-assisted communications. To tackle this issue, we present an optimized computation resource allocation model using cooperative evolutionary computation to solve the joint optimization problem of queue-based computation offloading and adaptive computing resource allocation. The developed method ensures the task computation delay of all MNs within a time block, optimizes the sum of MN's accessibility rates, and reduces the energy consumption of the UAV and MNs while meeting task computation restrictions. Moreover, we propose a multilayer data flow processing system to make full use of the computational capability across the system. The top layer of the system contains the cloud centre, the middle layer contains the UAV-assisted MEC (U-MEC) servers, and the bottom layer contains the mobile devices. Our numerical analysis and simulation results prove that the proposed scheme outperforms conventional techniques such as equal offloading time allocation and straight-line flight.

A Survey on Machine Learning Techniques for Massive MIMO Configurations: Application Areas, Performance Limitations and Future Challenges

The deployment of fifth-generation (5G) broadband wireless cellular networks has enabled the support of highly demanding applications, paving the way towards global broadband connectivity. In the same context, related advances in network infrastructure, such as network function virtualization via the decoupling of related functionalities from hardware equipment has leveraged end-to-end service support in highly heterogeneous environments. To this end, uninterrupted provision of service necessitates a holistic network redesign where access points (APs) should be placed and configured dynamically. One of the key supporting technologies of 5G communications are massive multiple input multiple output (m-MIMO) configurations, deployed over various APs, either in a centralized or in a distributed manner. However, such a configuration would pose an additional degree of complexity during network planning, since on one hand accurate channel estimation for all potential links is limited due to the pilot contamination effect, and on the other hand the number of actually deployed radio frequency chains is again limited when compared to the fully digital approach, in an effort to relax hardware and computational burden. Moreover, additional issues should be taken into consideration as well, such as the coexistence of m-MIMO configurations with other novel technologies in the 5G air interface as well as the need for provision of acceptable quality of service to a large number of mobile users. In an effort to deal with the aforementioned issues, machine learning (ML) algorithms have emerged over the last decade as a promising solution that can deal with complex optimization problems. In this context, the main goal of this survey paper is to present the state-of-the-art approaches in the deployment of ML methods in m-MIMO configurations. Unlike other related survey papers, this is the first scientific attempt to cover all aspects of m-MIMO practical implementations (i.e., channel estimation, MIMO precoding and signal detection, hybrid beamforming, distributed configurations, user pairing and power allocation in non-orthogonal multiple access), in the context of ML-aided calculations. To this end, all presented works are categorized according to the studied aspect, while in the same context potential limitations and future challenges are highlighted as well.

Digital Object Architecture for IoT Networks

The Internet of Things (IoT) is a recent technology, which implies the union of objects, “things”, into a single worldwide network. This promising paradigm faces many design challenges associated with the dramatic increase in the number of end-devices. Device identification is one of these challenges that becomes complicated with the increase of network devices. Despite this, there is still no universally accepted method of identifying things that would satisfy all requirements of the existing IoT devices and applications. In this regard, one of the most important problems is choosing an identification system for all IoT devices connected to the public communication networks. Many unique software and hardware solutions are used as a unique global identifier; however, such solutions have many limitations. This article proposes a novel solution, based on the Digital Object Architecture (DOA), that meets the requirements of identifying devices and applications of the IoT. This work analyzes the benefits of using the DOA as an identification platform in modern telecommunication networks. We propose a model of an identification system based on the architecture of digital objects, which differs from the well-known ones. The proposed model ensures an acceptable quality of service (QoS) in the common architecture of the existing public communication networks. A novel interaction architecture is developed by introducing a Middle Handle Register (MHR) between the global register, i.e., Global Handle Register (GHR), and local register, i.e., Local Handle Register (LHR). The aspects of the network interaction and the compatibility of IoT end-devices with the integrated DOA identifiers in heterogeneous communication networks are presented. The developed model is simulated for a wide-area network with allocated registers, and the results are introduced and discussed.

Performance Evaluation of UDSM Network to Deliver Multimedia eLearning Contents: Case of CoICT

The use of technology in education in Sub-Saharan Africa has been reported to be hindered by many factors including inadequate Information and Communication Technology (ICT) infrastructure, low internet speed, and lack of skills to use educational technologies. This paper aim at evaluating how the available ICT infrastructure at the College of Information and Communication Technologies (CoICT) can support, with acceptable Quality of Service (QoS), the delivery of multimedia applications for eLearning services. The evaluation was performed over a network testbed implemented in a computer laboratory for wired and wireless connections. The testbed consisted of five personal computers (PCs) connected via a switch and a router to the internet for the wired connection. The wireless connection consisted of three PCs and a laptop connected through a switch and a router to the internet via a wireless access point. Four different multimedia applications were run through the PCs, and traffic data was mirrored and captured at one of the PC using Wireshark network analyzer. The traffic included streamed and conversational video and audio using YouTube and Zoom applications, respectively; interactive web browsing using web browsers; bulk file transfer, and a mixture of all applications running on the computers. Traffic data captured during the running of these applications were packet loss, delay, jitter, and throughput. The QoS performance parameters obtained were within the acceptable threshold values as per International Telecommunication Union, Telecommunication Standardization Sector (ITU-T) values. Results revealed that ICT infrastructure at CoICT can support delivery of multimedia content for eLearning purposes for both wired and wireless connections. Having the infrastructure capable of supporting multimedia applications with the acceptable QoS implies that instructors are equipped with a conducive environment that supports multimedia application. Hence, they are expected to use and continue using educational technologies to facilitate teaching and learning processes.

Performance of Narrow Band Wide Area Networks with Gateway Diversity.

This paper quantifies the coverage area of Low-Power Wide-Area Networks (LPWAN) for Packet Success Rates (PSR) above 85%, where acceptable Quality of Service (QoS) can be achieved. The network consists of battery-operated end-nodes (ENs) and multiple stationary gateways (GWs). We consider asynchronous communication that uses ALOHA-based random channel access. Each transmission from the ENs can be received by multiple GWs. Such spatial diversity results in favorable Signal-to-Noise ratios (SNR). The LoRa modulation is assumed and its specific features, such as IQ inversion, further contribute to decreasing the impact of interference. An increase in the GW density improves network performance, which allows support for a larger density of end-nodes as well as increasing the coverage area. Our simulation results show that a suburban area of up to 1.44 km2 can be covered with five GWs with up to fifty end-nodes with a PSR greater than 86%.

The relation between transit service availability and productivity with customers satisfaction

Limited access to public service and a lack of measurable quality of service attributes composes a challenge for providing acceptable service quality. This research addresses the question: does the transit service performance (spatial and temporal coverage) influences customer satisfaction? Available data from the relevant sources were collected, and field surveys were conducted to collect data unavailable. Different tools and approaches include descriptive and inferential statistics and GIS-based spatial analysis, including Moran's I, the data envelopment analysis for the super efficiency analysis.The service coverage is relatively poor, with less than 60% of the transit-supportive area (TSA) served by transit. The spatial service assessment based on four attributes (the number and length of transit lines, line density, and % TSA served) showed a small value of Moran's I. Only transit density and the percent of the transit-supportive area served were statistically significant, showing a cluster in the city center. The primary road network correlates with the transit network density and % of TSA served. The results showed that poor service productivity varies by the season and the day of the week, and the system is more efficient in winter. The customers rated the service spatial and temporal coverage above the scale's mid-point, but less than the fare rating and their satisfaction differs by the service season. Customer satisfaction can be predicted based on fare, comfort, and safety for the inter-urban service and service fare, travel time, and convenience of intra-urban service.

Probabilistically Certified Management of Data Centers Using Predictive Control

Data centers are facilities with large number of servers providing cloud services. The increasing number of data centers in the last years has generated environmental concern due to the large amount of energy consumed by them. This also includes some auxiliary services such as the cooling equipment which is known to be very costly. For that reason, efficient data center strategies are needed in order to provide an acceptable quality of service (QoS) and suitable temperature for every server while using the least amount of resources possible. This article presents some strategies to deal with the unified workload and temperature problem that appears in the data center. As the system is modeled as a queue and the control variables have an hybrid nature, some highly parallelizable particle-based optimization algorithms are proposed to solve the optimization problem. Numerical simulations are provided in order to illustrate the effectiveness of the strategy. These simulations also show the improvements obtained from the GPU computing. Finally, a probabilistic evaluation approach is developed in order to provide certificates on the probability of constraint satisfaction without increasing the computational burden of the online problem. Note to Practitioners—This article addresses the problem of deciding in real-time the number of active servers in a data center that is required to meet the quality of service (QoS) demands while keeping energy consumption at a minimum. The temperature set point of the cooling equipment must also be taken into account, as it is advisable to use the minimum cooling that keeps the servers running in safe conditions. The management strategy proposed is based on predictive control. In this way, the number of active servers and temperature set point will be chosen so that the future energy consumption is minimized while guaranteeing that QoS and safety demands are met under different possible operating conditions. Furthermore, the proposed management strategy can be tuned depending on the QoS that it is desirable to provide. The proposed strategy will lead to energy-consumption improvements while having guarantees on the data center performance.

An intelligent sustainable efficient transmission internet protocol to switch between User Datagram Protocol and Transmission Control Protocol in IoT computing

AbstractToday, Internet of things (IoT), Cloud and Fog networks have spread out around the world. The more these networks grow, the more their energy consumption comes to attention. Many efforts have been made during recent years to decrease this energy consumption, mainly focused on utilizing low‐power devices. Green algorithms are recently proposed to reduce energy consumption by modifying the structure of many algorithms employed in the network and its protocols. This paper proposes a new green reliability algorithm for Transmission Control Protocol/Internet Protocol (TCP/IP protocol) in Fog computing. The proposed algorithm does not require extensive TCP/IP protocol changes or relevant hardware. It is based on transferring less number of packets in the network by using the advantage of differences between TCP and User Datagram Protocol (UDP). TCP and User Datagram Protocol (UDP) are different in nature as the number of total packets in UDP is half that of TCP. As a result, the number of complete packets in UDP is half that of TCP. The proposed method is built around the loss of some packets in applications, such as voice and online video, does not severely degrade the end results. Therefore, the UDP protocol can substitute TCP in such situations. The criterion to switch between the two is the minimum acceptable Quality of Service (QoS) of the overall network. In other words, the UDP protocol will be used as long as QoS requirements are met. The switching process between UDP and TCP is dynamic, optimized by estimating network noise in the period. Additionally, we evaluated the proposed method based on several QoS functions, including delay, throughput, and energy usage.