Abstract: Emergency vehicles (EVs) are significant in disseminating sensitive information across the road-side communication networks. This amalgamation of vehicles and communication networks improves the reachability and accessibility of sensitive data along the driving scenario. However, the communication network experiences data and traffic congestion due to wireless medium and varying vehicle densities. In order to address the problem of data and traffic congestion, this paper introduces message dissemination with re-route planning (MDRP) method. This method initiates a dissemination boundary for selecting neighbors and weight for selecting the re-routing path. The weight is based on the traffic conditions of the road segment along with consideration of the timeout of the EV message. The joint process of rerouting and data transmission is supported by dependent queue management for improving the message delivery and reducing the impact of delaying instances in the travelling path.

Abstract: The efficiency of Transmission Control Protocol (TCP) is significantly influenced by Congestion Control. Many scientists have widely researched and suggested several improvements to conventional TCP Congestion Control over the past centuries. This subject, however, still inspires scholarly and industrial research groups due to changes in demands for internet applications and the evolution of internet technologies. In this review paper, illustrate the most important features of the TCP and its mechanisms. The challenges and its effect on the performance of the network communication have also been explained. Several research groups have been working on the improvement of the quality of services of the network. This paper reviews some recent studies and argues their latest findings to have a better understanding. Where the important features of sub-sections of the TCP were presented. Then we showed the two main classes of the existing router queue management system. Performance metrics are used to show the behaviour of a network during data transmission. The results in this study showed the performance of ARED was be better than that of RED.

Abstract: The transport of perishable products is in need of specific control and safety operations, either due to their short shelf life or their particular storage circumstances. This study investigates an extended Location-routing-inventory problem (LRIP) for perishable products, in which a two-phase hybrid mathematical model is developed. In the first phase, the location-routing problem (LRP) is formulated with stochastic demands and travel time, and then in the second phase, a queue system is employed to model the inventory control problem based on the established locations and routes. Moreover, the effects of reneging and balking behaviors are studied in the second phase, and hereby, holding, shortage, product expiration, customer waiting times, and customer loss costs are calculated. To tackle the complexity of the problem, an improved genetic algorithm (IGA) is designed and is compared with the classic genetic algorithm (GA) and GAMS software. Finally, two small and large-sized illustrative examples and then different problem instances are taken into account to test the applicability of the suggested methodology. The obtained results demonstrate that the developed methodology of the research has an appropriate performance to deal with the high complexity of the problem.

Abstract: Advancements in sensor and hardware technology have surged the growth of smart devices (SDs), including smartphones, and wearable devices The data generated by the built-in sensors are utilized by

Abstract: The mobile users have acquired the benefits of cloud computing with the help of Mobile Edge Computing (MEC) technology in order to satisfy the increasing data demands. The efficiency of the system is highly limited by the bandwidth limitations and limitations associated with the mobile devices despite the rapid development of MEC as well as the cloud computing technology. Our aim is to provide an optimal method to optimize the energy consumption in the mobile edge computing. In this regard, the research paper proposed a Green Cloud based Queue Management system for 5G networks that helps in addressing the issues related to latency and energy consumption. While serving the users, the proposed methodology results in less amount of energy being wasted and hence the reduced latency. By means of alleviating the congestion and implementing the virtual list, this issue can be resolved greatly. Simulation is done with the help of NS2 green cloud simulator and the results are obtained by comparing the proposed model to conventional cloud model and cloudlet based on throughput, latency, energy consumption and normalized overhead as these are the evaluation measures. The results show that there has been considerable enhancement in the energy consumption. As the throughput increases, the quality of the service also increases.

Abstract: Power Internet of Things (PIoT) is a promising solution to meet the increasing electricity demand of modern cities, but real-time processing and analysis of huge data collected by the devices is challengeable due to limited computing capability of devices and long distance from the cloud center. In this paper, we consider the edge computing assisted PIoT where the computing tasks of the devices can be either processed locally by the devices, or offloaded to edge servers. Aiming to maximize the long-term system utility which is defined as a weighted sum of reduction in latency and energy consumption, we propose a novel task offloading algorithm based on deep reinforcement learning, which jointly optimizes task scheduling, transmit power of the PIoT devices, and computing resource allocation of the edge servers. Specifically, the task execution on each edge server is modeled as a queuing system, in which the current queue state may affect the waiting time for the next tasks. The transmit power and computing resource allocation is first optimized, respectively, and then a deep Q-learning algorithm is adopted to make task scheduling decisions. Numerical results show that the proposed method can improve the system utility.

Abstract: Post-earthquake housing recovery monitoring is necessary, especially since the housing sector usually represents 50 percent of the total monetary disaster loss. However, very scarce recovery data, ...

Abstract: In emergency services, maximizing population coverage with the lowest cost at the peak of the demand is important. In addition, due to the nature of services in emergency centers, including hospitals, the number of servers and beds is actually considered as the capacity of the system. Hence, the purpose of this paper is to propose a multi-objective maximal covering facility location model for emergency service centers within an M (t)/M/m/m queuing system considering different levels of service and periodic demand rate.,The process of serving patients is modeled according to queuing theory and mathematical programming. To cope with multi-objectiveness of the proposed model, an augmented e-constraint method has been used within GAMS software. Since the computational time ascends exponentially as the problem size increases, the GAMS software is not able to solve large-scale problems. Thus, a NSGA-II algorithm has been proposed to solve this category of problems and results have been compared with GAMS through random generated sample problems. In addition, the applicability of the proposed model in real situations has been examined within a case study in Iran.,Results obtained from the random generated sample problems illustrated while both the GAMS software and NSGA-II almost share the same quality of solution, the CPU execution time of the proposed NSGA-II algorithm is lower than GAMS significantly. Furthermore, the results of solving the model for case study approve that the model is able to determine the location of the required facilities and allocate demand areas to them appropriately.,In the most of previous works on emergency services, maximal coverage with the minimum cost were the main objectives. Hereby, it seems that minimizing the number of waiting patients for receiving services have been neglected. To the best of the authors’ knowledge, it is the first time that a maximal covering problem is formulated within an M (t)/M/m/m queuing system. This novel formulation will lead to more satisfaction for injured people by minimizing the average number of injured people who are waiting in the queue for receiving services.

Abstract: Waiting time in a queue is a common problem in all the service disciplines and some people may reluctant to join a queue due to long wait. These phenomena can be seen in the case of a railway ticket service also. A well-drafted model is needed for the management to comprehend the circumstances better. This paper centers on a single server queuing model in which the arrival process is a Poisson process and the service times follow an exponential distribution or a constant. In this work, we have studied queue management at a railway ticket counter with a single server. For this, we have collected data for one week from an NSG-3 category railway station and analyzed the data to find the pattern of arrival and service distribution. It has been seen that performance measures and service distributions of neither of the queuing models M/M/1 and M/D/1 conform to reality. So, we propose an approach to apply the mathematical queuing model more efficiently for such systems. We have used an M/G/1 model in which service time is calibrated based on a normal distribution. Based on the data collected from an NSG-3 category railway station for a day, a normal distribution is fitted for the service rate and found that the fit is good statistically. It is found that the resulted performance measures show significant conformity with the observed field data.

Abstract: In many public and private sectors that provide services there are physical queues. Waiting in the queue can affect the clients experience and can exhaust them as well. On the other hand, idle time in many sectors of valuable resources, such as, healthcare sectors, can be expensive. In this paper we aim to enhance the clients experience when they are looking for non critical services. The paper suggests an approach for appointments using queue management system. The customers in the queue are divided into several priority classes that is considered in computing the expected waiting time. To show the efficiency of the suggested approach, we applied it on healthcare vaccination system.

Abstract: Congestion control in Wireless Sensor Networks (WSNs) is one of the key areas of research and different algorithms have been proposed using either of the notions of fair rate allocation, traffic class priority, and queue management. Use of the any one of the above is not adequate to address the challenges. Hence, in this paper, we have proposed a novel congestion control algorithm using the combined notions of fair allocation of bandwidth, prioritizing traffic classes, and Adaptive Queue Management (ADQM). The proposed Weighted Priority based Fair Queue Gradient Rate Control (WPFQGRC) scheme achieves the fair distribution of spare bandwidth by considering the traffic class priority, average queue size, and the connected loads of a node. Average queue size at every node is adapted based on the proposed notion of the gradient of the differential of Global Priority (GP) with respect to the differential of queue size. The output rate of a given node is computed based on the GP of the node and the average queue size. The spare bandwidth of a node is fairly distributed by taking into account the connected load of the given node. The proposed algorithm is developed to suit to a general topology of WSN, however for the sake of illustration, we have considered a tree topology network that deals with both Real-Time (RT) and Non-Real Time (NRT) traffic classes. The proposed algorithm is implemented in NS3 platform in Linux environment and the performance of the algorithm is evaluated in terms of throughput, packet loss, packet delay, traffic class patterns, node mobility, and the average queue size. The performance of the proposed algorithm is found to be superior to that of Yaghmaee et al. ’s, Brahma et al. ’s, Monowar et al. ’s, Sarode et al. ’s, Difference of Differentials Rate Control (DDRC), Weighted Priority based DDRC (WPDDRC), and Priority based Fairness Rate Control (PFRC) algorithms respectively.

Abstract: The growing demand for outpatient departments and prolonged waiting time of patients in recent years has made physician scheduling necessary to provide timely medical services. This study focused on the problem of scheduling physicians in an outpatient system with a nonhomogeneous patient arrival and priority queue, which exists in many Asian hospitals. In such a system, both patients with appointments and walk-in patients wait in a priority queue to see physicians, with the patients’ arrivals fluctuating throughout the day. In order to respond to time-related demands while simultaneously respecting physicians’ preferences for being on or off duty in some specific slots, a staffing optimization model was formulated. In addition, a physician rescheduling model was proposed for the case where a physician is unexpectedly absent. To solve the problem, a calibrated waiting time approximation-based genetic algorithm methodology was proposed. Its main contribution is the use of a data-driven analytical method to estimate the average waiting time of the two types of patients in a complex queuing system. The results of a numerical study and real case study in a Shanghai hospital showed that physician scheduling optimization on the basis of the proposed waiting time approximation method was effective and efficient when applied to the proposed outpatient system.

Abstract: An optimal control problem with heterogeneous servers to minimize the average age of information (AoI) is considered. Each server maintains a separate queue, and each packet arriving to the system is randomly routed to one of the servers. Assuming Poisson arrivals and exponentially distributed service times, we first derive an exact expression of the average AoI for two heterogeneous servers. Next, to solve for the optimal average AoI, a close approximation is derived, called the approximate AoI, this is shown to be useful for multi-server systems as well. We show that for the optimal approximate AoI, server utilization (ratio of arrival rate and service rate) for each server should be same as the optimal server utilization with a single server queue. For two identical servers, it is shown that the average AoI is approximately 5/8 times the average AoI of a single server. Furthermore, the average AoI is shown to decrease considerably with the addition of more servers to the system.

Abstract: The behavior of many systems of practical interest in communications and other areas is well modeled by a single-server exponential queuing system in which the arrival and service rates are dependent upon the state of a Markov chain, the dynamics of which are independent of the queue length. Formal solutions to such models based on Neuts’s matrix geometric approach have appeared frequently in the literature. A major problem in using the matrix geometric approach is the computation of the rate matrix, which requires the solution of a matrix polynomial. In particular, computational times appear to be unpredictable and excessive for many problems of practical interest, especially under heavy traffic loads. In this paper, we present an efficient method of determining the rate matrix that performs well at all traffic loads. We then show how the rate matrix may be applied to determine not only the joint and marginal queue distributions, but also many other quantities of practical interest, such as the virtual and stochastic equilibrium waiting time 162distributions. We present numerical examples that illustrate the application of our techniques.

Abstract: An upper bound for the convergence rate of the distribution of a queuing system state with infinitely many servers is obtained for the case where the intensities of the incoming and service flows depend on the state of the system.

Abstract: Recent increases in computational power and the development of specialized architecture led to the possibility to perform machine learning, especially inference, on the edge. OpenVINO is a toolkit based on convolutional neural networks that facilitates fast-track development of computer vision algorithms and deep learning neural networks into vision applications, and enables their easy heterogeneous execution across hardware platforms. A smart queue management can be the key to the success of any sector. In this paper, we focus on edge deployments to make the smart queuing system (SQS) accessible by all also providing ability to run it on cheap devices. This gives it the ability to run the queuing system deep learning algorithms on pre-existing computers which a retail store, public transportation facility or a factory may already possess, thus considerably reducing the cost of deployment of such a system. SQS demonstrates how to create a video AI solution on the edge. We validate our results by testing it on multiple edge devices, namely CPU, integrated edge graphic processing unit (iGPU), vision processing unit (VPU) and field-programmable gate arrays (FPGAs). Experimental results show that deploying a SQS on edge is very promising.

Abstract: One of the most important issues accepted by researchers in LTE cellular systems is to develop Queue Management Algorithms for RLC (Radio Link Control). The performance of queue-management algorithms depends on parameters such as latency, packet dropping, and bandwidth usage. Simulation software is used to evaluate the queue-management algorithms developed and to test their performance. In the literature, active queue management algorithms have been compared with wired and wireless networks. In contrast to prior works, in this study, we have analyzed active queue management algorithms using the LTE model in the NS-3 network simulator. When the data and the results obtained from the simulations have been evaluated, it is concluded that the RED algorithm using probabilistic methods and the threshold value is more successful than the other algorithms in LTE networks.

Abstract: Space-air-ground integrated networks (SAGINs) are heterogeneous, self-organizing and time-varying wireless networks providing massive and global connectivity. These three characteristics of SAGINs bring great challenges for routing design. In this paper, the important parameters affecting performance of SAGINs are analyzed, based on which the heterogeneous network framework is described as a vector weighted topology. Instead of a scale, the weighted parameter of the topology is a vector with elements of signal-to-noise ratio (SNR), variation of SNR, end-to-end delay and queuing length. To meet the time-varying requirements, a Wiener predictor is adopted for obtaining the estimated channel information, the expectation of queuing delay is also acquired by modeling the process of packets waiting the transmitting buffer as a M/M/1 queuing system. Considering the Ant Colony Optimization (ACO) algorithm sharing the common decentralized feature with routing algorithm in SAGINs, a novel ACO-based cross-layer routing algorithm for SAGINs is proposed. The proposed algorithm takes the link quality and end-to-end packed delay in the physical layer as deciding factors in searching for optimal routing. Simulations performed in different scenarios show that this proposed algorithm demonstrates a higher packet delivery rate.

Abstract: The weighted averaging operators are one of the popular methods for aggregating information. In recent years, ordered weighted averaging operators (OWA) have attained a great attention by researchers. These OWA operators due to their versatility are very useful to model many real world situations. Several extensions of OWA operators are presented in the literature which can handle a situation with uncertainty. Although many queuing models have been proposed in numerous healthcare studies, the inclusion of OWA operators is still rare. In this research study, we propose a novel method using the uncertain generalized ordered weighted average and illustrate its application to the uncertain queue modeling in a hospital emergency room; where incoming flux of patients and the required level of service for each patient is unknown and uncertain. The model with multilateral decision making process has been described which will provide several alternatives to decision makers to select the best alternative for their challenging situations. The proposed method has resulted an improved performance of the queuing system, increased customer satisfaction as well as a significant reduction in the operational cost. This study will enable decision makers to operate a flexible and cost-effective system in the event of uncertainty, uncontrollable and unpredicted situations.

Abstract: One of the primary difficulties of fishermen engaged in deep-sea fishing is the lack of effective communication systems to the shore. The Offshore Communication Network(OCN) resolves this problem by providing Internet over the ocean through a fishing vessel network. OCN is a multi-layered architecture with heterogeneous connectivity ranges, directionality, resources, and mobility patterns. Connectivity maintenance is challenging due to the lack of infrastructure, expanded mobility, network sparsity, and sea-wave-induced movements. This paper discusses a framework to improve OCN connectivity with a multi-level optimization strategy. We propose a predictive model to generate real-time forecasts of link status. At the physical level, node position re-orientations to higher connectivity locations are suggested. The transmission queue management and prioritized scheduling in the link-layer minimize the queuing delay. A reinforcement routing strategy in the network layer determines the best next-hop for message dissemination. The proposed three-level optimization approach facilitates communication capability enhancement in OCN.

Abstract: We consider a queue with an unobservable backlog by the incoming users. There is an information designer that observes the queue backlog and makes recommendations to the users arriving at the queue whether to join or not to join the queue. The arriving users have payoff relevant private types. The users, upon arrival, send a message, that is supposed to be their type, to the information designer if they are willing to hear a recommendation. The information designer then creates a recommendation for that specific type of user. The users have to pay a tax in exchange for the information they receive. In this setting, the information designer has two types of commitments. The first commitment is the recommendation policy and the second commitment is the tax function. We combine mechanism design and information design to study a queuing system with heterogeneous users. In this setting, the information designer is a sender of the information in the information design aspect and a receiver in the mechanism design aspect of the model. We formulate an optimization problem that characterizes the solution of the joint design problem. We characterize the tax functions and provide structural results for the recommendation policy of the information designer.

Abstract: COVID-19 is a pandemic crisis that has introduced new norm to the world where we are not encouraged to be in 3C areas, namely crowded place, confined space, and close conservation. We must also ensure that we are at least one meter apart from one another at all time even while queuing. The queuing process can be seen at any organization that offer services. Adhering to the new norm can be a challenge for organization such as banks, hospitals, and government offices when the number of clients waiting in queue increases while in confined space. On the client’s side, they must go through the queue process of obtaining a queue number ticket and then wait to be served in confined and sometimes crowded space every time they require a service. Thequeue process will be repeated at different premise. This study proposes real-time multi-organizationsC19-SmartQ system which use predictive modelling to generate single or consecutive queue number tickets for any client requiring services from two different organizations located within the same building. C19-SmartQsystemmanages queues thus administer social distancing and streamline queues to reduce waiting periods and improve service efficiency. To ensure operability of C19-SmartQ system, itwas tested on the functionality and web server speed performance. The web server speed performance results show that data transfer and web loading were stable since there was only an increase of 0.2 seconds or 0.08% as the number of users per session increases. In the future, the system can be designed to accommodate queuing for more organizations located within the same building. Machine learning can also be integrated in the system to improve the predictive modelling based on current environment at each organization.

Abstract: Many service providers often categorize their users into multi-classes, depending on their service requirements. Each class has strict quality of service (QoS) demands (e.g., minimum required service rate or transfer time) that must be ensured throughout its service. In some cases, priorities are also assigned in a multi-class user’s environment to ensure that the important class user shall be serviced first. In this paper, we have developed a novel Markov chain based analytical model to investigate and evaluate a multi-class queuing system with a strict QoS requirement and priority constraints. Experimental analysis is conducted for two users classes, i.e., class-1 (may be free/student users) and class-2 (may be paid/research users). Each class requests have strict QoS requirements in terms of the minimum required rate (MRR) that must be ensured throughout its lifetime once the request is admitted into the system. Secondly, class-2 requests have preemption priority over class-1, i.e., if there is no room for newly arriving class-2 requests, then one or more active flows of class-1 can be ejected in order to accommodate high-class requests. Model results are validated through simulation results and performance measures of our interest include blocking probability (BP) of individual classes and the overall system, effect of higher-class jobs on lower-class jobs, and link capacity utilization. The proposed model can be instrumental in developing advanced connection admission control (CAC), efficient resource dimensioning, and capacity planning of the queuing system.

Abstract: One of the issues that has attracted many researchers in the last decade is the problem of locating facilities i.e. hospitals, shops, banks and ATMs. One of the basic needs of the people of the community is easy access to the facilities, so that by spending little time can reach to the facility and with spending low cost to receive their facilities. In this paper, the location-pricing problem of the congested facilities by considering competition between available facilities and new facilities were investigated and bi-objective non-linear mathematical model that follow from M/M/m/k queuing system, was presented. In the first goal, maximize the profit of system by minimizing the total cost of establishing the facilities, shipping costumers and expectation time of the costumers in the queue and in the second goal the share of facility market minimized. The proposed model is in the category of non-linear integer programming problems, that, due to the complexity of the problem in the large scales and in order to solve the model, different approaches such as multi-objective meta-heuristic algorithms including non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO) has been presented. At the end, by applying the Taguchi method, the efficiency performance of NSGA-II algorithm perform better than MOPSO.

Abstract: The electronic exchange of business to business information (e.g. purchase orders, inventory data and shipment notices between departments or organizations) can eliminate the need for human intervention and paper copy trails. Incorporating Electronic Data Interchange (ED I) standards into an organization can drastically improve the efficiency of processing times. Modelling the behaviour of EDI messages within a Supply Chain network's queuing system has many purposes, from understanding the efficiency of queue behaviour to process re-engineering. In this paper we demonstrate that these messages are heterogeneous, suffer from correlation, are not stationary and are challenging to model. We investigate whether a parametric or non-parametric approach is appropriate to model message service and inter-arrival times. Our results show that parametric distribution models are suitable for modelling the distribution's tail, whilst non-parametric Kernel Density Estimation models are better suited for modelling the head.

Abstract: Queues are waiting for lines from customers to get service. The queue is caused by the needs of consumers to be served beyond the ability of service facilities, so consumers who come cannot immediately get service. Queue problems at ports often occur every day. Ship loading and unloading services is a queuing phenomenon in daily life. This queue can be caused by damage to the equipment that supports loading and unloading, labor, availability of warehouses, and the limited capacity of berths at berths and others. In maintaining optimal port services, it is necessary to avoid long waiting times and low queuing system utilities. Queues at the mooring service system occur due to mismatch between planning and realization. Waiting time for vessels inside and outside the port area is calculated using the simulation method in queuing theory so that it becomes a queuing model. Until finally, it produces the optimum berthing service system. Based on the simulation results obtained value, and which is much shorter than the conditions on realization. The potential causes of these conditions are divided into three factors, namely, internal factors, external factors, and weather factors.

Abstract: With the development of social networks, more and more mobile social network devices have multiple interfaces. Multipath TCP (MPTCP), as an emerging transmission protocol, can fit multiple link bandwidths to improve data transmission performance and improve user experience quality. At the same time, due to the large-scale deployment and application of emerging technologies such as the Internet of Things and cloud computing, cyber attacks against MPTCP have gradually increased. More and more network security research studies point out that low-rate distributed denial of service (LDDoS) attacks are relatively popular and difficult to detect and are recognized as one of the most severe threats to network services. This article introduces six classic queue management algorithms: DropTail, RED, FRED, REM, BLUE, and FQ. In a multihomed network environment, we perform the performance evaluation of MPTCP under LDDoS attacks in terms of throughput, delay, and packet loss rate when using the six algorithms, respectively, by simulations. The results show that in an MPTCP-enabled multihomed network, different queue management algorithms have different throughput, delay, and packet loss rate performance when subjected to LDDoS attacks. Considering these three performance indicators comprehensively, the FRED algorithm has better performance. By adopting an effective active queue management (AQM) algorithm, the MPTCP transmission system can enhance its robustness capability, thus improving transmission performance. We suggest that when designing and improving the queue management algorithm, the antiattack performance of the algorithm should be considered: (1) it can adjust the traffic speed by optimizing the congestion control mechanism; (2) the fairness of different types of data streams sharing bandwidth is taken into consideration; and (3) it has the ability to adjust the parameters of the queue management algorithm in a timely and accurate manner.