Network Response Time Research Articles

Modeling and optimization of two-stage emergency supply chain network under uncertain environment

In order to formulate an effective emergency supply chain network planning scheme, improve the efficiency of emergency organization rescue and realize the reasonable allocation of resources and materials, considering the uncertainty of supply and demand, a distribution mode combining multiple transportation modes is adopted, and the optimization objectives are to minimize network response time, cost and carbon emissions. A two-stage emergency supply chain mixed integer programming model is constructed. At the same time, an adjustable robust optimization model is constructed based on robust optimization theory to enhance the network's ability to cope with uncertain factors, and the constraints with uncertain parameters are transformed through linear duality theory. In order to improve the solution effect of the model, an optimize cuckoo search (OCS) algorithm is proposed, and a benchmark example is introduced to verify the superiority and applicability of the OCS algorithm in solving multi-objective functions. Finally, the emergency material distribution data during the COVID-19 epidemic in Wuhan is used to study the emergency supply chain network decision-making problem with uncertain parameters, and the effective suppression of the robust control coefficient on uncertain disturbances is proved through sensitivity analysis.

Multi-strategy improved sand cat optimization algorithm-based workflow scheduling mechanism for heterogeneous edge computing environment

Edge computing is one of the predominant technologies which facilitates the option of bringing out the computing resources closer to the location of the end users when they are utilized by them. This facility offered by edge computing technology need to reduce the utilization of network bandwidth and response time with respect to the user’s workflow. In this paper, Multi-Strategy Improved Sand Cat Swarm Optimisation Algorithm (MSISCSOA)-based workflow scheduling mechanism is proposed for handling the challenges of workflow scheduling in cloud-edge computing environment. The core objective of this MSISCSOA-based workflow scheduling algorithm targets on minimizing the execution latency and energy consumption to facilitate timely and on-demand end users’ satisfaction of resources. This MSISCSOA scheme is adopted with the improvement introduced using random variation and elite collaborative strategies, such that well-balanced the trade-off between exploration and exploitation is achieved. This improvement is introduced over Sand Cat Optimization Algorithm (SCOA) using the merits of dynamic random search and joint opposite selection strategies that accelerates the convergence of the algorithm with increased global optimization and searching efficiency. It specifically improved SCOA using random variation for escaping from the local point of optimality. It also used well distributed pareto fronts and population evolution multi-strategy that aids in searching solutions with maximized diversity. The simulation experiments conducted using the datasets of Montage, Cybershake, LIGO and SIPHT an average confirmed minimized execution latency of 21.38 % and energy consumptions of 19.56 %, better than the baseline Ant Colony Optimization Algorithm-Based Workflow Scheduling (IACOAWS), Quadratic Penalty Function-based Particle Swarm Optimization Algorithm (QPF-PSOA), Biogeography Optimization (BBO) Algorithm based Multi-Objective Task Scheduling (BBOAMOTS) and Different Evolution-based Task Clustering and Scheduling (DETCS) approaches used for comparative investigation.

A scenario-based game theory integrating with a location-allocation-routing problem in a pre- and post-disaster humanitarian logistics network under uncertainty

PurposePre-positioning and distributing relief items are important parts of disaster management as it simultaneously considers activities from both pre- and post-disaster stages. This study aims to address this problem with a novel mathematical model.Design/methodology/approachIn this research, a bi-objective mixed-integer linear programming model is developed to tackle pre-positioning and distributing relief items, and it is formulated as an integrated location-allocation-routing problem with uncertain parameters. The humanitarian supply chain consists of relief facilities (RFs) and demand points (DPs). Perishable and imperishable relief commodities (RCs), different types of vehicles, different transportation modes, a time window for delivering perishable commodities and the occurrence of unmet demand are considered. A scenario-based game theory is applied for purchasing RCs from different suppliers and an integrated best-worst method-technique for order of preference by similarity to ideal solution technique is implemented to determine the importance of DPs. The proposed model is used to solve several random test problems for verification, and to validate the model, Iran’s flood in 2019 is investigated as a case study for which useful managerial insights are provided.FindingsManagers can effectively adjust their preferences towards response time and total cost of the network and use sensitivity analysis results in their decisions.Originality/valueThe model locates RFs, allocates DPs to RFs in the pre-disaster stage, and determines the routing of RCs from RFs to DPs in the post-disaster stage with respect to minimizing total costs and response time of the humanitarian logistics network.

Enhancing modular application placement in a hierarchical fog computing: A latency and communication cost-sensitive approach

Over the years, cloud computing has been a key enabler for handling complex applications and services for Internet of Things (IoT) devices situated at the edge of the network. Services and applications that are driven by the IoT environment commonly have stringent latency-sensitive requirements and may experience long network delays due to the long physical distance of cloud-based computational resources from IoT devices. Fog computing gained adoption as a solution in this case because it shortens this distance by spreading the computing power around the edge of the network in tiers. This contributes to network latency reduction and response times improvements of applications that have sensitive temporal requirements, besides improving the overall data traffic management in the network. Nevertheless, when certain requirements of an application are prioritized over others during the resource allocation process, a fog tier closer to IoT devices may experience resource depletion, forcing other latency-sensitive applications to use resources from a distant fog level and causing them to become non-responsive. To address this issue, this work proposes an approach for allocating modular applications in a hierarchical tier-based fog computing architecture. The proposed approach, named Least Impact - X (LI-X), aims to minimize the response time of latency-sensitive applications and reduce data traffic on the network by mitigating the idle time of resources at the lower levels of the hierarchical fog. This is achieved by distributing the application modules among the fog tiers in order to minimize the response time of delay-sensitive applications, while also reducing the overall network traffic. The performance of LI-X was compared to previous studies in a simulated iFogSim environment. Results have demonstrated that LI-X outperforms these studies in most of the proposed scenarios, effectively reducing response time and minimizing communication data costs on the network.

IoT-Edge Healthcare Solutions Empowered by Machine Learning

Managing the overwhelming volume of data collected by medical sensors presents a challenge in extracting relevant insights. This paper advocates for the development of an algorithm tailored to body sensor networks to identify outliers in collected data. Leveraging machine learning and statistical sampling methodologies, this research aims to optimize real-time response, particularly as computational tasks migrate to backend systems. Addressing the increasing dispersion of computing power across various domains, this study highlights the potential bottleneck posed by computation as Internet-of-Things (IoT) devices proliferate. To mitigate battery drain, a common approach involves offloading processing to background servers. However, the widespread adoption of IoT devices has sparked concerns about privacy and security. Current measures are deemed insufficient in light of escalating cyber threats. Machine learning methods offer promise in identifying vulnerabilities within IoT systems. Edge computing emerges as a solution to enhance network response times, decentralization, and security. By leveraging distributed-edge computing within an IoT framework, this paper investigates the fusion of cloud and edge computing with machine learning. Specifically, it explores how these technologies can be harnessed in the medical field, utilizing sensor-equipped IoT devices to collect extensive data for analysis. The proposed approach involves proactive decision-making at the front end, guided by an IoT server, and employs machine learning algorithms at the backend to identify pertinent data signatures. This paper underscores the significance of combining cloud, edge computing, and machine learning in a distributed-edge-computing-based IoT framework, offering a potential avenue for real-time, efficient solutions in various domains.

An improved energy-efficient cloud-optimized load-balancing for IoT frameworks

As wireless communication grows, so does the need for smart, simple, affordable solutions. The need prompted academics to develop appropriate network solutions ranging from wireless sensor networks (WSNs) to the Internet of Things (IoT). With the innovations of researchers, the necessity for enhancements in existing researchers has increased. Initially, network protocols were the focus of study and development. Regardless, IoT devices are already being employed in different industries and collecting massive amounts of data through complicated applications. This necessitates IoT load-balancing research. Several studies tried to address the communication overheads produced by significant IoT network traffic. These studies intended to control network loads by evenly spreading them across IoT nodes. Eventually, the practitioners decided to migrate the IoT node data and the apps processing it to the cloud. So, the difficulty is to design a cloud-based load balancer algorithm that meets the criteria of IoT network protocols. Defined as a unique method for controlling loads on cloud-integrated IoT networks. The suggested method analyses actual and virtual host machine needs in cloud computing environments. The purpose of the proposed model is to design a load balancer that improves network response time while reducing energy consumption. The proposed load balancer algorithm may be easily integrated with peer-existing IoT frameworks. Handling the load for cloud-based IoT architectures with the above-described methods. Significantly boosts response time for the IoT network by 60 %. The proposed scheme has less energy consumption (31 %), less execution time (24\\%), decreased node shutdown time (45 %), and less infrastructure cost (48\\%) in comparison to existing frameworks. Based on the simulation results, it is concluded that the proposed framework offers an improved solution for IoT-based cloud load-balancing issues.

A multi-constraint and multi-objective optimization layout method for a mine water inrush monitoring network

Mine water inrush can cause property losses and casualties, but current theoretical and technological approaches cannot accurately predict such events. Through the networked deployment of water level sensors along a mine roadway, a mine water inrush monitoring network was developed, and a multi-constraint and multi-objective optimal deployment method was established. By setting practical constraints of the mining area, water inrush risk level, and installation at specified locations, and considering two objective functions of minimum total cost and minimum average monitoring time, a mathematical model was established. The non-dominated sorting genetic algorithm II (NSGA-II) was designed to solve the model. The method temporally and spatially optimized the network, which was then verified in the Beiyangzhuang coal mine in north China. The average response time of the monitoring network was 916 s using only 28 water level sensors. The higher the water inrush risk level, the shorter the monitoring network response time. Under the 2, 3, and 4 risk levels, the network’s response time to simulated water inrush accidents was less than 3000, 2100, and 900 s, respectively. The multi-constraint and multi-objective optimization layout method further enhanced the effectiveness of the network, providing a novel system for the early warning of mine water inrush.

SDN-Controlled Resource-Tailored Analytics for Healthcare IoT System

In this work, we propose phSDN, a programmable healthcare network architecture for resource-tailored management and on-demand allocation of analytic modules in Internet of Things (IoT) healthcare system. The identification, dynamic management of services, and fast analysis of healthcare data are a few challenges in existing remote healthcare monitoring devices. phSDN programs and reconfigures the resources and services, while updating the associated flow-rules in the data-plane of software-defined network (SDN) architecture. It is especially useful in scenarios with intermittent or constrained network connectivity and/or device availability. The newly identified flows associated with different resources are explicitly considered, while forwarding services over the SDN network. The on-the-fly programmable services enable healthcare devices to provide resource-specific analytics. The programmability over services also allows unified control over the end-devices by reconfiguring on-the-fly vendor-specific heterogeneity of resource types. Our prototype of phSDN supports three different types of resources: 1) body temperature, 2) pulse rate, and 3) blood pressure. Experiments using our implementation show that the proposed architecture minimizes the network response time by at least 43.87% and 78.4%, compared to the fog- and cloud-based approaches, respectively. The implemented system's scalability is further evaluated using Mininet by considering latency, packet delivery ratio, and throughput, as the performance metrics.

An SDN perspective IoT-Fog security: A survey

The utilization of the Internet of Things (IoT) has burst in recent years. Fog computing is a notion that solves cloud computing’s limitations by offering low latency to IoT network user applications. However, the significant number of networked IoT devices, the large scale of the IoT, security concerns, users’ critical data, and heterogeneity in this extensive network significantly complicate the implementation. The IoT-Fog architecture consists of fog devices (servers) at the fog layer, which decreases network utilization and response time due to their closeness to IoT devices. However, as the number of IoT and fog devices under the IoT-Fog architecture grows, new security concerns and requirements emerge. Because incorporating fog computing into IoT networks introduces some vulnerabilities to IoT-Fog networks, the nodes in the fog layer are the target of security threats. Software-Defined Networking (SDN) is a novel paradigm that decouples the data plane from control plane, resulting in better programmability and manageability. Attack defense mechanisms can be implemented in the IoT-Fog network without SDN. But SDN paradigm provides the IoT-Fog with some characteristics that facilitate counterattacks. This survey briefly explains some works that utilized the SDN features in the IoT-Fog network for security threats in the IoT-Oriented fog layer. To this end, we examine IoT-Fog, SDN, and SDN-based IoT-Fog networks. We describe security threats in IoT-Fog networks and briefly explain the vulnerabilities and attacks in the fog layer. Then, we describe the fog layer’s most common IoT-Fog security defense mechanisms. Following that, we present the SDN features, explore how SDN can help defensive mechanisms in IoT-Fog networks, and categorize the works based on the SDN features they use. We explain their features and present a comparison between them. Finally, we discuss the disadvantages of SDN in IoT-Fog networks.

Effects of motor imagery based brain-computer interface on upper limb function and attention in stroke patients with hemiplegia: a randomized controlled trial

BackgroundSeeking positive and comprehensive rehabilitation methods after stroke is an urgent problem to be solved, which is very important to improve the dysfunction of stroke. The aim of this study was to investigate the effects of motor imagery-based brain-computer interface training (MI-BCI) on upper limb function and attention in stroke patients with hemiplegia.MethodsSixty stroke patients with impairment of upper extremity function and decreased attention were randomly assigned to the control group (CR group) or the experimental group (BCI group) in a 1:1 ratio. Patients in the CR group received conventional rehabilitation. Patients in the BCI group received 20 min of MI-BCI training five times a week for 3 weeks (15 sessions) in addition to conventional rehabilitation. The primary outcome measures were the changes in Fugl-Meyer Motor Function Assessment of Upper Extremities (FMA-UE) and Attention Network Test (ANT) from baseline to 3 weeks.ResultsAbout 93% of the patients completed the allocated training. Compared with the CR group, among those in the BCI group, FMA-UE was increased by 8.0 points (95%CI, 5.0 to 10.0; P < 0.001). Alert network response time (32.4ms; 95%CI, 58.4 to 85.6; P < 0.001), orienting network response (5.6ms; 95%CI, 29.8 to 55.8; P = 0.010), and corrects number (8.0; 95%CI, 17.0 to 28.0; P < 0.001) also increased in the BCI group compared with the CR group. Additionally, the executive control network response time (− 105.9ms; 95%CI, − 68.3 to − 23.6; P = 0.002), the total average response time (− 244.8ms; 95%CI, − 155.8 to − 66.2; P = 0.002), and total time (− 122.0ms; 95%CI, − 80.0 to − 35.0; P = 0.001) were reduced in the BCI group compared with the CR group.ConclusionMI-BCI combined with conventional rehabilitation training could better enhance upper limb motor function and attention in stroke patients. This training method may be feasible and suitable for individuals with stroke.Trial registration: This study was registered in the Chinese Clinical Trial Registry with Portal Number ChiCTR2100050430(27/08/2021).

Machine-Learning-Based IoT–Edge Computing Healthcare Solutions

The data that medical sensors collect can be overwhelming, making it challenging to glean the most relevant insights. An algorithm for a body sensor network is needed for the purpose of spotting outliers in the collected data. Methods of machine learning and statistical sampling can be used in the research process. Real-time response optimization is a growing field, as more and more computationally intensive tasks are offloaded to the backend. Optimizing data transfers is a topic of study. Computing power is dispersed across many domains. Computation will become a network bottleneck as more and more devices gain Internet-of-Things capabilities. It is crucial to employ both task-level parallelism and distributed computing. To avoid running down the battery, the typical solution is to send the processing to a server in the background. The widespread deployment of Internet-of-Things (IoT) devices has raised serious privacy and security concerns among people everywhere. The rapid expansion of cyber threats has rendered our current privacy and security measures inadequate. Machine learning (ML) methods are gaining popularity because of the reliability of the results that they produce, which can be used to anticipate and detect vulnerabilities in Internet-of-Things-based systems. Network response times are improved by edge computing, which also increases decentralization and security. Edge nodes, which frequently communicate with the cloud, can now handle a sizable portion of mission-critical computation. Real-time, highly efficient solutions are possible with the help of this technology. To this end, we use a distributed-edge-computing-based Internet-of-Things (IoT) framework to investigate how cloud and edge computing can be combined with ML. IoT devices with sensor frameworks can collect massive amounts of data for subsequent analysis. The front-end component can benefit from some forethought in determining what information is most crucial. To accomplish this, an IoT server in the background can offer advice and direction. The idea is to use machine learning in the backend servers to find data signatures of interest. We intend to use the following ideas in the medical field as a case study. Using a distributed-edge-computing-based Internet-of-Things (IoT) framework, we are investigating how to combine the strengths of both cloud and edge computing with those of machine learning.

K-Test: An Analytical Model for Measurement-Based Server Selection Based on Response Time

Different paradigms employ server selection to establish a fast connection to end users, thereby reducing the delay associated with exchanging data between mobile users and servers. To efficiently infer the statistics of the server response, we propose an analytical model for server selection based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> measured response times, namely, the K-Test method. First, the probability that the user device correctly selects the best server and the corresponding time taken in K-Test method are investigated. Then, we study the server switch when connection failure is encountered. The proposed analytical model for K-Test method considers the distribution of the response time of candidate servers and derives three metrics, namely the probability of selecting the best server, the expected latency of the server selection process, and the false connection failure detection probability. The analytical model is validated by the simulation. Numerical results show that both the value of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> and the number of candidate servers significantly affect the selection probability. Also, it needs more test rounds to correctly find the best server when users experience a variation in the network response times.

Impairment of attention network function in posterior circulation ischemia-evidence from the Attention Network Test.

This study aimed to investigate the effect of posterior circulation ischemia (PCI) on attention network function and to determine whether PCI is holistic or selective attention network deficit and which attention network is affected. Thirty-six PCI patients aged 30 to 75 were assessed using the Attention Network Test and the Mini-Mental State Examination (MMSE). There were no significant differences in age, sex, and education between PCI group and the control group (n = 32). All data were statistically analyzed by SPSS 23.0 software. There were no significant difference in the MMSE scores between the two groups. Compared with the control group, the PCI group had significantly shorter response time for alerting and orienting network. The executive control network response time was significantly longer in PCI group than in the control group. The overall mean response time was also significantly longer in PCI group than in normal control group. There was no significant difference in mean accuracy between the two groups. The alerting, orienting, and executive control networks were significantly less efficient in PCI group than in the control group (P < 0.01). This indicates impaired attention network in PCI patients. Since transient nerve seizures caused by vertebrobasilar ischemia may precede posterior circulation stroke, early assessment of cognitive function in patients with PCI is particularly important, and ANT is an excellent tool for this assessment.

Detecting Network Scanning Through Monitoring and Manipulation of DNS Traffic

In this paper, we propose an approach for detecting internal and external network scanning attacks on enterprise networks. In our approach, an inline scan detection system (SDS) monitors the ingress and egress flows of an enterprise network subnet and detects scanning probes based on the correlation of flows with preceding DNS query/responses and reducing TTL values of DNS Resource Records (RR). Through rigorous evaluation, we show that our method is effective against both external and internal port scanners and network worms, its effectiveness is independent of scanning rate or technique, and its deployment incurs negligible overhead on DNS and network response times. While the idea of detecting scans by correlating network flows with preceding DNS query/responses has been proposed in the literature, this work extends the state-of-the-art by offering four contributions: 1) we show that without decreasing TTL values of RRs in DNS responses, attackers can piggyback on cached DNS records to bypass our detection; thus we incorporate a TTL reduction mechanism to enhance the effectiveness of this approach, especially against stealthy and adaptive scanners; 2) while prior works work against internal scanners, we use the relatively new extension of DNS protocol, ENDS0 Client Subnet (ECS) option, to expand this approach toward detecting external scanners; 3) we present a novel adaptive scanning technique, called DNS-cache-based scanning, that exploits local DNS cache to bypass prior detection methods, and shows that, while prior approaches fail to defeat this threat model, our approach is effective against this evolved threat model as well; and 4) contrary to existing work that focuses on defeating fast network scanning worms, this approach is effective against any scanning, including stealthy scanning that uses conservative timing profiles to evade detection.

A fog-assisted information model based on priority queue and clinical decision support systems.

Telehealth monitoring applications are latency-sensitive. The current fog-based telehealth monitoring models are mainly focused on the role of the fog computing in improving response time and latency. In this paper, we have introduced a new service called "priority queue" in fog layer, which is programmed to prioritize the events sent by different sources in different environments to assist the cloud layer with reducing response time and latency. We analyzed the performance of the proposed model in a fog-enabled cloud environment with the IFogSim toolkit. To provide a comparison of cloud and fog computing environments, three parameters namely response time, latency, and network usage were used. We used the Pima Indian diabetes dataset to evaluate the model. The fog layer proved to be very effective in improving the response time while handling emergencies using priority queues. The proposed model reduces response time by 25.8%, latency by 36.18%, bandwidth by 28.17%, and network usage time by 41.4% as compared to the cloud. By combining priority queues, and fog computing in this study, the network usage, latency time, bandwidth, and response time were significantly reduced as compared to cloud computing.

Collaborative Authentication for 6G Networks: An Edge Intelligence Based Autonomous Approach

The conventional device authentication of wireless networks usually relies on a security server and centralized process, leading to long latency and risk of single-point of failure. While these challenges might be mitigated by collaborative authentication schemes, their performance remains limited by the rigidity of data collection and aggregated result. They also tend to ignore attacker localization in the collaborative authentication process. To overcome these challenges, a novel collaborative authentication scheme is proposed, where multiple edge devices act as cooperative peers to assist the service provider in distributively authenticating its users by estimating their received signal strength indicator (RSSI) and mobility trajectory (TRA). More explicitly, a distributed learning-based collaborative authentication algorithm is conceived, where the cooperative peers update their authentication models locally, thus the network congestion and response time remain low. Moreover, a situation-aware secure group update algorithm is proposed for autonomously refreshing the set of cooperative peers in the dynamic environment. We also develop an algorithm for localizing a malicious user by the cooperative peers once it is identified. The simulation results demonstrate that the proposed scheme is eminently suitable for both indoor and outdoor communication scenarios, and outperforms some existing benchmark schemes.

Fault Tolerant Load Balancing with Quadruple Osmotic Hybrid Classifier and Whale Optimization for Cloud Computing

Cloud Computing (CC) environment is developing as a recently discovered caliber for computing applications over the network. Fault tolerance is one of the foremost issues in CC environment. Since the negligence in resource have a profound effect on job execution, throughput, response time and performance of the entire network. In this work, in order to address the issue, Quadruple Osmotic Hybrid Classification and Whale Optimization (QOHC-WO) is introduced to fault-tolerance under the requirement of different user request tasks. Initially, Quadruple Fault Tolerance Level is applied to allocate the fault tolerance level. Followed by, Hybrid Vector Classifier is used to categorize the user request tasks (task) and cloud server nodes (node). Then, the Osmotic function is employed for performing the migration among virtual machines with lesser response time. This helps to solve the maximum level of fault issue. Finally, Improved Whale Optimization Algorithm is applied to find the optimal allocation of tasks with the corresponding node. In addition, the Bandit function and Whale optimization are used to address the trade-off between exploitation and exploration. Experimental setup of the proposed QOHC-WO method and existing methods are carried out with different factors such as task response time, the number of VM migrations, and percentage of fault detected rate with respect to a number of tasks. The analyzed results validate that the proposed QOHC-WO method achieves a higher fault detection rate with minimum response time as well as task migration than the state-of-the-art methods.

Self-Evolutionary Neuron Model for Fast-Response Spiking Neural Networks

We propose two simple and effective spiking neuron models to improve the response time of the conventional spiking neural network. The proposed neuron models adaptively tune the presynaptic input current depending on the input received from its presynapses and subsequent neuron firing events. We analyze and derive the firing activity homeostatic convergence of the proposed models. We experimentally verify and compare the models on MNIST handwritten digits and FashionMNIST classification tasks. We show that the proposed neuron models significantly increase the response speed to the input signal. Experiment codes are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/anvien/Evol-SNN</uri> .

Additive manufacturing integration in E-commerce supply chain network to improve resilience and competitiveness

In light of recently increased e-commerce, also a result of the COVID-19 pandemic, this study examines how additive manufacturing (AM) can contribute to e-commerce supply chain network resilience, profitability and competitiveness. With the recent competitive supply chain challenges, companies aim to decrease cost performance metrics and increase responsiveness. In this work, we aim to establish utilisation policies for AM in a supply chain network so that companies can simultaneously improve their total network cost and response time performance metrics. We propose three different utilisation policies, i.e. reactive, proactive – both with 3D printing support – and a policy excluding AM usage in the system. A simulation optimisation process for 136 experiments under various input design factors for an (s, S) inventory control policy is carried out. We also completed a statistical analysis to identify significant factors (i.e. AM, holding cost, lead time, response time, demand amount, etc.) affecting the performance of the studied retailer supply chain. Results show that utilising AM in such a network can prove beneficial, and where the reactive policy contributes significantly to the network performance metrics. Practically, this work has important managerial implications in defining the most appropriate policies to achieve optimisation of supply network operations and resilience with the aid of AM, especially in times of turbulence and uncertainty.

A priority queue-based telemonitoring system for automatic diagnosis of heart diseases in integrated fog computing environments.

Various studies have shown the benefits of using distributed fog computing for healthcare systems. The new pattern of fog and edge computing reduces latency for data processing compared to cloud computing. Nevertheless, the proposed fog models still have many limitations in improving system performance and patients' response time.This paper, proposes a new performance model by integrating fog computing, priority queues and certainty theory into the Edge computing devices and validating it by analyzing heart disease patients' conditions in clinical decision support systems (CDSS). In this model, a Certainty Factor (CF) value is assigned to each symptom of heart disease. When one or more symptoms show an abnormal value, the patient's condition will be evaluated using CF values in the fog layer. In the fog layer, requests are categorized in different priority queues before arriving into the system. The results demonstrate that network usage, latency, and response time of patients' requests are respectively improved by 25.55%, 42.92%, and 34.28% compared to the cloud model. Prioritizing patient requests with respect to CF values in the CDSS provides higher system Quality of Service (QoS) and patients' response time.