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Live video applications are getting popular, and content providers widely use adaptive bitrate (ABR) streaming to improve QoE while maintaining low latency. However, users' increasing preference to watch videos on mobile devices poses great challenges for ABR algorithm due to the dramatically varying cellular network. Existing learn-based ABR algorithms face difficulties to generalize to various network conditions because of their reliance on training traces, and model/rule-based ABR schemes suffer from rebuffering under low latency constraint since they cannot robustly control the buffer occupancy within a small range. To address it, this work proposes Cratus, a lightweight and robust ABR algorithm for mobile live streaming, which achieves high QoE and low latency by accurately regulating the buffer at a small level. To enhance the control ability, Cratus controls the buffer dynamic behavior rather than the buffer occupancy. By using sliding mode control approach, Cratus robustly controls the buffer dynamic and ensures that the buffer occupancy is bounded around the target level regardless of network uncertainties. Trace-driven experiments show that Cratus outperforms existing ABRs: average QoE is increased by 12.3% to 28.6%, and rebuffering time is limited within 0.8s on average, which is reduced by 53.5% to 92.3%.

This paper presents a multinomial choice model with social interactions in an incomplete network. Individuals form heterogenous rational expectations about the behavior of peers by taking into account their characteristics and the strength of their ties. We show the network conditions under which the endogenous and exogenous effects can be separately identified even in the presence of correlated effects at the group level. Conditions for unique equilibrium are established. We apply our empirical model to occupational choice in nineteenth century London. Thanks to a newly constructed dataset, we use ecclesiastical parish boundaries as proxies for social groups and geographic distances between individuals as measures of the strength of their ties. Our results show that endogenous network effects were important above and beyond correlated and exogenous effects. We uncover distinct impact by occupation type: peers in professional and industrial occupations have a positive impact on the likelihood of following a similar occupation while commercial have a negative one.

The HTTP adaptive streaming technique opened the door to cope with the fluctuating network conditions during the streaming process by dynamically adjusting the volume of the future chunks to be downloaded. The bitrate selection in this adjustment inevitably involves the task of predicting the future throughput of a video session, owing to which various heuristic solutions have been explored. The ultimate goal of the present work is to explore the theoretical upper bounds of the QoE that any ABR algorithm can possibly reach, therefore providing an essential step to benchmarking the performance evaluation of ABR algorithms. In our setting, the QoE is defined in terms of a linear combination of the average perceptual quality and the buffering ratio. The optimization problem is proven to be NP-hard when the perceptual quality is defined by chunk size and conditions are given under which the problem becomes polynomially solvable. Enriched by a global lower bound, a pseudo-polynomial time algorithm along the dynamic programming approach is presented. When the minimum buffering is given higher priority over higher perceptual quality, the problem is shown to be also NP-hard, and the above algorithm is simplified and enhanced by a sequence of lower bounds on the completion time of chunk downloading, which, according to our experiment, brings a 36.0% performance improvement in terms of computation time. To handle large amounts of data more efficiently, a polynomial-time algorithm is also introduced to approximate the optimal values when minimum buffering is prioritized. Besides its performance guarantee, this algorithm is shown to reach 99.938% close to the optimal results, while taking only 0.024% of the computation time compared to the exact algorithm in dynamic programming.

This work studies the jitter buffer management algorithm for Voice over IP in WebRTC. In particular, it details the core concepts of WebRTC’s jitter buffer management. Furthermore, it investigates how jitter buffer management algorithm behaves under network conditions with packet bursts. It also proposes an approach, different from the default WebRTC algorithm, to avoid distortions that occur under such network conditions. Under packet bursts, when the packet buffer becomes full, the WebRTC jitter buffer algorithm may discard all the packets in the buffer to make room for incoming packets. The proposed approach offers a novel strategy to minimize the number of packets discarded in the presence of packet bursts. Therefore, voice quality as perceived by the user is improved. ITU-T Rec. P.863, which also confirms the improvement, is employed to objectively evaluate the listening quality.

Referring to the conditions in Lowokwaru sub-district, there was a reduction in the area of rice fields due to an increase in the conversion of paddy fields to residential areas. This study aims to determine changes in the irrigation network system that affect the water distribution system in the irrigation area. This study was conducted using data from 2012 and 2017 which includes a comparative analysis of rice fields using the arc GIS application. analysis of existing irrigation network conditions for comparison of clean water needs of paddy fields. and analysis of the difference in the efficiency of the use of water needs to show whether or not it is still feasible to irrigate the reduced fields due to land use changes in each of the irrigation areas. The results of the study include a comparative analysis of rice fields_Area is losing 9%, losing 7%, losing 18% and losing 6%. Analysis_of_the difference in efficiency_in the use of_water needs_in each Irrigation Area is an increase_ of 10.5%, an increase of 24.33%, an increase of 40.55% and decrease of 34.8%.

This paper discusses the distributed coordination function (DCF) access mechanism which is a carrier sense multiple access with collision avoidance (CSMA/CA) scheme. Simulation projects for different DCF performance parameters have been built using the OPNET network simulator. The projects are mainly basic service set (BSS) topology simulated under different parameter values (data rate, fragmentation, RTS/CTS, number of nodes, and load condition). Simulation results show when the DCF access mechanism is better under what load condition, and how to choose the best fragmentation threshold and other access-mechanism specific parameters according to the network conditions. Simulation results were validated against a theoretically calculated maximum throughput (the simulation maximum throughput was about 70% of the theoretically calculated maximum throughput).

Telementoring generalist surgeons as they treat patients can be essential when in situ expertise is not available. However, unreliable network conditions, poor infrastructure, and lack of remote me...

Modern techniques that enable identification and targeted manipulation of neuron groups are frequently used to bolster theories that attribute specific behavioral functions to specific neuron types. These same techniques can also be used, however, to highlight limitations of such attribution, and to develop the argument that the question ‘what is the function of these neurons?’ is ill-posed in the absence of temporal and network constraints. Here we do this by first reviewing evidence that neural responses are dynamic at multiple time scales, making the point that such changes in firing rates imply changes in what the neuron is doing. Studies involving brief perturbations of neural populations confirm this point, showing that the functions in which these populations participate change across seconds and even milliseconds. Based on these studies, we suggest that it is inappropriate to assign function to sets of neurons without contextualizing that assignment to specific times and network conditions.

This letter proposes an analytical approach to estimate local and the network wide influence of a static compensator (STATCOM), configured for voltage unbalance (VU) mitigation. In addition, it provides guidelines for determining the most appropriate location for active mitigation. The proposed generalised formulation is able to demonstrate the VU mitigation provided by a STATCOM regardless of the control strategy used or the network conditions.

Vehicular ad hoc networks are wireless network technologies that can be used to communicate without the need for fixed infrastructure. The use of zone routing protocol which is a hybrid routing protocol in a vehicular ad hoc network environment can reduce delay, packet flooding, and excess bandwidth usage on the network. However, traditional zone routing protocol is only configured for one fixed radius value, which makes it not adapt to existing network conditions. Zone dynamics with adaptive radius values in zone routing protocol are used so that zones formed by nodes are more optimal. In adapting the radius value to make the zone dynamics necessary, the optimal configuration of the radius update time is required. Simulations and tests that have been carried out with NS-2 show that the proper update time can improve zone routing protocol performance with a low end-to-end delay and routing overhead value, but has a high packet delivery ratio.

The problem of low rate attack detection has been well studied in different situations. However the methods suffer to achieve higher performance in low rate attack detection. The multimedia transmission is focused on transmitting video and audio which claims higher bandwidth conditions. There exists no such algorithm in detecting low rate attacks for invariant network conditions. To solve this issue, an invariant feature based approach is presented in this paper. The method maintains the network features like the routes, bandwidth conditions and traffic. Based on these features, a set of routes has been identified for each data transmission. Here, low rate attack detection is performed at the reception of any packet and the data transmission is performed using cooperative routing. From the packet features, and the route being followed, the method identifies the class of route, traffic and bandwidth conditions of the route. Using these features, the method computes Network Transmission Support measure. Based on the NTS value, the method performs low rate attack detection and improves the performance.

The ever increasing threat and complexity of modern cyber-attacks requires search for integrated and flexible intelligent defense mechanisms. Such approaches can provide optimal countermeasures, reliable credentials extraction and self-adjusting potential. Given the widespread scale of modern networks and the complexity of cyber-attacks, the problem of self-adaptation goes far beyond the capabilities of network Intrusion Detection Systems (IDS). The main weakness of IDS is the fact that they cannot adapt to new network conditions (“zero day” attacks). This research tries to overcome the above limitation, by introducing a Semi-supervised Discriminant Autoencoder (AUE) which combines Denoising AUEs with a heuristic method of class separation. In essence, the proposed algorithm learns to remodel the displaced specimens instead of the original ones in the super-sphere defined by their closest neighbors. The purpose is to understand the nature of an attack, based on generalized transformed features derived directly from unknown web environments and data.

Video applications are projected as a stressing and significant service of the Nationwide Public Safety Broadband Network. There is an need to empirically experiment and evaluate services across the actual network, rather than purely theoretical or simulation-based assessments. Our contribution to addressing this need, is being one of the first to demonstrate, using open source technology, how to conduct video experiments at the FirstNet Innovation and Test Lab. We transmitted video from the dataset across public safety and commercial broadband networks and under different network conditions. We demonstrated that data preemption and priority were dependent upon a variety of factors.

In this paper, we identify the unique quality of service (QoS) needs of emerging IoT applications and propose SDN-based Application-aware Dynamic Internet of things Quality of service (SADIQ), a software-defined network (SDN) framework that addresses these needs. SADIQ provides location-aware, context-driven QoS for IoT applications by allowing applications to express their requirements using a location-based abstraction and a high-level SQL-like policy language, and the network to support these requirements through recent advances in SDNs. We implement SADIQ using commodity OpenFlow-enabled switches and an open-source SDN controller and evaluate its effectiveness using traces from two real IoT applications. Our results show that SADIQ improves the percentage of regions with error in their reported temperature for the Weather Signal application up to 45x, and improves the percentage of incorrect parking statuses for regions with high occupancy for the Smart Parking application up to 30x, under the same network conditions and drop rates.

Reduction of error in water distribution network (WDN) models leads to simulations that are more representative of actual network conditions and allows for more realistic system responses. Technolo...

This paper focuses on a fast finite-set model predictive control (FFS-MPC) for three-phase four-arm active front end modular multilevel converters (AFE-MMCs) under unbalanced and distorted network conditions. The main aim of this paper is to enhance the steady-state performance of the whole system while remaining computationally feasible. Firstly, a novel topology, which has a good potential to improve the fault tolerance ability of MMCs, is presented in this literature. Secondly, in order to enhance the steady-state control performance, a new FFS-MPC methodology is proposed to serve this purpose. Specifically, the philosophy behind the proposed solution is to formulate a user-predefined cost function formula by embedding a power compensation term and an integral error term at the same time, which improves the power quality under normal and under abnormal conditions. However, it is important to notice that the computational complexity will be increased while applying the proposed solution to the control of three-phase four-arm AFE-MMCs. To solve this issue, a fast MPC is introduced into the proposed methodology to improve the computational efficiency, making it suitable for multilevel converters control. Finally, the effectiveness and feasibility of the proposed FFS-MPC methodology can be validated by the comprehensive results for regulated three-phase four-arm AFE-MMCs.

Movie trailers are prepared using a one-size-fits-all framework. These days, however, streaming platforms seek to overcome this problem and provide personalized trailers via the investigation of centralized server-side solutions. This can be achieved by analyzing personal user data, and can lead to two major issues: privacy violation and enormous demand in computational resources. This paper proposes an innovative, low-power, client-driven method to facilitate the personalized trailer generation process. It tackles the complex process of detecting personalized actions in real-time from lightweight thumbnail containers. The HTTP live streaming (HLS) server and client are locally configured to validate the proposed method. The system is designed to support a wide range of client hardware with different computational capabilities and has the flexibility to adapt to network conditions. To test the effectiveness of this method, twenty-five broadcast movies, specifically in the western and sports genres, are evaluated. To the best of our knowledge, this is the first-ever client-driven framework that uses thumbnail containers as input to facilitate the trailer generation process.

TCP congestion control is a vital component for the latency of Web services. In practice, a single congestion control mechanism is often used to handle all TCP connections on a Web server, e.g., Cubic for Linux by default. Considering complex and ever-changing networking environment, the default congestion control may not always be the most suitable one. Adjusting congestion control to meet different networking scenarios usually requires modification of TCP stacks on a server. This is difficult, if not impossible, due to various operating system and application configurations on production servers. In this paper, we propose Mystique , a light-weight, flexible, and dynamic congestion control switching scheme that allows network or server administrators to deploy any congestion control schemes transparently without modifying existing TCP stacks on servers. We have implemented Mystique in Open vSwitch (OVS) and conducted extensive test-bed experiments in both public and private cloud environments. Experiment results have demonstrated that Mystique is able to effectively adapt to varying network conditions, and can always employ the most suitable congestion control for each TCP connection. More specifically, Mystique can significantly reduce latency by 18.13% on average when compared with individual congestion controls.

International audience; As a field of research arc fault detection in domestic appliances has existed for a long time and many detection algorithms have been published, patterned or implemented on commercial products. None of them, however, guarantees perfect discrimination and all are susceptible to false negatives or false positives (i.e. indicating the absence of arcing fault, when in reality it is present, or recognizing normal functioning as an arcing condition). This phenomenon can be explained by the fact that all methods have been based on some features of arc fault which can be shared with load and network conditions such as noisy loads, the plugging-in or unplugging of appliances, the change of functioning mode of an appliance on a network and so on. A solution for limiting this phenomenon is multi arc-fault feature recognition. This research presents a method for finding and combining arc fault features in order to obtain better performance than using a single arc fault feature. The choice of arc-fault features and the algorithm for combining them are based on machine learning techniques. The method proposed here can be used for different network conditions and loads. The effectiveness of this method has been verified by a number of experimental tests including not only the requirements of standard arc fault detection, but also the most difficult situations such as multiple loads masking and transient loads.

For long-distance flying, drones often need to charge their battery at quick battery-charging machines (QCMs) because of their limited battery capacity. If a drone individually chooses a QCM without any coordination, a drone network may experience QCM congestion when multiple drones select the same QCM. This QCM congestion may lead to an increasing drone traffic delay. In order to solve this problem, we propose a cloud-based drone navigation (CBDN) system for efficient drone battery charging in drone networks. In order to achieve this goal, the CBDN gathers drone traffic information and determines efficient drone routes so as to minimize the overall QCM congestion level for drone battery charging using cloud-based management. Our key idea is to find globally coordinated drone routes so as to minimize the total traffic delay in a drone network by reducing the overall QCM congestion level. In order to demonstrate the effectiveness of the proposed system, we evaluated the performance of CBDN by simulating a drone network under various network conditions. The simulation results show that CBDN is more efficient than the existing shortest-path-based drone route planning algorithms in terms of end-to-end traffic delay and QCM average utilization.

With the dramatically increasing video streaming in the total network traffic, it is critical to develop effective algorithms to ensure the quality of content delivery service. Adaptive bitrate (ABR) control is the most essential technique which determines the proper bitrate to be chosen based on network conditions, thus realize high-quality video streaming. In this paper, a novel ABR strategy is proposed based on Ising machine by using the quadratic unconstrained binary optimization (QUBO) method and Digital Annealer (DA) for the first time. The proposed method is evaluated by simulation with the real-world measured throughput and compared with other state-of-the-art methods. Experiment results show that the proposed QUBO-based method can outperform the existing methods, which demonstrating the superior of the proposed QUBO-based method.

In the age of digitalization, the impact of Internet of Things (IoT) networks can be seen in many different areas, and there are significant issues with networking and capacity due to the low smartness and cost issues of IoT devices. Congestion and queue management, especially in an IoT network buffer, is one of the most important issues to consider. In this paper, we explained how to develop, implement and test the algorithm built to manage the queue and to avoid potential congestion by adding some intelligence to dumb nodes with a lightweight method called AQM-of- Things (AQMoT) which we have proposed recently. Extensive-form game model is used for defining decision making criteria of both IoT nodes (when to send) and the gateway node (when to drop). We implement a game model according to the queue level by including other network conditions as well. We propose a content-aware priority assignment method for the packets. We develop a realistic test environment and evaluate effectiveness of the AQMoT algorithm. Test results suggest that AQMoT effectively avoids packet drops for high priority data and reduces the burden on the gateway queue caused by redundant data sent by dumb IoT devices.

Nowadays, the interest of real-time video streaming reaches a peak. To deal with the problem of packet loss and optimize users' Quality of Experience (QoE), Forward error correction (FEC) has been studied and applied extensively. The performance of FEC depends on whether the future loss pattern is precisely predicted, while the previous researches have not provided a robust packet loss prediction method. In this work, we propose LightFEC to make accurate and fast prediction of packet loss pattern. By applying long short-term memory (LSTM) networks, clustering algorithms and model compression methods, LightFEC is able to accurately predict packet loss in various network conditions without consuming too much time. According to the results of well-designed experiments, we find out that LightFEC outperforms other schemes on prediction accuracy, which improves the packet recovery ratio while keeping the redundancy ratio at a low level.

This paper focuses on the design and evaluation of an educational solution centered on environments that do not have internet access. Two different prototypes were evaluated, one focused on creating the class content aimed at teachers, and the other focused on displaying and using the class content aimed at students. For this, twenty teachers and ten students were reached from African and Asian countries where the network conditions are not proper for remote educational environments. Preliminary results state that the participants were favorable to these solutions.

Congestion control algorithms (CCA) are devoted to improve their performance on long-term metrics (e.g., throughput, fairness), whereas the increasing low-latency application demand poses strict requirements on and motivates us to evaluate the transient performance of CCAs. However, due to the complexity of existing CCAs and changeable network conditions, it is challenging to quantify the transient performance. Our observation is that the transient performance could be modelled as damped oscillations. In this poster, we propose a framework to quantify the transient performance by calculating the extent of the damped oscillation. We use our framework to evaluate 3 widely-deployed CCAs and offer a comparison between their transient performance.

The popularity of permissioned blockchain systems demands BFT SMR protocols that are efficient under good network conditions (synchrony) and robust under bad network conditions (asynchrony). The state-of-the-art partially synchronous BFT SMR protocols provide optimal linear communication cost per decision under synchrony and good leaders, but lose liveness under asynchrony. On the other hand, the state-of-the-art asynchronous BFT SMR protocols are live even under asynchrony, but always pay quadratic cost even under synchrony. In this paper, we propose a BFT SMR protocol that achieves the best of both worlds -- optimal linear cost per decision under good networks and leaders, optimal quadratic cost per decision under bad networks, and remains always live.

When a router fails in IP-over-EON, some traffics are affected. Each affected traffic attempts to be restored through the best alternate path among alternate paths offered. Some conventional routing schemes are commonly used to solve problems in IP over optical networks, namely k-shortest paths (KSP), hop count (HC), and link-disjoint (LD). The three schemes are fixed-alternate routing (FAR) schemes. However, the alternate paths it generates cannot adjust to the current network conditions due to a router outage. Therefore, this study proposes an adaptive routing, i.e., adaptive lightpath reconfiguration paths (ALRP) routing. ALRP routing explores the number of paths between the source and destination pairs for each affected traffic and then selects the best alternate paths by maximizing the utilization of unaffected lightpaths. The goal is to minimize costs relate to the number of lightpath reconfiguration and additional power consumption due to the recovery of affected traffic. Based on simulations conducted using two network topologies, namely seven-node topology and NSFNET topology, it was found that the proposed ALRP routing produced better results compared to the three conventional routing schemes in minimizing the addition of both costs.

For Audio and Video (AV) Production, time synchronization between different devices is an important part to achieve a high-quality AV production. A well-established method for synchronizing devices is the use of clock signals like wordclock, which might not be a convenient solution when the spatial distance between devices increases. This work is aiming to introduce to the possibility to use an IP-based standard to perform the time synchronization between different devices: IEEE 1588 Precision-Time-Protocol (PTP). Therefore the protocol is evaluated according to his performance under different network conditions using a network emulator and a test-scenario through a Wide-Area-Network. The performance of the time synchronization using PTP shows promising results.

Reducing end-to-end streaming latency is critical to HTTP-based live video streaming. There are currently two technologies in this domain: Low-Latency HTTP Live Streaming (LL-HLS) and Low-Latency Dynamic Adaptive Streaming over HTTP (LL-DASH). Many players support LL-HLS and/or LL-DASH protocols, including Apple's AVPlayer, Shaka player, HLS.js Dash.js, and others. This paper is dedicated to the analysis of the performance of low-latency players and streaming protocols. The evaluation is based on a series of live streaming experiments, repeated using identical video content, encoders, encoding profiles, and network conditions, emulated by using traces of real-world networks. Several performance metrics, such as average stream bitrate, the amounts of downloaded media data, streaming latency, as well as buffering and stream switching statistics are captured and reported in our experiments. These results are subsequently used to describe the observed differences in the performance of LL-HLS and LL-DASH-based players.

Financial exchanges have begun a move from on-premise and custom-engineered datacenters to the public cloud, accelerated by a rush of new investors, the rise of remote work, cost savings from the cloud, and the desire for more resilient infrastructure. While the promise of the cloud is enticing, the cloud's varying network latencies can lead to market unfairness: orders can be processed out of sequence, and market data can be disseminated to market participants at incorrect times due to varying latencies between participants and the exchange. We present CloudEx, a fair-access cloud exchange, which leverages high-precision software clock synchronization to compensate for noisy network conditions in the public cloud. We also discuss refinements to the CloudEx design that were informed by lessons learned from deploying CloudEx in two academic courses and conclude by outlining future research directions.

Unmanned Aerial Vehicle (UAV) networks are an emerging technology, useful not only for the military, but also for public and civil purposes. Their versatility provides advantages in situations where an existing network cannot support all requirements of its users, either because of an exceptionally big number of users, or because of the failure of one or more ground base stations. Networks of UAVs can reinforce these cellular networks where needed, redirecting the traffic to available ground stations. Using machine learning algorithms to predict overloaded traffic areas, we propose a UAV positioning algorithm responsible for determining suitable positions for the UAVs, with the objective of a more balanced redistribution of traffic, to avoid saturated base stations and decrease the number of users without a connection. The tests performed with real data of user connections through base stations show that, in less restrictive network conditions, the algorithm to dynamically place the UAVs performs significantly better than in more restrictive conditions, reducing significantly the number of users without a connection. We also conclude that the accuracy of the prediction is a very important factor, not only in the reduction of users without a connection, but also on the number of UAVs deployed.

Governments face numerous challenges in sustaining road network conditions. This is attributed to road authorities’ shortages of financial and physical infrastructure. As a result, low-cost automated solutions are being pursued to solve these problems and provide people with appropriate road conditions. Several attempts have been made to improve these technologies and incorporate them into a Pavement Management System (PMS) but limited attempts are made for developing countries. This study aimed to design a low-cost pavement management system for flexible pavement maintenance. A detailed literature review has been carried out, followed by a qualitative assessment of the various indicators considered for PMS. The priority ranks of the PMS indicators were made using an Analytical Network Process (ANP) and each rank was validated by a sensitivity assessment test using the Super Decision-Making tool. This paper also provides the conceptual framework for the low-cost PMS, followed by a fishbone diagram of the indicators and sub-indicators. It is concluded that an emergency maintenance plan with an ANP weight of (0.41) is one of the most significant plans for a low-cost PMS, followed by a routine with an ANP weight of (0.39) and periodic maintenance plans with a (0.20) ANP weight. Moreover, the functional indicators with an ANP weight of (0.32) are the most significant indicators for a low-cost PMS, followed by structural (0.26), safety (0.24), and serviceability(0.18) indicators. This model will assist the road planners in making better decisions on pavement maintenance management plans. The model will suggest the pavement sections on a higher priority to be added in the maintenance plans, especially where the maintenance budget is limited.

This article investigates group fragmentation in the al-Qaeda and Islamic State ecosystems, employing network analysis to examine the impact of specific network conditions on the probability of a f...

In the emergency management of disruptions, efficient emergency material scheduling (EMS) is a key factor to save people's lives and reduce loss. Based on the literature of EMS and related areas in recent years, the research was summarized from two aspects of EMS optimization model and algorithms. It is concluded that the EMS optimization models mainly aim at the shortest time, shortest distance, minimum cost, maximum satisfaction and fairness, etc. The constraints usually include the quantity of supply depots, relief supply and vehicles, the types of commodities, the road network conditions, the budgets and the demand forecast of emergency materials. Multi-objective model is more complex and it usually considers more than one objective. To find the optimized solution, the multi-objective model with complex constraints needs more efficient algorithms. The existing algorithms, including mathematic algorithm and heuristic algorithm, have been categorized. For NP-hard (non-deterministic polynomial hard) problems, heuristic algorithms should be designed, which mainly include genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), etc. Based on the characteristics of the optimization model and various algorithms, appropriate algorithm or tools should be chosen and designed to obtain the optimized solution of EMS model. Finally, the development trends of EMS optimization model and algorithm in the future are proposed. Keywords: Emergency material scheduling (EMS), Optimization model, Heuristic algorithm, Disruption

Expert networks are formed by a group of expert-professionals with different specialties to collaboratively resolve specific queries posted to the network. In such networks, when a query reaches an expert who does not have sufficient expertise, this query needs to be routed to other experts for further processing until it is completely solved; therefore, query answering efficiency is sensitive to the underlying query routing mechanism being used. Among all possible query routing mechanisms, decentralized search, operating purely on each expert’s local information without any knowledge of network global structure, represents the most basic and scalable routing mechanism, which is applicable to any network scenarios even in dynamic networks. However, there is still a lack of fundamental understanding of the efficiency of decentralized search in expert networks. In this regard, we investigate decentralized search by quantifying its performance under a variety of network settings. Our key findings reveal the existence of network conditions, under which decentralized search can achieve significantly short query routing paths (i.e., between O (log n ) and O (log 2 n ) hops, n : total number of experts in the network). Based on such theoretical foundation, we further study how the unique properties of decentralized search in expert networks are related to the anecdotal small-world phenomenon. In addition, we demonstrate that decentralized search is robust against estimation errors introduced by misinterpreting the required expertise levels. The developed performance bounds, confirmed by real datasets, are able to assist in predicting network performance and designing complex expert networks.

The need for better linkages between evidence and policymaking has been well established in the literature, with a heightened focus on the relationship between researchers, research utilisation and policy development. However, fostering such linkages using a policy network approach has seldom been discussed. This article revisits a key framework in the policy network literature: the policy community, to shed further light on such linkages. Using an empirical approach built on the experience of the Social Policy Research Centre, this article discusses the utility of a policy community network between academic researchers and policymakers. In doing so, the study makes clear that the establishment and strengthening of policy communities is time-bound. Furthermore, this paper argues the need to adhere to network conditions as interactions remain fluid and mature over time.

ICT is one of the learning media that is widely used in various fields of educationbecause it increases the effectiveness and efficiency in the learning process. Theuse of ICT in learning includes tutorials, application tools and communication,while the application of ICT in the world of education is in the form of electronicbooks and e-learning. The use of information and communication technology ineducation is absolutely necessary to answer problems in the education sector,especially for access and equity and the quality of education. Everything will runsmoothly depending on the technology and network conditions available.Standardization and utilization of ICT in education is very important to ensure thequality of educational processes and outcomes. Teachers as implementers ofeducation are required to design the learning process by presenting the rightlearning atmosphere so that the learning process can take place effectively,efficiently and pleasantly. One learning model that can be used as a reference formodel design and learning based material is the ADDIE model.

To evaluate the performance of multi-player online games (MPOGs) (e.g., the online Chinese poker game), this paper presents a novel Effective Quality of Experience (EQoE) concept that considers the interaction among multiple players. A novel QoE evaluation and adaptation method is proposed utilizing EQoE. Specifically, the proposed method assigns different weights to different players according to their roles in the multi-player online poker game. Some players with a lower weight are willing to reduce their own QoE to save network resources for players with a higher weight in order to keep the game going under poor network conditions. Our simulation study demonstrates the feasibility of the proposed QoE evaluation and adaptation method.

Dual Connectivity (DC) is an important lower-layer feature accelerating the transition from 4G to 5G that also is expected to play an important role in standalone 5G. However, even though the packet reordering introduced by DC can significantly impact the performance of upper-layer protocols, no prior work has studied the impact of DC on QUIC. In this paper, we present the first such performance study. Using a series of throughput and fairness experiments, we show how QUIC is affected by different DC parameters, network conditions, and whether the DC implementation aims to improve throughput or reliability. Our findings provide insights into the impacts of splitting QUIC traffic in a DC environment. With reasonably selected DC parameters and increased UDP receive buffers, QUIC over DC performs similarly to TCP over DC and achieves optimal fairness under symmetric link conditions when DC is not used for packet duplication.

Packet loss is one of the main factors that can affect and/or impair the video’s quality, with a direct influence on the Quality of Experience (QoE) perceived by the end-user. For this reason, investigating the effects of packet loss on the quality of the video is crucial and could lead to a better understanding of the QoE perceived by the end-user. In this context, for purposes of teaching and learning on computer networks, this paper discusses a practical study on video transmissions in networks with packet loss. For this, based on a reference scenario, a computational environment is used for purposes of experimentation and discussion, with video transmissions under normal network conditions and with packet loss emulation of 5%, 10%, 15%, and 20%. The experiments and discussions are based in a computer network laboratory carried out with second-year students of an undergraduate computer course to help them understand the effects of packet loss on the quality of the video, as well as on the QoE perceived by the end-user. This work aims to complement other approaches to teaching and learning on computer networks.