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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.

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...

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.

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.

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.

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.

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.

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.

Clusters of firewalls is an architecture that can enhance cyber security and protect from network attackers. Deep Packet Inspection (DPI) is a sophisticated method used in firewalls to stop malicious activity within a network. However, DPI creates a performance drawback in terms of further processing and associated latency. There are very few published realistic experiments on firewall clusters and to the best of our knowledge, this is the first time that a study with real network equipment under realistic network conditions has measured and evaluated firewall clusters against single firewalls using DPI. We measured the induced latency while varying data traffic conditions such as speed and packet payload sizes. Our results clearly identify and quantify the conditions and architectures where deterioration in performance is quite significant. Our measurements show that in some cases the use of firewall clusters or DPI can increase latency and/or reduce speed by more than 10%. This new contribution to knowledge can help engineers and researchers when designing a network.

Client-side adaptive bitrate (ABR) algorithms based on deep reinforcement learning (RL) can continuously improve its adaptability to network conditions. However, most existing methods adopt fixed reward functions to train the ABR policy, which leads the results being not consistent with user-perceived quality of experience (QoE) in a long duration under various network conditions. In order to optimize the QoE, this paper proposes a novel ABR algorithm considering user preference based on short trajectory segments. The user-specific preference feedback, which is selected by the user from a pair of short track segments in advance, is collected and applied to define the training goal of RL. Specifically, we train a deep neural network to define the RL reward and integrate it with A3C-based ABR algorithm. The experiment results show that the accuracy of the proposed reward model outperforms most existing fixed reward functions by 13.6% in user preference prediction, and the optimized ABR algorithm improves QoE by 16.4% on average.

Dependable measurement data are essential for the accuracy and integrity of power system state estimation and actuation. However, distribution grids empowered by wireless connectivity are subject to missing sensor observations due to channel stochasticity. In this paper, we leverage the potential of dynamical models to extract knowledge from the ambient measurement space under unfavorable and ideal network conditions. A rigorous assessment of various network configurations based on empirical evaluations reveals meaningful performance trends for model fitting and recovery of incomplete trajectories. In particular, while the imperfect connectivity may curtail the ability of dynamical models to exploit the intrinsic spatio-temporal structure, measurement imputation under ideal network configuration exhibits favorable performance. Our research outcomes highlight the impact of the underlying wireless connectivity on measurement data acquisition and handling in distribution grids.

In this paper we present Joint Unicast-Multicat Panoramic Streaming (JUMPS) over the cellular network. JUMPS optimizes the resource allocation for a group of eMBMS (evolved Multicast Broadcast Multimedia Systems) users to enhance their experience while leveraging the inherent diversity in both users' network conditions and field of view (FoV). The key intuition is combining unicast and multicast, for tiled panoramic content, would enable facilitate using the right amount of resources for every tile considering the tile popularity and receiving user link quality. We compare JUMPS performance to state-of-the-art solutions and show that it significantly improves users' received FoV bitrate and reduces their battery by reducing the number of resource blocks that users have to listen to. These results are consistent across various scenarios that vary across user group link conditions, FoV diversity, and available network resources.

Wireless powered communication networks (WPCN) which allow an energy source (ES) to wirelessly transfer its power to a wireless device (WD) is becoming a promising technology in modern wireless networks. Besides extending network longevity, WPCN eliminates the need for frequent manual battery replacement or recharging. Even WPCN is a potential solution, how to optimize network lifetime to guarantee network substantiality and reliability is challenging. This problem has not yet been considered carefully in literature. In addition, existing related works do not consider both uplink and downlink traffic which directly impact device lifetime as well as network lifetime. This paper, therefore, deals with the problem of optimizing network lifetime by appropriately scheduling transmitted power from an ES to each WD. To this end, the paper first proposes models for device lifetime and network lifetime. Based on the models, the strategy to optimize network lifetime is derived. The paper then proposes a power scheduling algorithm performed at an ES to maximize network lifetime under several network conditions. The proposed algorithm takes into account bi-directional traffic at each WD. The proposed models and the performance of power scheduling algorithm are verified by simulations. The paper is an useful guideline to assess reliability duration of the network and provides several directions to ensure network reliability and substantiality.

Communication is vitally important during disasters. This paper proposes a new messaging protocol that is resilient to the damaged network conditions caused by disasters based on the principles of dew computing. The goal of this messaging protocol is to make messengers adapt to both normal conditions and disastrous conditions. In a normal condition, the client-server architecture supports efficient communications; in a disastrous condition, the messengers will make full use of P2P and other possible communication links so that vital information can be transmitted whenever it is possible.

The use of Machine Learning (ML) techniques in Intrusion Detection Systems (IDS) has taken a prominent role in the network security management field, due to the substantial number of sophisticated attacks that often pass undetected through classic IDSs. These are typically aimed at recognising attacks based on a specific signature, or at detecting anomalous events. However, deterministic, rule-based methods often fail to differentiate particular (rarer) network conditions (as in peak traffic during specific network situations) from actual cyber attacks. In this paper we provide an experimental-based review of neural-based methods applied to intrusion detection issues. Specifically, we i) offer a complete view of the most prominent neural-based techniques relevant to intrusion detection, including deep-based approaches or weightless neural networks, which feature surprising outcomes; ii) evaluate novel datasets (updated w.r.t. the obsolete KDD99 set) through a designed-from-scratch Python-based routine; iii) perform experimental analyses including time complexity and performance (accuracy and F-measure), considering both single-class and multi-class problems, and identifying trade-offs between resource consumption and performance. Our evaluation quantifies the value of neural networks, particularly when state-of-the-art datasets are used to train the models. This leads to interesting guidelines for security managers and computer network practitioners who are looking at the incorporation of neural-based ML into IDS., Early Access on IEEE Transactions on Network and Service Management

Machine learning models, particularly reinforcement learning (RL), have demonstrated great potential in optimizing video streaming applications. However, the state-of-the-art solutions are limited to an "offline learning" paradigm, i.e., the RL models are trained in simulators and then are operated in real networks. As a result, they inevitably suffer from the simulation-to-reality gap, showing far less satisfactory performance under real conditions compared with simulated environment. In this work, we close the gap by proposing OnRL, an online RL framework for real-time mobile video telephony. OnRL puts many individual RL agents directly into the video telephony system, which make video bitrate decisions in real-time and evolve their models over time. OnRL then aggregates these agents to form a high-level RL model that can help each individual to react to unseen network conditions. Moreover, OnRL incorporates novel mechanisms to handle the adverse impacts of inherent video traffic dynamics, and to eliminate risks of quality degradation caused by the RL model's exploration attempts. We implement OnRL on a mainstream operational video telephony system, Alibaba Taobao-live. In a month-long evaluation with 543 hours of video sessions from 151 real-world mobile users, OnRL outperforms the prior algorithms significantly, reducing video stalling rate by 14.22% while maintaining similar video quality.

Today almost half the world's population does not have Internet access. This is particularly the case in rural and undeserved regions where providing Internet access infrastructure is challenging and expensive. To this end, we present demonstration of WhiteHaul [5], a low-cost hybrid cross-layer aggregation system for TV White Space (TVWS) based backhaul. WhiteHaul features a custom-designed frequency conversion substrate that efficiently handles multiple noncontiguous chunks of TVWS spectrum using multiple low-cost COTS 802.11n/ac cards but with a single antenna. At the software layer, WhiteHaul uses MPTCP as a link-level tunnel abstraction to efficiently aggregate multiple chunks of the TVWS spectrum via a novel uncoupled, cross-layer congestion control algorithm. This demo illustrates the unique features of the WhiteHaul system based on a prototype implementation employing a modified version of MPTCP Linux Kernel and a custom-designed conversion substrate. Using this prototype, we highlight the performance of the WhiteHaul system under various configurations and network conditions.

The Internet of Things is now part of everyday life and there has been a wide range of novel IoT applications collecting cyber-physical data and providing information on the environment. As it is expected that the IoT traffic will count for a major part of the Internet traffic, it is essential to characterize the IoT traffic and to identify each device, and especially in the case of cyberattacks. In this paper, we present a new method to identify IoT devices based on traffic entropy. We compute the entropy values of traffic features and we rely on Machine Learning algorithms to classify the traffic. Our method succeeds in identifying devices under various network conditions with performances up to 94% in all cases. Our method is also robust to unpredictable network behavior with anomalies spreading into the network.

In recent years, we witness dramatic growing attention in immersive media technologies like 360-degree videos and virtual reality (VR). However, measuring the quality-of-experience (QoE) for 360-degree VR videos is not a trivial task. Streaming such videos to head mounted displays (HMDs) is extremely bandwidth-demanding when compared to traditional 2D videos. In HTTP adaptive streaming, QoE tends to deteriorate significantly during fluctuating network conditions, which results in various bitrate changes and causes multiple stalling events during playback. Thus, understanding how the human visual system perceives 360-degree video with the effect of stalling and different bitrate levels becomes inevitable. In this paper, we investigate the impact of stalling on users QoE under different bitrate levels and the interaction between stalling event and bitrate level for 360-degree videos in VR. To aim this, we first build a 360-degree videos database by encoding videos in three different bitrate levels (1, 5, and 15 Mbps) with 4K resolutions ($3840\times1920$ pixels). We then simulate various stalling events in the videos and conduct a subjective experiment in a virtual reality environment to investigate the human responses. Finally, we use a Bayesian method to estimate and predict the QoE while measuring the quality drop owing to various stalling events and bitrate changes. Proposed solution and prediction results show a strong dependency between playback stalling and bitrate of 360-degree video in VR. Stalling always impacts the QoE of 360-degree videos, but the strength of this negative impact depends on the video bitrate level. The adverse effect of stalling events is more profound when bitrate level approaches to the high and low end, which is in close agreement with subjective opinion.

Quality of Experience (QoE) assessment in video games is notorious for its burdensomeness. Employing human subjects to understand network impact on the perceived gaming QoE presents major drawbacks in terms of resources requirement, results interpretability and poor transferability across different games. To overcome these shortcomings, we propose to substitute human players with artificial agents trained with state-of-the-art Deep Reinforcement Learning techniques. Equivalently to traditional QoE assessment, we measure the in-game score achieved by an artificial agent for the game of Doom for varying network parameters. Our results show that the proposed methodology can be applied to understand fine-grained impact of network conditions on gaming experience while opening a lot of new opportunities for network operators and game developers.

Quality of Experience (QoE) for various types of websites has gained significant attention in recent years. In order to design and evaluate websites, a metric that can estimate a user's experienced quality robustly for diverse content is necessary. SpeedIndex (SI) has been widely adopted to estimate perceived web page loading progress. It measures the speed of rendering pixels for the webpage that is visible in the browser window. This is termed Above-The-Fold (ATF). The influence of animated content on the perception of ATF has been less comprehensively explored. In this paper, we present an experimental design and methodology to measure ATF perception for websites with and without animated elements for various page content categories. We found that pages with animated elements caused people to have more varied perceptions of ATF under different network conditions. Animated content also impacts the page load estimation accuracy of SI for websites. We discuss how the difference in the perception of ATF will impact the QoE management of web applications. We explain the necessity of revisiting the visual assessment of ATF to include the animated contents and improve the robustness of metrics like SI.

The demand and usage of 360-degree video services are expected to increase. However, despite these services being highly bandwidth intensive, not much is known about the potential value that basic bandwidth saving techniques such as server or edge-network on-demand caching (e.g., in a CDN) could have when used for delivery of such services. This problem is both important and complicated as client-side solutions have been developed that split the full 360-degree view into multiple tiles, and adapt the quality of the downloaded tiles based on the user's expected viewing direction and bandwidth conditions. To better understand the potential bandwidth savings that caching-based techniques may offer for this context, this paper presents the first characterization of the similarities in the viewing directions of users watching the same 360-degree video, the overlap in viewports of these users (the area of the full 360-degree view they actually see), and the potential cache hit rates for different video categories and network conditions. The results provide substantial insight into the conditions under which overlap can be considerable and caching effective, and can inform the design of new caching system policies tailored for 360-degree video.

The aim of this work is to assess whether actual distorted conditions of the network are really affecting the accuracy of inductive current transformers. The study started from the need to evaluate the accuracy performance of inductive current transformers in off-nominal conditions, and to improve the related standards. In fact, standards do not provide a uniform set of distorted waveforms to be applied on inductive or low-power instrument transformers. Moreover, there is no agreement yet, among the experts, about how to evaluate the uncertainty of the instrument transformer when the operating conditions are different from the rated ones. To this purpose, the authors collected currents from the power network and injected them into two off-the-shelf current transformers. Then, their accuracy performances have been evaluated by means of the well-known composite error index and an approximated version of it. The obtained results show that under realistic non-rated conditions of the network, the tested transformers show a very good behavior considering their nonlinear nature, arising the question in the title. A secondary result is that the use of the composite error should be more and more supported by the standards, considering its effectiveness in the accuracy evaluation of instrument transformers for measuring purposes.

Packet-In message injection attack is severe in Software Defined Network (SDN), which will cause a single point of failure of the centralized controller and the crash of the entire network. Nowadays, there are many detection methods for it, including entropy detection and so on. We propose an adaptive detection method to proactively defend against this attack. We establish a Poisson probability distribution detection model to find the attack and use the flow table filter to mitigate it. We also use the EWMA method to update the expectation value of the model to adapt the actual network conditions. Our method has no need to send additional packets to request the switch information. The experiment results show that there is 92% true positive rate in case of attack with random destination IP packets injected, and true positive rate is 98.2% under the attack with random source IP packets injected.

Protocols for secure Multi-Party Computation (MPC) can be classified according to the underlying communication model. Two prominent communication models considered in the literature are the synchronous and asynchronous models, which considerably differ in terms of the achievable security guarantees. Synchronous MPC protocols can achieve the optimal corruption threshold n/2 and allow every party to give input, but become completely insecure when synchrony assumptions are violated. On the other hand, asynchronous MPC protocols remain secure under arbitrary network conditions, but can tolerate only n/3 corruptions and parties with slow connections unavoidably cannot give input.

As end-user devices often have multiple access networks available, choosing the most suitable network can help to improve application performance and user experience. However, selecting the best access network for HTTP Adaptive Streaming (HAS) is non-trivial, e.g., due to complex interactions between network conditions and the Adaptive Bit-Rate algorithm (ABR), which adapts to network conditions by selecting which video representation to load. In this paper, we propose to use an application-informed approach, Informed Access Network Selection (IANS), to select the most suitable access network for each video segment. We evaluate the impact of IANS on HAS performance in a testbed under a variety of network conditions and using different workloads. We find that IANS improves HAS performance substantially, in particular in cases where the available downstream capacity is low. In the Capacity Decrease scenario, where capacity decreases drastically during the video load, IANS can improve the estimated Mean Opinion Score (MOS) compared to using a single network from 2.1 to 2.8. We compare IANS to MPTCP using the Lowest-RTT-first scheduler, which continues to use a low downstream capacity network, resulting in lower performance. This confirms that IANS can improve video streaming performance. © ACM, 2020. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published here, http://dx.doi.org/https://doi.org/10.1145/3339825.3391865

File transfer is based on the reliable TCP protocol. However, when the network is of poor quality, TCP-based transmission will still perform less effectively due to some reasons. Existing approaches mainly optimize file transmission by modifying network strategies or optimize transmission mechanism. But when network conditions cannot be selected or modified, a suitable parameter adjustment method would be needed. The experiments in this paper firstly perform TCP-based file transmission tests on a poor network condition and find two problems. Then, multiple tests were performed on multiple platforms to detect the conditions under which these problems occurred. Next, to address the issue and improve the transmission performance, we propose an intelligent optimization scheme. By adjusting the transmission parameters and adding policies, the scheme equips the intermediate parameters with intelligent self-adaptation capabilities. We also test and evaluate the performance of the intelligent scheme. The result shows that the file transfer under the new scheme not only basically avoids the target problems, but also reduces the practical upload time under poor network condition from the perspective of decreasing the number of retransmissions and reducing the failure rate.

International audience; With the rise of the complexity of the IEEE 802.11 standard, rate adaptation algorithms have to deal with a large set of values for all the different parameters which impact the network throughput. Simple trial-and-error algorithms can no longer explore solution space in reasonable time and smart solutions are required. Most of the WiFi controllers rely on proprietary code and the used rate adaptation algorithms in these controllers are unknown. Very few WiFi controllers provide their rate adaptation algorithms when they do not rely on the Minstrel-HT algorithm, which is implemented in the Linux kernel. Intel WiFi controllers come with their own rate adaptation algorithms that are implemented in the Intel IwlWifi Linux Driver which is open-source. In this paper, we have reverse-engineered the Intel rate adaptation mechanism from the source code of the IwlWifi Linux driver, and we give, in a comprehensive form, the underlying rate adaptation algorithm named Iwl-Mvm-Rs. We describe the different mechanisms used to seek the best throughput adapted to the network conditions. We have also implemented the Iwl-Mvm-Rs algorithm in the ns-3 simulator. Thanks to this implementation, we can evaluate the performance of Iwl-Mvm-Rs in different scenarios (static and with mobility, with and without fast fading). We also compare the performances of Iwl-Mvm-Rs with the ones of Minstrel-HT and IdealWifi, also implemented in the ns-3 simulator.

Augmented reality is an emerging application on mobile devices. However, there is a lack of understanding of the communication requirements and challenges of multi-user AR scenarios. In this position paper, we propose several important research issues that need to be addressed for low-latency, accurate shared AR experiences: (a) Systems tradeoffs of AR communication architectures used today in mobile AR platforms; (b) Understanding AR communication patterns and adapting the AR application layer to dynamically changing network conditions; and (c) Tools and methodologies to evaluate AR quality of experience in real time on mobile devices. We present preliminary measurements of off-the-shelf mobile AR platforms as well as results from our AR system, ShareAR, illustrating performance tradeoffs and indicating promising new research directions.

Software-Defined Networking (SDN) is one of the key technologies of 5th generation mobile networks (5G). However, like the traditional network architecture, SDN is also vulnerable to the Distributed Denial of Service (DDoS) attack. This paper explores the dynamic threshold for DDoS attack in the SDN environment. Through the characteristics of SDN, we propose a feasible DDoS detection and defense mechanism. The proposed mechanism calculates the entropy of the network environment by the collected traffic status, and derives a dynamic threshold according to the network conditions to determine whether the environment is subject to DDoS attacks. In the event of a DDoS attack, the proposed mechanism discards the traffic from the malicious nodes to the victim nodes with a flow entry. In addition, if no DDoS attacks occur in the environment, the proposed system can disperse the traffic of the SDN switch, thereby balance the traffic load in the environment.

Data from phasor measurement units (PMUs) enable the efficient functioning of critical power applications supporting real-time monitoring, advanced protection, and dynamic control. Given the time-sensitive and critical nature of these measurements, PMU data are attractive targets for malicious attackers aiming to cause widespread grid damage. This paper addresses the problem of detecting a class of cyber threats called gray-hole attack, where the adversary drops PMU data packets as they traverse the network, resulting in loss of observability and subsequently, incorrect control decisions. This paper proposes an online mechanism for detecting gray-hole attacks in real-time. The proposed mechanism uses the timing information inherent in the PMU data for detecting packet drop attacks, and does not impose any overheads or require any support from the network infrastructure. Extensive simulation results are presented to verify the effectiveness and accuracy of the proposed detection mechanism under different network conditions.

Currently, the 4th Generation District Heating (4GDH) is an attractive topic in the energy field as it concerns a sustainable and efficient means of delivering heat to houses. The 4GDH concept is characterised by low temperatures, low heat distribution losses, renewable and excess energy utilisation, as well as high efficiency. As a result of implementing the 4GDH concept, existing district heating systems (DHS) are undergoing massive improvement. The barriers faced by existing DHS over the course of the transition process towards the 4th generation are reviewed in the paper; the methodology for the evaluation of the DHS transition process towards the 4th generation is also presented. This methodology allows to assess the transition process dynamics, as well as helps to focus on DHS characteristics, which need to be improved. A large-scale DHS in Tallinn (Estonia) was analysed with the help of the proposed methodology. Supply and return temperatures, the share of renewable energy, and network conditions demonstrated the highest potential for improvement and had the most notable impact on the Tallinn DHS transition process.

Sharing members with other online communities can be problematic for community responsiveness, as less member time and attention become available for the focal community. However, member time and attention spent externally have the potential to generate knowledge that bolsters the focal community’s responsiveness. Little is known about when member time and attention spent externally become such generative resources. Building on and extending the knowledge collaboration theory of online communities, we examine the network conditions that enable the generative effect of member time and attention spent externally on community responsiveness. We propose a contingent model wherein a focal community’s external bridging (the extent of bridging nonredundant communities) and internal bonding (the extent of cohesion among members) jointly moderate the effects of member time and attention spent externally on community responsiveness. We theorize that external bridging provides the opportunity to access novel external knowledge and that internal bonding improves the motivation to share knowledge to assist others. Our results show that member time and attention spent externally have a positive effect on community responsiveness only in the presence of both external bridging and internal bonding. The study has important implications for advancing the boundary conditions regarding generative resources and online knowledge collaboration.

Small cells deliver cost-effective capacity and coverage enhancement in a cellular network. In this work, we present the interplay of two technologies, namely Wi-Fi offloading and small-cell cooperation that help in achieving this goal. Both these technologies are also being considered for 5G and B5G (Beyond 5G). We simultaneously consider Wi-Fi offloading and small-cell cooperation to maximize average user throughput in the small-cell network. We propose two heuristic methods, namely Sequential Cooperative Rate Enhancement (SCRE) and Sequential Offloading Rate Enhancement (SORE) to demonstrate cooperation and Wi-Fi offloading, respectively. SCRE is based on cooperative communication in which a user data rate requirement is satisfied through association with multiple small-cell base stations (SBSs). However, SORE is based on Wi-Fi offloading, in which users are offloaded to the nearest Wi-Fi Access Point and use its leftover capacity when they are unable to satisfy their rate constraint from a single SBS. Moreover, we propose an algorithm to switch between the two schemes (cooperation and Wi-Fi offloading) to ensure maximum average user throughput in the network. This is called the Switching between Cooperation and Offloading (SCO) algorithm and it switches depending upon the network conditions. We analyze these algorithms under varying requirements of rate threshold, number of resource blocks and user density in the network. The results indicate that SCRE is more beneficial for a sparse network where it also delivers relatively higher average data rates to cell-edge users. On the other hand, SORE is more advantageous in a dense network provided sufficient leftover Wi-Fi capacity is available and more users are present in the Wi-Fi coverage area.

The operation of a voltage source converter (VSC)-based medium-voltage (MV) direct current (DC) link is described. The use of a DC link in an MV distribution network is presented, considering salient similarities and differences to a high-voltage DC link. A PSCAD/EMTDC model was used to demonstrate the dynamic operation of an MVDC link, under normal and faulted network conditions. The study shows that MVDC can provide flexibility in a distribution network by decoupling real (P) and reactive (Q) power flows through the link, under normal operation. During a fault, however, the current injected from the converter could interfere with the detection and discrimination of AC system faults, in distribution networks with current-based protection scheme.

In a paper appearing in a recent issue of this journal ( Studies in Microeconomics), the authors explored a new method to allocate a divisible resource efficiently among cooperating agents located at the vertices of a connected undirected network. It was shown in that article that maximizing social welfare of the agents produces Pareto optimal allocations, referred to as dominance over neighbourhood (DON), capturing the notion of dominance over neighbourhood in terms of network degree. In this article, we show that the allocation suggested by the method competes well with current cooperative game-theoretic power centrality measures. We discuss the conditions under which DON turns exactly equivalent to a recent ‘fringe-based’ Shapley Value formulation for fixed networks, raising the possibility of such solutions being both Pareto optimal in a utilitarian social welfare maximization sense as well as fair in the Shapley value sense.

Repeated loading in district heating networks is occurring inconstantly. There are small and great load amplitudes in the system. As small load amplitudes are usually observed more frequently than great load amplitudes, their accumulation with respect to great load amplitudes is of common interest. A growing share of renewable energy can be noted in energy markets. Because of this, flexible operation of combined heat and power plants is necessary. For the connected district heating network this means additional temperature amplitudes in the system, causing higher stresses for the network. Repeated loading in the system has to be considered in the fatigue analysis. For a suggested lifetime of a district heating network of 30 years, an estimated number of cycles for the temperature load were accumulated for standardization. Nevertheless a longer lifetime than 30 years for district heating pipes is expected in the district heating sector, which also means higher cyclic loading. Due to individual operation parameters and individual network conditions, the equivalent action cycles are expected to be different at every element in the network. The paper is based on a review on damage accumulation theories that may be used for district heating systems regarding future developments. Some examples are shown for equivalent full load cycles for different locations in district heating networks.

We propose a “living network” as a key enabler for open reconfigurable optical add/drop multiplexer (ROADM) networks that, unlike today's use of static planning tools, adapts to varying network conditions, and allows network operation close to actual performance. We experimentally demonstrate the living network in various network conditions that autonomously keeps record of its path-level performance. The more services are added, the more accurate the performance of a newly to be established service is estimated through a learning process, which shows the feasibility of living network with an allocated margin of only 1.2 dB.

We present the first power over Wi-Fi system that delivers power to low-power sensors and devices and works with existing Wi-Fi chipsets. We show that a ubiquitous part of wireless communication infrastructure, the Wi-Fi router, can provide far field wireless power without significantly compromising the network's communication performance. Building on our design, we prototype battery-free temperature and camera sensors that we power with Wi-Fi at ranges of 20 and 17 ft, respectively. We also demonstrate the ability to wirelessly trickle-charge nickel--metal hydride and lithium-ion coin-cell batteries at distances of up to 28 ft. We deploy our system in six homes in a metropolitan area and show that it can successfully deliver power via Wi-Fi under real-world network conditions without significantly degrading network performance.

An innovative real time rate adaptation algorithm is proposed for smooth media playback and effective network bandwidth utilization.The algorithm helps the system to choose the next likely segment download bit representation by real time monitoring of segment duration or client buffer duration.The efficiency of the algorithm measured using key performance indices and compared with conventional throughput and segment fetch time based algorithms.Provides improved quality of service in terms of stability, fairness and effective use of available resources. We propose a real-time rate-adaptation approach for adaptive Hypertext Transfer Protocol streaming. In this advanced real-time rate-adaptation (ARTRA) approach, we monitor the parameters at the client in real time, and select the most likely available bit representation for the next segment download. The algorithm dynamically triggers the rate evaluation and decides the rate switch according to metrics calculated using the segment download duration, measured throughput, and buffer data-level indication. The performance of the ARTRA is compared with those of throughput-based adaptation and segment-fetch time-based adaptation algorithms. An analysis of the experimental results shows that ARTRA enables clients to use the maximum available end-to-end network capacity and delivers the best user experience by reducing re-buffering events and bitrate level changes. ARTRA performs remarkably well under challenging network conditions and supports system stability and robustness. Display Omitted

Power distribution networks have extensive and long length medium voltage (MV) feeders due to direct contact with the customer. Network extension will be followed by an increase in the number of installed insulators. Pin ceramic insulators cause various problems for the operation of the distribution network because they are made of two-piece (TP). The expansion and contraction of cementitious material between two pieces of insulators create some defects for poor quality of production and critical conditions of insulation problem for these insulators. In this paper, the ultrasonic diagnosis method based on monitoring of partial discharge (PD) signals of TP pin ceramic insulators have been studied and the leakage losses of these insulators in network conditions for a MV feeder of North Khorasan Electrical Distribution Company (NKEDC), called Khanloq feeder, were calculated. Finally, the economic consideration of a sample project of insulator replacement and estimating the project investment return for Khanloq feeder by using PD signals analysis have been discussed.

This letter proposes a novel Gaussian mixture model-based ensemble Kalman filter approach to the accurate calibration of the parameters of machine dynamic models. This approach aims to overcome some practical challenges affecting parameter calibration accuracy. Results show the proposed approach can provide precise calibrated parameters even when the machine operates under unbalanced network conditions with nonGaussian measurement noises.

With the recent rise in artificial intelligence, several areas of computer science have begun to explore the use of machine learning algorithms. Among them, recent video encoding techniques exploit in large-scale machine learning algorithms, either to aid in compression efficiency or to reduce computational cost. However, this is not a simple task, especially if the objective is to reduce computational cost, since it is necessary that the machine learning algorithm used does not interfere negatively in the total coding process time and in compression efficiency. As video transrating is an essential task in streaming service providers that need to transmit and deliver different versions of the same content for a multitude of users operating under different network conditions, it is critical to find an efficient strategy that yields computational cost reduction with small loss in compression efficiency. This work discusses the use of different machine learning algorithms in the video transcoding concept, comparing them in terms of accuracy and overhead processing time. The experimental results and discussions provide insights that can be useful in the definition of future fast transcoding schemes in real-world transcoding architectures.

BBR is a new congestion control algorithm (CCA) deployed for Chromium QUIC and the Linux kernel. As the default CCA for YouTube (which commands 11+% of Internet traffic), BBR has rapidly become a major player in Internet congestion control. BBR's fairness or friendliness to other connections has recently come under scrutiny as measurements from multiple research groups have shown undesirable outcomes when BBR competes with traditional CCAs. One such outcome is a fixed, 40% proportion of link capacity consumed by a single BBR flow when competing with as many as 16 loss-based algorithms like Cubic or Reno. In this short paper, we provide the first model capturing BBR's behavior in competition with loss-based CCAs. Our model is coupled with practical experiments to validate its implications. The key lesson is this: under competition, BBR becomes window-limited by its 'in-flight cap' which then determines BBR's bandwidth consumption. By modeling the value of BBR's in-flight cap under varying network conditions, we can predict BBR's throughput when competing against Cubic flows with a median error of 5%, and against Reno with a median of 8%.

This short paper presents a detailed empirical study of BBR's performance under different real-world and emulated testbeds across a range of network operating conditions. Our empirical results help to identify network conditions under which BBR outperforms, in terms of goodput, contemporary TCP congestion control algorithms. We find that BBR is well suited for networks with shallow buffers, despite its high retransmissions, whereas existing loss-based algorithms are better suited for deep buffers.To identify the root causes of BBR's limitations, we carefully analyze our empirical results. Our analysis reveals that, contrary to BBR's design goal, BBR often exhibits large queue sizes. Further, the regimes where BBR performs well are often the same regimes where BBR is unfair to competing flows. Finally, we demonstrate the existence of a loss rate "cliff point" beyond which BBR's goodput drops abruptly. Our empirical investigation identifies the likely culprits in each of these cases as specific design options in BBR's source code.

We study the quality of frequency response in a noisy optical network. Such a response can be useful in traditional frequency-domain industrial loop controllers. In particular, we analyse a (step, frequency) response of a simulated computer network, where the stimulus is one of the coefficients which regulate the network’s strategy of packet transmission, and the response is the network’s momentary performance. This way, we find a frequency range, where an instantaneous dependence between the stimulus and the response can direct a self-adaptation scheme of the proposed strategy due to changing network conditions. To stay in the safe limits of the network’s behaviour, we make the stimulus weak. We use a bursty traffic model to test the limits of this approach. We use a model of an optical ring of an experimental NGREEN network developed at NOKIA. The discussed technique was capable of optimising the network’s behaviour.

Today’s end-user devices have multiple access networks available and can achieve better application performance by distributing traffic across access networks. However, matching application traffic to the most suitable access network or bundling them is non-trivial, given varying application needs and network performance characteristics. Therefore, we propose an application-informed approach for access network selection (IANS). Based on the size of a Web resource, we select the better access network in terms of latency and available downstream capacity. We implement IANS within our Socket Intents prototype and evaluate its benefits for Web page loads under a variety of network conditions and for various Web pages. IANS provides the highest speedups for scenarios with asymmetric network conditions and for scenarios with low downstream capacity. Here, IANS improves relevant Web metrics by between 500 and 1000 ms in the median, compared to using the better of the two access networks, and may also outperform MPTCP. This confirms that IANS improves application performance over using a single network and, in several scenarios, even using MPTCP.

We present BurstTracker, the first tool that developers can use to detect if the LTE downlink is the bottleneck for their applications. BurstTracker is driven by our discovery that the proprietary LTE downlink schedulers running on LTE base stations allocate resources to users in a way that reveals if a user's downlink queue runs empty during a download. We demonstrate that BurstTracker works across Tier-1 cellular providers and across a variety of network conditions. We also present a case study that shows how application developers can use this tool in practice. Surprisingly, with BurstTracker, we find that the LTE downlink may not be the bottleneck for video streaming on several Tier-1 providers, even during peak hours at busy locations. Rather, transparent TCP middleboxes deployed by these providers lead to downlink underutilization, because they force Slow-Start Restart. With a simple workaround, we improve video streaming bitrate on busy LTE links by 35%.