Changing Network Conditions Research Articles

A low-complexity, energy-efficient data securing model for wireless sensor network based on linearly complex voice encryption mechanism of GSM technology

A wireless sensor network is the formation of a temporary network of sensor nodes equipped with limited resources working in an ad hoc environment. Routing protocol is one of the key challenges while designing a wireless sensor network, which requires optimum use of limited resources of a sensor node, such as power and so on. Similarly, data security and integrity is another open issue that has emerged as a flash point in research community in the last decade. This article proposes a secure model for routing data from source to destination named as secure and energy-efficient routing. The proposed secure and energy-efficient routing is inherited from authentication and voice encryption scheme developed for Global System for Mobile Communications. Necessary modifications have been carried out in order to fit the Global System for Mobile Communications technology in a wireless sensor network ad hoc environment. Due to its low complexity, the secure and energy-efficient routing consumes lesser battery power both during encryption/decryption and for routing purposes. It is due to the XoR operation used in the proposed scheme which is considered as the most inexpensive process with respect to time and space complexity. It is observed through simulations that secure and energy-efficient routing can work effectively even in critical power level in a sensor network. The article also presents a simulation-based comparative analysis of the proposed secure and energy-efficient routing with two notable existing secure routing protocols. We proved that the proposed secure and energy-efficient routing helps to achieve the desired performance under dynamically changing network conditions with various numbers of malicious nodes. Moreover, in Global System for Mobile Communications, generally three linear feedback shift registers are used to fragment the key in data encryption mechanism. In this article, a mathematical model is proposed to increase the number of possible combinations of shift register in order to make the data encryption mechanism more secure which has never been done before. Due to its liner complexity, lesser power consumption, and more dynamic route updating, the secure and energy-efficient routing can easily find its use in the emerging Internet-of-Things systems.

Evaluation of objective video quality assessment methods on video sequences with different spatial and temporal activity encoded at different spatial resolutions

With the development of Video on Demand applications due to the availability of high-speed internet access, adaptive streaming algorithms have been developing and improving. The focus is on improving user’s Quality of Experience (QoE) and taking it into account as one of the parameters for the adaptation algorithm. Users often experience changing network conditions, so the goal is to ensure stable video playback with satisfying QoE level. Although subjective Video Quality Assessment (VQA) methods provide more accurate results regarding user’s QoE, objective VQA methods cost less and are less time-consuming. In this article, nine different objective VQA methods are compared on a large set of video sequences with various spatial and temporal activities. VQA methods used in this analysis are: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), MultiScale Structural Similarity Index (MS-SSIM), Video Quality Metric (VQM), Mean Sum of Differences (DELTA), Mean Sum of Absolute Differences (MSAD), Mean Squared Error (MSE), Netflix Video Multimethod Assessment Fusion (Netflix VMAF) and Visual Signal-to-Noise Ratio (VSNR). The video sequences used for testing purposes were encoded according to H.264/AVC with twelve different target coding bitrates, at three different spatial resolutions (resulting in a total of 190 sequences). In addition to objective quality assessment, subjective quality assessment was performed for these sequences. All results acquired by objective VQA methods have been compared with subjective Mean Opinion Score (MOS) results using Pearson Linear Correlation Coefficient (PLCC). Measurement results obtained on a large set of video sequences with different spatial resolutions show that VQA methods like SSIM and VQM correlate better with MOS results compared to PSNR, SSIM, VSNR, DELTA, MSE, VMAF and MSAD. However, the PLCC results for SSIM and VQM are too low (0.7799 and 0.7734, respectively), for the usage of these methods in streaming services instead of subjective testing. These results suggest that more efficient VQA methods should be developed to be used in streaming testing procedures as well as to support the video segmentation process. Furthermore, when comparing results obtained for different spatial resolutions, it can be concluded that the quality of video sequences encoded at lower spatial resolutions in cases of lower target coding bitrate is higher compared to the quality of video sequences encoded at higher spatial resolutions at the same target coding bitrate, particularly when video sequences with higher spatial and temporal information are used.

Research on New Generation of Power Line Carrier Communication Trusted Networking Algorithm

With the safe and stable operation of the new generation of power line carrier communication, it is of great significance to the rapid development of smart power grid. However, due to its own physical characteristics and the impact of the external environment, communication cables usually have some security risks, such as aging, damage, theft. In this paper, a new generation of power line carrier communication trusted access network is studied. In this paper, the current situation of the new generation of power line carrier trusted access and network requirements are analyzed, then the principle and characteristics of basic ant colony algorithm are briefly introduced, then the characteristics of power line carrier communication system network and network requirements are analyzed, the improved ant colony algorithm is applied to network optimization of the new generation of PLC communication system. The algorithm makes the power line carrier communication system have certain destruction resistance and recombination ability when the network state changes, it can guarantee the reliable operation of power network communication system.

Genetic Improvement of Routing Protocols for Delay Tolerant Networks

Routing plays a fundamental role in network applications, but it is especially challenging in Delay Tolerant Networks (DTNs). These are a kind of mobile ad hoc networks made of, e.g., (possibly, unmanned) vehicles and humans where, despite a lack of continuous connectivity, data must be transmitted while the network conditions change due to the nodes’ mobility. In these contexts, routing is NP-hard and is usually solved by heuristic “store and forward” replication-based approaches, where multiple copies of the same message are moved and stored across nodes in the hope that at least one will reach its destination. Still, the existing routing protocols produce relatively low delivery probabilities. Here, we genetically improve two routing protocols widely adopted in DTNs, namely, Epidemic and PRoPHET, in the attempt to optimize their delivery probability. First, we dissect them into their fundamental components, i.e., functionalities such as checking if a node can transfer data, or sending messages to all connections. Then, we apply Genetic Improvement (GI) to manipulate these components as terminal nodes of evolving trees. We apply this methodology, in silico, to six test cases of urban networks made of hundreds of nodes and find that GI produces consistent gains in delivery probability in four cases. We then verify if this improvement entails a worsening of other relevant network metrics, such as latency and buffer time. Finally, we compare the logics of the best evolved protocols with those of the baseline protocols, and we discuss the generalizability of the results across test cases.

S-DPS: An SDN-Based DDoS Protection System for Smart Grids

Information Communication Technology (ICT) environment in traditional power grids makes detection and mitigation of DDoS attacks more challenging. Existing security technologies, besides their efficiency, are not adequate to cater to DDoS security in Smart Grids (SGs) due to highly distributed and dynamic network environments. Recently, emerging Software Defined Networking- (SDN-) based approaches are proposed by researchers for SG’s DDoS protection; however, they are only able to protect against flooding attacks and are dependent on static thresholds. The proposed approach, i.e., Software Defined Networking-based DDoS Protection System (S-DPS), is efficiently addressing these issues by employing light-weight Tsallis entropy-based defense mechanisms using SDN environment. It provides early detection mechanism with mitigation of anomaly in real time. The approach offers the best deployment location of defense mechanism due to the centralized control of network. Moreover, the employment of a dynamic threshold mechanism is making detection process adaptive to the changing network conditions. S-DPS has demonstrated its effectiveness and efficiency in terms of Detection Rate (DR) and minimal CPU/RAM utilization, considering DDoS protection focusing smurf attacks, socket stress attacks, and SYN flood attacks.

EVeREst: Bitrate Adaptation for Cloud VR

Cloud Virtual Reality (VR) technology is expected to promote VR by providing a higher Quality of Experience (QoE) and energy efficiency at lower prices for the consumer. In cloud VR, the virtual environment is rendered on the remote server and transmitted to the headset as a video stream. To guarantee real-time experience, networks need to transfer huge amounts of data with much stricter delays than imposed by the state-of-the-art live video streaming applications. To reduce the burden imposed on the networks, cloud VR applications shall adequately react to the changing network conditions, including the wireless channel fluctuations and highly variable user activity. For that, they need to adjust the quality of the video stream adaptively. This paper studies video quality adaptation for cloud VR and improves the QoE for cloud VR users. It develops a distributed, i.e., with no assistance from the network, bitrate adaptation algorithm for cloud VR, called the Enhanced VR bitrate Estimator (EVeREst). The algorithm aims to optimize the average bitrate of cloud VR video flows subject to video frame delay and loss constraints. For that, the algorithm estimates both the current network load and the delay experienced by separate frames. It anticipates the changes in the users’ activity and limits the bitrate accordingly, which helps prevent excess interruptions of the playback. With simulations, the paper shows that the developed algorithm significantly improves the QoE for the end-users compared to the state-of-the-art adaptation algorithms developed for MPEG DASH live streaming, e.g., BOLA. Unlike these algorithms, the developed algorithm satisfies the frame loss requirements of multiple VR sessions and increases the network goodput by up to 10 times.

Optimizing Video QoE for Mobile eMBMS Users in Cellular Networks

Evolved Multimedia Broadcast Multicast Service (eMBMS) is used in cellular networks to improve the utilization of scarce wireless resources in high user density service areas. However, eMBMS configuration involves interwoven decisions including which base stations (eNB) to synchronize to form Single Frequency Networks (SFN), which video qualities to be serviced, and how to distribute resources among different videos. These decisions should accommodate disparate channel conditions for eMBMS users, and the impact of eNB's unicast-load in the service area. In this paper, we formulate eMBMS configuration as an optimization problem that maximizes the video QoE for users. Additionally, we present NIMBLE as an eMBMS configuration heuristic, guided by our optimization framework, to solve the problem in realtime. Furthermore, NIMBLE's design integrates elements to accommodate the dynamic nature of cellular networks resulting from changes in both user, and network state over time. We developed a simulation testbed, and performed extensive experiments to show that, in comparison to state-of-the-art schemes, NIMBLE can increase the average user throughput by 150%, and reduce the bitrate switches by 75%.

Multi-Objective Control Optimization for Congestion Avoidance in Computer Networks

Effective congestion control is an issue strongly impacting basic features demanded from modern network environment as reliability, high and stable throughput, and low delays. These characteristics define the quality of communication channels. Optimizing network nodes configuration for only one of mentioned features, can exacerbate other parameters. This paper focuses on avoiding and alleviating network congestions using multi-objective optimization for gain setting of used controllers. Unlike in other presented approaches, in this case the non-stationary, discrete, dynamical model is discussed. The significant advantage of this approach is in the better reflection of the real environment conditions, where the transmission delay is floating. As the further development of the control strategy, the controller with the memory of previous steps have been deployed. Such control strategy mitigates the unfavorable impact of extended delays. Both proposed control strategies tune the presented model of communication channel to alleviate the results of sudden, unexpected network state changes. It is obtained by maximization of available bandwidth usage combined with minimization of buffer utilization. This supports avoiding undesirable congestion effects like packet dropping, retransmissions, high delay, and low network throughput.

Q -Learning-Based High Credibility and Stability Routing Algorithm for Internet of Medical Things

With the outbreak of COVID-19, people’s demand for using the Internet of Medical Things (IoMT) for physical health monitoring has increased dramatically. The considerable amount of data requires stable, reliable, and real-time transmission, which has become an urgent problem to be solved. This paper constructs a health monitoring-enabled IoMT network which is composed of several users carrying wearable devices and a coordinator. One of the important problems for the proposed network is the unstable and inefficient transmission of data packets caused by node congestion and link breakage in the routing process. Based on these, we propose a Q -learning-based dynamic routing selection (QDRS) algorithm. First, a mathematical model of path optimization and a solution named Global Routing selection with high Credibility and Stability (GRCS) is proposed to select the optimal path globally. However, during the data transmission through the optimal path, the node and link status may change, causing packet loss or retransmission. This is a problem not considered by standard routing algorithms. Therefore, this paper proposes a local link dynamic adjustment scheme based on GRCS, using the Q -learning algorithm to select the optimal next-hop node for each intermediate forwarding node. If the selected node is not the same as the original path, the chosen node replaces the downstream node in the original path and so corrects the optimal path in time. This paper considers the congestion state, remaining energy, and mobility of the node when selecting the path and considers the network state changes during packet transmission, which is the most significant innovation of this paper. The simulation results show that compared with other similar algorithms, the proposed algorithm can significantly improve the packet forwarding rate without seriously affecting the network energy consumption and delay.

Prediction-Based End-to-End Dynamic Network Slicing in Hybrid Elastic Fiber-Wireless Networks

In recent years, hybrid fiber-wireless (Fi-Wi) networks that integrate optical network technology and wireless communication technology have received widespread attention. The technical integration in Fi-Wi networks provides users with the possibility to establish high-quality connections that guarantee flexibility and economy. However, there are heterogeneous structures and resource types between the subnets of the Fi-Wi network. This article considers the segmented application of network slicing technology in the optical backbone and hybrid access parts of Fi-Wi networks. In this way, the seamless connection of wired and wireless networks can be realized, and meanwhile adapt to the multiple network service forms and differentiated service quality requirements introduced in the 5G network era. In this article, we design a dynamic slicing (PDSM) scheme based on traffic prediction. This scheme includes all four procedures of service provisioning, which are network slice creation, traffic prediction, online reconfiguration of network slices and intra-slice routing, and resource allocation based on the prediction results. It is more flexible than the traditional static slicing scheme in the scenario, where the network state changes dynamically. We develop a prediction-based dynamic slicing algorithm (PD-MSRA) with intra-slice multi-path routing and shared resource allocation for the entire workflow of PDSM. We evaluate the performance of PD-MSRA on a variety of network topologies, and also implement several comparison algorithms including static slicing strategies. Simulation results show that the PDSM scheme using PD-MSRA achieves low bandwidth blocking rate and the optimization of resource utilization.

Optimal valid path prediction method for inter-domain networks considering commercial relationships*

In the security research of large-scale inter-domain networks, it is often necessary to consider network state changes under the condition of failure or attack. In this case, the connection between ASes often switches between disconnection and recovery frequently, requiring frequent prediction of the AS-level path. Previous AS-level path prediction methods generally require detailed network information data and are complicated to calculate, which is difficult to meet the needs of such large-scale dynamic computation. In this paper, an optimal valid path prediction method for inter-domain networks considering commercial relations is proposed. This method predicts the optimal valid path according to the commercial relationships between ASes, which requires less network information data and low computational complexity. Through experiments, it is found that compared with the shortest path method commonly used in the security simulation of large-scale inter-domain networks, the prediction results are closer to the actual path of the network and can better reflect the actual path situation of the inter-domain network with lower computational complexity.

Exploring Edge Computing for Multitier Industrial Control

Industrial automation traditionally relies on local controllers implemented on microcontrollers or programmable logic controllers. With the emergence of edge computing, however, industrial automation evolves into a distributed two-tier computing architecture comprising local controllers and edge servers that communicate over wireless networks. Compared to local controllers, edge servers provide larger computing capacity at the cost of data loss over wireless networks. This article presents switching multitier control (SMC) to exploit edge computing for industrial control. SMC dynamically optimizes control performance by switching between local and edge controllers in response to changing network conditions. SMC employs a data-driven approach to derive switching policies based on classification models trained based on simulations while guaranteeing system stability based on an extended Simplex approach tailored for two-tier platforms. To evaluate the performance of industrial control over edge computing platforms, we have developed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WCPS-EC</i> , a real-time hybrid simulator that integrates simulated plants, real computing platforms, and real or simulated wireless networks. In a case study of an industrial robotic control system, SMC significantly outperformed both a local controller and an edge controller in face of varying data loss in a wireless network.

World-Models for Bitrate Streaming

Adaptive bitrate (ABR) algorithms optimize the quality of streaming experiences for users in client-side video players, especially in unreliable or slow mobile networks. Several rule-based heuristic algorithms can achieve stable performance, but they sometimes fail to properly adapt to changing network conditions. Fluctuating bandwidth may cause algorithms to default to behavior that creates a negative experience for the user. ABR algorithms can be generated with reinforcement learning, a decision-making paradigm in which an agent learns to make optimal choices through interactions with an environment. Training reinforcement learning algorithms for bitrate streaming requires building a simulator for an agent to experience interactions quickly; training an agent in the real environment is infeasible due to the long step times in real environments. This project explores using supervised learning to construct a world-model, or a learned simulator, from recorded interactions. A reinforcement learning agent that is trained inside of the learned model, rather than a simulator, can outperform rule-based heuristics. Furthermore, agents that are trained inside the learned world-model can outperform model-free agents in low sample regimes. This work highlights the potential for world-models to quickly learn simulators, and to be used for generating optimal policies.

A Comprehensive Analysis of ACO for Wireless Sensor Network Routing

The proliferations of IoT technologies and applications have led to an increased interest in Wireless Sensor Networks (and in particular, multi-hop networks). Wireless sensor networks are composed of small mobile terminals which have limited system resources. Due to this, these networks are vulnerable to changes in network status arising from changes in the network parameters such as, position / layout of sensors, signal strength, environmental conditions, etc. In addition, the network nodes are also constrained in terms of energy provided by the battery. It is an significant consideration to be accounted so as to prolong their operational time, since this adds to the network lifetime. Lot of research has gone into routing and transmission technologies for wireless sensor networks. Conventional routing mechanisms for WSNs still suffer from energy-hole problem caused by difficulties in adaptive route management. Thus, it is imperative that efficient routing mechanisms be developed in order to conserve energy and improve network lifetime. One popular approach is to use meta-heuristic algorithms for optimal path selection in a WSN route management system. A very popular meta-heuristic algorithm used for this objective is the Ant Colony Optimization (ACO) algorithms. ACO has been used as a base for many routing management systems. In this paper an extensive analysis of the performance of ACO based route selection mechanism is reported and also reporting a comparative analysis of efficacy of the ACO routing algorithm over the standard Greedy algorithm in finding routes with different count of sensor nodes and different count of ants. Then find that the ACO routing algorithm outdoes the Greedy algorithm with respect to the number of routes identified.

A Novel High Performance Routing Algorithm for Mobile Multi-hop Tunneling Networks

By the increased number of mobile devices, the demand of high speed connection oriented tunneling networks also increases. But, in mobile tunneling networks, the full route will have to be reconstructed more frequently because of more frequent route failures, where the requirements for extra route reconstructions (RRC) will cause extra delay and a decreased system throughput. In this work, a novel routing algorithm, optimum path routing (OPR) is proposed to extend the route life time of mobile multi hop tunneling networks and to increase the system throughput. On the other hand, as the most recent works in the literature indicate that, one of the greatest challenges of tunneling networks is to provide the network flexibility by instant adaptation on changing network conditions to keep the delay at lowest levels even in case of high traffic loads or node failures. To solve the aforementioned flexibility problem of the tunneling networks, a novel algorithm applicable to all kinds of routing algorithms called “avoid congested nodes (ACN)” is proposed. The results show that, the OPR algorithm proposed to increase the route life has succeeded in decreasing the RRC delay by 84% and improving the throughput by 20.17% with respect to the Fastest Path routing algorithm in the literature. And the ACN algorithm, proposed to provide a rapid adaptation of the nodes to the changing traffic conditions, has also succeeded in carrying back the throughput of the network with traffic load from 75.5% of its best performance evaluated with no traffic to its 94%.

Structural Controllability Predicts Functional Patterns and Brain Stimulation Benefits Associated with Working Memory.

The brain is an inherently dynamic system, and much work has focused on the ability to modify neural activity through both local perturbations and changes in the function of global network ensembles. Network controllability is a recent concept in network neuroscience that purports to predict the influence of individual cortical sites on global network states and state changes, thereby creating a unifying account of local influences on global brain dynamics. While this notion is accepted in engineering science, it is subject to ongoing debates in neuroscience as empirical evidence linking network controllability to brain activity and human behavior remains scarce. Here, we present an integrated set of multimodal brain-behavior relationships derived from fMRI, diffusion tensor imaging, and online repetitive transcranial magnetic stimulation (rTMS) applied during an individually calibrated working memory task performed by individuals of both sexes. The modes describing the structural network system dynamics showed direct relationships to brain activity associated with task difficulty, with difficult-to-reach modes contributing to functional brain states in the hard task condition. Modal controllability (a measure quantifying the contribution of difficult-to-reach modes) at the stimulated site predicted both fMRI activations associated with increasing task difficulty and rTMS benefits on task performance. Furthermore, fMRI explained 64% of the variance between modal controllability and the working memory benefit associated with 5 Hz online rTMS. These results therefore provide evidence toward the functional validity of network control theory, and outline a clear technique for integrating structural network topology and functional activity to predict the influence of stimulation on subsequent behavior.SIGNIFICANCE STATEMENT The network controllability concept proposes that specific cortical nodes are able to steer the brain into certain physiological states. By applying external perturbation to these control nodes, it is theorized that brain stimulation is able to selectively target difficult-to-reach states, potentially aiding processing and improving performance on cognitive tasks. The current study used rTMS and fMRI during a working memory task to test this hypothesis. We demonstrate that network controllability correlates with fMRI modulation because of working memory load and with the behavioral improvements that result from a multivisit intervention using 5 Hz rTMS. This study demonstrates the validity of network controllability and offers a new targeting approach to improve efficacy.

Self-Aware Networks That Optimize Security, QoS, and Energy

The need to adaptively manage computer systems and networks so as to offer good Quality of Service (QoS) and Quality of Experience (QoE) with secure operation at relatively low levels of energy consumption is challenged by their sheer complexity and the wide variability of the workloads. A possible way forward is through self-awareness, whereby self-measurement and self-observation, together with on-line control mechanisms, operate adaptively to attain the required performance and QoE. We survey the premises for these ideas arising from cognitive science and active networks and review recent work on self-aware computer systems and networks, including those that propose the use of software-defined networks as a means to implement these concepts. Then we provide some examples from the literature on self-aware systems to illustrate the performance gains that they can provide. Finally, we detail an example system and its working algorithms to allow the reader to understand how such a system may be implemented. Measurements showing how it can react rapidly to changing network conditions regarding QoS and security are presented. Some conclusions and suggestions for further work are listed.

Adaptive distributed SDN controllers: Application to Content-Centric Delivery Networks

This paper aims to propose a deployment solution for Content-Centric Delivery Networks (C-CDN) based on the SDN technology in an IoT-like environment. To meet today’s network requirements in terms of reliability, scalability and performance and to match the specific IoT needs, the SDN architecture needs to be physically-distributed, but logically-centralized. The key idea is to consider the knowledge plane natively provided by the SDN controllers to build Information-Centric Networking (ICN) applications (e.g. Video distribution services). To achieve this, we put forward an adaptive and continuous consistency model for the distributed SDN controllers in large-scale deployments. More specifically, we propose to turn the standard replication technique into a scalable and intelligent replication strategy following Quorum-replicated consistency. That strategy uses the read and write Quorum parameters as adjustable control knobs for a fine-grained consistency level tuning. The main purpose is to find at run-time appropriate partial Quorum configurations that achieve, under changing network and workload conditions, balanced trade-offs between the SDN application’s continuous performance and consistency requirements. When compared to ONOS’s static consistency model, our approach which was implemented for a Content-Centric Delivery Network (C-CDN) application that we designed on top of ONOS, proved efficient in minimizing the application’s inter-controller overhead, while satisfying the SLA-style application requirements.

Near real-time optimization of fog service placement for responsive edge computing

In recent years, computing workloads have shifted from the cloud to the fog, and IoT devices are becoming powerful enough to run containerized services. While the combination of IoT devices and fog computing has many advantages, such as increased efficiency, reduced network traffic and better end user experience, the scale and volatility of the fog and edge also present new problems for service deployment scheduling.Fog and edge networks contain orders of magnitude more devices than cloud data centers, and they are often less stable and slower. Additionally, frequent changes in network topology and the number of connected devices are the norm in edge networks, rather than the exception as in cloud data centers.This article presents a service scheduling algorithm, labeled “Swirly”, for fog and edge networks containing hundreds of thousands of devices, which is capable of incorporating changes in network conditions and connected devices. The theoretical performance is explored, and a model of the behaviour and limits of fog nodes is constructed. An evaluation of Swirly is performed, showing that it is capable of managing service meshes for at least 300.000 devices in near real-time.

A Machine-Learning-Based Framework for Optimizing the Operation of Future Networks

5G and beyond are not only sophisticated and difficult to manage, but must also satisfy a wide range of stringent performance requirements and adapt quickly to changes in traffic and network state. Advances in machine learning and parallel computing underpin new powerful tools that have the potential to tackle these complex challenges. In this article, we develop a general machinelearning- based framework that leverages artificial intelligence to forecast future traffic demands and characterize traffic features. This makes it possible to exploit such traffic insights to improve the performance of critical network control mechanisms, such as load balancing, routing, and scheduling. In contrast to prior works that design problem-specific machine learning algorithms, our generic approach can be applied to different network functions, allowing reuse of existing control mechanisms with minimal modifications. We explain how our framework can orchestrate ML to improve two different network mechanisms. Further, we undertake validation by implementing one of these, mobile backhaul routing, using data collected by a major European operator and demonstrating a 3 reduction of the packet delay compared to traditional approaches.