A Cross-Layer Solution for Dynamic Selection of Data-Rate in WLAN using Traffic Differentiation

The COVID-19 pandemic is recognized as one of the most serious global health problems, and many countries implemented lockdown measures to mitigate the effects of the crisis caused by this respiratory infectious disease. In this study, we investigated the relationship between social distancing policies and changes in traffic volume in Sinchon Station, South Korea. We used an official COVID-19 report provided by the Korea Disease Control and Prevention Agency (KCDA) and Seoul Metropolitan Government (SMG) to review social distancing policies, and the changes in traffic patterns before and during the COVID-19 pandemic between January 2020 and November 2021 were analyzed. Our study reveals that the changes in the overall traffic patterns from acceleration phases to deceleration phases of COVID-19 were related to the alert levels of social distancing policies implemented to tackle the situation resulting from the COVID-19 pandemic. Herein, we found that a significant decline in traffic volume took place from August to September 2020 (13.5–19.7%, weekday; 19.4–31.7%, weekend), from December 2020 to January 2021 (20.0%−26.6%, weekday; 26.8–34.0%, weekend), and from July to September 2021 (3.2–13.1%, weekday; 38.3–44.7%, weekend) when compared to the corresponding periods in 2019 (paired t-test; p < 0.001). The results of this study provide strong support for the effectiveness of Seoul’s preemptive measures, namely, the central government’s intensive social distancing campaign, in managing and reducing the impact of the pandemic situation based on the precise analysis of 10 types of facilities.

Although reinforcement learning (RL) shows advantages in cellular network load balancing, it suffers from a low generalization ability, preventing it from real-world applications. Specifically, if network traffic pattern changes, the learned RL policy cannot adapt accordingly, resulting in system performance degradation. To address this issue, we propose a Multi-teacher MOdel BAsed Reinforcement Learning algorithm (MOBA), which leverages multi-teacher knowledge distillation theory to learn a generalized load balancing policy for adapting the real-world traffic pattern changes. The key is that different teachers represent different traffic patterns, and can learn various system models. By distilling and transferring the teacher knowledge, the student network is able to learn a generalized system model that covers different traffic patterns and unseen situations. Moreover, to improve the robustness of multi-teacher knowledge transfer, we learn a set of student models and use an ensemble method to jointly predict system dynamics. Results show that, compared with state-of-the-art RL methods, MOBA improves the minimal throughput and total throughput of a cellular network by up to 28.6% and 23.2%. Results also show that MOBA improves the training efficiency by up to 64%.

Software DefinedNetworking (SDN) is considered as a promising approach in networking paradigm. It distinguishes the control plane of the network from the plane which is used for data forwarding. This technique not only helps in optimal resource utilization in the network, but it also simplifies the complexity in management of the network, reduces the operating cost of the network and also encourages the innovative and evolutionary ideas. An emerging way to achieve this requirement that optimizes a network and helps in the improvisation of the network robustness is traffic engineering. Traffic Engineering can reduce link failure and service degradation in the network. Research communities have worked a lot on methods for traditional networking structures, which allows the network to adapt to the changes in traffic patterns. This paper mainly focuses on the review of the various state of the art methods for traffic engineering for Software Defined Network. It mainly concentrates on the techniques for traditional network architecture and to study the challenges in the implementation and future scope of such methods.

Development of network cybersecurity technology needs IDS on network technology because modern networks face against dynamic attacks which display various attack methods. To address this problem, we present a different combination of a hybrid ideal solution for an adaptive intrusion detection framework that is incorporated with various analytical techniques approach to achieve accuracy and reliability in detecting threats. This proposed ideal design combines Random Forest based noise-handling classifier and recognizing structured and also static traffic behaviour, while the LSTM neural model helps to analyze traffic behavior by understanding temporal patterns. Security professionals can track network-based attack behavior through their ability to monitor traffic pattern changes which occur over time. The forecast detector system detects unusual network traffic patterns which the fusion model layer uses to create precise threat responses. The framework receives validation through testing with the CICIDS2017 is the dataset which contains various type of traffic patterns from both normal and attack activities. It performs comprehensive data preprocessing, feature selection and normalization to get better input data quality. The model becomes more adaptable through pseudo-labeling which enables it to analyze and learn from unclassified network traffic while utilizing this each set of information for subsequent training cycles to detect new traffic patterns. The system performs continuous network behavior monitoring through its clustering with retraining process. The method demonstrates that uniting statistical analysis with temporal data and adaptive learning into one system framework produces better intrusion detection results which also enable detecting intrusion for network at large scales.

Smallcell technology is gaining significance as part of the next-generation cellular systems due to their performance benefits in terms of increased network capacity and improved indoor and local coverage. Hybrid access smallcells, which provide service to both indoor as well as neighboring users, adopt adhoc policies to guarantee performance benefits to indoor home users in the presence of external neighboring users. Such policies must be able to stabilize user queues as well as to provision performance benefits in terms of delay and throughput, especially for the indoor users. As a result, classification of user data in terms of traffic type and user type is required to effectively achieve the differentiated QoS performance. In this paper, a traffic-aware utility function is proposed, which takes into account for the user's priority index and traffic characteristics to efficiently provide differentiated QoS benefits to users served under an OFDMA hybrid smallcell. The problem of the traffic-aware utility based scheduling under power constraints is posed as an optimization objective and an optimal algorithm for the scheduling problem is presented. The results show that the proposed scheme achieves QoS performance benefits in terms of throughput and delay.

In this paper we investigate the impact of asymmetric traffic patterns on the energy consumption and throughput in a wireless multi hop network. Network coding is a novel technique for communication systems and a viable solution for wireless multi hop networks. State of the art research is mainly focusing on ideal scenarios with symmetric traffic patterns that are not realistic in a real life scenario. The main contribution of this paper is the investigation of the asymmetric traffic patterns in terms of throughput and energy consumption, and a validation of these results by real measurements on commercial platforms. The outcome of this paper confirms the analytical expression, and the results shows that even with a large asymmetric data rate there is a gain in terms of energy consumption and throughput when network coding is applied in compare to the case when network coding is not applied.

A TCP/IP satellite infrastructure for sensing operations in emergency contexts

5G resources should be properly planned for users to have a good quality of service. This planning includes defining the most suitable numerology indexes and best spectrum allocation considering the requirements of current traffic, which may change over time. Furthermore, when accounting for changes in traffic pattern, any necessary reconfigurations should be minimized. Here in this article, an optimization model is developed for the planning of spectrum allocation to the best mix numerology. The model considers adapting the operating numerology mix according to the current presence (or not) of traffic requirements. The model also works under any wireless communication scenario in 5G, and under any traffic pattern.

BackgroundThe Democratic National Convention (DNC) in Boston, Massachusetts in 2004 provided an opportunity to evaluate the impacts of a localized and short-term but potentially significant change in traffic patterns on air quality, and to determine the optimal monitoring approach to address events of this nature. It was anticipated that the road closures associated with the DNC would both influence the overall air pollution level and the distribution of concentrations across the city, through shifts in traffic patterns.MethodsTo capture these effects, we placed passive nitrogen dioxide badges at 40 sites around metropolitan Boston before, during, and after the DNC, with the goal of capturing the array of hypothesized impacts. In addition, we continuously measured elemental carbon at three sites, and gathered continuous air pollution data from US EPA fixed-site monitors and traffic count data from the Massachusetts Highway Department.ResultsThere were significant reductions in traffic volume on the highway with closures north of Boston, with relatively little change along other highways, indicating a more isolated traffic reduction rather than an across-the-board decrease. For our nitrogen dioxide samples, while there was a relatively small change in mean concentrations, there was significant heterogeneity across sites, which corresponded with our a priori classifications of road segments. The median ratio of nitrogen dioxide concentrations during the DNC relative to non-DNC sampling periods was 0.58 at sites with hypothesized traffic reductions, versus 0.88 for sites with no changes hypothesized and 1.15 for sites with hypothesized traffic increases. Continuous monitors measured slightly lower concentrations of elemental carbon and nitrogen dioxide during road closure periods at monitors proximate to closed highway segments, but not for PM2.5 or further from major highways.ConclusionWe conclude that there was a small but measurable influence of DNC-related road closures on air quality patterns in the Boston area, and that a low-cost monitoring study combining passive badges for spatial heterogeneity and continuous monitors for temporal heterogeneity can provide useful insight for community air quality assessments.

This paper presents a low complexity macroblock (MB) level H.264/AVC rate control (RC) algorithm from system-level design viewpoint for video encoder hardware implantation. MB pipeline structure and data reuse efficiency are simultaneously take into consideration. In order to improve the data processing efficiency, hardware encoder usually adopts zigzag scanning MB encoding sequence order instead of raster one. The proposed low-cost RC algorithm resolves the problem of data dependency without increasing processing latency. Moreover, the proposed algorithm adopts a new MB level mean absolute difference (MAD) prediction method to improve the MAD prediction efficiency in G012 proposal. Linear prediction method in G012 resulting in lower complexity and memory requirements not only reduce the complexity of RC algorithm but also the memory buffer. Simulation results show only 32%, 68%, and 88% hardware cycles are desired for QCIF, CIF and D1 format video real-time coding respectively in the proposed RC algorithm compared with G012, while maintaining the same PSNR compared with G012.

Operating room (OR) human traffic has been implicated as a cause of surgical site infection. We first observed the normal human traffic pattern in our Pediatric Orthopedic ORs, and then examined the effect of surveillance on that traffic pattern. This study consisted of 2 phases: phase I sought to observe the OR traffic pattern (number of door swings, maximum and minimum number of OR personnel, number of OR personnel at 30-minute intervals, or changes in nursing, anesthesia, or surgeon staff) during surgical cases without OR personnel being notified, and for phase II, the same traffic pattern was monitored with their knowledge. Two thousand four hundred forty-two minutes of surgical time were observed in phase I, and 1908 minutes were observed in phase II. There was no difference (P=0.06) in the time between door swings between phase I (1.39 min) and phase II (1.70 min), no difference (P=1.000) in the maximum number of people in the OR between phase I (11.5 people, range: 7-15 people) and phase II (11.5 people, range: 8-20 people), and no difference (P=1.000) in the minimum number of people in the OR between phase I (4.67 people, range: 4-6 people) and phase II (4.71 people, range: 3-6 people). There was a difference in the time between door swings (P=0.03) and maximum number of people in the OR (P=0.005) based on the length of the surgery (less or more than 120 min). There was no difference in the time between door swings (P=0.11), but there was a difference in the maximum number of people in the OR (P=0.002) based on type of surgery (spine vs. others). There was no role of surveillance of human traffic in the OR. To achieve any change in the OR traffic pattern, monitoring alone may not be sufficient; other novel techniques or incentives may need to be considered.

Neural networks may be useful alternatives for statistical classification techniques when the available data is incomplete. This paper discusses the results obtained from a statistical technique called hierarchical grouping and the Kohonen neural network for classifying traffic patterns. The Kohonen neural network is shown to be a reasonable approximation of the hierarchical-grouping technique. It is suggested that hierarchical grouping of a small sample of typical traffic patterns may be a useful first step in setting up a Kohonen neural network for traffic-pattern classification. The Kohonen neural network can be used for classifying a large number of complete as well as incomplete traffic patterns as they become available. The neural network can also adapt the classification process to the change in the typical traffic patterns over time.

S.1719-23

In this paper we develop a distributed rate control algorithm for multiple-unicast-sessions when network coding is allowed. Building on our recent flow-based characterization of network coding, we formulate the problem as a convex optimization problem. The formulation exploits pairwise coding possibilities between any pair of sessions, where the objective function is the sum of the utilities based on the rates supported by each session. With some manipulation on the Lagrangian of the formulated problem, a distributed algorithm is developed with no interaction between intermediate nodes, and each source having the freedom to choose its own utility function. The only information required by the source is the weighted sum of the queue length updates of each link, which can be piggy-backed on the acknowledgment messages. In addition to the optimal rate control algorithm, we propose a decentralized pairwise random coding scheme (PRC) that is optimal when a sufficiently large finite field is used for network coding. The convergence of the rate control algorithm is proved analytically and verified by extensive simulations. Simulations also demonstrate the advantage of our algorithm over the state-of-the-art in terms of throughput and fairness.

Network coding-based approach for efficient video streaming over MANET