Adaptive Spectrum Allocation for 5G Wireless Communication Scenarios

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%.

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.

Wireless devices operating under IEEE 802.11 x standards have the ability to transmit at various data-rates. However, there is no specification in the standard for dynamically selecting a suitable value of data-rate. Numerous solutions are available, mainly from device manufacturers and research community; focusing on a particular algorithm and its suitability for the overall system. However, none of the previous designs were flexible to change their behavior with changes in traffic nature and pattern. Not all traffic types have the same requirements and so should be the behavior of underlying algorithms for data-rate control. This paper highlights various data-rate control algorithms; classifying them on the basis of several parameters and discusses their suitability for different traffic patterns. It also proposes an algorithm, specifically designed for real time services. The cross-layer framework monitors traffic nature and patterns at the transport layer and activates one of three available rate control algorithms which suits the requirements of a particular type of traffic. According to the specific design of a selected algorithm, it then selects data-rate values at runtime. The results verify the tremendous difference among the three algorithms in terms of throughput and delay and signify their usability for specific traffic types.

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.

Although cellular wireless telecommunication is now available worldwide, it still constitutes only a small percentage of overall telecommunications traffic. Even in advanced countries such as the US, in comparison to wireline, at year-end ’96, wireless service still remained somewhat elitist with no more than 16 percent of the population having cellular phones and — more tellingly — less than three percent of the total call traffic volume being carried by these phones. As various national governments make new spectrum available for wireless PCS service, and existing cellular service transforms itself into PCS, the public is demanding ubiquitous, affordable, high-quality wireless service. PCS will be defined not only by its new digital technology or advanced service capabilities, but also by its universal affordability. This paper describes some of the strategies new PCS service-providers are adopting to meet this challenge. While the penetration of wireless telecommunication in any market depends on such factors as terminal cost, per-capita income, quality of service, and cultural preferences, a major driver for achieving the goal of ubiquitous service is pricing. We analyze the trends in the US and Israel markets using principles of marketing science to illustrate this point. At year-end 1996, in the US, wireless cost 17 times more than wireline service. Using the theory of diffusion of consumer durables, we project that, at this price, the potential for wireless in US is only 93 million subscribers (34 percent penetration, 52 percent relative to wireline teledensity), around 90 minutes of use (MoU) (5 percent relative to wireline) and $49 revenue per subscriber per month. In contrast, in Israel, the new PCS operator Cellcom has introduced wireless service at only three times the cost of wireline. Our analysis of the Israel data indicates that, with this revolutionary pricing strategy, Israel can reach a wireless penetration of 100 percent relative to wireline teledensity, and an average subscriber usage of 60 percent relative to wireline. Based on the Israel experience, we infer that, in the US market, a similar pricing strategy will yield 180 million subscribers, 600 MoU and $54 revenue per subscriber per month. While a lower price would help PCS realize the goal of ubiquitous service, it would also result in a highly competitive, low-revenue per-minute-of-use environment for the operator. So to succeed in such an environment, operators such as Cellcom are restructuring their businesses in fundamental ways. As revenue per-MoU falls, operators must make similar reductions in expenditure on marketing, network infrastructure and operations. In the rest of the paper, we discuss changes in traffic patterns, new marketing strategies, wireless digital technologies, advanced planning techniques, and innovative network management systems that are some of the key enablers of this transformation. In 1997, several new PCS operators have entered the market in the US, causing a lowering of tariffs, but there is yet no indication of as dramatic a reduction as experienced in Israel. In this paper, we also describe the pricing strategies currently being employed by US PCS operators and the impact they have had on subscriber penetration thus far.

Traffic growth and changes in traffic patterns have caused increasing congestion and delay in the National Airspace System (NAS). A national traffic flow management (TFM) strategy that reduces both congestion and delay could potentially yield the dual benefit of increasing safety and decreasing cost. An operations research (OR) optimization model called the Time Assignment Model has been formulated to generate such strategies. This article presents this model, the TFM problems that it was used to solve, computational results, performance of alternative solutions, and plans for future research. © 1994 John Wiley & Sons, Inc.

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.

This paper proposes a methodology for reconfiguring a wavelength division multiplexed (WDM) optical network to adapt to changing traffic requirements at the ATM layer. The ATM network layer is reconfigured to adapt to changes in traffic patterns. These changing ATM network topologies or connectivities are known and are used to design the network. The proposed method sizes the ATM switches and then assigns wavelengths between pairs of ports so as to support the required ATM network topologies and their reconfiguration in a hitless manner.

JUE insight: Demand for transportation and spatial pattern of economic activity during the pandemic

This paper's primary goal is to examine the detection and mechanism of ARP spoofing. One may argue that the Address Resolution Protocol, or ARP for simple terms, is crucial to computer science and forensics. ARP spoofing is one of the various computers hacking techniques used nowadays by individuals to transmit phony ARP packets across a Local Area Network (LAN). Such attacks might lead to changes in traffic patterns or, worse yet, a temporary or permanent stoppage of traffic. Even though this attack is only possible on networks with Address Resolution Protocols, ARP spoofing can be a precursor to more dangerous assaults that have the potential to do considerably more harm. An attacker seeking to launch this sort of attack will search for the Address Resolution Protocol's vulnerabilities. He may, for instance, be trying to take advantage of flaws like the message's inability to properly verify the sender. Because of this, hackers may find it very simple to alter or steal users' data. Because ARP spoofing poses a genuine risk to the security of every user on the network, all appropriate precautions must be taken to minimize harm.

The two papers that follow report the results of painstaking epidemiological work on pedestrian accidents involving children. Because these papers relate the age of the child to specific behavior patterns that result in accidents and to specific injury patterns that result from such accidents, they would appear to offer a sound empirical base for the development of a variety of countermeasures, whether such countermeasures are intended to reduce accidents or to limit the severity of the resulting injuries. Unfortunately, however, the ability to generalize such data is extremely limited. A given pattern of child-pedestrian behavior is the result of a highly complex set of variables: the social class and ethnicity of the child, the ecological characteristics of the neighborhood and the broader community, the characteristics of the traffic pattern and the types and density of the vehicles that make it up, the current enforcement policy, climatic conditions, road and highway characteristics, and a host of other conditions that combine and interact in various ways. Consequently, few localities are sufficiently similar to justify the application of data from one to another. The injury patterns described by Ryan, for example, are those produced by Australian vehicles on children walking, playing, and cycling in a specific Australian city. It seems quite unlikely that the injury patterns produced in an American urban environment would resemble those that Ryan reports. Indeed, data from another Australian city might show striking differences. For the same reasons, such data as Read presents cannot be used reliably as a before-and-after measure to assess the effectiveness of a specific countermeasure. Any significant changes in the data after the introduction of a countermeasure might well be attributable not to the countermeasure itself but to changes in traffic patterns, ecology, the weather, or other events which operated to reinforce or counteract the countermeasure in question. The overwhelming obstacle to systematic research in vehicular accidents and on the assessment of countermeasure effectiveness is the investigator's inability to control the numerous variables that affect both the incidence and the consequences of accidents. The present papers, despite their inherently interesting data, are seriously limited by this problem.

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.

The aviation industry contributes to global warming by releasing CO2 and non-CO2 species into the atmosphere. The climate impacts of non-CO2 emissions have been claimed to be two times higher than the effects of CO2 alone [1]. Unlike CO2 emission, the climate impacts of non-CO2 species highly depend on geographical location, altitude, and time of the emissions. Thus, performing more efficient maneuvers to avoid climate hotspots can potentially mitigate their associated climate effects. So far, several studies have been conducted on micro-scale climate optimal aircraft trajectory planning (i.e., trajectory level) [2]. However, generating a climatically optimal flight plan for each aircraft is not the ultimate solution to this problem when it comes to global traffic scenarios.&amp;#160;&amp;#160;&amp;#160;&amp;#160; Besides increasing the operating costs as the aircraft fly longer routes ( mainly due to the tendency to avoid climate-sensitive regions), the climate-optimal trajectories also alter the traffic pattern by increasing the congestion around climate hotspots, which may have negative implications, including, but not limited to, high traffic density, increased workload, complexity, and conflicts. Therefore, the evolution toward an environmentally friendly trajectory planning framework required a holistic perspective on the consequences of adopting climate-optimal routes at network scale. Nonetheless, in the literature, the problem of aircraft trajectory planning for the benefits of climate at a network scale is explored only in a free-routing airspace, considering a regional scenario (i.e., only Spain airspace), and constant altitude for trajectory optimization [3].&amp;#160;&amp;#160;&amp;#160; In this study, we aim to explore this problem considering a real large-scale scenario including &amp;#8776; 6000 flights on December 20th, 2018, from 12:00 to 16:00 over European airspace. The flight information, including the time and altitude of the first crossed waypoint within the considered time interval, has been extracted from the DDR2 dataset. For flights that start or land outside ECAC airports, we model only the segment of the flight that takes place within ECAC airspace. The algorithmic climate change functions proposed by [4] are employed to quantify the climate impact of each species, including contrails, and emissions of nitrogen oxides, CO2, and water vapor, in terms of average temperature response over the next 20 years. Our recently developed tool for climate-optimal aircraft trajectory planning, ROOST, is then used to optimize each trajectory 1 within the current structured airspace [5]. The effects of adopting climate optimized trajectories are assessed in terms of complexity, demand, and the number of conflicts. A performance map associated with each indicator is generated to spatially analyze the overall behavior of optimized trajectories and detect congested areas.&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160; For the considered scenario, the results indicate that by adopting trajectories with less climate impact, the complexity, demand, and conflicts are increased around climate hotspots. This trend is mainly due to the tendency to avoid climate-sensitive regions. In order to mitigate such changes in traffic patterns, an efficient resolution strategy is needed to find the optimal mechanisms to manage the ATM system from a climatic perspective.&amp;#160;&amp;#160;