User Density Research Articles

Coalitional Game-Based User Association Integrated With Open Loop Power Control for Green Communications in Uplink HCNs

In heterogeneous cellular networks (HCNs), the energy reduction has a great impact on the operational cost and global carbon footprint, which has attracted more and more attention. To enhance the network energy efficiency (EE), we design two types of association mechanisms integrated with open loop power control (OLPC) for uplink code-division-multiple-access (CDMA) enabled HCNs. Specifically, they are formulated into two optimization problems that maximize the sum of users’ EEs and the one of users’ EE utilities. It is noteworthy that the one of these two mechanisms guarantees the user fairness by employing a logarithmic function. Considering that these formulated problems are in a fractional mixed-integer form and hard to tackle, we utilize a coalitional game to design two types of association algorithms with guaranteed convergence. To show the effectiveness of designed algorithms, another one is introduced for comparison in the simulation. As for them, we mainly investigate the impacts of user and BS densities on the association performance.

Efficiency analysis of cellular/LiFi traffic offloading.

Data offloading is a promising low-cost and power-efficient solution for the expected high demands for high-speed connectivity in the near future. We investigate offloading efficiency in a cellular/light fidelity (LiFi) network. This offloading efficiency is a measure of the ratio of traffic carried by the LiFi network to the total traffic carried by both LiFi and cellular networks. We consider the two scenarios of opportunistic and delayed offloading. Effects of user density, user mobility, LiFi-signal blocking, and channel characteristics are investigated. We use Zemax to simulate LiFi channels in the proposed model. Based on our results, delayed offloading can achieve up to 60% offloading efficiency while opportunistic offloading achieves up to 18% offloading efficiency.

AI-Assisted Service Virtualization and Flow Management Framework for 6G-Enabled Cloud-Software-Defined Network-Based IoT

The sixth-generation (6G) communication technology provides a high level of interoperability through terahertz data transfer and latency-less service sharing. Due to its interoperable nature, the integration of heterogeneous networks, such as the Internet of Things (IoT) and cloud radio access networks (CRANs), is performed at ease. This integration is managed using software-defined networks (SDNs) for managing the Quality of Service (QoS) experience of the users, irrespective of the application. This manuscript proposes the service virtualization and flow management framework (SVFMF) for the reliable utilization of resources in the 6G-cloud environment. The imbalance in a service request and response due to overloaded and idle virtual resources is addressed in this framework. For this purpose, this framework endorses service virtualization and user allocation modules for mitigating the drawbacks of imbalanced service allocations. Linear decision making of the service virtualization process helps to reduce the computation and service discovery by identifying overloaded services and performing a reallocation. The purpose of user allocation is to distribute the service requests to the idle service providers to reduce the prolonged wait time of the increasing user requests. The performance of the proposed framework is verified using experimental analyses, for the metrics service discovery and computation time, service failure ratio, and flows. The reliability of SVFMF is proved by varying the density of users, virtual machines, service requests, and user allocation per virtual machine, respectively.

Information Dissemination Mechanism Based on Cloud Computing Cross-Media Public Opinion Network Environment

As an important expression of social public opinion, network public opinion develops rapidly with the popularization of the internet and then affects the real society. Therefore, the use of computer technology to study the network public opinion information transmission mechanism has strong practical significance. The purpose of this paper is to use cloud computing to realize the research of information dissemination mechanism in the context of cross-media public opinion network. Researched from three aspects of operator supervision, number of media, and user density, the hotspot propagation mechanism of Storm platform given in this paper can solve the efficiency problems of traditional algorithms while ensuring accuracy, improve efficiency, and lay the foundation for the research on the monitoring of Internet public opinion propagation.

Time-Efficient Indoor Navigation and Evacuation With Fastest Path Planning Based on Internet of Things Technologies

In this paper, we propose a time-efficient indoor navigation and evacuation (TINE) framework to minimize moving time for mobile users based on Internet of Things (IoT) technologies. In normal time, the proposed TINE framework can estimate the density of mobile users in each area and determine the moving speeds to pass through different areas. Based on the determined moving speed of each area, an indoor navigation path can be planned to provide the shortest moving time for a mobile user. In emergent time, TINE can accurately estimate the escaping time for groups of mobile users by jointly considering the length and moving time of passageways, the capacity of passageways/doors/exits, the present distribution and parallel moving of mobile users, and the possible congestion caused by other groups. Based on the estimated escaping time, TINE can efficiently alleviate the congestion of all passageways/exits and evenly distribute the evacuation load among exits to minimize the total escaping time. According to our review of relevant research, this is the first solution that can both provide the fastest navigation path to arbitrary target places and evacuate all groups of mobile users to safe places in the shortest escaping time. Simulation results show that TINE outperforms existing schemes and can significantly reduce the total walking and escaping times of indoor navigation and evacuation, respectively. In particular, an Android-based prototype with iBeacon IoT localization is implemented to verify the feasibility of our TINE system.

An EDA Analysis of Seoul Metropolitan Area’s Mountain Usage Patterns of Users in Their 20~30s after COVID-19 Occurrence

Background and objective: The purpose of this study was to comprehensively analyze the user behavior in order to cope appropriately with the increasing demand for mountain usage of those in their 20s and 30s and to allocate resources efficiently. Methods: To analyze the behavior of mountain hiking users, an exploratory data analysis (EDA) was conducted on the data which had been collected in the app Tranggle. The main target are users in their 20s and 30s who visited the mountains in the metropolitan area in 2019-2020. Among them, we have selected data on the top 13 mountains based on the frequency of visits. After data pre-processing, mountain usage patterns were analyzed through statistical analysis and visualization. Results: Compared to 2019, the number of users in 2020 increased 1.36 times. The utilization rate of the well-established hiking trails has also increased. The usage of mountain on weekends (Saturday > Sunday) was still the highest, and the difference in the usage between the days of the week decreased. Outside of work hours, early morning usage has increased and night-time usage has decreased. There was no significant change in usages depending on activity type, level (experience point) and exercise properties. Conclusion: Since the COVID-19 outbreak, the usage of mountains has been changing towards low user density and short-distance trip. in the post-COVID-19 era, the function and role of forests in daily life are expected to increase. To cope with this, further research needs to be carried out with consideration of the wider demographic and social characteristics. © 2021 by the Society for People, Plants, and Environment.

Multi-criteria handover management using entropy‐based SAW method for SDN-based 5G small cells

The high data traffic requirements of the new generation 5G networks will be satisfied with effective and efficient mobility and handover management. However, dense or ultra-dense small cell (eNB) placements in 5G networks may lead to some problems, such as latency, handover failures, frequent handover, ping-pong effect, etc. In this study, we proposed an Entropy-based simple additive weighting decision-making method for multi-criteria handover in software-defined networking (SDN) based 5G small cells for the solution of the aforementioned problems. This method provides the connection of the mobile node to the most suitable eNB using bandwidth, user density and SINR parameters. The proposed handover method is compared with conventional LTE handover and distributed approach in terms of delay, block ratio, handover failure and throughput according to the varying number of mobile users. The scalability of handovers for both approaches according to the user number are also analysed. According to the simulation results, the proposed approach achieved 15%, 48% and 22% improvement in handover delay, blocking probability and throughput, respectively, compared to the conventional LTE handover.

Intelligent Physical Layer Secure Relay Selection for Wireless Cooperative Networks with Multiple Eavesdroppers

The modern and emerging wireless communication technologies are benefiting from relaying techniques, which is already adopted in long term evolution (LTE) Release 10. The relay technology has great potential to improve the communication distance and system capabilities. However, due to the use of short-range frequencies for communication and increasing user density, relay technology requires the integration of machine learning and physical layer security (PLS) to attain its full potential. Therefore, this paper presents the machine learning (ML) algorithms for relay selection (RS) to improve the physical layer security of a dual-hop non-regenerative wireless cooperative network. The secure RS is converted into a prediction and decision-making problem. To the best of our knowledge, this is the first attempt to study the secured RS as a prediction and decision-making problem. We propose two reinforcement-learning techniques, namely the physical layer secured Q-learning algorithm for RS (PQL-RS) and the modified physical layer secured Q-learning algorithm for RS (MPQL-RS) for different scenarios. Markov decision process (MDP) is used with the introduced algorithms to select a single relay from a group of relays and multiple eavesdroppers. RS without requiring the prior data and network model knowledge are the unique advantages of the proposed algorithms. The introduced algorithms are compared to the different RS schemes and simulation results show that the MPQL-RS can deliver the best performance and save the convergence time. Moreover, the secrecy performance of different modulation techniques is also analyzed for the proposed algorithms. It is shown that the secrecy of the system improves by 7% on average for every double in modulation order.

Investigation of the Scaling Formula Accuracy for Poly-energetic Kernel Calculation in 6 MV Photon Beam

Background: Dose distribution can be obtained from total energy released per unit mass (TERMA) and inhomogeneous energy deposition kernel (EDK) convolution. Since inhomogeneous EDK data is location-dependent, it is calculated by employing the density scaling method rather than Monte Carlo based user code EDKnrc. Objective: The present study aimed at investigating EDK scaling formula accuracy in the presence of lung and bone inhomogeneities.Material and Methods: In this theoretical-practical study, six EDKs datasets with lung and bone inhomogeneity in different radii were generated using EDKnrc user code and density scaling formula. Then the scaling method data and corresponding EDKnrc-generated ones were compared to enhance the calculations, and some correction factors for error reduction were also derived to create more consistency between these data. Results: The study has shown that the errors in the theoretical method for calculating inhomogeneous EDKs were significantly reduced based on the attenuation coefficient and ραrel parameter, with α equal to 1.2 and 0.8 for bone and lung voxels, respectively. Conclusion: Although the density scaling method has acceptable accuracy, the error values are significant at the location of lung or bone voxels. By using the mentioned correction factors, the calculation inaccuracy of heterogeneous EDKs can be reduced down to 5%. However, the lung heterogeneity results corrected by the method are not as good as the bone cases.

What influenced the vitality of the waterfront open space? A case study of Huangpu River in Shanghai, China

What influenced the vitality of the waterfront open space? A case study of Huangpu River in Shanghai, China

Beach carrying capacity management under Covid-19 era on the Basque Coast by means of automated coastal videometry

Beach carrying capacity management under Covid-19 era on the Basque Coast by means of automated coastal videometry

Extensive Edge Intelligence for Future Vehicular Networks in 6G

The 6th generation mobile network (6G) is expected to achieve a fully connected world. As a key enabling technology in 6G, edge intelligence (EI) combines artificial intelligence (AI) with mobile edge computing (MEC), further releasing the potential of edge-side data. However, edge resources from roadside units (RSUs) and base stations are insufficient to match the wide variety of services in future vehicular networks. Extensive EI with auxiliary edge resources from parked vehicles is therefore introduced in this article. To achieve the coordination between extensive edge resources and EI-assisted services for vehicular networks in 6G, we propose an intelligent service-ori-ented edge resource management architecture, which makes full use of knowledge extracted from a large amount of sensory data in vehicular networks. As a case study, a prediction-based edge resource activation method under this architecture is proposed, where resources of parked vehicles are activated based on the predicted user density. Simulation results demonstrate that our prediction model has high accuracy and the pre-diction-based resource activation approach can reduce deployment cost.

Selection of relays based on the classification of mobility‐type and localized network metrics in the Internet of Vehicles

Abstract The ever increasing amount of connected mobile devices and users in the Internet of vehicles and the fact bicycles, electric scooters and users' smartphones are also connected poses a real challenge in terms of ensuring quality of service. The constantly changing topology due to the high‐mobility of devices and users greatly impacts its stability and the connectivity of devices. Furthermore, to ensure safety of people in the case of autonomous vehicles it is of paramount importance to ensure excellent reliability. This is why we have developed a solution that is based on machine learning to classify devices according to their mobility profile and that uses a scoring system to select the best candidates to act as mobile relays amongst devices with a suitable mobility profile. The scoring system allows to find critical locations in terms of user density. This solution does not require a dedicated infrastructure such as road side units. Simulations results will show the proposed solution increases the packet delivery ratio by up to 6%, reduces the energy consumption by up to 30% and increases the efficiency of bandwidth usage without sacrificing the end delay of users and devices compared with the state‐of‐the‐art.

A Poisson Process-Based Random Access Channel for 5G and Beyond Networks

The 5th generation (5G) wireless networks propose to address a variety of usage scenarios, such as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC). Due to the exponential increase in the user equipment (UE) devices of wireless communication technologies, 5G and beyond networks (B5G) expect to support far higher user density and far lower latency than currently deployed cellular technologies, like long-term evolution-Advanced (LTE-A). However, one of the critical challenges for B5G is finding a clever way for various channel access mechanisms to maintain dense UE deployments. Random access channel (RACH) is a mandatory procedure for the UEs to connect with the evolved node B (eNB). The performance of the RACH directly affects the performance of the entire network. Currently, RACH uses a uniform distribution-based (UD) random access to prevent a possible network collision among multiple UEs attempting to access channel resources. However, in a UD-based channel access, every UE has an equal chance to choose a similar contention preamble close to the expected value, which causes an increase in the collision among the UEs. Therefore, in this paper, we propose a Poisson process-based RACH (2PRACH) alternative to a UD-based RACH. A Poisson process-based distribution, such as exponential distribution, disperses the random preambles between two bounds in a Poisson point method, where random variables occur continuously and independently with a constant parametric rate. In this way, our proposed 2PRACH approach distributes the UEs in a probability distribution of a parametric collection. Simulation results show that the shift of RACH from UD-based channel access to a Poisson process-based distribution enhances the reliability and lowers the network’s latency.

Rotated read head design for high-density heat-assisted shingled magnetic recording

In heat-assisted shingled magnetic recording, recorded tracks are erased on one side. The transitions are no longer symmetric relative to the track center, especially when the transitions are highly curved as a result of the temperature profile generated by the near-field transducer. To optimally utilize these asymmetrically curved transitions, the read head is rotated to match the curvature. For a single rotated head, a more than 10% improvement in user density is achieved compared to that of a single non-rotated head. We found that the optimal rotation angle generally follows the transition shape. With an array of two rotated heads, a track pitch of 15 nm, and a minimum bit length of 6.0 nm, the user areal density reaches 6.2 Tb/in2, more than 30% above previous projections for recording on granular media.

Inferring the trip purposes and uncovering spatio-temporal activity patterns from dockless shared bike dataset in Shenzhen, China

Inferring the trip purposes and uncovering spatio-temporal activity patterns from dockless shared bike dataset in Shenzhen, China

Joint Optimization of BS Clustering and Power Control for NOMA-Enabled CoMP Transmission in Dense Cellular Networks

Non-orthogonal multiple access (NOMA)-enabled coordinated multipoint (CoMP) transmission has great potential in balancing spectrum utilization and interference mitigation in dense cellular networks. However, NOMA reforms the spectrum sharing policy of CoMP transmission due to introducing additional interference among coordinated base stations (BSs), which deteriorates the CoMP transmission rate. In this paper, we investigate the joint optimization of BS clustering and power control for NOMA-enabled CoMP transmission in dense cellular networks to maximize system sum-rate. We first characterize the impact of interference among coordinated BSs on CoMP transmission rate and find that the BS clustering is dependent on NOMA user grouping and restricted by the NOMA decoding condition. We then derive a tight lower bound on the system sum-rate and exploit it to design a joint BS clustering and power control scheme. Specifically, a power control algorithm is designed by a penalty convex-concave procedure to satisfy the NOMA decoding condition and users’ rate requirements. A BS clustering algorithm based on successive convex approximation is designed to iteratively update the BS clustering and NOMA user grouping to increase system sum-rate. Finally, two algorithms are alternately performed until all users successfully access and the system sum-rate converges. Simulation results show that the proposed scheme can efficiently alleviate interference among coordinated BSs to improve system sum-rate and spectrum efficiency of NOMA-enabled CoMP transmission even under high user density.

How does the beach ecosystem change without tourists during COVID-19 lockdown?

Urban tourist beach ecosystems provide the essential service of recreation. These ecosystems also support critical ecological functions where biodiversity conservation is not usually a priority. The sudden lockdown due to the COVID-19 pandemic created a unique opportunity to evaluate the effects of human absence in these urban-coastal ecosystems. This study examined bioindicators from 29 urban tourist beaches in seven Latin-American countries and assesses their response to lockdown about some relevant anthropogenic stressors such as pollution, noise, human activities, and user density. The presence of animals and plants, as well as the intensity of stressors, were assessed through a standardized protocol during lockdown conditions. Additionally, the environmental conditions of the beaches before and during lockdown were qualitatively compared using multivariate non-parametric statistics. We found notable positive changes in biological components and a clear decrease in human stressors on almost all the beaches. Dune vegetation increased on most sites. Similarly, high burrow densities of ghost crabs were observed on beaches, except those where cleaning activity persisted. Because of the lockdown, there was an exceptionally low frequency of beach users, which in turn reduced litter, noise and unnatural odors. The observed patterns suggest that tourist beaches can be restored to natural settings relatively quickly. We propose several indicators to measure changes in beaches once lockdown is relaxed. Adequate conservation strategies will render the recreational service of tourist beaches more environmental-friendly.

Environmental and economic sustainability in public contexts: the impact of hand-drying options on waste management, carbon emissions and operating costs

Due to the high density of users hosted everyday, public buildings are important producers of waste and emissions of greenhouse gases (GHGs). Public restrooms play an underrated role in waste generation and GHG emissions, especially if paper towels are used as the hand-drying method. The choice of the hand-drying method (i.e., paper towels vs. electrical hand dryers) also affects the economic balance of a public institution, involving costs for the purchase of hand-drying materials/apparatuses, energy and waste disposal. The present paper aims at evaluating the economic and environmental impact of the introduction of electrical hand dryers (alternative scenario) in place of paper towels (reference scenario) in a public building. The paper presents a solid methodology, based on a numerical experiment approach, to identify a decision criterion for establishing the economical convenience of adopting the alternative scenario in public restrooms. Key factors affecting the choice between the proposed alternatives are presented and discussed in a dedicated sensitivity analysis. From the environmental point of view, this study evaluates the impact of each scenario in terms of GHG emissions, related to multiple waste treatment options and different electric grid mixes. Based on the experimental assumptions, the method allowed concluding that the alternative scenario becomes economically convenient when the number of daily usages (N) is > 57 ± 4. The environmental convenience of the alternative scenario is visible even at N < 10. The method here described can be successfully used to support strategic decisions for cost optimization and environmental mitigation in institutional buildings.

Measuring the impact of ICNIRP vs. stricter-than-ICNIRP exposure limits on QoS and EMF from cellular networks

Measuring the impact of ICNIRP vs. stricter-than-ICNIRP exposure limits on QoS and EMF from cellular networks