Current Deployment Research Articles

A feasibility study of 5G positioning with current cellular network deployment

This research examines the feasibility of using synchronization signals broadcasted by currently deployed fifth generation (5G) cellular networks to determine the position of a static receiver. The main focus lies on the analysis of synchronization among the base stations of a real 5G network in Milan, Italy, as this has a major impact on the accuracy of localization based on time of arrival measurements. Understanding such properties, indeed, is fundamental to characterize the clock drifts and implement compensation strategies as well as to identify the direct communication beam. The paper shows how the clock errors, i.e., inaccurate synchronization, among 5G base stations exhibit a significant bias, which is detrimental for precise cellular positioning. By compensating the synchronization errors of devices’ clocks, we demonstrate that it is in principle possible to localize a static user with an accuracy of approximately 8–10 m in non-obstructed visibility conditions, for urban and rural scenarios, using the deployed 5G network operating at 3.68 GHz and relying on broadcast signals as defined by 5G Release 15 standard. This work has been funded by the European Space Agency (ESA) Navigation Innovation and Support Program (NAVISP) Element 2 pillar which aims at improving the competitiveness of the industry of the participating States in the global Positioning, Navigation and Timing (PNT) market.

Forecast-Assisted Service Function Chain Dynamic Deployment for SDN/NFV-Enabled Cloud Management Systems

Software-defined network (SDN) and network function virtualization (NFV) are acknowledged as the most promising technologies to effectively allocate resource for network service. A service function chain (SFC), which can deploy virtualized network functions (VNFs) and chain them with associated flows allocation, can be used to represent each network service owing to the introduction of the SDN/NFV technology. Co-hosted applications on multiple Internet of Things terminals have dynamic and time-varying service requirements, in order to allocate network resources optimally and meet the end-to-end delay requirements of services, sufficient strategies are required to satisfy the continuously changing service demands. In this article, a deep learning model that combines the multitask regression layer above the graph neural networks is first presented to predict the future resource requirements of each VNF instance. The SFC deployment problem is then solved using the integer nonlinear programming (INLP) approach, and a novel prediction-assisted Viterbi algorithm is presented to overcome the scalability problem of the INLP approach. According to the simulation findings, the proposed deep model provides a minimum of a 6.2% improvement in prediction accuracy over baseline prediction models, and the proposed SFC deployment strategy has been demonstrated to deliver better performance in terms of acceptance ratio and revenue, compared to the current passive deployment algorithms.

Exploring LoRaWAN Traffic: In-Depth Analysis of IoT Network Communications.

In the past decade, Long-Range Wire-Area Network (LoRaWAN) has emerged as one of the most widely adopted Low Power Wide Area Network (LPWAN) standards. Significant efforts have been devoted to optimizing the operation of this network. However, research in this domain heavily relies on simulations and demands high-quality real-world traffic data. To address this need, we monitored and analyzed LoRaWAN traffic in four European cities, making the obtained data and post-processing scripts publicly available. For monitoring purposes, we developed an open-source sniffer capable of capturing all LoRaWAN communication within the EU868 band. Our analysis discovered significant issues in current LoRaWAN deployments, including violations of fundamental security principles, such as the use of default and exposed encryption keys, potential breaches of spectrum regulations including duty cycle violations, SyncWord issues, and misaligned Class-B beacons. This misalignment can render Class-B unusable, as the beacons cannot be validated. Furthermore, we enhanced Wireshark's LoRaWAN protocol dissector to accurately decode recorded traffic. Additionally, we proposed the passive reception of Class-B beacons as an alternative timebase source for devices operating within LoRaWAN coverage under the assumption that the issue of misaligned beacons can be addressed or mitigated in the future. The identified issues and the published dataset can serve as valuable resources for researchers simulating real-world traffic and for the LoRaWAN Alliance to enhance the standard to facilitate more reliable Class-B communication.

Technological sustainable materials and enabling in semiconductor memory industry: A review

This paper provides an overview of current and future deployment of recyclable direct materials and sustainable materials through recycling of interconnect metals used in semiconductor assembly and packaging. Also, with the aim to study the key technical barriers, challenges of deployment and its reliability performance of impacts of sustainable materials used in semiconductor device packaging, this paper lays out the importance of noble metals and polymeric materials in terms of its materials sustainability for the next decade. This article briefly reviews key aspects of recycled materials reliability concerns, deployment of recycled gold (Au) wire, recycled copper (Cu), and other noble metals in semiconductor packaging and assembly. Frontend chip fabrication sustainability issues and their corresponding countermeasures are covered in this review article as well.

In vitro exposure of neuronal networks to the 5G-3.5 GHz signal.

The current deployment of the fifth generation (5G) of wireless communications raises new questions about the potential health effects of exposure to radiofrequency (RF) fields. So far, most of the established biological effects of RF have been known to be caused by heating. We previously reported inhibition of the spontaneous electrical activity of neuronal networks in vitro when exposed to 1.8 GHz signals at specific absorption rates (SAR) well above the guidelines. The present study aimed to assess the effects of RF fields at 3.5 GHz, one of the frequencies related to 5G, on neuronal activity in-vitro. Potential differences in the effects elicited by continuous-wave (CW) and 5G-modulated signals were also investigated. Spontaneous activity of neuronal cultures from embryonic cortices was recorded using 60-electrode multi-electrode arrays (MEAs) between 17 and 27 days in vitro. The neuronal cultures were subjected to 15 min RF exposures at SAR of 1, 3, and 28 W/kg. At SAR close to the guidelines (1 and 3 W/kg), we found no conclusive evidence that 3.5 GHz RF exposure impacts the activity of neurons in vitro. On the contrary, CW and 5G-modulated signals elicited a clear decrease in bursting and total firing rates during RF exposure at high SAR levels (28 W/kg). Our experimental findings extend our previous results, showing that RF, at 1.8 to 3.5 GHz, inhibits the electrical activity of neurons in vitro at levels above environmental standards.

Optimization of Roadside Unit Deployment on Highways under the Evolution of Intelligent Connected-Vehicle Permeability

With the increasing number of Connected and Autonomous Vehicles (CAVs), the heterogeneous traffic flow on highways now consists of a mix of CAVs and Non-networked Autonomous Vehicles (NAVs). The current deployment of Roadside Units (RSUs) on highways is mostly based on uniform or hotspot locations. However, when the permeability of CAVs on the road varies, the communication network may face challenges such as excessive energy consumption due to closely spaced RSU deployments at high CAV permeability or communication interruptions due to widely spaced RSU deployments at low CAV permeability. To address this issue, this paper proposes an improved D-LEACH clustering algorithm based on vehicle clustering; analyzes the impact of RSU and vehicle communication radius, mixed traffic density, and different CAV permeabilities in the heterogeneous traffic flow on the RSU deployment interval; and calculates the rational and effective RSU deployment interval schemes under different CAV permeabilities on highways in the heterogeneous traffic flow. When the heterogeneous traffic flow density is stable and CAV continues to penetrate, the RSU communication radius and deployment interval can be adjusted to ensure that the network connectivity is maintained at a high level. When the RSU and vehicle communication radius are stable, the mixed traffic density is 0.05, and the CAV permeability is 0.2, the RSU deployment interval can be set to 1235 m; when the mixed traffic density is 0.08 and the CAV penetration rate is 0.7, the RSU deployment interval can be set to 1669 m to ensure that the network connectivity is maintained at a high level.

(Invited) Current Status of SOFC Deployment and Technology Developments in Korea

The current deployment of stationary fuel cells in Korea is about 880 MW including the 220 MW SOFC systems, About 40 MW in 2021 and 50 MW in 2022 were installed respectively. In Korea, the fuel cell deployment is accelerated by the mandatory supply and the promotion regulations driven by government. Several Korean companies have developed the SOFC and SOEC technologies under the national program, and major projects are the development of a 200 kW SOFC and a 20 kW SOEC systems. The 2~8kW class SOFC products have been developed already and are in deployment. The Korean government enacted the Hydrogen Law in 2020, and under this law, development and deployment of hydrogen and fuel cell-related technologies are in progress. The achievements of the SOFC R&D and deployment, and national hydrogen roadmap in Korea are introduced in more detail.

Deep neural networks in the cloud: Review, applications, challenges and research directions

Deep neural networks (DNNs) are currently being deployed as machine learning technology in a wide range of important real-world applications. DNNs consist of a huge number of parameters that require millions of floating-point operations (FLOPs) to be executed both in learning and prediction modes. A more effective method is to implement DNNs in a cloud computing system equipped with centralized servers and data storage sub-systems with high-speed and high-performance computing capabilities. This paper presents an up-to-date survey on current state-of-the-art deployed DNNs for cloud computing. Various DNN complexities associated with different architectures are presented and discussed alongside the necessities of using cloud computing. We also present an extensive overview of different cloud computing platforms for the deployment of DNNs and discuss them in detail. Moreover, DNN applications already deployed in cloud computing systems are reviewed to demonstrate the advantages of using cloud computing for DNNs. The paper emphasizes the challenges of deploying DNNs in cloud computing systems and provides guidance on enhancing current and new deployments.

The global status of carbon capture and storage: ambition to action

As the global response to climate change advances from ambition to action, the past few years have witnessed unprecedented levels of investments in carbon capture and storage (CCS), with the pipeline of commercial facilities continuing to grow as the technology becomes increasingly competitive and commercial in many countries. Key trends underpinning this momentum include the rise of the network project model enabling the use of shared infrastructure, the incorporation of CCS in private sector investment strategies, ESG (environmental, social and governance) considerations, green taxonomies and the industrial-scale production of hydrogen to meet demand. With CCS receiving increased awareness in international climate policy negotiations, a renewed focus on developing supportive policy, legal and regulatory mechanisms by national governments are also driving CCS deployment. Established leaders and early-movers in CCS-relevant policy, such as the United States, European Union and the UK, continue to maintain or strengthen their positions by allocating significant levels of funding for tax credits, subsidies and grants. Similarly, countries in the Asia-Pacific region, notably China, Indonesia, Malaysia and Thailand, are also advancing policies to incentivise deployment, including through the establishment of CCS-specific legal and regulatory frameworks. The outlook for CCS is optimistic, however, current deployment rates fall far short of what is required to keep global warming below 1.5 or even 2 degrees. Further action is needed to unlock the full potential of CCS and realise the Paris climate goals.

The Effect of Applying an Online Learning System on Islamic Religious Education and the Characteristics of Learning Media Use

The current deployment of the online learning system is novel for educators and students, and it starkly contrasts the more traditional face-to-face model of education. This research aims to ascertain if and how an online learning system influences the utilisation of learning media in the context of Islamic theological and moral instruction. The study takes a quantitative, survey-based approach; data were gathered using questionnaires, records, and relevant literature reviews; descriptive statistics were used to analyse the results. The application of the online learning system has a significant effect on Islamic religious education subjects and character education through the use of learning media, as determined by the product-moment correlation test, the correlation test of determination, and the significance test. The following is the description: The Rcount value of 0.61 is greater than the Rtable value of 0.294 with a significant level of 5% in the strength of influence test, indicating that Rcount Rtable (0.61 0.294) means Ha is accepted and H0 is rejected. While the coefficient of determination test results is 37.21%, the significant correlation test results are 0.43, indicating a moderate correlation coefficient.

Optimization of Fire Brigade Deployment by Means of Mathematical Programming

The content of this research paper is focused on the applications of selected operation research methods in the field of rescue services. The first theoretical part aims at identifying the most important aspects of the real system, which should be taken into account whenever a rescue system is to be redesigned or optimized. It also contains an in-depth description of system performance. Based on the found criteria, a mathematical model for a new system design is proposed. The second part of this paper is practically oriented. A short case study performed with real data on the rescue service system in Slovakia is reported herein, and the obtained results are compared with current fire brigade deployment. We concentrate on improving the most important criteria of the real system. If the suggested mathematical model proves to be hard to solve with common optimization tools, we will develop an efficient solving algorithm. From a general point of view, the main scientific goal of this research article is to summarize current trends in location science, which could be useful for the optimization of fire brigade deployment. Since the strategic decisions in location service centers to some nodes of the network are usually made for long periods, different properties and possible failures are discussed to be considered with the mathematical models and associated solving tools.

Spectral Efficiency of Time-Variant Massive MIMO Using Wiener Prediction

Massive multiple-input multiple-output (MIMO) serves as technological pillar in current 5G deployments. The spectral efficiency (SE) reduction due to time-variance of the wireless channel, and respective mitigation strategies, are still not sufficiently understood. In this paper, we propose a multi-step Wiener predictor with a general temporal covariance function to quantify the impact of time-variance on the SE for different numbers of consecutive pilot symbols and prediction horizons. We provide asymptotic expressions of the SE for maximum ratio combining (MRC) beam-forming that show excellent agreement to numerical simulations. We investigate the channel hardening capability of massive MIMO systems in time-variant propagation channels and show that it is largely independent of the prediction horizon. The numerical evaluations show that channel prediction allows to double SE utilizing four instead of one uplink pilot symbol for a prediction horizon of 0.3 λ for both MRC and regularized zero-forcing (RZF) beam-forming.

Repeat Ivermectin Mass Drug Administrations for Malaria Control II: Protocol for a Double-blind, Cluster-Randomized, Placebo-Controlled Trial for the Integrated Control of Malaria

The gains made against malaria have stagnated since 2015, threatened further by increasing resistance to insecticides and antimalarials. Improvement in malaria control necessitates a multipronged strategy, which includes the development of novel tools. One such tool is mass drug administration (MDA) with endectocides, primarily ivermectin, which has shown promise in reducing malaria transmission through lethal and sublethal impacts on the mosquito vector. The primary objective of the study is to assess the impact of repeated ivermectin MDA on malaria incidence in children aged ≤10 years. Repeat Ivermectin MDA for Malaria Control II is a double-blind, placebo-controlled, cluster-randomized, and parallel-group trial conducted in a setting with intense seasonal malaria transmission in Southwest Burkina Faso. The study included 14 discrete villages: 7 (50%) randomized to receive standard measures (seasonal malaria chemoprevention [SMC] and bed net use for children aged 3 to 59 months) and placebo, and 7 (50%) randomized to receive standard measures and monthly ivermectin MDA at 300 μg/kg for 3 consecutive days, provided under supervision to all eligible village inhabitants, over 2 successive rainy seasons. Nonpregnant individuals >90 cm in height were eligible for ivermectin MDA, and cotreatment with ivermectin and SMC was not permitted. The primary outcome is malaria incidence in children aged ≤10 years, as assessed by active case surveillance. The secondary safety outcome of repeated ivermectin MDA was assessed through active and passive adverse event monitoring. The trial intervention was conducted from July to November in 2019 and 2020, with additional sampling of humans and mosquitoes occurring through February 2022 to assess postintervention changes in transmission patterns. Additional human and entomological assessments were performed over the 2 years in a subset of households from 6 cross-sectional villages. A subset of individuals underwent additional sampling in 2020 to characterize ivermectin pharmacokinetics and pharmacodynamics. Analysis and unblinding will commence once the database has been completed, cleaned, and locked. Our trial represents the first study to directly assess the impact of a novel approach for malaria control, ivermectin MDA as a mosquitocidal agent, layered into existing standard-of-care interventions. The study was designed to leverage the current SMC deployment infrastructure and will provide evidence regarding the additional benefit of ivermectin MDA in reducing malaria incidence in children. ClinicalTrials.gov NCT03967054; https://clinicaltrials.gov/ct2/show/NCT03967054 and Pan African Clinical Trials Registry PACT201907479787308; https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=8219. DERR1-10.2196/41197.

Is Carbon Capture and Storage (CCS) Really So Expensive?

Carbon capture and storage (CCS) is an essential technology to mitigate global CO2 emissions from power and industry sectors. Despite the increasing recognition of its importance to achieve the net-zero target, current CCS deployment is far behind targeted ambitions. A key reason is that CCS is often perceived as too expensive.

An RSU Deployment Scheme for Vehicle-Infrastructure Cooperated Autonomous Driving

For autonomous driving vehicles, there are currently some issues, such as limited environmental awareness and locally optimal decision-making. To increase the capacity of autonomous cars’ environmental awareness, computation, decision-making, control, and execution, intelligent roads must be constructed, and vehicle–infrastructure cooperative technology must be used. The Roadside unit (RSU) deployment, a critical component of vehicle–infrastructure cooperative autonomous driving, has a direct impact on network performance, operation effects, and control accuracy. The current RSU deployment mostly uses the large-spacing and low-density concept because of the expensive installation and maintenance costs, which can accomplish the macroscopic and long-term communication functions but fall short of precision vehicle control. Given these challenges, this paper begins with the specific requirements to control intelligent vehicles in the cooperative vehicle–infrastructure environment. An RSU deployment scheme, based on the improved multi-objective quantum-behaved particle swarm optimization (MOQPSO) algorithm, is proposed. This RSU deployment scheme was based on the maximum coverage with time threshold problem (MCTTP), with the goal of minimizing the number of RSUs and maximizing vehicle coverage of communication and control services. Finally, utilizing the independently created open simulation platform (OSP) simulation system, the model and algorithm’s viability and effectiveness were assessed on the Nguyen–Dupuis road network. The findings demonstrate that the suggested RSU deployment scheme can enhance network performance and control the precision of vehicle–infrastructure coordination, and can serve as a general guide for the deployment of RSUs in the same application situation.

Analysis of Hydrogen Production Methods Using the Analytic Hierarchy Process

The ever-growing evidence of climate emergency continues to drive the need for innovative solutions to reduce the release of anthropogenic harmful gases. Continuing to heavily rely on energy originating from fossil fuels remains non-sustainable owing to their limited, exhausting supplies, dependence on politically insecure sources and adverse environmental impacts. These considerations, coupled with the current desperate need for energy security, have driven research towards finding cleaner alternatives. Hydrogen has the potential to reduce 5Gt of CO2 emissions annually, create 30 million new jobs and power over 400 million cars by 2050. However, for hydrogen to be justified and to be successful in feeding the world's appetite for energy, its full life cycle, including its production methods, is required to be safe, efficient, affordable and environmentally benign. This paper aims to review the status of the potential hydrogen production methods that have strong eligibility within the UK and facilitate the much-required wider discussion around hydrogen by employing a multi-disciplinary approach. The analytic hierarchy process (AHP) has been employed to allow for pairwise comparisons of decision criteria and ranks decision alternatives using expert knowledge. Six hydrogen production methods (Green Electrolysis, SMR, ATR, POX, Anaerobic Digestion and Gasification) were chosen for review against nine parameters to determine which hydrogen production method(s) present most viable for the UK to aid in decarbonising the energy system. Collating performance scores against relevant parameters from industry experts allowed for a non-bias, holistic view of the production methods. Green electrolysis was found to be the better method (score 131) when assessed against all but one parameter, resulting in the method being considered the most viable option for the UK, however, considerations are to be made for electrolyser longevity and cost. SMR scored second most viable option (score 125), however, despite presenting as an effective production method for the UK based on the performance scores, this does not reflect the current deployment or construction rate of SMR with CCS projects in the UK. ATR (score 114) and POX (score 116) carry similar characteristics and subsequently scored similarly in performance scores. Anaerobic digestion (score 104) and gasification (score 101) scored amongst the lowest as their challenge is to scale in order to reach government goals as set out in the Government Hydrogen Strategy. 

Is Carbon Capture and Storage (CCS) Really So Expensive? An Analysis of Cascading Costs and CO2 Emissions Reduction of Industrial CCS Implementation on the Construction of a Bridge.

Carbon capture and storage (CCS) is an essential technology to mitigate global CO2 emissions from power and industry sectors. Despite the increasing recognition of its importance to achieve the net-zero target, current CCS deployment is far behind targeted ambitions. A key reason is that CCS is often perceived as too expensive. The costs of CCS have however traditionally been looked at from the industrial plant perspective, which does not necessarily reflect the end user's one. This paper addresses the incomplete view by investigating the impact of implementing CCS in industrial facilities on the overall costs and CO2 emissions of end-user products and services. As an example, we examine the extent to which an increase in costs of raw materials (cement and steel) due to CCS impacts the costs of building a bridge. Results show that although CCS significantly increases cement and steel costs, the subsequent increment in the overall bridge construction cost remains marginal (∼1%). This 1% cost increase, however, enables a deep reduction in CO2 emissions (∼51%) associated with the bridge construction. Although more research is needed in this area, this work is the first step to a better understanding of the real cost and benefits of CCS.

Quantifying global carbon dioxide removal deployment

Despite the importance of carbon dioxide removal (CDR) in most climate change mitigation scenarios that limit warming to well below 2 °C, the study of CDR is still a nascent field with basic questions to be resolved. Crucially, it is not known how much CDR is currently deployed at a global scale, nor how that compares to mitigation scenario estimates. Here, we address this problem by developing an estimate of global current CDR activity. We draw on national greenhouse gas inventory data combined with CDR registries and commercial databases to estimate that global anthropogenic activity presently generates ∼1985 MtCO2yr−1 of atmospheric removals. Almost all of these—1983 MtCO2yr−1—are removals from land-use, land-use change and forestry. Non-land-management CDR projects such as bioenergy with carbon capture and storage, direct air capture with carbon capture and storage and biochar remove only about 2 MtCO2yr−1. We compare this estimate with Shared Socioeconomic Pathways projections of CDR deployed in ‘well-below 2°C’ mitigation pathways. In so doing we demonstrate current CDR deployment would need to grow exponentially to keep the world aligned with most ‘well-below 2°C’ scenarios, which see CDR deployment growing between 75% and 100% per year between 2020 and 2030, adding ∼300–2500 MtCO2 in total CDR capacity. To conclude we discuss uncertainties related to our estimates, and suggest priorities for the future collection and management of CDR data, particularly related to the role of the land sink in generating CDR.

Experiments and Evaluation of a Container Migration Data-Auditing System on Edge Computing Environment

With the proliferation of IoT sensors and devices, storing collected data in the cloud has become common. A wide variety of data with different purposes and forms are not directly stored in the cloud but are sent to the cloud via edge servers. At the edge server, applications are running in containers and virtual machines to collect data. However, the current deployment and movement mechanisms for containers and virtual machines do not consider any conventions or regulations for the applications and the data it contains. Therefore, it is easy to deploy and migrate containers and virtual machines. However, the problem arises when it is deployed or migrated, which may violate the licensing terms of the contained applications, the rules of the organization, or the laws and regulations of the concerned country. We have already proposed a data-audit control mechanism for the migration of virtual machines. The proposed mechanism successfully controls the unintentional and malicious migration of virtual machines. We expect similar problems with containers to occur as the number of edge servers increases. Therefore, we propose a policy-based data-audit control system for container migration. The proposed system was verified in the implemented edge computing environment and the results showed that adding the proposed data-audit control mechanism had a minimal impact on migration time and that the system was practical enough. In the future, we intend to conduct verification not in a very compact and short-range environment such as this one but on an existing wide-area network.

A Survey on UAV-Aided Maritime Communications: Deployment Considerations, Applications, and Future Challenges

Maritime activities represent a major domain of economic growth with several emerging maritime Internet of Things use cases, such as smart ports, autonomous navigation, and ocean monitoring systems. The major enabler for this exciting ecosystem is the provision of broadband, low-delay, and reliable wireless coverage to the ever-increasing number of vessels, buoys, platforms, sensors, and actuators. Towards this end, the integration of unmanned aerial vehicles (UAVs) in maritime communications introduces an aerial dimension to wireless connectivity going above and beyond current deployments, which are mainly relying on shore-based base stations with limited coverage and satellite links with high latency. Considering the potential of UAV-aided wireless communications, this survey presents the state-of-the-art in UAV-aided maritime communications, which, in general, are based on both conventional optimization and machine-learning-aided approaches. More specifically, relevant UAV-based network architectures are discussed together with the role of their building blocks. Then, physical-layer, resource management, and cloud/edge computing and caching UAV-aided solutions in maritime environments are discussed and grouped based on their performance targets. Moreover, as UAVs are characterized by flexible deployment with high re-positioning capabilities, studies on UAV trajectory optimization for maritime applications are thoroughly discussed. In addition, aiming at shedding light on the current status of real-world deployments, experimental studies on UAV-aided maritime communications are presented and implementation details are given. Finally, several important open issues in the area of UAV-aided maritime communications are given, related to the integration of sixth generation (6G) advancements. These future challenges include physical-layer aspects, non-orthogonal multiple access schemes, radical learning paradigms for swarms of UAVs and unmanned surface and underwater vehicles, as well as UAV-aided edge computing and caching.