End-user Devices Research Articles

Evaluating the effects of ICT core elements on CO2 emissions: Recent evidence from OECD countries

This research aimed to inform policymakers about the actual climate relevance of the information and communication technology (ICT) ecosystem, which includes data centers and content provision, broadband networks, and consumer devices. To provide robust empirical evidence on the net environmental impact of these core ICT elements, we used OECD panel data for 34 Member States for the years 2002–2019 and panel fixed-effects regression models. In contrast to previous literature, we measured basic broadband and new fiber-based wireline broadband and 3G/4G mobile broadband network deployment. We also captured variations in end-user devices, ICT affinity, and famous online content. We found that, on average, the CO2-reducing effects appear to outweigh the CO2-increasing effects. In particular, we found that, in addition to the reduction effect associated with the use of basic broadband connections, there is a further, albeit smaller, reduction effect associated with new fiber-based broadband connections. Other ICT elements showed insignificant coefficient estimates, suggesting largely offsetting environmental impacts. Our main findings were subject to various robustness checks and were broadly consistent with previous literature using data from developed countries. Our results suggested that old and new broadband networks as enabling technology could generate positive environmental effects for society.

Recent Trends on Privacy-Preserving Technologies under Standardization at the IETF

End-users are concerned about protecting the privacy of their sensitive personal data that are generated while working on information systems. This extends to both the data they actively provide including personal identification in exchange for products and services as well as its related metadata such as unnecessary access to their location. This is when certain privacy-preserving technologies come into a place where Internet Engineering Task Force (IETF) plays a major role in incorporating such technologies at the fundamental level. Thus, this paper offers an overview of the privacy-preserving mechanisms for layer 3 (i.e. IP) and above that are currently under standardization at the IETF. This includes encrypted DNS at layer 5 classified as DNS-over-TLS (DoT), DNS-over-HTTPS (DoH), and DNS-over-QUIC (DoQ) where the underlying technologies like QUIC belong to layer 4. Followed by that, we discuss Privacy Pass Protocol and its application in generating Private Access Tokens and Passkeys to replace passwords for authentication at the application layer (i.e. end-user devices). Lastly, to protect user privacy at the IP level, Private Relays and MASQUE are discussed. This aims to make designers, implementers, and users of the Internet aware of privacy-related design choices.

Techno-economic study of very dense optical wireless access using visible or infrared light

Future wireless networks need to serve a high density of end-user devices with a high quality of service. The density of wireless access points and the infrastructure behind them lead to substantial cost. We study this cost impact for the particular case of optical wireless communication [also denoted as light fidelity (LiFi)] in a large manufacturing hall. We compare different LiFi network topologies using visible or infrared light. Considering aspects such as the fronthaul cabling layout, equipment investments, and installation and operational costs for energy and maintenance, different LiFi solutions are analyzed for greenfield and brownfield scenarios. The techno-economic analysis is performed in terms of total cost of ownership as well as the impact of each cost category to the final cost. Furthermore, the cost per delivered Mbps is compared to take into account the expected densification (more bits/s/m2) and satisfy the demand for future wireless traffic. Both delivered capacity and power consumption have been identified as the two most critical parameters.

ReaLPrune: ReRAM Crossbar-Aware Lottery Ticket Pruning for CNNs

Training machine learning (ML) models at the edge (on-chip training on end user devices) can address many pressing challenges including data privacy/security, increase the accessibility of ML applications to different parts of the world by reducing the dependence on the communication fabric and the cloud infrastructure, and meet the real-time requirements of AR/VR applications. However, existing edge platforms do not have sufficient computing capabilities to support complex ML tasks such as training large CNNs. ReRAM-based architectures offer high-performance yet energy efficient computing platforms for on-chip CNN training/inferencing. However, ReRAM-based architectures are not scalable with the size of the CNN. Larger CNNs have more weights, which requires more ReRAM cells that cannot be integrated in a single chip. Moreover, training larger CNNs on-chip will require higher power, which cannot be afforded by these smaller devices. Pruning is an effective way to solve this problem. However, existing pruning techniques are either targeted for inferencing only, or they are not crossbar-aware. This leads to sub-optimal hardware savings and performance benefits for CNN training on ReRAM-based architectures. In this paper, we address this problem by proposing a novel crossbar-aware pruning strategy, referred as ReaLPrune, which can prune more than 90% of CNN weights. The pruned model can be trained from scratch without any accuracy loss. Experimental results indicate that ReaLPrune reduces hardware requirements by 77.2% and accelerates CNN training by ∼20× compared to unpruned CNNs. ReaLPrune also outperforms other crossbar-aware pruning techniques in terms of both performance and hardware savings. In addition, ReaLPrune is equally effective for diverse datasets and more complex CNNs.

Analysis And Prediction of COVID-19 Based on Machine Learning

Artificial intelligence (AI) technology is in high demand today for wireless infrastructure integration, real-time data collecting, and end-user device processing. The most innovative answer at hand right now is the use of AI to recognize and forecast widespread epidemics. The pandemic, which started with COVID-19, has had a terrible impact on world society and placed a significant load on the most developed healthcare systems globally. According to the European Centre for Disease Prevention and Control (ECDC), as of May 11, 2020, there had been 282,244 fatalities and more than 4,063,525 confirmed cases. However, given the current exponential and rapid development in the number of patients, it is essential to make use of AI technology to quickly and accurately estimate the prognosis of infected individuals.

Improving yields, compatibility and tailoring the properties of hydrothermal liquefaction bio-crude using yellow grease

Low quality waste feedstocks like food wastes, waste fats, manures etc. offer huge potential for replacing an appreciable quantity of petroleum crude. Owing to high water content of some of these feedstocks, hydrothermal liquefaction is the preferred route for its transformation. The present work performed in a continuous pilot scale hydrothermal liquefaction system studies the influence of yellow grease on the yields and quality of bio-crude, properties of bio-crude/blends, and its compatibility with petroleum fractions. Blends of food waste, yellow grease, and algae were used as feedstocks. Key fuel properties such as density and viscosity of bio-crudes and its blends with diesel and butanol were evaluated. Compatibility of different bio-crudes with petroleum fraction was investigated using microscopy imaging of blends of bio-crude and commercial diesel. Enhanced yields of bio-crude and lower asphaltene content were observed with the addition of waste lipids in the form of yellow grease while also decreasing the viscosity and density of bio-crude. Bio-crude from food wastes showed regions of compatibility at higher bio-crude concentrations while lower concentrations of bio-crude were incompatible. A combination of feedstock mix and blending of bio-crude could be used to improve the properties and compatibility of bio-crude for specific end-use devices and processes.

Attacking Windows Hello for Business: Is It What We Were Promised?

Traditional password authentication methods have raised many issues in the past, including insecure practices, so it comes as no surprise that the evolution of authentication should arrive in the form of password-less solutions. This research aims to explore the problems that password authentication and password policies present and aims to deploy Windows Hello for Business (WHFB) on-premises. This includes creating three virtual machines (VMs) and evaluating WHFB as a password-less solution and showing how an attacker with privileged access may retrieve the end user’s domain password from the computer’s memory using Mimikatz and describing the possible results. The conducted research tests are in the form of two attack methods. This was feasible by the creation of three VMs operating in the following way. The first VM will act as a domain controller (DC) and certificate authority server (CA server). The second VM will act as an Active Directory Federation Service (ADFS). The third VM will act as the end-user device. The test findings research summarized that password-less authentication is far more secure than the traditional authentication method; this is evidenced throughout the author’s tests. Within the first test, it was possible to retrieve the password from an enrolled device for WHFB while it was still in the second phase of the deployment. The second test was a brute-force attack on the PIN of WHFB; since WHFB has measures to prevent such attacks, the attack was unsuccessful. However, even though the retrieval of the password was successful, there are several obstacles to achieving this outcome. It was concluded that many organizations still use password authentication as their primary authentication method for accessing devices and applications. Larger organizations such as Microsoft and Google support the adoption of password-less authentication for end-users, and the current usage of password-less authentication shared by both organizations is encouraged. This usually leads organizations to adopt this new solution for their IT infrastructure. This is because it has been used and tested by millions of people and has proven to be safe. This supports the findings of increased usage and the need for password-less authentication by today’s users.

Optimal service decomposition for mobile augmented reality with edge cloud support

Mobile augmented reality (MAR) applications are starting to attract significant attention due to the enhanced capabilities stemming from both the network and the end devices that propel their realization. However, despite the progress on the end user devices, MAR applications are inherently hugely demanding in terms of computational and memory requirements since they combine, inter alia, video streams, computer generated images, intense computer vision algorithms and geolocation. To this end, edge cloud computing is envisioned as a key technology for supporting such applications where part of the computationally demanding algorithms could be offloaded to suitably selected edge clouds. Within that context the inherent user mobility should be considered to allow an efficient service continuum between edge and the end-terminal. To this end, in this paper, an optimal edge cloud resource MAR service decomposition is presented that takes explicitly into account the AR service composition as well as the inherent user mobility to proactively allocate resources to satisfy the required strict latency and frame accuracy requirements of MAR applications. In addition to the optimal decision making using mathematical programming, and as a mean to provide real-time decision making two advanced heuristic techniques are proposed. A Simulated Annealing based mobility aware AR algorithm (SAMAR) is developed to enhance computing efficiency and a Long Short-Term Memory (LSTM) neural network which is trained offline with optimal solutions. Numerical investigations reveal that significant gains can be achieved by the proposed schemes compare to a number of baseline previously proposed techniques.

Multi-Job Intelligent Scheduling With Cross-Device Federated Learning

Recent years have witnessed a large amount of decentralized data in various (edge) devices of end-users, while the decentralized data aggregation remains complicated for machine learning jobs because of regulations and laws. As a practical approach to handling decentralized data, Federated Learning (FL) enables collaborative global machine learning model training without sharing sensitive raw data. The servers schedule devices to jobs within the training process of FL. In contrast, device scheduling with multiple jobs in FL remains a critical and open problem. In this article, we propose a novel multi-job FL framework, which enables the training process of multiple jobs in parallel. The multi-job FL framework is composed of a system model and a scheduling method. The system model enables a parallel training process of multiple jobs, with a cost model based on the data fairness and the training time of diverse devices during the parallel training process. We propose a novel intelligent scheduling approach based on multiple scheduling methods, including an original reinforcement learning-based scheduling method and an original Bayesian optimization-based scheduling method, which corresponds to a small cost while scheduling devices to multiple jobs. We conduct extensive experimentation with diverse jobs and datasets. The experimental results reveal that our proposed approaches significantly outperform baseline approaches in terms of training time (up to 12.73 times faster) and accuracy (up to 46.4% higher).

CernVM-FS ephemeral publishers on Kubernetes

The CernVM File System (CernVM-FS) is a global read-only POSIX file system that provides scalable and reliable software distribution to numerous scientific collaborations. It gives access to more than a billion binary files of experiment application software stacks and operating system containers to end user devices, grids, clouds, and supercomputers. CernVM-FS is asymmetric by construction. Writing into the repository is a centralized operation called publishing, while reading is allowed for many clients from many locations. The classic publishing process needs a dedicated “release manager machine” that provides the editable repository copy. This classic approach was improved thanks to the introduction of the CernVM-FS Gateway that provides concurrent access to the repository backend storage through a REST API. In this contribution, we present further improvements to the CernVM-FS publishing process. Our main contribution is the construction of ephemeral containers that are created on demand and used to provide a temporary, editable repository copy for a single publish operation. The container construction makes careful use of Linux namespaces and a user-space implementation of overlayfs. We further show that both the gateway and the containers used for publishing can be instantiated as pods in a kubernetes cluster. Thus, we demonstrate a kubernetes-native CernVM-FS publishing workflow.

Covid-19 Patient Health Prediction Using Boosted Random Forest Algorithm

COVID-19, also known as 2019-nCoV, is no longer a pandemic but an endemic disease that has killed many people worldwide. COVID-19 has no precise treatment or remedy at this time, but it is unavoidable to live with the disease and its implications. By quickly and efficiently screening for covid, one may determine whether or not one has COVID-19 and thus limit the financial and administrative burdens on healthcare systems. This reality puts a huge demand on these countries' healthcare systems, especially in emerging nations, due to the poor healthcare systems around the world. Although the COVID-19 pandemic cannot be stopped by any licenced vaccine or antiviral medicine, there are other possible solutions that could lighten the burden of the virus on healthcare systems and the economy. The most promising approaches for usage outside of a clinical environment include non-clinical approaches like machine learning, data mining, deep learning, and other artificial intelligence technologies. Artificial intelligence (AI) approaches are increasingly being integrated into wireless infrastructure, real-time data collection, and end-user device processing. A positive and negative COVID-19 case dataset is used to validate artificial intelligence (AI) systems such decision trees, support vector machines, artificial neural networks, and naive Bayesian models. The correlation coefficients between various dependent and independent variables were examined to determine the strength of the relationship between the dependent features. The model was tested 20% of the time while being trained 80% of the time during the preparation phase. The Random Forest had the highest precision (94.99%), according to the evaluation of success.

A Secure Mutual Authentication and Key Agreement Protocol that Preserves Internet of Things (Iot) Modules

IoT systems need a high security to protect information exchanged between devices and users. Securing the information and providing authentication are the most significant features of end-user devices on vulnerable and open network infrastructure. Thus, it is more often and significant to reinforce and modernize the security protocol for IoT devices headed for security perception. The security concern of IoT devices can be fulfilled with effective cryptography algorithm techniques. Several private and public cryptography techniques provide an extensive choice of services to protect users’ information in the form of scrambled text, that can be authenticated only by authorized users or devices. Particularly, end user-device authentication is essential for the Internet of Things (IoT). IoT devices are linked via the internet and they are deployed in open and public places, which creates them susceptible activity by unauthorized agents. Thus, it is significant to design an effective and strong authentication protocol to protect the end user-device with affordable cost and rapid communication. In the proposed work, the linear matrix-based, block cipher cryptography protocol scheme is used. The protocol encrypts confidential data using cryptography to generate a different scenario of the same text. In contrast to simple block cipher algorithms based on linear matrix algorithms, symmetric cryptography reduces encryption time but is not concerned about security. The objective of the research paper is to establish secure links for endto-end communication with a proper and high speed authentication scheme.

End-to-end network slicing in vehicular clouds using the MobFogSim simulator

Fog computing and network slicing are drawing attention as promising technologies in modern networks, such as Vehicular Ad-Hoc Networks (VANETs). On the one hand, fog computing lets end-user devices offload part of their computation and data to micro data centres (i.e., fog nodes) that are pervasively deployed in their proximity. On the other hand, network slicing creates different logical networks over a common physical infrastructure, wherein each logical network, called a slice, aims at meeting the requirements and characteristics of a specific class of applications. In this context, simulators allow the evaluation of new solutions in a cost-efficient and repeatable way. MobFogSim is specifically targeted at allowing the evaluation of resource management solutions in modern networks, including the support of device mobility, fog service migration, and network slicing. In this work, we further extend MobFogSim by presenting new functionalities, namely: (i) the support of VANETs; (ii) the modelling of end-to-end (E2E) slices, which include storage and processing resources of fog nodes besides the network resources; and (iii) improvements to the scalability of our simulator. We validate this work by conducting experiments over MobFogSim to assess its improved scalability as well as to test network slicing and fog computing solutions in three different VANET scenarios: (i) fog_only, wherein fog nodes are deployed only at the edge of the fixed infrastructure; (ii) vehicular_only, where fog nodes are provided only by nearby vehicles; and (iii) hybrid, which combines the previous two. Results show that the vehicular_only approach can reduce the latency provided to end-user devices by around 50% with respect to the fog_only approach. Besides, the improved version of MobFogSim reduces the simulation time by around 65% if compared to the previous version.

Effective Resource Allocation Technique to Improve QoS in 5G Wireless Network

A 5G wireless network requires an efficient approach to effectively manage and segment the resource. A Centralized Radio Access Network (CRAN) is used to handle complex distributed networks. Specific to network infrastructure, multicast communication is considered in the performance of data storage and information-based network connectivity. This paper proposes a modified Resource Allocation (RA) scheme for effectively handling the RA problem using a learning-based Resource Segmentation (RS) technique. It uses a modified Random Forest Algorithm (RFA) with Signal Interference and Noise Ratio (SINR) and position coordinates to obtain the position coordinates of end-users. Further, it predicts Modulation and Coding Schemes (MCS) for establishing a connection between the end-user device and the Remote Radio Head (RRH). The proposed algorithm depends on the accuracy of positional coordinates for the correctness of the input parameters, such as SINR, based on the position and orientation of the antenna. The simulation analysis renders the efficiency of the proposed technique in terms of throughput and energy efficiency.

Sweet Streams Are Made of This: The System Engineer’s View on Energy Efficiency in Video Communications [Feature

In recent years, the global use of online video services has increased rapidly. Today, a manifold of applications, such as video streaming, video conferencing, live broadcasting, and social networks, make use of this technology. A recent study found that the development and the success of these services had as a consequence that, nowadays, more than 1% of the global greenhouse-gas emissions are related to online video, with growth rates close to 10% per year. This article reviews the latest findings concerning energy consumption of online video from the system engineer’s perspective, where the system engineer is the designer and operator of a typical online video service. We discuss all relevant energy sinks, highlight dependencies with quality-of-service variables as well as video properties, review energy consumption models for different devices from the literature, and aggregate these existing models into a global model for the overall energy consumption of a generic online video service. Analyzing this model and its implications, we find that end-user devices and video encoding have the largest potential for energy savings. Finally, we provide an overview of recent advances in energy efficiency improvement for video streaming and propose future research directions for energy-efficient video streaming services.

Base Station Energy Use in Dense Urban and Suburban Areas

Growing energy consumption is a global problem. The information and communications technology (ICT) industry is in a critical role as an enabler of energy savings in other sectors. However, the power consumption of the ICT sector also needs to be addressed, to contribute to the overall reduction of power consumption and carbon emissions. A new era has begun as the fifth generation (5G) mobile data connection rollouts are advancing globally and are expected to reach a 10% share of end-user devices and connections by 2023. The available references on energy consumption in global mobile networks are rather old and highly averaged – only estimates of energy consumption relative to data volumes are available. There is an information gap regarding the energy consumption of emerging 5G and advanced 4G technologies. Therefore, it has been difficult to understand the actual electricity consumption differences between generations and spatially aggregated electricity consumption once these generations are combined to offer capacity and coverage. This article fills this gap by providing a reference on the energy consumption of base transceiver stations for reported mobile data usage for different Radio Access Technologies; 3G, 4G and 5G respectively. To the best of our knowledge, there is no reference to scientific research on the comparison of energy intensity per square kilometer for 3G, 4G and 5G mobile radio technologies, using actual operator data. The objective of this research was to improve the understanding of the actual energy consumption of different Radio Access Technologies (RAT). The results also give insight to decision makers on when to modernize the operator radio access network. The article reports on the results of field measurements on data and visitor volumes and shares of different RATs. The research contains two statistical RAT combination cases, one representing the European average and the other Finnish mobile networks. The analyses were done for dense urban (DU) and suburban (SU) areas.

A DRL Strategy for Optimal Resource Allocation Along With 3D Trajectory Dynamics in UAV-MEC Network

Advances in Unmanned Air Vehicle (UAV) technology have paved a way for numerous configurations and applications in communication systems. However, UAV dynamics play an important role in determining its effective use. In this article, while considering UAV dynamics, we evaluate the performance of a UAV equipped with a Mobile-Edge Computing (MEC) server that provides services to End-user Devices (EuDs). The EuDs due to their limited energy resources offload a portion of their computational task to nearby MEC-based UAV. To this end, we jointly optimize the computational cost and 3D UAV placement along with resource allocation subject to the network, communication, and environment constraints. A Deep Reinforcement Learning (DRL) technique based on a continuous action space approach, namely Deep Deterministic Policy Gradient (DDPG) is utilized. By exploiting DDPG, we propose an optimization strategy to obtain an optimal offloading policy in the presence of UAV dynamics, which is not considered in earlier studies. The proposed strategy can be classified into three cases namely; training through an ideal scenario, training through error dynamics, and training through extreme values. We compared the performance of these individual cases based on cost percentage and concluded that case II (training through error dynamics) achieves minimum cost i.e., 37.75 %, whereas case I and case III settles at 67.25% and 67.50% respectively. Numerical simulations are performed, and extensive results are obtained which shows that the advanced DDPG based algorithm along with error dynamic protocol is able to converge to near optimum. To validate the efficacy of the proposed algorithm, a comparison with state-of-the-art Deep Q-Network (DQN) is carried out, which shows that our algorithm has significant improvements.

Integrated Privacy Preserving Healthcare System Using Posture-Based Classifier in Cloud

Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare system. Online patient data processing from remote places may lead to severe privacy problems. Moreover, the existing cloud-based healthcare system takes more latency and energy consumption during diagnosis due to offloading of live patient data to remote cloud servers. Solve the privacy problem. The proposed research introduces the edge-cloud enabled privacy-preserving healthcare system by exploiting additive homomorphic encryption schemes. It can help maintain the privacy preservation and confidentiality of patients’ medical data during diagnosis of Parkinson’s disease. In addition, the energy and delay aware computational offloading scheme is proposed to minimize the uncertainty and energy consumption of end-user devices. The proposed research maintains the better privacy and robustness of live video data processing during prediction and diagnosis compared to existing healthcare systems.

Sistem Penjadwalan Pemupukan dan Pengapuran untuk Peternakan Udang Vanami Menggunakan Teknologi IoT dan Algoritma Fuzzy Logic

The most common type of ponds found in most areas of Lamongan is shrimp ponds (vanami). One of the problems is the disease that often affects shrimp farmers. The cause of this disease include rapid changes in pH, which will stress the shrimp and will affect the immunity of the shrimp itself. The solution to this rapid change in pH is to immediately add fertilizer or liming. In this study a system for monitoring PH will be created so that fertilization or liming can be carried out as early as possible. This system is built using a PH sensor and a fuzzy logic algorithm. This research also applies the MQTT protocol to send data from the PH monitoring system to end user devices. The test results show that the PH monitoring system that is made works as needed with an error rate of 0.018. Besides that, it also meets expectations when testing fuzzy logic on the system because the calculation results are the same between the manual and the system.

Dynamic Partitioning and Popularity based Caching for Optimized Performance in content-centric fog networks: DPPCOP

With the massive growth of information generation, processing, and distribution in the Internet of Things (IoT), the existing cloud architectures need to be designed more effectively using fog networks. The current IP-address-based Internet architecture is unable to deliver the desired Quality-of-Service (QoS) towards the increasing demands of fog networking-based applications. To this end, Content-Centric Networking (CCN) has been developed as the potential future Internet architecture. CCN provides name-based content delivery and is established as an architecture for next-generation fog applications. The CCN-based fog environment uses the cache of in-network fog nodes to place the contents near the end-user devices. Generally, the caching capacity of the fog nodes is very small as compared to the content catalog size. Therefore, efficient content placement decisions are vital for improving the network performance. To enhance the content retrieval performance for the end-users, a novel content caching scheme named “Dynamic Partitioning and Popularity based Caching for Optimized Performance (DPPCOP)” has been proposed in this paper. First, the proposed scheme partitions the fog network by grouping the fog nodes into non-overlapping partitions to improve content distributions in the network and to ensure efficient content placement decisions. During partitioning, the scheme uses the Elbow method to obtain the “good” number of partitions. Then, the DPPCOP scheme analyzes the partition’s information along with the content popularity and distance metrics to place the popular contents near the end-user devices. Extensive simulations on realistic network topologies demonstrate the superiority of the DPPCOP caching strategy on existing schemes over various performance measurement parameters such as cache hit ratio, delay, and average network traffic load. This makes the proposed scheme suitable for next-generation CCN-based fog networks and the futuristic Internet architectures for industry 4.0.