Latency Of Internet Research Articles

Study On The Effect Of Edge Computing On Latency In Iot Applications

This article examines the effect of implementing edge computing on latency in Internet of Things (IoT) applications. Edge computing moves data processing closer to the data source to reduce latency and improve efficiency. This research compares the performance of IoT applications with and without edge computing. The results show a significant reduction in latency and an increase in application response speed.

Latency and Reliability Analysis of a 5G-Enabled Internet of Musical Things System

Latency and Reliability Analysis of a 5G-Enabled Internet of Musical Things System

Mobile edge computing servers deployment with improved genetic algorithm in cellular Internet of Things

Mobile edge computing (MEC) provides services to devices and reduces latency in cellular internet of things (IoT) networks. However, the challenging problem is how to deploy MEC servers economically and efficiently. This paper investigates the deployment problem of MEC servers of the real-world road network by employing an improved genetic algorithm (GA) scheme. We first use the threshold-based K-means algorithm to form vehicle clusters according to their locations. We then select base stations (BSs) based on clustering center coordinates as the deployment locations set for potential MEC servers. We further select BSs using a combined simulated annealing (SA) algorithm and GA to minimize the deployment cost. The simulation results show that the improved GA deploys MEC servers effectively. In addition, the proposed algorithm outperforms GA and SA algorithms in terms of convergence speed and solution quality.

Locomotion-Based UAV Control Toward the Internet of Senses

The emergence of 5th-generation networks and the introduction of the ultra-low latency Internet, namely, tactile internet, by the International Telecommunication Union (ITU), has opened up a wide range of applications. xr, holoportation, and remote control of machines are among the ones that would revolutionize the future of factories, smart cities, and digital healthcare. vr technology provides users with highly realistic visual and auditory experiences, enabling a high sense of immersion and embodiment in virtual environments. In the real world, however, we use more senses than only vision and hearing to perceive our surroundings. Particularly, tactile sensation is the only bidirectional modality that enables us to perceive and interact with the objects and surfaces around us. This paper introduces a real-life testbed uav-based system that leverages different technologies, including vr, 360∘ video streaming over 4G/5G, and edge computing to enable 6dof view with haptic feedback for a maximized immersion. The demonstration videos of the testbed are made publicly available on the following links: https://www.youtube.com/watch?v=WTUvWkDJFIQ and https://www.youtube.com/watch?v=Bmc9ddydjYY.

New Instances in IOT Technology

Abstract: Future of the Internet of Things with enabled Artificial Intelligence, block chain technology, Big data technology, cloud AWS - Azure endpoints with little coding or no coding to manager enamors powerful device management. Few of the application of Internet of Things like B2C sector of summers like smart homes, smart appliances and elderly care. In infrastructure area in internet of things like smart city, smart energy & utilities and environmental monitoring. In the Commerce area in the Internet of Things like Medical & healthcare, transportation and building accommodations. Coming to Industry and farming area in the Internet of Things like smart manufacturing and smart agricultures and more. Coming to IT Datacenters need to use Internet of Things and IoT enabled ship, airplanes and trains exceptional application going use in multiple manners. IoT application development services enables on all IoT devices / machines connect virtually with global wireless standard using fifth generation 5G mobile network (ultra low latency internet via embedded IoT devices). In Asia-Pacific zone, 5G adoption is going to hit 4 billion in 2025 as per Ericson mobility report.

Time-Variant RGB Model for Minimal Cell Allocation and Scheduling in 6TiSCH Networks

IEEE has standardized the 802.15.4e Time Slotted Channel Hopping (TSCH) mode to provide stringent latency, higher reliability, and low duty-cycle in various Internet of Things (IoT) applications. TSCH eliminates interference and multi-path fading on channels, but its channel hopping feature severely affects the 6TiSCH (IPv6 over IEEE 802.15.4e TSCH mode) network formation. Further, 6TiSCH Minimal Configuration standard does not provide sufficient bandwidth (i.e., minimal cell) for quick transmission of control packets required by the new nodes (i.e., pledges) during their network association. Many works have been proposed on 6TiSCH network formation as it has high impact on network performance and lifetime. However, the existing works either did not use all the available physical channels while allocating minimal cell(s) or are not stable with topology changes. Therefore, this work proposes a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> ime-Variant <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RGB</u> (TRGB) model for minimal cell allocation and scheduling, which results in faster association of pledges and maintains network stability. We evaluate the TRGB using Markov Chain model and also on a real 60-node testbed in FIT IoT-LAB. Testbed results show that TRGB achieves 51% and 23% improvement over the state-of-the-art scheme in terms of joining time and energy consumption, respectively, while maintaining stability of the network.

Segment routing for traffic engineering and effective recovery in low-earth orbit satellite constellations

Low-Earth Orbit Satellite Constellations (LEO-SCs) provide global, high-speed, and low latency Internet access services, which bridges the digital divide in the remote areas. As inter-satellite links are not supported in initial deployment (i.e. the Starlink), the communication between satellites is based on ground stations with radio frequency signals. Due to the rapid movement of satellites, this hybrid topology of LEO-SCs and ground stations is time-varying, which imposes a major challenge to uninterrupted service provisioning and network management. In this paper, we focus on solving two notable problems in such a ground station-assisted LEO-SC topology, i.e., traffic engineering and fast reroute, to guarantee that the packets are forwarded in a balanced and uninterrupted manner. Specifically, we employ segment routing to support the arbitrary path routing in LEO-SCs. To solve the traffic engineering problem, we proposed two source routings with traffic splitting algorithms, Delay-Bounded Traffic Splitting (DBTS) and DBTS+, where DBTS equally splits a flow and DBTS ​+ ​favors shorter paths. Simulation results show that DBTS ​+ ​can achieve about 30% lower maximum satellite load at the cost of about 10% more delay. To guarantee the fast recovery of failures, two fast reroute mechanisms, Loop-Free Alternate (LFA) and LFA+, are studied, where LFA pre-computes an alternate next-hop as a backup while LFA ​+ ​finds a 2-segment backup path. We show that LFA ​+ ​can increase the percentage of protection coverage by about 15%.

An Energy Efficient Local Popularity Based Cooperative Caching for Mobile Information Centric Networks

The usage of social media applications such as Youtube, Facebook, and other applications is rapidly increasing with each passing day. These applications are used for uploading informational content such as images, videos, and voices, which results in exponential traffic overhead. Due to these overheads (high bandwidth consumption), the service providers fail to provide high-speed and low latency internet to users around the globe. The current internet cannot cope with such high data traffic due to its fixed infrastructure (host centric-network) degrading the network performance. A new internet paradigm known as Information Centric Networks (ICN) was introduced based on content-oriented addressing. The concept of ICN is to entertain the request locally by neighbor nodes without accessing the source, which will help offload the network’s data traffic. ICN can mitigate traffic overhead and meet future Internet requirements. In this work, we propose a novel decentralized placement scheme named self-organized cooperative caching (SOCC) for mobile ICN to improve the overall network performance through efficient bandwidth utilization and less traffic overhead. The proposed scheme outperforms the state-of-the-art schemes in terms of energy consumption by 55%, average latency, and cache hit rate by a minimum of 35%.

Scalable routing in low-Earth orbit satellite constellations: Architecture and algorithms

Low-Earth orbit satellite constellations (LEO-SCs) are attractive for provisioning global, high-speed and low latency Internet access services. Due to the fast movement of satellites and the lack of inter-satellite links (ISLs), the LEO-SC topology is highly dynamic. Applying shortest path routing directly to LEO-SCs may suffer from poor scalability and frequent route changes. In this paper, a scalable two-layer routing architecture is first proposed. Based on it, two stable routing algorithms, delay-bounded routing (DBR) and delay-aware routing (DAR), are designed to minimize route changes. DBR is flow-based. It provides bounded network latency but at the cost of a larger forwarding table. DAR is destination-based. Although network latency is not bounded, we show that the further reduction in route changes is significant and the increase in average latency is minimal.

Socially-Aware-Clustering-Enabled Federated Learning for Edge Networks

Edge Intelligence based on federated learning (FL) can be considered to be a promising paradigm for many emerging, strict latency Internet of Things (IoT) applications. Furthermore, a rapid upsurge in the number of IoT devices is expected in the foreseeable future. Although FL enables privacy-preserving, on-device machine learning, it still exhibits a privacy leakage issue. A malicious aggregation server can infer the sensitive information of other end-devices using their local learning model updates. Furthermore, centralized FL aggregation server might stop working due to security attack or a physical damage. To address the aforementioned issues, we propose a novel concept of socially-aware-clustering-enabled dispersed FL. First, we present a novel framework for socially-aware-clustering-enabled dispersed FL. Second, we formulate a problem for minimizing the loss function of the proposed FL scheme. Third, we decompose the formulated problem into three sub-problems, such as local devices relative accuracy minimization (i.e., end-devices local accuracy maximization) sub-problem, clustering sub-problem, and resource allocation sub-problem, due to the NP-hard nature of the formulated problem. The clustering and resource allocation sub-problems are solved using low complexity schemes based on a matching theory. The end devices' relative accuracy minimization problem is solved by using a convex optimizer. Finally, numerical results are provided for validation of the proposed FL scheme. Furthermore, we show the convergence of the proposed FL scheme for image classification tasks using the MNIST dataset.

Time Sensitive Networking for 5G NR Fronthauls and Massive Iot Traffic

Integration of public fifth generation mobile network (5G) backhaul and non-public networks is key for the private/local 5G network ecosystem. However, such shared networks, namely, x-haul, must satisfy the extremely diverse traffic demands including low latency and massive number of internet of things (IoT) devices. We propose an x-haul platform with autonomous time aware shaper and adaptive mobile fronthaul compression, which simultaneously accommodates two 5G NR-class fronthauls (2*29.5 Gbps CPRI-equivalent rate) and backhaul traffic with 1,000 IoT devices. From an experiment we conducted to evaluate the proposed platform, mobile fronthaul traffic was transferred with 76-μs deterministic latency via 4 switches and 10-km optical fiber by autonomous time aware shaper while improving the achievable transmission rate of IoT traffic owing to the integration of adaptive compression.

Transforming higher education in Ghana in times of disruption: flexible learning in rural communities with high latency internet connectivity

Purpose The purpose of this study is to explore the characteristics and potential effects of teaching and learning through audio teleconferencing (dial-in) with a cell phone. In addition, the study aims to identify the associations between the audio teleconferencing and video teleconferencing in a 12-week postgraduate course. Design/methodology/approach The study is a cross-sectional survey conducted at the Department of Educational Innovations at the Kwame Nkrumah University of Science and Technology from March to June 2020. The purposive sampling technique was used to sample 100 postgraduate students who registered for a course in the department. The data for the study were collected using the System Usability Scale (SUS) and 17-item self-administered eQuestionnaire. Multiple Linear Regression analysis, ANOVA, Independent sample T-test and Mann–Whitney U-test were used to estimate the differences in course achievements of students who experienced education through audio teleconferencing and those who experienced education through video teleconferencing. Findings In total, 59% of the participating postgraduate students chose to attend the synchronous online lectures via audio teleconferencing (dial-in). The participants gave a high SUS score (SUS &gt; 80.3; Grade A; Excellent) for audio conferencing service. Among the students in the audio teleconferencing cohort, the results evidenced a strong positive linear correlation, (r (57) = 0.79, p &lt; 0.05), between the individual adjective ratings and the SUS scores. There was marginal significance among demography of students in the audio teleconference (AT) cohort with regards to their perception about the dial-in lecture. There was no statistically significant difference, (t (98) = 1.88, p = 0.063), in the achievement test for AT students and video teleconference (VT) students. The instructors and the students were satisfied with the AT. Practical implications Based on the students’ preference, AT offers equal benefit as VT with regards to system satisfaction and perceived quality of online teaching. AT, as teaching modality, should be an option for students who reside in communities with high latency internet connectivity. It is recommended that instructors are trained on how to engage and motivate students via AT. Originality/value Higher education institutions in Ghana are facing decisions about how to continue learning and teaching through flexible pedagogy, while keeping their faculty members and students protected from the COVID-19 pandemic. Many of these institutions have canceled the brick-and-mortar education and other conventional learning practices and have instructed faculty to adopt online teaching through synchronous video teleconferencing platforms. However, the learning experience is not the same for students who reside in remote or rural communities with low bandwidth. There is very little research in this topic, especially in developing countries like Ghana, and the present study aims to bridge the gap in the literature by exploring the characteristics and potential effects of teaching and learning through audio teleconferencing (dial-in) with a cell phone, in the context of a 12-week postgraduate course.

DRLR: A Deep-Reinforcement-Learning-Based Recruitment Scheme for Massive Data Collections in 6G-Based IoT Networks

Recently, rapid deployment on the fifth-generation (5G) networks has brought great opportunities for enabling data-intensive applications and brings an extending expectation on the developments of 6G. A basic requirement to develop 6G networks is to reach data with low latency, low cost, and high coverage in smart Internet of Things (IoT). Therefore, this article proposes a novel machine learning-based approach to collect data from multiple sensor devices by cooperation between vehicle and unmanned aerial vehicle (UAV) in IoT. First, a genetic algorithm is utilized to select vehicular collectors to collect massive data from sensor devices, which aims to maximize coverage ratio and to minimize employment cost. Second, we design a novel deep reinforcement learning (DRL)-based route policy to plan collection routes of UAVs with constrain energy, which simplifies the network model, accelerates training speeds, and realizes dynamic planning of flight paths. The optimal collection route of a UAV is a series of outputs based on the proposed DRL-based route policy. Finally, our extensive experiments demonstrate that the proposed scheme can comprehensively improve the coverage ratio of massive data collections and reduce collection costs in smart IoT for the future 6G networks.

Energy-Efficient Deep Reinforced Traffic Grooming in Elastic Optical Networks for Cloud–Fog Computing

Cloud-fog computing emerges to satisfy the low latency and high computation requirements of Internet of Things (IoT) services. Elastic optical networks (EONs) are excellent substrate communication networks between fog datacenters and cloud datacenters. However, the uneven traffic of massive cloud-fog services incurs many spectrum fragments, leading to high extra energy consumption. To solve this problem, we propose an energy-efficient deep reinforced traffic grooming (EDTG) algorithm based on deep reinforcement learning. Unlike existing manually network features extracting methods, we convert the traditional network modal and the service routing path into colored network images to represent their states and extract the features automatically by MobilenetV3 according to these images. With the extracted features, we implement an advantage actor-critic (A2C) algorithm, whose actor module and critic module share an artificial neural network (ANN) to get optimal grooming actions. Additionally, after repeated attempts and experiments, we set up an objective reward and punishment mechanism to evaluate the grooming actions. We conduct extensive simulations for performance evaluation, and the results have shown that EDTG can significantly reduce energy consumption compared with two well-performed traffic grooming algorithms.

Survey of various bandwidth enhancement techniques used for 5G antennas

AbstractFifth-generation technology is not fully deployed in the world wireless communication till date. Millimeter-wave (mm-wave) band needs to be used due to plenty of available bandwidth and for achieving the goals of 5G such as greater data rate, ultra-high-speed video broadcasting, low latency services, and many more. Wideband antenna is required for 5G applications to access the high speed, low latency Internet services, and ultra-high-definition video streaming. Various bandwidth enhancement techniques have been reported by the researchers for microstrip antennas operating at microwave bands. High link losses, small wavelength, limited coverage, and environmental losses are the major challenges of mm-wave band. To mitigate these issues and satisfy 5G standard, an antenna with wide bandwidth, high gain, narrow steerable beam, high isolation, low side lobe levels, and multiband characteristics is required. Modifications in conventional antenna design techniques are required to achieve broader bandwidth along with stable radiation characteristics, improved gain, and low side lobe levels at mm-wave frequencies. This paper presents the survey of various bandwidth enhancement techniques which has been used in the 5G antennas designed by researchers. Reviews of some wideband 5G antennas with their performance comparisons are also discussed.

Heuristic Edge Server Placement in Industrial Internet of Things and Cellular Networks

Rapid developments in industry 4.0, machine learning, and digital twins have introduced new latency, reliability, and processing restrictions in Industrial Internet of Things (IIoT) and mobile devices. However, using current information and communications technology (ICT), it is difficult to optimally provide services that require high computing power and low latency. To meet these requirements, mobile-edge computing is emerging as a ubiquitous computing paradigm that enables the use of network infrastructure components such as cluster heads/sink nodes in IIoT and cellular network base stations to provide local data storage and computation servers at the edge of the network. However, optimal location selection for edge servers within a network out of a very large number of possibilities, such as to balance workload and minimize access delay, is a challenging problem. In this article, the edge server placement problem is addressed within an existing network infrastructure obtained from Shanghai Telecom's base station data set that includes a significant amount of call data records and locations of actual base stations. The problem of edge server placement is formulated as a multiobjective constraint optimization problem that places edge servers strategically to balance between the workloads of edge servers and reduce access delay between the industrial control center/cellular base stations and edge servers. To search randomly through a large number of possible solutions and selecting those that are most descriptive of optimal solution can be a very time-consuming process, therefore, we apply the genetic algorithm and local search algorithms (hill climbing and simulated annealing) to find the best solution in the least number of solution space explorations. Experimental results are obtained to compare the performance of the genetic algorithm against the above-mentioned local search algorithms. The results show that the genetic algorithm can quickly search through the large solution space as compared to local search optimization algorithms to find an edge placement strategy that minimizes the cost function.

Optimal deployment of cloudlets based on cost and latency in Internet of Things networks

With the development of the Internet of Things (IoT), a large amount of data is generated on the network edge. Given the limited computing power of mobile devices (MDs) and access to computing resources from remote clouds, which leads to high latency to MDs, edge computing provides a way to reduce service latency by building a miniature cloud (Cloudlet). MDs transfer tasks they generate to nearby cloudlets for lower latency. Although a lot of research has been done in the field of edge computing, little attention has been paid to how to deploy cloudlets in the network. In this paper, we study the cloudlet deployment on a large number of wireless access points (APs) in an IoT network to optimize both deployment cost and network latency. When the cloudlets has been deployed in the network, we propose a fault-tolerant cloudlet deployment scheme. When the original cloudlets in the network fail, the software-defined network technology is used to start the fault-tolerant cloudlets in time to ensure the stability of the network latency. To address the above problems, we propose a binary-based differential evolution cuckoo search (BDECS) algorithm, which selects the permanent cloudlet deployment location among a large number of APs on the network. Extensive simulations reveal that the proposed algorithm has better performance in minimizing cost and latency compared with other deploymegt algorithms. Moreover, the convergence speed of the BDECS algorithm is also superior to other algorithms.

Data Replication in Mobile Edge Computing Systems to Reduce Latency in Internet of Things

The progress in the development in the field of information technology has brought the Internet of Things (IoT) into existence to play a crucial role in our daily lives. There are interconnected sensors or devices that can both collect and also exchange various data among themselves by employing a modern network of communication as an infrastructure that has been connected by many millions of the IoT nodes. After this, there are various applications of the IoT that may be able to provide accurate and fine-grained services to the users. Using this as a strategy which can mitigate an escalation to the congestion of resources, Edge Computing is emerging as the new paradigm that solves the needs of localized computing and the IoT. The Mobile Edge Computing (MEC) has been emerging to handle the volume of data produced and this can reach a latency of demand of the IoT applications that are intensive in terms of computation. Even though the MEC has advanced in terms of latency of service and has been solidly investigated, the efficiency of data usage and security are not identified clearly. Replication of data is well suited for improving the time taken for a response, global traffic and data sharing as even at the time of server disconnection this can be done. In this work, efficient techniques of data replication for the Mobile Ad hoc Networks (MANET) like the simple and the random applications are evaluated for improving availability of data which considers all the issues that are related to the MANET like consumption of power, availability of resource, time taken for response and consistency management. The results of the experiment have shown that a random algorithm for replication can achieve a bandwidth that is better in terms of savings compared to a simple replication algorithm.

Energy-Optimal Dynamic Computation Offloading for Industrial IoT in Fog Computing

Fog computing is emerging as a promising mode to meet the stringent requirement of low latency in industrial Internet of Things (IIoT). By dynamically offloading part of the computation-intensive tasks from a fog node to a cloud server, the computation experience of users can be further improved in fog computing systems. In this paper, we develop an energy-optimal dynamic computation offloading scheme (EDCO) for IIoT in a fog computing scenario. The purpose is to minimize energy consumption when computation tasks are accomplished within a desired energy overhead and delay. Specifically, we first formulate an energy minimization computation offloading problem with delay, energy and other network resource constraints. To address this optimization problem, an accelerated gradient algorithm with joint optimization of the offloading ratio and transmission time is proposed; it can find the optimal value with a fast speed that improves the convergence speed of traditional methods. Subsequently, to better meet the stringent energy and latency requirements of IIoT applications, the dynamic voltage scaling (DVS) technique is integrated into the above solution, and we develop an alternating minimization algorithm to achieve energy-optimal fog computation offloading by jointly optimizing the offloading ratio, transmission power, local CPU computation speed and transmission time. Finally, the numerical results reveal that the proposed offloading scheme is superior to the local computing, full offloading and partial offloading with fixed computation speed schemes in terms of energy consumption and completion time. We also confirm the convergence rate advantage of the accelerated algorithm.

Resource-Aware Distributed Epilepsy Monitoring Using Self-Awareness From Edge to Cloud.

The integration of wearable devices in humans' daily lives has grown significantly in recent years and still continues to affect different aspects of high-quality life. Thus, ensuring the reliability of the decisions becomes essential in biomedical applications, while representing a major challenge considering battery-powered wearable technologies. Transferring the complex and energy-consuming computations to fogs or clouds can significantly reduce the energy consumption of wearable devices and result in a longer lifetime of these systems with a single battery charge. In this work, we aim to distribute the complex and energy-consuming machine-learning computations between the edge, fog, and cloud, based on the notion of self-awareness that takes into account the complexity and reliability of the algorithm. We also model and analyze the trade-offs in terms of energy consumption, latency, and performance of different Internet of Things (IoT) solutions. We consider the epileptic seizure detection problem as our real-world case study to demonstrate the importance of our proposed self-aware methodology.