Average Round-trip Time Research Articles

Environmental Monitoring System using Wireless Multi-Node Sensors based Communication System on Volcano Observations Drones

Indonesia is on the Ring of Fire and has the world's most active volcanoes. Volcanic activity has a significant effect on the landscape and on the people who live there. The difficulty of evacuating and helping victims requires hard work and sometimes even the safety of the rescue team itself. For this reason, high-tech tools are needed. Unmanned aerial vehicles (UAVs), also called drones, have become a hopeful tool for remote environmental monitoring in recent years. The system design has a monitoring platform, gateway, and sensor nodes attached to the UAV, which monitors the content of toxic gas contamination in the air. Using IoT technology, sensor data is sent wirelessly to a central monitoring station for a thorough and accurate volcanic activity study. This system is a flexible and complete way to monitor volcanic activity, learn more about it, and make it easier to respond to disasters. Tests are also done to measure system speed, including latency, and determine network service quality. The results show that data is successfully sent in real-time from the sensor nodes to the monitoring system. The average Round-Trip time for the payload transmission is 446.046226 ms. This shows how well the system works to send data from the sensors connected to the UAV to the monitoring station. The UAV has sensor nodes and a monitoring system platform. These can be used to build and optimize disaster mitigation systems.

A hybrid congestion control scheme for Named Data Networking via SDN

As the mainstream congestion control in Named Data Networking (NDN), the hybrid control mechanism integrates the advantages of consumer and router control mechanisms. However, The concurrent control by both consumers and routers may results in degraded data transmission performance. Therefore, we propose a novel hybrid congestion control scheme for NDN, called ndnSieve. By using the characteristics of Software Defined Network (SDN) that can achieve global network information acquisition as well as global resource scheduling, ndnSieve manages to invoke consumer and router control mechanisms based on different congestion states in the network respectively and achieves the synergy between two kinds of mechanisms. The results of experiment demonstrate that, compared with existing works, ndnSieve has the lowest average round-trip time, relatively high level of throughput and fairness, effectively improving the Quality of Service (QoS) of the network.

Optimum Batch Scheduling Model for Quality Aware Delay Sensitive Data Transmission over Fog Enabled IOT Network

The emerging fog networks in the internet of things (IoT) applications provide flexibility and agility for service providers. The combination of fog nodes and edge nodes enable them to deliver a given network service. However, the selection of suitable edge and fog nodes and their scheduling still remain a research challenge. Finding a globally optimal scheduling of oversized data transmission over IoT applications for industrial requirements is crucial. Optimal batch scheduling has been regarded as a viable way to achieve optimal scheduling in other contemporary network models. This manuscript has projected an Optimum Batch Scheduling Model (OBSM) for Quality aware Delay Sensitive Data Transmission over Fog Enabled IoT Networks. A novel clustering technique has been proposed in this manuscript to group the transmission nodes (fog or edge nodes) and data packets, which further pairs each group of data with one of the corresponding node group to achieve delay sensitivity and other quality factors such as energy efficiency. The data scheduling between data and node group is drawn from the previous contribution -"Quality aware Energy Efficient Scheduling Model (QEESM) for Fog Enabled IoT Network". The simulation results have shown that, in terms of average make span rate, average round trip time, and energy consumption, the batch scheduling model OBSM performs noticeably better than the contemporary scheduling models. The OBSM scheduling model's average make-span rate, roundtrip time, as well as energy consumption per make span are 23.3 7.03, 17.8 5.2, and 11.33 6.9 joules, respectively, which conclusively demonstrate that the OBSM model outperforms the existing models. A novel batch scheduling algorithm has been proposed using a unique unsupervised learning approach that suggested to cluster the transmission requests and transmission channels in to multiple clusters.

SCALABILITY IMPROVEMENT OF ACTIVE PROBING FOR FAILURE DETECTION IN LARGE-SCALE SOFTWARE DEFINED NETWORKS USING COMMUNITY DETECTION ALGORITHMS

Software-Defined Networking (SDN) is a networking approach that separates the network's control plane and data plane to provide better network control. The nature of SDN requires an essential focus on its reliability and availability, which includes failure detection aspects. Several techniques have been proposed to enhance SDN failure detection, including passive and active approaches. However, the Route Inspection (RI) algorithm, an example of the active probing method in active detection, has scalability issues when deployed in large-scale networks as it generates inefficient and lengthy probe paths. To address this, we propose using community detection (CD) algorithms to improve the probe path generation of the RI algorithm, resulting in the 'Failure Detection using Community Detection and probing (FDCD)' framework. With the aim to reduce travel paths, the proposed framework minimizes the network topology using CD algorithms to reduce the topology size into smaller communities. Then, the RI algorithm calculates the probe path in the communities instead of the single large topology, thus improving the efficiency of the failure detection mechanism. This paper adopts three community detection algorithms: Louvain, Label Propagation, and Girvan-Newman. We evaluate the framework using three well-known network topologies, COST266, PIORO40, and GERMANY50, in Mininet to observe the framework's performance. The results indicate that the proposed approach improves the algorithm's scalability against its baseline approach, improving the average probe round trip time by 66.87% and the average path installation time by 2.67%.

A Novel Deep-Learning Model for Remote Driver Monitoring in SDN-Based Internet of Autonomous Vehicles Using 5G Technologies

The rapid advancement in the Internet of Things (IoT) and its integration with Artificial Intelligence (AI) techniques are expected to play a crucial role in future Intelligent Transportation Systems (ITS). Additionally, the continuous progress in the industry of autonomous vehicles will accelerate and increase their short adoption in smart cities to allow safe, sustainable and accessible trips for passengers in different public and private means of transportation. In this article, we investigate the adoption of 5G different technologies, mainly, the Software-Defined Networks (SDN) to support the communication requirements of delegation of control of level-2 autonomous vehicles to the Remote-Control Center (RCC) in terms of ultra-low delay and reliability. This delegation occurs upon the detection of a drowsy driver using our proposed deep-learning-based technique deployed at the edge to reduce the level of accidents and road congestion. The deep learning-based model was evaluated and produced higher accuracy, precision and recall when compared to other methods. The role of SDN is to implement network slicing to achieve the Quality of Service (QoS) level required in this emergency case. Decreasing the end-to-end delay required to provide feedback control signals back to the autonomous vehicle is the aim of deploying QoS support available in an SDN-based network. Feedback control signals are sent to remotely activate the stopping system or to switch the vehicle to direct teleoperation mode. The mininet-WiFi emulator is deployed to evaluate the performance of the proposed adaptive SDN framework, which is tailored to emulate radio access networks. Our simulation experiments conducted on realistic vehicular scenarios revealed significant improvement in terms of throughput and average Round-Trip Time (RTT).

Toward the Design of an Efficient and Secure System Based on the Software-Defined Network Paradigm for Vehicular Networks

The advent of Intelligent Transport Systems (ITS) has led to the appearance of vehicles increasingly connected to their environment on global road networks. Due to the strict requirements for low latency and secure interactions in a vehicular environment, the proposal of new architectures is a crucial topic for discussion. This paper aims to develop a vehicular network using several access technologies based on SDN (Software Defined Network) paradigm, to take advantage of the capacities of the various access networks and provide flexibility in their control and management. Confidentiality, integrity, and authentication are essential services to prevent an adversary from compromising the security of vehicular networks. Therefore, good security and privacy management system is necessary to ensure this protection. We represent then a hybrid SDN-VANET architecture that can address all of the challenges we mentioned earlier. We are in the process of implementing a dynamic approach to optimize the positioning of controllers according to changes in network topology due to fluctuations in road traffic. We will also detail the topology estimation service based on machine learning techniques to provide network control functions with potential insight into the future state of the network, unlocking proactive and intelligent network control. We also provide a scheme that prevents and informs about basic and compound attacks and reacts to the privacy and security conditions of the vehicular network, managing the requirements of security management systems. The simulation results showed the effectiveness of the proposed schemes in terms of message loss rates, packet delivery rates (PDR), Round Trip Time (RTT), and delays. With used our scheme, the performance of the network is improved when SDN triggers the change of the RSU entity. Such as, we notice that the average RTT is lowered by 68 ms and that the PDR remains around 94%. We also notice with the integration of the security and privacy scheme (SPS) that the performance of the network is improved, the average RTT is reduced by 51 ms and the PDR persists around 99%.

Effects on random early detection of the packet drop probability function with an adjustable nonlinearity

This paper proposed a packet drop probability function with an adjustable nonlinearity parameter in random early detection (RED) for active queue management of a router to control network congestion. We investigated the effect of nonlinearity on the average queue size, average throughput, average retransmission rate, average round-trip time, and fairness index for two widely used loss-based congestion control algorithms: Reno and CUBIC. Simulations were performed with Tail-Drop and the original RED to clarify the effect of nonlinearity under different traffic loads. The results showed that the RED with a nonlinear function did not aggravate the network performance statistics because achieved high throughput while maintaining a low-queuing delay, such as the original RED with a linear function under the extremely heavy traffic condition. Under the light and the heavy traffic conditions, increasing the bending degree of the nonlinear function accomplished high throughput by preventing excessive discarding of packets.

Exploration and Prediction of the Elderly Travel Behavior Based on a Novel GR-GA-BP Hybrid Model

With the aging trend in megacities, the travel behaviors of the elderly have attracted much attention. Accurate prediction of the travel behaviors of the elderly is a key link to meet the traffic demand and public facilities’ optimization. The aim of this paper was to explore the link between the travel characteristics and variables of the daily activities of the elderly. Based on a stratified sampling survey, the internal relationship between the characteristics of the elderly and their travel behavior was studied and discussed in this work. A novel grey correlation degree–genetic algorithm–back propagation (GR-GA-BP) hybrid model was proposed to predict the travel behavior of the elderly. Then, a grey correlation degree module was established and used to analyze the correlation between the individual elderly characteristics and their travel behavior. The results showed the following: (1) Both the times of weekly trips (y1) and average round-trip travel time (y2) were highly sensitive to the external environment, especially buses, subway stations, and recreational facilities. The size of the family was less sensitive to the travel behavior. (2) Referring to prediction of the times of weekly trips, the MRE of the proposed model was 23.12%, which was 15.22% less than the baseline models. (3) In terms of the prediction of round-trip travel time, the MRE of the proposed model was 7.13%, which was 14.00–69.41% lower than the baseline models. (4) The times of trips per week were 3.5. In summary, this paper provides technical support for formulating traffic demand policies and facilitates the configuration of cities for an aging society.

Design and Implementation of Low-Cost Distributed Tabletop Magnetic Particle Imaging System

Magnetic particle imaging (MPI) is a novel tomography model that mainly benefits from the nonlinear magnetization response of magnetic nanoparticles under a dramatically changing magnetic field. Low-cost and compact system construction is beneficial to the application of education, biomedical research, preclinical and clinical medicine. Conversely, many aspects of the production cost, infrastructure requirements, and distributed measurements can be prohibitively expensive for them. In this article, a low-cost, compact tabletop MPI scanner based on a field-programmable gate array (FPGA) framework with an Advanced RISC Machine (ARM) cores was proposed to address these issues. First, the proposed low-cost MPI scanner was realized by substituting discrete elements, which is affordable to cover the most economically underdeveloped regions. Besides, the ARM cores embedded in the FPGA framework which has the inherent advantages of low-power operation, are exploited to execute high-power real-time data transmission tasks instead of conventional X86. And a customized tabletop MPI system was constructed via the proposed architecture for advanced distributed MPI research. Simulative vessel experiments with commercial superparamagnetic nanoparticles (SPIONs) have been performed in the proposed system by vessel phantoms. The experimental images showed that the proposed system could achieved an image resolution of approximately 2 mm, which can distinguish spatial locations of brain veins (2–4 mm in diameter). Moreover, the experiment of distributed transmission showed that the proposed system could perform the collection and transmission of distributed particle signals based on the inherent user datagram protocol (UDP) network transmission protocol with an average round trip time (RTT) of 28.044 ms. Hence, the proposed system has the potential to apply the network cloud to get distributed biomedical information for remote education or diagnosis.

Distributed memory access architecture and control for fully disaggregated datacenter network

In this paper, we propose novel disaggregated memory module (dMM) architecture and memory access control schemes to solve the collision and contention problems of memory disaggregation, reducing the average memory access time to less than 1 μs. In the schemes, the distributed scheduler in each dMM determines the order of memory read/write access based on delay-sensitive priority requests in the disaggregated memory access frame (dMAF). We used the memory-intensive first (MIF) algorithm and priority-based MIF (p-MIF) algorithm that prioritize delay-sensitive and/or memory-intensive (MI) traffic over CPU-intensive (CI) traffic. We evaluated the performance of the proposed schemes through simulation using OPNET and hardware implementation. Our results showed that when the offered load was below 0.7 and the payload of dMAF was 256 bytes, the average round trip time (RTT) was the lowest, ~0.676 μs. The dMM scheduling algorithms, MIF and p-MIF, achieved delay less than 1 μs for all MI traffic with less than 10% of transmission overhead.

Adopting MQTT for a multi protocols IoMT system

<span>Coronavirus disease (COVID-19) altered the way of caregiving and the new pandemic forced the health systems to adopt new treatment protocols in which remote follow-up is essential. This paper introduces a proposed system to link a remote healthcare unit as it is inside the hospital. Two different network protocols; a global system for mobile communication (GSM) and Wi-Fi were used to simulate the heath data transfer from the two different geographical locations, using Raspberry Pi development board and Microcontroller units. Message queuing telemetry transport (MQTT) protocol was employed to transfer the measured data from the healthcare unit to the hospital’s Gateway. The gateway is used to route the aggregated health data from healthcare units to the hospital server, doctors’ dashboards, and the further processing. The system was successfully implemented and tested, where the experimental tests show that the remote healthcare units using a GSM network consumed about 900 mWh. A high percentage of success data packets transfer was recorded within the network framework as it reaches 99.89% with an average round trip time (RTT) of 7.5 milliseconds and a data transfer rate up to 12.3 kbps.</span>

Implementation of MQTT Protocol in Health Care Based on IoT Systems: A Study

Internet of things IoT systems have become one of the most promising technologies in all fields. Data transmission is one of the important aspects, and the tendency to messaging protocols is an important aspect of IoT systems. One of these most important protocols is MQTT. This protocol depends on the Publish/Subscribe model, and it is a lightweight protocol. Reliability, simplicity, quality of service levels, and being Resource-constrained make MQTT common in the IoT industry. This paper designed an IoT device that consists of the sensor MLX 90614 non-contact IR Temperature connected to a development board (Node MCU ESP8266). A person's temperature is one of the important vital signs. This system measures human temperature values and transmits the measured values to the Mosquitto broker by using the MQTT protocol in real-time. The technology used is Wi-Fi. The person or the doctor can read the patient’s temperature remotely through a program (Flutter Android Client) representing the subscriber. Also, MQTT protocol control packets of the system were analyzed using Wireshark. The three levels of QoS were used in subscriber clients to compare the throughput. The results indicate that QoS2 is more reliable and offers more throughput but more delay. The results also show that the average round trip time (RTT) of the MQTT protocol is five milliseconds which means optimal performance for IoT applications.

An application layer technique to overcome TCP incast in data center network using delayed server response

Applications running in data center networks exhibits barrier-synchronized many-to-one communication pattern. Under this pattern, multiple workers synchronously transmit data to single aggregator. Synchronized transmission from multiple workers overload small-sized buffer of switch connected to aggregator. This causes severe packet drops and leads to overall application goodput degradation. This is named as TCP Incast problem in data center network. In this paper, an application layer technique called Delayed Server Response at Application Layer (DSRAL) is proposed to mitigate TCP Incast problem. It is delay-based sequential data transmission technique. In DSRAL, aggregator first determines amount of time required by each worker to finish its data transmission. This can be done by knowing Server Request Unit (SRU) size, average Round Trip Time (RTT) and packet scheduling time. Then along with request packet, aggregator communicates distinct SRU transmission time with each worker and enforces them to transmit SRUs sequentially. Through simulation it is proved that DSRAL avoids TCP Incast problem and provides improved performance than existing application layer serialized method. It supports large number of workers and simultaneously achieves high application goodput without causing any packet loss.

BBR-S: A Low-Latency BBR Modification for Fast-Varying Connections

The new possibilities offered by 5G and beyond networks have led to a change in the focus of congestion control from capacity maximization for web browsing and file transfer to latency-sensitive interactive and real-time services, and consequently to a renaissance of research on the subject, whose most well-known result is Google's Bottleneck Bandwidth and Round-trip propagation time (BBR) algorithm. BBR's promise is to operate at the optimal working point of a connection, with the minimum Round Trip Time (RTT) and full capacity utilization, striking the balance between resource use efficiency and latency performance. However, while it provides significant performance improvements over legacy mechanisms such as Cubic, it can significantly overestimate the capacity of fast-varying mobile connections, leading to unreliable service and large potential swings in the RTT. Our BBR-S algorithm replaces the max filter that causes this overestimation issue with an Adaptive Tobit Kalman Filter (ATKF), an innovation on the Kalman filter that can deal with unknown noise statistics and saturated measurements, achieving a 40% reduction in the average RTT over BBR, which increases to 60% when considering worst-case latency, while maintaining over 95% of the throughput in 4G and 5G networks.

CDRA: A Community Detection based Routing Algorithm for Link Failure Recovery in Software Defined Networks

The increase in size and complexity of the Internet has led to the introduction of Software Defined Networking (SDN). SDN is a new networking paradigm that breaks the limitations of traditional IP networks and upgrades the current network infrastructures. However, like traditional IP networks, network failures may also occur in SDN. Multiple research studies have discussed this problem by using a variety of techniques. Among them is the use of the community detection method is one of the failure recovery technique for SDN. However, this technique have not considered the specific problem of multiple link multi-community failure and inter-community link failure scenarios. This paper presents a community detection-based routing algorithm (CDRA) for link failure recovery in SDN. The proposed CDRA scheme is efficient to deal with single link intra-community failure scenarios and multiple link multi-community failure scenarios and is also able to handle the inter-community link failure scenarios in SDN. Extensive simulations are performed to evaluate the performance of the proposed CDRA scheme. The simulation results depicts that the proposed CDRA scheme have better simulations results and reduce average round trip time by 35.73%, avg data packet loss by 1.26% and average end to end delay 49.3% than the Dijkstra based general recovery algorithm and also can be used on a large scale network platform.

6LE-SDN: An Edge-Based Software-Defined Network for Internet of Things

IPv6 over low-power wireless personal area network (6LoWPAN) has been widely used for large-scale sensing and actuating purposes in the Internet of Things (IoT). Though promising, many challenges, such as high latency, heterogeneity, and packet loss persist. To mitigate these challenges, the software-defined network (SDN) technique can be hybridized with existing IoT structures that can address many of them. In this article, we propose an approach—edge-based 6LoWPAN-SDN (6LE-SDN) architecture, which can improve the system limitations mentioned. It uses an edge-based computational capability to improve the network performance over 6LoWPAN. To reduce heterogeneity, we develop a hybrid-edge switch that helps to enable communication among 6LoWPAN and SDN entities. For efficient communication between different devices, a new protocol—edge-based 6LoWPAN-SDN protocol (6LE-SDNP) is proposed, which is capable of ensuring optimal routing of the packet for efficient communication among the devices. We use the SDN-based edge controller for reducing the latency of the network apart from improving the interoperability feature. The testbed evaluation of the proposed solution indicates satisfactory performance in terms of reducing latency by 60% and network overhead by 91%. The 6LE-SDN network also succeeded in reducing the average round trip time (RTT) by 31% and the packet loss by 70% as compared to that of the traditional 6LoWPAN-based IoT.

Does self-reported water collection time differ from observed water collection time? Evidence from the Upper West Region of Ghana

Abstract Water collection time is a key indicator in tracking access to drinking water. Over the years, water surveillance agencies have relied on water collectors for information on water collection time to measure progress of water supply. However, emerging evidence shows that water collection times reported by water collectors in developing countries are often imprecise. To contribute to knowledge about the validity or otherwise of self-reported water collection times, this study explored the association between self-reported water collection times and observed water collection times in the Upper West Region of Ghana. Data on water collection times were gathered from 412 water collectors in Daffiama-Issa-Bussie District and Lawra Municipality, first through interviews, and second by observation. From the results, self-reported water collection times were significantly (P < 0.01) lower than observed water collection times. The average round-trip water collection time reported by water collectors (32 minutes) was 8 minutes lower than observed water collection times (40 minutes). This implies that existing statistics on basic water coverage that are largely based on self-reported water collection times are overestimated. Going forward, we call on water surveillance agencies to collect data on water collection times through observation. This article has been made Open Access thanks to the generous support of a global network of libraries as part of the Knowledge Unlatched Select initiative.

Software-Defined Networking load distribution technique for an internet service provider

Internet Service Providers (ISPs) are expected to continuously provide services without interruptions. However, planning for this activity requires a number of tasks such as forecasting clients' demand that needs to be considered in fulfilling their objectives particularly in face of calamity and traffic load variations. We present a Software-Defined Networking (SDN) load distribution technique for a typical ISP. We have implemented an optimization method employing linear programming to achieve this goal. A multi-objective model has been developed for this purpose. The multiple objectives are maximization of link utilization, maximization of link availability and minimization of average round-trip time for the top visited websites by users. Furthermore, an automated load distribution system has been developed for carrying out these optimization related activities. In addition, an example is presented to illustrate traffic load distribution under different operational modes (normal, critical, and catastrophic). The findings of this study should be of great help to the service provider to better plan for load distribution between international links, and help in approving down times and maintenance windows requested by cable providers.

ECCO: A Novel End-to-End Congestion Control Scheme in Multi-Hop Cognitive Radio Ad Hoc Networks

End-to-end congestion control, aiming to find out how much traffic load offered by the source can be handled by a network, is an essential function of a transport layer protocol. Although various efforts have been made to address the end-to-end congestion control issue in traditional wireless ad hoc networks, these approaches usually lead to excessively long delay for the source node to react to the congestion in cognitive radio (CR) ad hoc network because of the non-uniform channel availability. Thus, the end-to-end throughput suffers significant degradation. In this paper, a novel end-to-end congestion control scheme named ECCO is proposed that considers the unique features in multi-hop CR ad hoc networks, such as spectrum sensing, channel rendezvous, and licensed user activities. The average packet round trip time for multi-hop CR ad hoc networks is derived. In addition, through extensive simulation, it is shown that our proposed novel end-to-end congestion control scheme outperforms the existing congestion control mechanisms in terms of higher end-to-end throughput. To the best of our knowledge, this is the first paper that investigates the end-to-end congestion control issue in multi-hop CR ad hoc networks.

An RTT-Aware Virtual Machine Placement Method

Virtualization is a key technology for mobile cloud computing (MCC) and the virtual machine (VM) is a core component of virtualization. VM provides a relatively independent running environment for different applications. Therefore, the VM placement problem focuses on how to place VMs on optimal physical machines, which ensures efficient use of resources and the quality of service, etc. Most previous work focuses on energy consumption, network traffic between VMs and so on and rarely consider the delay for end users’ requests. In contrast, the latency between requests and VMs is considered in this paper for the scenario of optimal VM placement in MCC. In order to minimize average RTT for all requests, the round-trip time (RTT) is first used as the metric for the latency of requests. Based on our proposed RTT metric, an RTT-Aware VM placement algorithm is then proposed to minimize the average RTT. Furthermore, the case in which one of the core switches does not work is considered. A VM rescheduling algorithm is proposed to keep the average RTT lower and reduce the fluctuation of the average RTT. Finally, in the simulation study, our algorithm shows its advantage over existing methods, including random placement, the traffic-aware VM placement algorithm and the remaining utilization-aware algorithm.