scholarly journals Smart SDN Management of Fog Services to Optimize QoS and Energy

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3105
Author(s):  
Piotr Fröhlich ◽  
Erol Gelenbe ◽  
Jerzy Fiołka ◽  
Jacek Chęciński ◽  
Mateusz Nowak ◽  
...  
Keyword(s):  
Power Consumption ◽  
Response Times ◽  
Goal Function ◽  
Time Varying ◽  
Test Bed ◽  
Average Response Time ◽  
Client Server ◽  
Control Scheme ◽  
Iot Devices ◽  
Network Power

The short latency required by IoT devices that need to access specific services have led to the development of Fog architectures that can serve as a useful intermediary between IoT systems and the Cloud. However, the massive numbers of IoT devices that are being deployed raise concerns about the power consumption of such systems as the number of IoT devices and Fog servers increase. Thus, in this paper, we describe a software-defined network (SDN)-based control scheme for client–server interaction that constantly measures ongoing client–server response times and estimates network power consumption, in order to select connection paths that minimize a composite goal function, including both QoS and power consumption. The approach using reinforcement learning with neural networks has been implemented in a test-bed and is detailed in this paper. Experiments are presented that show the effectiveness of our proposed system in the presence of a time-varying workload of client-to-service requests, resulting in a reduction of power consumption of approximately 15% for an average response time increase of under 2%.

scholarly journals Smart SDN Management of Fog Services

2020 ◽  
Author(s):  
Piotr Frohlich ◽  
Erol Gelenbe ◽  
Mateusz P. Nowak
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Measurement Data ◽  
Experimental Results ◽  
Time Varying ◽  
Test Bed ◽  
Iot Devices ◽  
Smart Service ◽  
Service Manager ◽  
Sdn Controller

<p>We present a smart Service Manager whose role is</p> <p>to direct user requests (such as those coming from IoT devices)</p> <p>at the edge towards appropriate servers where the services they</p> <p>request can be satisfied, when services can be housed at different</p> <p>Fog locations, and the system is subject to variations in workload.</p> <p>The approach we propose is based on using an SDN controller as</p> <p>a decision element, and to incorporate measurement data based</p> <p>machine learning that uses Reinforcement Learning to make the</p> <p>best choices. The system we have developed is illustrated with</p> <p>experimental results on a test-bed in the presence of time-varying</p> <p>loads at the servers. The experiments confirm the ability of the</p> <p>system to adapt to significant changes in system load so as to</p> <p>preserve the QoS perceived by end users.</p>

scholarly journals Smart SDN Management of Fog Services

2020 ◽  
Author(s):  
Piotr Frohlich ◽  
Erol Gelenbe ◽  
Mateusz P. Nowak
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Measurement Data ◽  
Experimental Results ◽  
Time Varying ◽  
Test Bed ◽  
Iot Devices ◽  
Smart Service ◽  
Service Manager ◽  
Sdn Controller

<p>We present a smart Service Manager whose role is</p> <p>to direct user requests (such as those coming from IoT devices)</p> <p>at the edge towards appropriate servers where the services they</p> <p>request can be satisfied, when services can be housed at different</p> <p>Fog locations, and the system is subject to variations in workload.</p> <p>The approach we propose is based on using an SDN controller as</p> <p>a decision element, and to incorporate measurement data based</p> <p>machine learning that uses Reinforcement Learning to make the</p> <p>best choices. The system we have developed is illustrated with</p> <p>experimental results on a test-bed in the presence of time-varying</p> <p>loads at the servers. The experiments confirm the ability of the</p> <p>system to adapt to significant changes in system load so as to</p> <p>preserve the QoS perceived by end users.</p>

scholarly journals Formation Tracking Control and Obstacle Avoidance of Unicycle-Type Robots Guaranteeing Continuous Velocities

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4374
Author(s):  
Jose Bernardo Martinez ◽  
Hector M. Becerra ◽  
David Gomez-Gutierrez
Keyword(s):  
Obstacle Avoidance ◽  
Tracking Control ◽  
Global Coordinate System ◽  
Avoidance Task ◽  
Time Varying ◽  
Control Laws ◽  
Formation Tracking ◽  
Control Scheme ◽  
First Time ◽  
Hierarchical Scheme

In this paper, we addressed the problem of controlling the position of a group of unicycle-type robots to follow in formation a time-varying reference avoiding obstacles when needed. We propose a kinematic control scheme that, unlike existing methods, is able to simultaneously solve the both tasks involved in the problem, effectively combining control laws devoted to achieve formation tracking and obstacle avoidance. The main contributions of the paper are twofold: first, the advantages of the proposed approach are not all integrated in existing schemes, ours is fully distributed since the formulation is based on consensus including the leader as part of the formation, scalable for a large number of robots, generic to define a desired formation, and it does not require a global coordinate system or a map of the environment. Second, to the authors’ knowledge, it is the first time that a distributed formation tracking control is combined with obstacle avoidance to solve both tasks simultaneously using a hierarchical scheme, thus guaranteeing continuous robots velocities in spite of activation/deactivation of the obstacle avoidance task, and stability is proven even in the transition of tasks. The effectiveness of the approach is shown through simulations and experiments with real robots.

scholarly journals Reliability Improvement of LoRa with ARQ and Relay Node

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 552 ◽  
Author(s):  
Rocksan Choi ◽  
SeungGwan Lee ◽  
Sungwon Lee
Keyword(s):  
Power Consumption ◽  
Low Power ◽  
Smart Cities ◽  
Wide Area ◽  
Forward Rate ◽  
Low Power Consumption ◽  
Modern World ◽  
Automatic Repeat Request ◽  
Actual Performance ◽  
Iot Devices

In our modern world, many Internet of Things (IoT) technologies are being researched and developed. IoT devices are currently being used in many fields. IoT devices use Wi-Fi and Bluetooth, however, communication distance is short and battery consumption is high. In areas such as smart cities and smart farms, IoT technology is needed to support a wide coverage with low power consumption. Low Power Wide Area (LPWA), which is a transmission used in IoT supporting a wide area with low power consumption, has evolved. LPWA includes Long Range (LoRa), Narrowband (NB-IoT), and Sigfox. LoRa offers many benefits as it communicates the longest distances, is cheap and consumes less battery. LoRa is used in many countries and covers a range of hundreds of square kilometers (km2) with a single gateway. However, if there are many obstacles to smart cities and smart farms, it causes communication problems. This paper proposes two (2) solutions to this problem: the relay method which is a multi-hop method and the Automatic Repeat Request (ARQ) system that detects packet loss in real-time and requests retransmission for LoRa. In this study, the actual performance of LoRa in the problematic environment was measured and the proposed method was applied. It was confirmed that the transmission rate of LoRa dropped when there were many obstacles such as trees. To use LoRa in a smart farm with a lot of space, multi-hop was observed to be better. An ARQ system is needed to compensate for the unexpected drop in the forward rate due to the increase in IoT devices. This research focused on reliability, however, additional network methods and automatic repeat request (ARQ) systems considering battery time should be researched in symmetry. This study covers the interdisciplinary field of computer science and wireless low power communication engineering. We have analyzed the LoRa/LoRaWAN technology in an experimental approach, which has been somewhat less studied than cellular network or WiFi technology. In addition, we presented and improved the performance evaluation results in consideration of various local and climatic environments.

Psychophysiological Markers of Performance and Learning during Simulated Marksmanship in Immersive Virtual Reality

2021 ◽  
pp. 1-18
Author(s):  
Sicong Liu ◽  
Jillian M. Clements ◽  
Elayna P. Kirsch ◽  
Hrishikesh M. Rao ◽  
David J. Zielinski ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Motor Control ◽  
Response Times ◽  
Immersive Virtual Reality ◽  
Test Bed ◽  
Beta Band ◽  
Neuroscience Research ◽  
Visual Motor ◽  
Behavioral Mechanisms ◽  
Visual Evoked

Abstract The fusion of immersive virtual reality, kinematic movement tracking, and EEG offers a powerful test bed for naturalistic neuroscience research. Here, we combined these elements to investigate the neuro-behavioral mechanisms underlying precision visual–motor control as 20 participants completed a three-visit, visual–motor, coincidence-anticipation task, modeled after Olympic Trap Shooting and performed in immersive and interactive virtual reality. Analyses of the kinematic metrics demonstrated learning of more efficient movements with significantly faster hand RTs, earlier trigger response times, and higher spatial precision, leading to an average of 13% improvement in shot scores across the visits. As revealed through spectral and time-locked analyses of the EEG beta band (13–30 Hz), power measured prior to target launch and visual-evoked potential amplitudes measured immediately after the target launch correlate with subsequent reactive kinematic performance in the shooting task. Moreover, both launch-locked and shot/feedback-locked visual-evoked potentials became earlier and more negative with practice, pointing to neural mechanisms that may contribute to the development of visual–motor proficiency. Collectively, these findings illustrate EEG and kinematic biomarkers of precision motor control and changes in the neurophysiological substrates that may underlie motor learning.

Iterative learning algorithm with a quadratic criterion for linear time-varying systems

2002 ◽  
Vol 216 (3) ◽  
pp. 309-316 ◽  
Author(s):  
S N Huang ◽  
K K Tan ◽  
T H Lee
Keyword(s):  
Learning Algorithm ◽  
Linear Time ◽  
Tracking Error ◽  
Iterative Learning ◽  
Tracking Performance ◽  
Time Varying ◽  
Control Scheme ◽  
Time Varying Systems ◽  
Simulation Results ◽  
Learning Law

A novel iterative learning controller for linear time-varying systems is developed. The learning law is derived on the basis of a quadratic criterion. This control scheme does not include package information. The advantage of the proposed learning law is that the convergence is guaranteed without the need for empirical choice of parameters. Furthermore, the tracking error on the final iteration will be a class K function of the bounds on the uncertainties. Finally, simulation results reveal that the proposed control has a good setpoint tracking performance.

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