Evaluation of advanced fuzzy logic PSS on analog network simulator and actual installation on hydro generators

1996 ◽  
Vol 11 (1) ◽  
pp. 125-131 ◽  
Author(s):  
T. Hiyama ◽  
S. Oniki ◽  
H. Nagashima
Keyword(s):  
Fuzzy Logic ◽  
Network Simulator

scholarly journals A Resource Oriented Route Selection Framework Using Contextual Information Based on Fuzzy Logic

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1023 ◽  
Author(s):  
Khadak Singh Bhandari ◽  
Gi Hwan Cho
Keyword(s):  
Fuzzy Logic ◽  
Objective Function ◽  
Smart City ◽  
Contextual Information ◽  
Network Dynamics ◽  
Residual Energy ◽  
Smart Devices ◽  
Route Selection ◽  
Network Simulator ◽  
Lossy Networks

Internet of Things (IoT) is expected to have a significant impact on city’s service provisioning and make a smart city more accessible and pragmatic since the deployment of heterogeneous smart devices in each infrastructure of cities is increasing. So far, the IPv6 routing protocol for low power and lossy networks (RPL) is considered to fit on IoT infrastructure for achieving the expected network requirements. While RPL meets the IoT network requirements quite well, there are some issues that need to be addressed, such as adaptability to network dynamics. This issue significantly limits the use of RPL in many smart city application scenarios, such as emergency alerts with high traffic flows. As part of a smart city vision, IoT applications are becoming more diverse, which requires context-awareness in routing protocols to support the behavior of the network. To address this issue, we design an objective function that performs the route selection based on fuzzy logic techniques while using contextual information from the application. For this, we present a new context-oriented objective function (COOF) that comprises both nodes as well as link metrics. Further, we suggest two new routing metrics, known as queue fluctuation index (QFI) and residual energy index (REI), which consider the status of queue utilization and remaining energy, respectively. The metrics used are designed to respond to the dynamic needs of the network. The proposed approach has been examined and evaluated in different scenarios when compared to other similar approach and default RPL objective functions. Simulation experiments are conducted in Cooja network simulator for Contiki OS. The evaluation results show that COOF can cope with network dynamics and IoT-based smart city application requirements.

scholarly journals Fuzzy Logic Aware QoS Multicasting in MANETs with Load Balance

2018 ◽  
Vol 7 (4.6) ◽  
pp. 269
Author(s):  
Nageswara Rao Sirisala ◽  
Raswitha Bandi
Keyword(s):  
Fuzzy Logic ◽  
Load Balance ◽  
Mobile Ad Hoc Network ◽  
Fuzzy Inference ◽  
Multicast Routing ◽  
Multicast Tree ◽  
Network Simulator ◽  
Inference System ◽  
Intermediate Node ◽  
Qos Parameters

Mobile ad-hoc network (MANET) is a decentralized and infrastructure less network where a nodes can communicate with other nodes within the access region. Due to mobility node can enter and leave a network at any moment. Due to unstable nature of MANETs, the provision of Quality of Service (QoS) to the applications is a difficult task. In this paper, fuzzy logic enabled QoS multicast routing is proposed. Here energy, bandwidth and link expiry time are considered as a QoS parameters. The existing methods lost their performance in handling multi constrained QoS protocols, since defining the dynamic priorities among the multiple QoS parameters is not a trivial task. In the proposed method “Fuzzy Logic Aware QoS Multicasting in MANETs with Load Balance-FQML”, this issue is overcome by using fuzzy logic. The Competency Factor of each intermediate node along the route is calculated by aggregating it’s QoS parameters using fuzzy inference system. In the classical multicast protocols, some of the nodes in the multicast tree are overloaded by having multiple branches towards the destination nodes. This leads to partition of the multicast tree and degrades the performance of protocols. In the proposed method, this problem is handled by limiting the number of branches at intermediate nodes. The results are taken in network simulator-ns2, where the proposed method could measure less number of path failures and improved results than existing methods. 

scholarly journals PERFORMANCE ENHANCEMENTS OF COGNITIVE RADIO NETWORKS USING THE IMPROVED FUZZY LOGIC

2019 ◽  
Vol 2019 (2) ◽  
pp. 57-68 ◽  
Author(s):  
Dr. P Ebby Darney ◽  
Dr. I. Jeena Jacob
Keyword(s):  
Fuzzy Logic ◽  
Cognitive Radio ◽  
Spectrum Allocation ◽  
Network Simulator ◽  
Channel Switching ◽  
Secondary Users ◽  
Efficient Management ◽  
User Request ◽  
Radio Technology ◽  
Effective Decision

The rapid increase in the mobile device and the different types of wireless communication has led to the necessity of the extra spectrum allocation for the proper transmission of the information. Since the additional spectrum allocation for every network involved in the data transmission is a strenuous process, the efficient management of the spectrum allocation is preferred. The cognitive radio technology does a befitting service in the managing the allocation of the spectrum efficiently by providing the vacant spaces of the licensed users to the secondary users and vacating the secondary users when the licensed user request for the spectrum. This results in the deterioration in the performance of the secondary users due to the immediate evacuating. The conventional methods in the deciding the channel switching remains unsuitable for the cognitive radio network, so to have an effective decision on switching and selecting the channel the paper put forth the improved fuzzy logic that relies on the decision (IFDSS-GA) support system to handle both the switching of the channels and genetic algorithm to select the proper spectrum for conveyance. The evaluation of the proposed approach using the network simulator -2 determines the competency the IFDSS in terms of the throughput and switching rate.

scholarly journals A Modification of the Fuzzy Logic Based DASH Adaptation Scheme for Performance Improvement

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Hyun Jun Kim ◽  
Ye Seul Son ◽  
Joon Tae Kim
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Buffer Overflow ◽  
Buffer Size ◽  
Network Simulator ◽  
Bit Rate ◽  
Lte Networks ◽  
Available Bandwidth ◽  
Streaming Service ◽  
Adaptation Scheme

We propose a modification of the fuzzy logic based DASH adaptation scheme (FDASH) for seamless media service in time-varying network conditions. The proposed scheme (mFDASH) selects a more appropriate bit-rate for the next segment by modification of the Fuzzy Logic Controller (FLC) and estimates more accurate available bandwidth than FDASH scheme by using History-Based TCP Throughput Estimation. Moreover, mFDASH reduces the number of video bit-rate changes by applying Segment Bit-Rate Filtering Module (SBFM) and employs Start Mechanism for clients to provide high-quality videos in the very beginning stage of the streaming service. Lastly, Sleeping Mechanism is applied to avoid any expected buffer overflow. We then use NS-3 Network Simulator to verify the performance of mFDASH. Upon the experimental results, mFDASH shows no buffer overflow within the limited buffer size, which is not guaranteed in FDASH. Also, we confirm that mFDASH provides the highest QoE to DASH clients among the three schemes (mFDASH, FDASH, and SVAA) in Point-to-Point networks, Wi-Fi networks, and LTE networks, respectively.

Hysteresis in Categorization: Fuzzy Logic Makes Fuzzy Boundaries

2012 ◽  
Author(s):  
Thomas M. Crawford ◽  
Justin Fine ◽  
Donald Homa
Keyword(s):  
Fuzzy Logic

Continuous Estimation of CO2 Production during Exercise

1997 ◽  
Vol 36 (04/05) ◽  
pp. 368-371
Author(s):  
R. Soma ◽  
Y. Yamamoto
Keyword(s):  
Fuzzy Logic ◽  
Steady State ◽  
Infusion Rate ◽  
Feedback System ◽  
Whole Body ◽  
Co2 Production ◽  
Cycle Exercise ◽  
Continuous Estimation ◽  
Breath Measurement ◽  
13Co2 Enrichment

Abstract.A new method was developed for continuous isotopic estimation of human whole body CO2 rate of appearance (Ra) during non-steady state exercise. The technique consisted of a breath-by-breath measurement of 13CO2 enrichment (E) and a real-time fuzzy logic feedback system which controlled NaH13CO3 infusion rate to achieve an isotopic steady state. Ra was estimated from the isotope infusion rate and body 13CO2 enrichment which was equal to E at the isotopic steady state. During a non-steady state incremental cycle exercise (5 w/min or 10 w/min), NaH13CO3 infusion rate was successfully increased by the action of feedback controller so as to keep E constant.

Modified fuzzy-based greedy routing protocol for VANETs

2020 ◽  
Vol 39 (6) ◽  
pp. 8357-8364
Author(s):  
Thompson Stephan ◽  
Ananthnarayan Rajappa ◽  
K.S. Sendhil Kumar ◽  
Shivang Gupta ◽  
Achyut Shankar ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Routing Protocol ◽  
Intelligent Transportation Systems ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Research Area ◽  
Transportation Systems ◽  
Delivery Ratio ◽  
Greedy Routing ◽  
Hoc Networks

Vehicular Ad Hoc Networks (VANETs) is the most growing research area in wireless communication and has been gaining significant attention over recent years due to its role in designing intelligent transportation systems. Wireless multi-hop forwarding in VANETs is challenging since the data has to be relayed as soon as possible through the intermediate vehicles from the source to destination. This paper proposes a modified fuzzy-based greedy routing protocol (MFGR) which is an enhanced version of fuzzy logic-based greedy routing protocol (FLGR). Our proposed protocol applies fuzzy logic for the selection of the next greedy forwarder to forward the data reliably towards the destination. Five parameters, namely distance, direction, speed, position, and trust have been used to evaluate the node’s stability using fuzzy logic. The simulation results demonstrate that the proposed MFGR scheme can achieve the best performance in terms of the highest packet delivery ratio (PDR) and minimizes the average number of hops among all protocols.

Design and Simulation of Smart Control System for Internet Traffic Distribution on Servers by Using Fuzzy Logic System

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Raid Daoud ◽  
Yaareb Al-Khashab
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Input Parameter ◽  
Traffic Density ◽  
The Internet ◽  
Linguistic Variables ◽  
Internet Service ◽  
Traffic Distribution ◽  
Smart Control ◽  
High Level

The internet service is provided by a given number of servers located in the main node of internet service provider (ISP). In some cases; the overload problem was occurred because a demand on a given website goes to very high level. In this paper, a fuzzy logic control (FLC) has proposed to distribute the load into the internet servers by a smart and flexible manner. Three effected parameters are tacked into account as input for FLC: link capacity which has three linguistic variables with Gaussian membership function (MF): (small, medium and big), traffic density with linguistic variables (low, normal and high) and channel latency with linguistic variables (empty, half and full); with one output which is the share server status (single, simple and share). The proposed work has been simulated by using MATLAB 2016a, by building a structure in the Fuzzy toolbox. The results were fixed by two manners: the graphical curves and the numerical tables, the surface response was smoothly changed and translates the well-fixed control system. The numerical results of the control system satisfy the idea of the smart rout for the incoming traffics from the users to internet servers. So, the response of the proposed system for the share of server ratio is 0.122, when the input parameter in the smallest levels; and the ratio is 0.879 when the input parameters are in highest level. The smart work and flexible use for the FLC is the main success solution for most of today systems control.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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