Optimization of smart traffic lights to prevent traffic congestion using fuzzy logic

Hartanti, Dian; Aziza, Rosida Nur; Siswipraptini, Puji Catur

doi:10.12928/telkomnika.v17i1.10129

Cited by 42 publications

(21 citation statements)

References 16 publications

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“…It contains basic analog input/output and basic digital input/output which can be applied to motor drivers and inverters for integration with remote SCADA systems based on IoT, and thus achieve efficient power management [12]- [14]. The study was conducted using a model based on the fuzzy inference system (FIS) to evaluate the performance of the block cipher algorithm based on three current metrics [15]- [17]. Two types of FIS models, the Mamdani FIS model, and the Sugeno FIS model were used for this evaluation.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy mamdani logic inference model in the loading of distribution substation transformer SCADA system

Ningrum

Siregar

Rusjdi

2021

IJ-AI

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<span id="docs-internal-guid-152e332e-7fff-7073-a541-1d420decb47b"><span>The research objective of supervisory control and data acquisition (SCADA), with fuzzy Mamdani logic simulation on the loading section of distribution transformer substations. Data acquisition is available when saving SAIFI SAIDI data and storing the results of monitoring equipment. The method used is Mamdani fuzzy logic, there are two input variables, namely current and voltage devices. The membership function in Mamdani fuzzy logic has been created based on the input current and voltage variables. Currently: parameter {0, 600} low is created {0, 350, 450, 600}, normal {400-650} parameter is created {400, 500, 550, 650}, parameter high {≥600} is created {600, 650, 750, 1000}, when determining the voltage: low {≤10.5} parameters {0 4 7 10.5}, normal {9-14} parameters {9, 10, 13, 14} and high {≥13} - parameters {13, 14, 15, 16}. Based on the results of the Mamdani logic rule test on the output current containing a transformer and a voltage sensor, the results obtained are IF (normal current; (630) AND voltage (high); (13.2) (high load transformer). The components in the simulation tool include miniature substations made with the 1A travel substation model, 3A substation as the main substation, the relay as distribution substation as the monitoring application. Telestatus and Telecontrol use a microcontroller. Initial scenario. After substation is resumed, data is stored after downtime, service life, duration, and data period. Initial scenario After substation is resumed, data is stored after downtime, service life, duration, and data period.</span></span>

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Section: Introductionmentioning

confidence: 99%

Fuzzy mamdani logic inference model in the loading of distribution substation transformer SCADA system

Ningrum

Siregar

Rusjdi

2021

IJ-AI

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“…A statistical report says that the rate of video traffic in a DCN is increasing in a heavily. An efficient way of controlling network devices is given in paper [5]. The network is partitioned into various sub-domains.…”

Section: Introductionmentioning

confidence: 99%

Traffic-aware adaptive server load balancing for software defined networks

Fancy

Pushpalatha

2021

IJECE

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Servers in data center networks handle heterogenous bulk loads. Load balancing, therefore, plays an important role in optimizing network bandwidth and minimizing response time. A complete knowledge of the current network status is needed to provide a stable load in the network. The process of network status catalog in a traditional network needs additional processing which increases complexity, whereas, in software defined networking, the control plane monitors the overall working of the network continuously. Hence it is decided to propose an efficient load balancing algorithm that adapts SDN. This paper proposes an efficient algorithm TA-ASLB-traffic-aware adaptive server load balancing to balance the flows to the servers in a data center network. It works based on two parameters, residual bandwidth, and server capacity. It detects the elephant flows and forwards them towards the optimal server where it can be processed quickly. It has been tested with the Mininet simulator and gave considerably better results compared to the existing server load balancing algorithms in the floodlight controller. After experimentation and analysis, it is understood that the method provides comparatively better results than the existing load balancing algorithms.

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“…Certain algorithm can be used to predict next events based on the frequency of occurrence, thus giving the smart home controller the capability to target the next particular events for automation [19]. Other method is based on fuzzy logic [20]. Other studies in many research areas implement neural network based algorithms to achieve more accurate control [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

The design of a smart home controller based on ADALINE

et al. 2020

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This paper proposes a prototype of an improved smart home controller that implements a neural network-based algorithm for enabling the controller to make decisions and act based on the current condition. Unlike previous approaches, this design also utilizes the use of IoT (internet of thing) technology and neural network based-algorithm for developing the controller. Since a smart home is equipped with various sensors, actuators, smart appliances, and mobile terminals, all of these devices need to be connected to the Internet to be able to communicate and provide services for its occupants. The construction of the proposed controller is carried out through several procedures, i.e. the implementation of the ADALINE (adaptive liniear) as the neural network method, the design of the smart home controller prototype, and the validation process using mean average percentage error (MAPE) calculation. This prototype integrates functionalities of several household appliances into one application controlled by a smartphone. ADALINE is applied as an algorithm to predict output when the controller is in automatic mode. Although the obtained accuracy value is still not satisfactory, the value is bound to change when testing on more data. The work published in this paper may encourage the implementation of smart technology in more households in Indonesia.

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Optimization of smart traffic lights to prevent traffic congestion using fuzzy logic

Cited by 42 publications

References 16 publications

Fuzzy mamdani logic inference model in the loading of distribution substation transformer SCADA system

Fuzzy mamdani logic inference model in the loading of distribution substation transformer SCADA system

Traffic-aware adaptive server load balancing for software defined networks

The design of a smart home controller based on ADALINE

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