scholarly journals Analisis Dan Pemodelan Posisi Access Point Pada Jaringan Wi-Fi Menggunakan Metode Simulate Annealing

2017 ◽  
Vol 1 (1) ◽  
pp. 134
Author(s):  
Anjar Wanto ◽  
Jaya T Hardinata ◽  
Herlan F Silaban ◽  
Widodo Saputra
Keyword(s):  
Signal Strength ◽  
Access Point ◽  
Received Signal Strength ◽  
Received Signal Strength Indication ◽  
Point Placement ◽  
Annealing Method

Laying the position of the access point on the Wi-Fi network in a room is needed to optimize the signal strength received from the transmitter to the receiver. The parameters that determine the performance of the access point is the value of the signal strength. Strong or weak a signal access point will be affected by distance and barriers that exist between the access point and a client that accesses the access point. This study has been performed several simulations in multiple rooms are placed the access point to the receiver. The parameters used to measure the signal strength using inSSIDer applications that generate value RSSI (Received Signal Strength Indication) of a transmitter to the receiver and barriers (barriers) that may influence the strength of the signal. From this research strength of the signal received by the receiver not only in pengaruhui by the distance between accespoint to the recipient, but rather influenced by barriers (barriers) which is in a room. From the results of the research are expected to be able to obtain appropriate modeling to optimize access point placement position using the Simulate annealing method.

scholarly journals Penggunaan Metode Simulated Annealing untuk Optimasi Penempatan Posisi Access Point pada Jaringan WI-FI

2019 ◽  
Vol 5 (1) ◽  
pp. 10
Author(s):  
Ganang Eko Noviardianto ◽  
Muhammad Novel ◽  
Mercurius Broto Legowo
Keyword(s):  
Simulated Annealing ◽  
Statistical Mechanics ◽  
Signal Strength ◽  
Access Point ◽  
Simulated Annealing Method ◽  
Access Points ◽  
Optimum Solution ◽  
Received Signal Strength Indication ◽  
Annealing Method

<p><em>Abstrak</em><strong> </strong>– <strong>Tujuan dari penelitian ini adalah untuk menempatkan titik akses pada jaringan Wi-Fi</strong><strong>. </strong><strong>Dengan demikian, kekuatan sinyal yang diterima dari pemancar ke penerima adalah optimal. Masalah muncul ketika menempatkan titik akses untuk mempengaruhi nilai kekuatan sinyal. Selanjutnya, nilai ini akan digunakan untuk menentukan area jangkauan (jangkauan sinyal) dari pemancar</strong><strong> (<em>access point</em></strong><strong>). Penelitian ini adalah studi kasus di Kampus Institut Perbanas di mana pengukuran kekuatan sinyal dari titik akses terhadap penerima di unit 2 dan unit 6 dari Perbanas Institute diukur menggunakan aplikasi inSSIDer. Studi kasus ini menghasilkan nilai Indikasi Kekuatan Sinyal yang Diterima (RSSI) dari pemancar ke penerima. Metode</strong><strong> </strong><strong><em>Simulated</em></strong><strong><em> </em></strong><strong><em>Annealing</em></strong><strong> diterapkan dalam penelitian ini, dengan alasan untuk optimasi generik. Berdasarkan probabilitas dan mekanika statistik, algoritma ini dapat digunakan untuk menemukan pendekatan pada solusi optimal untuk suatu masalah. Hasil penelitian menunjukkan bahwa </strong><strong>setelah dilakukan optimisasi menggunakan </strong><strong>m</strong><strong>etode</strong><strong> </strong><strong><em>Simulated</em></strong><strong><em> </em></strong><strong><em>Annealing</em></strong><strong><em> </em></strong><strong>prosentase <em>koverage area</em> sebesar 98.66 % dan, diperoleh</strong> <strong>kenaikan persentase <em>koverage area</em> sebesar 87.15%.</strong><strong> Dengan demikian, penelitian ini akan memberikan kontribusi untuk memodelkan penempatan </strong><strong>posis </strong><strong>titik akses</strong><strong> </strong><strong>dan kekuatan sinyal </strong><strong>pada jaringan Wi-Fi </strong><strong>yang diperoleh di Unit 2 dan Unit 6 Kampus Institut Perbanas</strong></p><p><em>Abstract</em> – <strong>The purpose of this study is to place access points on Wi-Fi networks. Thus, the signal strength received from the transmitter to the receiver is optimal. Problems arise when placing access points to influence the signal strength value. Furthermore, this value will be used to determine the koverage area (signal koverage) of a transmitter (access point). This research is a case study at the Perbanas Institute Campus where measurements of the signal strength of the access point towards recipients in unit 2 and unit 6 of Perbanas Institute were measured using the inSSIDer application. This case study produced a Received Signal Strength Indication (RSSI) value from a transmitter to the receiver. </strong><strong>The Simulated Annealing method applied in this study, with reasons for generic optimization. </strong><strong>Based on probability and statistical mechanics, this algorithm can be used to find an approach to the optimum solution to a problem. </strong><strong>The results showed that after optimization using the Simulated Annealing method the percentage of koverage area was 98.66%. And, the percentage of the koverage area was increased by 87.15%.</strong> <strong>Thus, this study will contribute to modeling the placement of access points and signal strength in Wi-Fi networks obtained in Units 2 and 6 of the Perbanas Institute Campus.</strong></p><p><strong><em>Keywords</em></strong> -  <em>Koverage Area</em><em>, </em><em>RSSI</em><em>, </em><em>Simulated Annealing</em><em>, </em><em>Propaga</em><em>tion, </em><em>Wi-F</em><em>i</em><strong><em></em></strong></p>

scholarly journals Optimisasi Penempatan Posisi Access Point pada Jaringan Wi-Fi Menggunakan Metode Simulated Annealing

2015 ◽  
Vol 2 (1) ◽  
pp. 51
Author(s):  
Nila Feby Puspitasari ◽  
Reza Pulungan
Keyword(s):  
Simulated Annealing ◽  
Field Measurements ◽  
Signal Strength ◽  
Access Point ◽  
Line Of Sight ◽  
Coverage Area ◽  
Point Position ◽  
Received Signal Strength Indication ◽  
Point Placement ◽  
Non Line Of Sight

Penempatan access point pada jaringan Wi-Fi yang tepat sangat diperlukan untuk mengoptimalkan kekuatan sinyal yang diterima dari transmitter terhadap receiver. Parameter yang paling mempengaruhi dalam menentukan performa access point adalah nilai kekuatan sinyal, karena nilai inilah yang akan digunakan untuk menentukan coverage area (cakupan sinyal) dari sebuah transmitter (access point).Pada penelitan ini telah dilakukan pengukuran terhadap kekuatan sinyal access point terhadap penerima di ruang dosen dan lobi gedung 2 lantai 1 STMIK AMIKOM Yogyakarta yang diukur menggunakan aplikasi inSSIDer dan menghasilkan nilai RSSI (Received Signal Strength Indication) dari sebuah transmitter terhadap receiver. Dalam pengukuran juga digunakan propagasi Line Of Sight (LOS) dan propagasi Non Line Of Sight (NLOS). Data yang diperoleh dari hasil pengukuran di lapangan digunakan untuk melakukan pemodelan penempatan access point menggunakan metode simulated anneling. Kekuatan sinyal RSSI yang diterima oleh receiver tidak hanya bergantung pada jarak antara transmitter dan receiver, akan tetapi menunjukkan variasi yang besar terhadap fading dan shadowing pada sebuah lokasi, juga pengaruh interferensi dapat menyebabkan penurunan sinyal (RSSI) yang diterima oleh receiver.Dari hasil penelitian yang dilakukan, diharapkan dapat menghasilkan pemodelan yang sesuai dan tepat guna dalam melakukan optimisasi penempatan access point pada jaringan Wi-Fi menggunakan metode simulated annealing.Access point placement on Wi-Fi networks is needed to optimize the strength of signal received by receiver from transmitter. The most influential parameter in determining the performance of access point position is signal strength, because this value will be used to determine the coverage area of a transmitter.In this research, signal strengths of access point to receiver in the faculty room and the lobby floor 1 of building 2 at STMIK AMIKOM Yogyakarta have been measured using inSSIDer application and results in the values of RSSI from transmitter to receiver. Also measured are LOS propagation and NLOS propagation. Data obtained from the field measurements are used to model and analyse the influence of access point placement using simulated annealing. It is found that the RSSI signal strength received by the receiver does not only depend on the distance between the transmitter and receiver, but also shows large variations caused by the fading and shadowing at a location. The interference also causes a decrease in the signal (RSSI) received by the receiver.The research conducted is expected to generate an appropriate model and analysis for further optimization of access point position on Wi-Fi network using simulated annealing.

scholarly journals A Generative Method for Indoor Localization Using Wi-Fi Fingerprinting

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2392
Author(s):  
Óscar Belmonte-Fernández ◽  
Emilio Sansano-Sansano ◽  
Antonio Caballer-Miedes ◽  
Raúl Montoliu ◽  
Rubén García-Vidal ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Indoor Localization ◽  
Hidden Markov ◽  
Signal Strength ◽  
Access Point ◽  
Received Signal Strength ◽  
Machine Learning Algorithms ◽  
Forward Algorithm ◽  
Generative Method

Indoor localization is an enabling technology for pervasive and mobile computing applications. Although different technologies have been proposed for indoor localization, Wi-Fi fingerprinting is one of the most used techniques due to the pervasiveness of Wi-Fi technology. Most Wi-Fi fingerprinting localization methods presented in the literature are discriminative methods. We present a generative method for indoor localization based on Wi-Fi fingerprinting. The Received Signal Strength Indicator received from a Wireless Access Point is modeled by a hidden Markov model. Unlike other algorithms, the use of a hidden Markov model allows ours to take advantage of the temporal autocorrelation present in the Wi-Fi signal. The algorithm estimates the user’s location based on the hidden Markov model, which models the signal and the forward algorithm to determine the likelihood of a given time series of Received Signal Strength Indicators. The proposed method was compared with four other well-known Machine Learning algorithms through extensive experimentation with data collected in real scenarios. The proposed method obtained competitive results in most scenarios tested and was the best method in 17 of 60 experiments performed.

Improved Algorithm on Wide Range Positioning for Partial Discharge in Substation

2013 ◽  
Vol 860-863 ◽  
pp. 2177-2181
Author(s):  
Xi Ran Wang ◽  
Huai Dong Liu ◽  
Yi Fan He ◽  
Qi Ming Zhao ◽  
He Wu
Keyword(s):  
Electrical Discharge ◽  
Partial Discharge ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Experimental Result ◽  
Wide Range ◽  
Positioning Algorithm ◽  
Received Signal Strength Indication ◽  
Improved Algorithm

This paper proposes a Improved positioning algorithm of electrical partial discharge applied for substations. This algorithm is based on received signal strength indication, and taken practical condition of sensors into consideration by replenishing beacon nodes. Compared with traditional trilateral weighting positioning algorithm, this paper introduces indefinite amount of localization perpendicular lines and combined them with trilateral districts to calculate the weighting result, which can reduce error. This model meets the requirement of reality that the height of electrical discharge spots differentiate from the height of the plane formed by beacon nodes (signal sensors). The experimental result indicates that the revised position model proposed by this paper can effectively fit the condition of monitoring hardware. Error of this algorithm is less than that of traditional trilateral localization.

Localization in Wireless Sensor Networks Using Soft Computing Approach

2017 ◽  
Vol 11 (3) ◽  
pp. 42-53 ◽  
Author(s):  
Sunil Kumar Singh ◽  
Prabhat Kumar ◽  
Jyoti Prakash Singh
Keyword(s):  
Ad Hoc ◽  
Low Cost ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Localization Scheme ◽  
Anchor Nodes ◽  
Received Signal Strength Indication ◽  
Logic System

Wireless sensor network (WSN) is formed by a large number of low-cost sensors. In order to exchange information, sensor nodes communicate in an ad hoc manner. The acquired information is useful only when the location of sensors is known. To use GPS-aided devices in each sensor makes sensors more costly and energy hungry. Hence, finding the location of nodes in WSNs becomes a major issue. In this paper, the authors propose a combination of range based and range-free localization scheme. In their scheme, for finding the distance, they use received signal strength indication (RSSI), which is a range based center of gravity technique. For finding the location of non-anchor nodes, the authors assign weights to anchor and non-anchor nodes based on received signal strength. The weight, which is assigned to anchor and non-anchor nodes, are designed by fuzzy logic system (FLS).

scholarly journals Research and Application of Underground WLAN Adaptive Radio Fingerprint Database

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1182
Author(s):  
Jiansheng Qian ◽  
Mingzhi Song
Keyword(s):  
Signal Strength ◽  
Access Point ◽  
Received Signal Strength ◽  
Reference Points ◽  
Normal Operation ◽  
Positioning System ◽  
Widespread Application ◽  
Radio Fingerprint ◽  
Fingerprint Database ◽  
User Location

Fingerprint positioning based on WiFi in coal mines has received much attention because of the widespread application of WiFi. Fingerprinting techniques have developed rapidly due to the efforts of many researchers. However, the off-line construction of the radio fingerprint database is a tedious and time-consuming process. When the underground environments change, it may be necessary to update the signal received signal strength indication (RSSI) of all reference points, which will affect the normal working of a personnel positioning system. To solve this problem, an adaptive construction and update method based on a quantum-behaved particle swarm optimization–user-location trajectory feedback (QPSO–ULTF) for a radio fingerprint database is proposed. The principle of ULTF is that the mobile terminal records and uploads the related dataset in the process of user’s walking, and it forms the user-location track with RSSI through the analysis and processing of the positioning system server. QPSO algorithm is used for the optimal radio fingerprint match between the RSSI of the access point (AP) contained in the dataset of user-location track and the calibration samples to achieve the adaptive generation and update of the radio fingerprint samples. The experimental results show that the radio fingerprint database generated by the QPSO–ULTF is similar to the traditional radio fingerprint database in the statistical distribution characteristics of the signal received signal strength (RSS) at each reference point. Therefore, the adaptive radio fingerprint database can replace the traditional radio fingerprint database. The comparable results of well-known traditional positioning methods demonstrate that the radio fingerprint database generated or updated by the QPSO–ULTF has a good positioning effect, which can ensure the normal operation of a personnel positioning system.

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