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Muttaqin Adharul - One of the best experts on this subject based on the ideXlab platform.
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SISTEM MONITORING DAYA PADA BEBAN LISTRIK DC BERBASIS RASPBERRY PI 3 TERINTEGRASI DENGAN WEBSITE DAN MEDIA SOSIAL TELEGRAM
Jurnal Mahasiswa TEUB, 2021Co-Authors: Sanusi, Muhammad Anwar, Maulana Eka, Muttaqin AdharulAbstract:Pada penerapan energi baru dan terbarukan, efisiensi penggunaan peralatan seperti lampu dan kipas seringkali terabaikan oleh karena itu dibutuhkan alat yang dapat me-monitor efisiensi daya dari beban listrik lampu dan kipas yang digunakan dengan akses yang mudah. Monitoring daya dari masing-masing beban lampu dan kipas dilakukan dengan mengetahui jumlah lampu dan kipas yang terhubung terlebih dahulu ditinjau dari karakteristik perubahan arus dari tiap bebannya. Proses monitoring ini juga dilakukan secara wireless melalui internet dengan menggunakan sebuah laman website dan media sosial Telegram. Sistem monitoring ini membutuhkan Sensor ACS712 dan Sensor tegangan yang berfungsi untuk membaca nilai arus dan tegangan, ESP32 yang berfungsi untuk mengolah data nilai pembacaan Sensor dan mengirim data tersebut menuju server serta sebuah web server yang dijalankan oleh Raspberry Pi 3 yang berfungsi untuk menerima data dari tiap node dan akan disimpan pada database MySQL. Pengolahan data yang dilakukan pada ESP32 menghasilkan data daya pada tiap beban dan jumlah beban yang terhubung, dalam hal ini beban lampu dan kipas yang di-monitor ialah Lampu LED Brighton 12v dan Kipas Angin Mini Sivicom 12v dengan melihat karakteristik rentang perubahan arus dari tiap bebannya untuk mendeteksi beban tersebut, karakteristik rentang nilai perubahan arus kipas berkisar diantara 0.4A hingga 0.51A, sedangkan rentang nilai perubahan arus lampu berkisar diantara 0.25A hingga 0.34A. Dari jumlah beban listrik yang diketahui, sistem dapat mengetahui daya yang dibutuhkan dari masing-masing beban tersebut dengan mengalikannya dengan karakteristik daya dari masing-masing beban yaitu sebesar 4.8watt untuk kipas dan 3.18watt untuk lampu. Selanjutnya data yang telah diolah akan dikirim menuju server Raspberry PI 3. Pengiriman data dari node membutuhkan pemrograman web menggunakan PHP yang berfungsi untuk menyimpan data tersebut pada database MySQL dan menampilkannya dalam bentuk grafik garis yang memanfaatkan library Highcharts. Terdapat 2 grafik pada tiap node-nya, grafik yang menampilkan nilai tegangan, arus, daya, jumlah daya dari masing-masing beban yang terpasang dan jumlah daya lain serta grafik yang menampilkan jumlah beban yang terpasang. Kedua grafik ini dapat dibuka melalui browser dengan menggunakan ip address dari server Raspberry Pi 3. Server juga berfungsi untuk mengolah data-data tersebut sehingga dapat dikirim kepada user melalui media sosial Telegram yang ekspor terlebih dahulu dalam bentuk file dengan tipe *.csv dengan menggunakan Python sebagai pemrograman web. User hanya perlu mengirimkan perintah tertentu melalui media sosial Telegram sehingga data yang dikirim sesuai dengan request user. Kata kunci: Monitoring beban listrik DC, Raspberry Pi 3, Media sosial Telegram, database MySQL Kata kunci: Monitoring beban listrik DC, Raspberry Pi 3, Media sosial Telegram, database MySQL ABSTRACK In the application of new and renewable energy, the efficiency of the use of equipment such as lights and fans is often overlooked therefore it takes a tool that can monitor the power efficiency of the electric load of the lamp and fan used with easy access. The power monitoring of each lamp and fan load is done by knowing the number of connected lights and fans first reviewed from the characteristics of the current change of each load. This monitoring process is also done wirelessly through the internet using a telegram website and social media page. This monitoring system requires an ACS712 Sensor and a voltage Sensor that serves to read current and voltage values, ESP32 which serves to process the Sensor Reading value data and send that data to the server as well as a web server run by Raspberry Pi 3 that serves to receive data from each node and will be stored on the MySQL database. The data processing performed on ESP32 generates power data on each load and the number of loads connected, in this case the lamp and fan load monitored are Brighton LED Light 12v and Sivicom Mini Fan 12v by looking at the characteristics of the current change range of each load to detect the load, characteristic of the fan current change value ranges from 0.4A to 0.51A , while the range of light current change values ranges from 0.25A to 0.34A. From the number of known electrical loads, the system can know the power required of each of these loads by multiplying them by the power characteristics of each load which is 4.8watts for the fan and 3.18watt for the lamp. Furthermore, the processed data will be sent to the Raspberry PI 3 server. Sending data from nodes requires web programming using PHP which serves to store that data in a MySQL database and display it in the form of a line graph utilizing the Highcharts library. There are 2 graphs on each node, a graph showing the voltage, current, power, amount of power of each installed load and the amount of other power and a graph showing the amount of load attached. Both charts can be opened through the browser using the ip address of the Raspberry Pi 3 server. The server also serves to process the data so that it can be sent to users via Telegram social media who export it first in the form of a file of type *.csv by using Python as web programming. Users only need to send certain commands via Telegram social media so that the data is sent according to the user's request. Keywords: Monitoring DC power load, Raspberry Pi 3, Telegram social media, MySQL databas
Adharul Muttaqin - One of the best experts on this subject based on the ideXlab platform.
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sistem monitoring daya pada beban listrik dc berbasis raspberry pi 3 terintegrasi dengan website dan media sosial telegram
Jurnal Mahasiswa TEUB, 2021Co-Authors: Muhammad Anwar Sanusi, Eka Maulana, Adharul MuttaqinAbstract:Pada penerapan energi baru dan terbarukan, efisiensi penggunaan peralatan seperti lampu dan kipas seringkali terabaikan oleh karena itu dibutuhkan alat yang dapat me- monitor efisiensi daya dari beban listrik lampu dan kipas yang digunakan dengan akses yang mudah. Monitoring daya dari masing-masing beban lampu dan kipas dilakukan dengan mengetahui jumlah lampu dan kipas yang terhubung terlebih dahulu ditinjau dari karakteristik perubahan arus dari tiap bebannya. Proses monitoring ini juga dilakukan secara wireless melalui internet dengan menggunakan sebuah laman website dan media sosial Telegram. Sistem monitoring ini membutuhkan Sensor ACS712 dan Sensor tegangan yang berfungsi untuk membaca nilai arus dan tegangan, ESP32 yang berfungsi untuk mengolah data nilai pembacaan Sensor dan mengirim data tersebut menuju server serta sebuah web server yang dijalankan oleh Raspberry Pi 3 yang berfungsi untuk menerima data dari tiap node dan akan disimpan pada database MySQL. Pengolahan data yang dilakukan pada ESP32 menghasilkan data daya pada tiap beban dan jumlah beban yang terhubung, dalam hal ini beban lampu dan kipas yang di- monitor ialah Lampu LED Brighton 12v dan Kipas Angin Mini Sivicom 12v dengan melihat karakteristik rentang perubahan arus dari tiap bebannya untuk mendeteksi beban tersebut, karakteristik rentang nilai perubahan arus kipas berkisar diantara 0.4A hingga 0.51A, sedangkan rentang nilai perubahan arus lampu berkisar diantara 0.25A hingga 0.34A. Dari jumlah beban listrik yang diketahui, sistem dapat mengetahui daya yang dibutuhkan dari masing-masing beban tersebut dengan mengalikannya dengan karakteristik daya dari masing-masing beban yaitu sebesar 4.8watt untuk kipas dan 3.18watt untuk lampu. Selanjutnya data yang telah diolah akan dikirim menuju server Raspberry PI 3. Pengiriman data dari node membutuhkan pemrograman web menggunakan PHP yang berfungsi untuk menyimpan data tersebut pada database MySQL dan menampilkannya dalam bentuk grafik garis yang memanfaatkan library Highcharts. Terdapat 2 grafik pada tiap node -nya, grafik yang menampilkan nilai tegangan, arus, daya, jumlah daya dari masing-masing beban yang terpasang dan jumlah daya lain serta grafik yang menampilkan jumlah beban yang terpasang. Kedua grafik ini dapat dibuka melalui browser dengan menggunakan ip address dari server Raspberry Pi 3. Server juga berfungsi untuk mengolah data-data tersebut sehingga dapat dikirim kepada user melalui media sosial Telegram yang ekspor terlebih dahulu dalam bentuk file dengan tipe *.csv dengan menggunakan Python sebagai pemrograman web. User hanya perlu mengirimkan perintah tertentu melalui media sosial Telegram sehingga data yang dikirim sesuai dengan request user . Kata kunci: Monitoring beban listrik DC, Raspberry Pi 3, Media sosial Telegram, database MySQL Kata kunci: Monitoring beban listrik DC, Raspberry Pi 3, Media sosial Telegram, database MySQL ABSTRACK In the application of new and renewable energy, the efficiency of the use of equipment such as lights and fans is often overlooked therefore it takes a tool that can monitor the power efficiency of the electric load of the lamp and fan used with easy access. The power monitoring of each lamp and fan load is done by knowing the number of connected lights and fans first reviewed from the characteristics of the current change of each load. This monitoring process is also done wirelessly through the internet using a telegram website and social media page. This monitoring system requires an ACS712 Sensor and a voltage Sensor that serves to read current and voltage values, ESP32 which serves to process the Sensor Reading value data and send that data to the server as well as a web server run by Raspberry Pi 3 that serves to receive data from each node and will be stored on the MySQL database. The data processing performed on ESP32 generates power data on each load and the number of loads connected, in this case the lamp and fan load monitored are Brighton LED Light 12v and Sivicom Mini Fan 12v by looking at the characteristics of the current change range of each load to detect the load, characteristic of the fan current change value ranges from 0.4A to 0.51A , while the range of light current change values ranges from 0.25A to 0.34A. From the number of known electrical loads, the system can know the power required of each of these loads by multiplying them by the power characteristics of each load which is 4.8watts for the fan and 3.18watt for the lamp. Furthermore, the processed data will be sent to the Raspberry PI 3 server. Sending data from nodes requires web programming using PHP which serves to store that data in a MySQL database and display it in the form of a line graph utilizing the Highcharts library. There are 2 graphs on each node, a graph showing the voltage, current, power, amount of power of each installed load and the amount of other power and a graph showing the amount of load attached. Both charts can be opened through the browser using the ip address of the Raspberry Pi 3 server. The server also serves to process the data so that it can be sent to users via Telegram social media who export it first in the form of a file of type *.csv by using Python as web programming. Users only need to send certain commands via Telegram social media so that the data is sent according to the user's request. Keywords: M onitoring DC power load, Raspberry Pi 3, Telegram social media, MySQL database
Adrian Perrig - One of the best experts on this subject based on the ideXlab platform.
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sia secure information aggregation in Sensor networks
Security of ad hoc and Sensor Networks, 2007Co-Authors: Haowen Chan, Bartosz Przydatek, Adrian Perrig, Dawn SongAbstract:In Sensor networks, data aggregation is a vital primitive enabling efficient data queries. An on-site aggregator device collects data from Sensor nodes and produces a condensed summary which is forwarded to the off-site querier, thus reducing the communication cost of the query. Since the aggregator is on-site, it is vulnerable to physical compromise attacks. A compromised aggregator may report false aggregation results. Hence, it is essential that techniques are available to allow the querier to verify the integrity of the result returned by the aggregator node. We propose a novel framework for secure information aggregation in Sensor networks. By constructing efficient random sampling mechanisms and interactive proofs, we enable the querier to verify that the answer given by the aggregator is a good approximation of the true value, even when the aggregator and a fraction of the Sensor nodes are corrupted. In particular, we present efficient protocols for secure computation of the median and average of the measurements, for the estimation of the network size, for finding the minimum and maximum Sensor Reading, and for random sampling and leader election. Our protocols require only sublinear communication between the aggregator and the user.
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sia secure information aggregation in Sensor networks
International Conference on Embedded Networked Sensor Systems, 2003Co-Authors: Bartosz Przydatek, Dawn Song, Adrian PerrigAbstract:Sensor networks promise viable solutions to many monitoring problems. However, the practical deployment of Sensor networks faces many challenges imposed by real-world demands. Sensor nodes often have limited computation and communication resources and battery power. Moreover, in many applications Sensors are deployed in open environments, and hence are vulnerable to physical attacks, potentially compromising the Sensor's cryptographic keys.One of the basic and indispensable functionalities of Sensor networks is the ability to answer queries over the data acquired by the Sensors. The resource constraints and security issues make designing mechanisms for information aggregation in large Sensor networks particularly challenging.In this paper, we propose a novel framework for secure information aggregation in large Sensor networks. In our framework certain nodes in the Sensor network, called aggregators, help aggregating information requested by a query, which substantially reduces the communication overhead. By constructing efficient random sampling mechanisms and interactive proofs, we enable the user to verify that the answer given by the aggregator is a good approximation of the true value even when the aggregator and a fraction of the Sensor nodes are corrupted. In particular, we present efficient protocols for secure computation of the median and the average of the measurements, for the estimation of the network size, and for finding the minimum and maximum Sensor Reading. Our protocols require only sublinear communication between the aggregator and the user. To the best of our knowledge, this paper is the first on secure information aggregation in Sensor networks that can handle a malicious aggregator and Sensor nodes.
Slawomir Stanczak - One of the best experts on this subject based on the ideXlab platform.
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robust analog function computation via wireless multiple access channels
IEEE Transactions on Communications, 2013Co-Authors: Mario Goldenbaum, Slawomir StanczakAbstract:Wireless Sensor network applications often involve the computation of pre-defined functions of the measurements such as for example the arithmetic mean or maximum value. Standard approaches to this problem separate communication from computation: digitized Sensor Readings are transmitted interference-free to a fusion center that reconstructs each Sensor Reading and subsequently computes the sought function value. Such separation-based computation schemes are generally highly inefficient as a complete reconstruction of individual Sensor Readings at the fusion center is not necessary to compute a function of them. In particular, if the mathematical structure of the channel is suitably matched (in some sense) to the function of interest, then channel collisions induced by concurrent transmissions of different nodes can be beneficially exploited for computation purposes. This paper proposes an analog computation scheme that allows for an efficient estimate of linear and nonlinear functions over the wireless multiple-access channel. A match between the channel and the function being evaluated is thereby achieved via some pre-processing on the Sensor Readings and post-processing on the superimposed signals observed by the fusion center. After analyzing the estimation error for two function examples, simulations are presented to show the potential for huge performance gains over time- and code-division multiple-access based computation schemes.
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robust analog function computation via wireless multiple access channels
arXiv: Information Theory, 2012Co-Authors: Mario Goldenbaum, Slawomir StanczakAbstract:Various wireless Sensor network applications involve the computation of a pre-defined function of the measurements without the need for reconstructing each individual Sensor Reading. Widely-considered examples of such functions include the arithmetic mean and the maximum value. Standard approaches to the computation problem separate computation from communication: quantized Sensor Readings are transmitted interference-free to a fusion center that reconstructs each Sensor Reading and subsequently computes the sought function value. Such separation-based computation schemes are generally highly inefficient as a complete reconstruction of individual Sensor Readings is not necessary for the fusion center to compute a function of them. In particular, if the mathematical structure of the wireless channel is suitably matched (in some sense) to the function, then channel collisions induced by concurrent transmissions of different nodes can be beneficially exploited for computation purposes. Therefore, in this paper a practically relevant analog computation scheme is proposed that allows for an efficient estimate of linear and nonlinear functions over the wireless multiple-access channel. After analyzing the asymptotic properties of the estimation error, numerical simulations are presented to show the potential for huge performance gains when compared with time-division multiple-access based computation schemes.
Bartosz Przydatek - One of the best experts on this subject based on the ideXlab platform.
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sia secure information aggregation in Sensor networks
Security of ad hoc and Sensor Networks, 2007Co-Authors: Haowen Chan, Bartosz Przydatek, Adrian Perrig, Dawn SongAbstract:In Sensor networks, data aggregation is a vital primitive enabling efficient data queries. An on-site aggregator device collects data from Sensor nodes and produces a condensed summary which is forwarded to the off-site querier, thus reducing the communication cost of the query. Since the aggregator is on-site, it is vulnerable to physical compromise attacks. A compromised aggregator may report false aggregation results. Hence, it is essential that techniques are available to allow the querier to verify the integrity of the result returned by the aggregator node. We propose a novel framework for secure information aggregation in Sensor networks. By constructing efficient random sampling mechanisms and interactive proofs, we enable the querier to verify that the answer given by the aggregator is a good approximation of the true value, even when the aggregator and a fraction of the Sensor nodes are corrupted. In particular, we present efficient protocols for secure computation of the median and average of the measurements, for the estimation of the network size, for finding the minimum and maximum Sensor Reading, and for random sampling and leader election. Our protocols require only sublinear communication between the aggregator and the user.
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sia secure information aggregation in Sensor networks
International Conference on Embedded Networked Sensor Systems, 2003Co-Authors: Bartosz Przydatek, Dawn Song, Adrian PerrigAbstract:Sensor networks promise viable solutions to many monitoring problems. However, the practical deployment of Sensor networks faces many challenges imposed by real-world demands. Sensor nodes often have limited computation and communication resources and battery power. Moreover, in many applications Sensors are deployed in open environments, and hence are vulnerable to physical attacks, potentially compromising the Sensor's cryptographic keys.One of the basic and indispensable functionalities of Sensor networks is the ability to answer queries over the data acquired by the Sensors. The resource constraints and security issues make designing mechanisms for information aggregation in large Sensor networks particularly challenging.In this paper, we propose a novel framework for secure information aggregation in large Sensor networks. In our framework certain nodes in the Sensor network, called aggregators, help aggregating information requested by a query, which substantially reduces the communication overhead. By constructing efficient random sampling mechanisms and interactive proofs, we enable the user to verify that the answer given by the aggregator is a good approximation of the true value even when the aggregator and a fraction of the Sensor nodes are corrupted. In particular, we present efficient protocols for secure computation of the median and the average of the measurements, for the estimation of the network size, and for finding the minimum and maximum Sensor Reading. Our protocols require only sublinear communication between the aggregator and the user. To the best of our knowledge, this paper is the first on secure information aggregation in Sensor networks that can handle a malicious aggregator and Sensor nodes.
