Role of Campus WiFi Infrastructure for Occupancy Monitoring in a Large University

Mohottige, Iresha Pasquel; Sutjarittham, Thanchanok; Raju, N. Mohan; Gharakheili, Hassan Habibi; Sivaraman, Vijay

doi:10.1109/iciafs.2018.8913341

Cited by 11 publications

(4 citation statements)

References 5 publications

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“…As a first measure of occupancy, the proposed solution was based on the hypothesis that people are increasingly connected to the network, especially in university environments, where it is common for there to be at least one connected device per person. In this way, the number of people will be controlled by verifying the number of Wi-Fi connections to the room's access points [45] , [46] , [47] .…”

Section: Methodsmentioning

confidence: 99%

Dynamic ventilation certificate for smart universities using artificial intelligence techniques

Macia-Perez¹,

Fonseca²,

Berná-Martínez³

2023

Computer Methods and Programs in Biomedicine

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Section: Methodsmentioning

confidence: 99%

Dynamic ventilation certificate for smart universities using artificial intelligence techniques

Macia-Perez¹,

Fonseca²,

Berná-Martínez³

2023

Computer Methods and Programs in Biomedicine

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“…Di Indonesia, untuk keperluan jaringan nirkabel sudah dialokasikan dalam industial scientific and medical bands (ISM Band) pada frekuensi 2.4 GHz dibagi dalam tiga kanal (channel) masing-masing 22 MHz [3]. Infrastruktur jaringan nirkabel di kampus modern menawarkan internet setiap hari ke seluruh penghuni kampus, yang mana lingkungan kampus terdiri dari berbagai ruangan yang berbeda termasuk ruang belajar, kantor, tempat tinggal dan bangunan ritel [4]. Akses internet bagi mahasiswa merupakan salah satu hal pokok yang mendukung perkuliahannya.…”

Section: Pendahuluanunclassified

Optimalisasi Jaringan Nirkabel Dengan Metode Rssi Di Aikom Ternate

Larumbia¹,

Hasan²,

Turuy³

2021

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Penelitian ini bertujuan untuk optimalisasi jaringan nirkabel dari titik buta atau blind spot di lingkungan kampus AIKOM Ternate. Area-area titik buta ini membuat pengguna jaringan nirkabel (dosen, staf, dan mahasiswa) tidak nyaman dikarenakan harus mendekat ke sumber jaringan (access point (AP)) terdekat agar dapat mengakses jaringan internet ataupun intranet. Dengan optimalisasi jaringan nirkabel ini, area jangkauan jaringan nirkabel disesuikan dengan kebutuhan agar tidak ada lagi titik buta. Penggunaan aplikasi Wireless Monitoring (Wirelessmon) untuk mendeteksi area jangkauan dari setiap AP yang dipasang, yang diukur adalah receive signal strength indicator (RSSI), termasuk penentuan penggunaan kanal dari setiap AP agar tidak tumpang tindih atau overlapping dalam penggunaan kanal pada setiap AP dan data rate yang mengalami peningkatan dan penurunan menyesuaikan dengan kualitas sinyal yang diterima. Hasil penelitian ini menunjukkan setelah dilakukannya optimalisasi dengan memasang AP pada lima titik. Optimalisasi jaringan nirkabel pada kampus AIKOM Ternate berhasil dengan RSSI -61dBm sampai dengan -44dBm, RSSI termasuk dalam kategori sangat bagus (very good). Disarankan pengukuran RSSI menggunakan software lebih dari satu sehingga dilakukan perbandingan, melakukan perbandingan RSSI yang diterima dengan throughput yang dihasilkan pada perangkat yang berbeda, dan pengelola jaringan dapat menggunakan hasil penelitian ini akan tetapi penggunaan perangkat dengan spesifikasi yang berbeda dengan yang digunakan dalam penelitian ini maka disarankan untuk melakukan pengambilan data ulang agar hasilnya maksimal.

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“…Despite these limitations, prior studies have shown that Wi-Fi connections are as accurate as dedicated physical sensors (e.g., infrared beam-break or thermal sensors) for estimating student occupancy of university rooms and buildings 20,21 . The daily pattern of student Wi-Fi connections also conforms to expectations for different sites on campus including teaching spaces, libraries, food courts, and residential buildings 20,22–24 . An important limitation of our study is that we did not investigate student mixing with university staff or visitors because we only had Wi-Fi data for students.…”

Section: Mainmentioning

confidence: 99%

A targeted e-learning approach to reduce student mixing during a pandemic

Yeo

Lai

Tan

et al. 2020

Preprint

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11The COVID-19 pandemic has resulted in widespread closure of schools and universities. These institutions 12 have turned to distance learning to provide educational continuity. Schools now face the challenge of how to 13 reopen safely and resume in-class learning. However, there is little empirical evidence to guide decision-14 makers on how this can be achieved. Here, we show that selectively deploying e-learning for larger classes is 15 highly effective at decreasing campus-wide opportunities for student-to-student contact, while allowing most 16 in-class learning to continue uninterrupted. We conducted a natural experiment at a large university that 17 implemented a series of e-learning interventions during the COVID-19 outbreak. Analyses of >24 million 18 student connections to the university Wi-Fi network revealed that population size can be manipulated by e-19 learning in a targeted manner according to class size characteristics. Student mixing showed accelerated growth 20 with population size according to a power law distribution. Therefore, a small e-learning dependent decrease in 21 population size resulted in a large reduction in student clustering behaviour. Our results show that e-learning 22 interventions can decrease potential for disease transmission while minimizing disruption to university 23 operations. Universities should consider targeted e-learning a viable strategy for providing educational 24 continuity during early or late stages of a disease outbreak. 25 26 Main 27The coronavirus disease 2019 (COVID-19) has had enormous socioeconomic impact 1 . In 5 months, 5.5 28 million COVID-19 cases have been confirmed, resulting in 350,000 deaths across 188 countries 2 . The severe 29 acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes COVID-19 is primarily spread when an 30 infected person sneezes or coughs 3 . The spread of infection can be slowed by public health measures that 31 reduce person-to-person contact. Nonpharmaceutical interventions that include restricted travel, staying at 32 home, and physical distancing can delay and flatten the peak of COVID-19 cases to avoid the overwhelming of 33 medical services 4-6 . Nonpharmaceutical interventions therefore play a critical role in controlling the spread of 34 disease until effective vaccines or drugs are available 7 . 35 School closure is a key strategy for controlling the spread of infectious diseases 7-9 . Empirical and 36 modelling studies show that closing schools and universities can suppress COVID-19 transmission when 37 combined with other nonpharmaceutical interventions 5,6,10 . The COVID-19 pandemic has resulted in an 38 unprecedented number of school and university closures, affecting over 1.2 billion learners worldwide 11 . 39Consequently, a massive shift from classroom learning to distance learning has occurred 12 . This has created 40 great strain on educational institutions which function not only as places of learning, but also as major 41 employers and drivers of local economies. It is therefore important to con...

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Role of Campus WiFi Infrastructure for Occupancy Monitoring in a Large University

Cited by 11 publications

References 5 publications

Dynamic ventilation certificate for smart universities using artificial intelligence techniques

Dynamic ventilation certificate for smart universities using artificial intelligence techniques

Optimalisasi Jaringan Nirkabel Dengan Metode Rssi Di Aikom Ternate

A targeted e-learning approach to reduce student mixing during a pandemic

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