scholarly journals On the analysis of received signal strength indicator from ESP8266

2019 ◽  
Vol 8 (3) ◽  
pp. 933-940
Author(s):  
Rafhanah Shazwani Rosli ◽  
Mohamed Hadi Habaebi ◽  
Md Rafiqul Islam
Keyword(s):  
Internet Of Things ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Characteristic Analysis ◽  
Received Signal Strength Indicator ◽  
Transceiver Module ◽  
Unique Parameter ◽  
Technological World ◽  
The Internet Of Things

Recently, the concept o Internet of Things has gained a tremendous momentum in the technological world. Internet of Things efficienty connects devices hence improving their quality of life from various aspects. One of the most heavily used device for Internet of Things application is ESP8266 WiFi serial transceiver module. It features access to the Received Signal Strength Indicator readings from the module. In this paper, a characteristic analysis of the Received Signal Strength Indicator readings collected using ESP8266 WiFi serial transceiver module is carried out. The aim is to explore the future possibilities of Received Signal Strength Indicator value as a stand-alone and unique parameter to be used in various applications especially in the domain of Internet of Things. In addition, the potential of the cheap yet sophisticated ESP8266 WiFi serial transceiver module is also highlighted. The findings have shown an insight into the characteristics of Received Signal Strength Indicator readings and how it can be utilized for other different purposes. The findings have brought up a few stimulating issues that may arise from some implementation of Received Signal Strength Indicator readings such as the significant effect of obstruction in the Line of Sight. However, its solution will thrust the Internet of Things’ technological advancementsahead.

scholarly journals Studi Performansi Jarak Jangkauan Lora-Dragino Sebagai Infrastruktur Konektifitas Nirkabel Pada WP-LAN

2019 ◽  
Vol 15 (1) ◽  
pp. 47 ◽  
Author(s):  
Eko Murdyantoro ◽  
Imron Rosyadi ◽  
Hilmi Septian
Keyword(s):  
Internet Of Things ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Line Of Sight ◽  
Received Signal Strength Indicator

Dalam sistem Internet of things (IoT), berbagai macam obyek fisik disekitar manusia akan disensor dan direpresentasikan menjadi data digital untuk mendukung produktifitas manusia. Sensor dan aktuator sebagai <em>node</em> akan terhubung satu sama lain untuk diproses oleh sistem cerdas. Ada beberapa pilihan teknologi bagi pengembang sistem IoT untuk mengimplementasikan konektivitas antar <em>node</em> tersebut. Teknologi konektivitas nirkabel tersebut antara lain dengan modul GSM, wifi, bluetooth LE, Zigbee, NB-IoT, Sigfox dan LoRa yang menjadi topik studi ini. Teknologi LoRa dikembangkan terutama diproyeksikan sebagai infrastruktur konektifitas nirkabel pada sistem IoT. Beberapa potensi kelebihan fitur LoRa yang diklaim LoRa Alliance antara lain berdaya rendah; dapat mendukung konektifitas IoT skala luas sampai ribuan <em>node</em> dalam satu sel; dan termasuk dalam kategori jangkauan radio jarak jauh. Studi ini bertujuan untuk menguji performansi jarak jangkauan radio dari modul LoRa OLG01 pada sistem IoT yang dikembangkan pada frekuensi ISM 915MHz di atmosfer Indonesia. Dalam studi ini konfigurasikan <em>node</em> yang terhubung ke <em>gateway</em> agar dapat terhubung ke internet menjadi sistem IoT dengan set SF=7 dan BW= 125 kHz. Pembahasan dibatasi pada pengujian performansi jarak jangkauan dengan parameter RSSI (Received Signal Strength Indicator) dan jarak saat LOS (<em>line of sight</em>) dan non LOS (ada halangan). Jangkauan LoRa saat ini yang berhasil diukur sekitar radius 400m. Jarak jangkauan ini masih belum sesuai dengan spesifikasi yang diharapkan yaitu sampai radius 5 km, sehingga masih perlu dicari solusi dan konfigurasi yang lebih optimal

scholarly journals Range-Based Localization of a Wireless Sensor Network for Internet of Things Using Received Signal Strength Indicator and the Most Valuable Player Algorithm

Technologies ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 42
Author(s):  
Mohammed A. Alanezi ◽  
Houssem R.E.H. Bouchekara ◽  
Mohammed. S. Javaid
Keyword(s):  
Internet Of Things ◽  
Signal Strength ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Local Network ◽  
Received Signal Strength Indicator ◽  
Outdoor Environments ◽  
Novel Method ◽  
Indoor And Outdoor Environments ◽  
The Internet Of Things

The localization of the nodes in wireless sensor networks is essential in establishing effective communication among different devices connected, within the Internet of Things. This paper proposes a novel method to accurately determine the position and distance of the wireless sensors linked in a local network. The method utilizes the signal strength received at the target node to identify its location in the localized grid system. The Most Valuable Player Algorithm is used to solve the localization problem. Initially, the algorithm is implemented on four test cases with a varying number of sensor nodes to display its robustness under different network occupancies. Afterward, the study is extended to incorporate actual readings from both indoor and outdoor environments. The results display higher accuracy in the localization of unknown sensor nodes than previously reported.

scholarly journals Analysis of different digital filters for received signal strength indicator

2019 ◽  
Vol 8 (3) ◽  
pp. 970-977
Author(s):  
Rafhanah Shazwani Rosli ◽  
Mohamed Hadi Habaebi ◽  
Md. Rafiqul Islam
Keyword(s):  
Signal Processing ◽  
Internet Of Things ◽  
Digital Signal Processing ◽  
Digital Filters ◽  
Moving Average ◽  
Digital Signal ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Alternative Methods ◽  
Received Signal Strength Indicator

Due to high demand in Internet of Things applications, researchers are exploring deeper alternative methods to provide efficiency in terms of application, energy, and cost among other factors. A frequently used technique is the Received Signal Strength Indicator value for different Internet of Things applications. It is imperative to investigate the digital signal filter for the Received Signal Strength Indicator readings to interpret it into more reliable data. A contrasting analysis of three different types of digital filters is presented in this paper, namely: Simple Moving Average filter, Alpha Trimmed Mean filter and Kalman filter. There are three criteria used to observe the performance of these digital filters which are noise reduction, data proximity and delays. Based on the criteria, the choice of digital signal processing filter can be determined in accordance with its implementations in [ractice. For example, Alpha-Trimmed Meanfilter is shown to be more efficient if used in the pre-processing of Received Signal Strength Indicator readings for physical intrusion detection due to its high data proximity. Hence, this paper illustrates the possibilities of the use of Received Signal Strength Indicator in different Internet of Things applications given a proper choice of digital signal processing filter.

KAJIAN KEKUATAN SINYAL RADIO (RSSI) XBEE DALAM RANGKA PEMASANGAN LANDSLIDE EARLY WARNING SYSTEM (LEWS) DI KABUPATEN GARUT, TASIKMALAYA DAN MAJALENGKA

2019 ◽  
Vol 3 (2) ◽  
pp. 88
Author(s):  
Riski Fitriani
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Received Signal Strength Indicator ◽  
Landslide Early Warning ◽  
Point To Point

Salah satu inovasi untuk menanggulangi longsor adalah dengan melakukan pemasangan Landslide Early Warning System (LEWS). Media transmisi data dari LEWS yang dikembangkan menggunakan sinyal radio Xbee. Sehingga sebelum dilakukan pemasangan LEWS, perlu dilakukan kajian kekuatan sinyal tersebut di lokasi yang akan terpasang yaitu Garut, Tasikmalaya, dan Majalengka. Kajian dilakukan menggunakan 2 jenis Xbee yaitu Xbee Pro S2B 2,4 GHz dan Xbee Pro S5 868 MHz. Setelah dilakukan kajian, Xbee 2,4 GHz tidak dapat digunakan di lokasi pengujian Garut dan Majalengka karena jarak modul induk dan anak cukup jauh serta terlalu banyak obstacle. Topologi yang digunakan yaitu topologi pair/point to point, dengan mengukur nilai RSSI menggunakan software XCTU. Semakin kecil nilai Received Signal Strength Indicator (RSSI) dari nilai receive sensitivity Xbee maka kualitas sinyal semakin baik. Pengukuran dilakukan dengan meninggikan antena Xbee dengan beberapa variasi ketinggian untuk mendapatkan kualitas sinyal yang lebih baik. Hasilnya diperoleh beberapa rekomendasi tinggi minimal antena Xbee yang terpasang di tiap lokasi modul anak pada 3 kabupaten.

Robust Networking: Dynamic Topology Evolution Learning for Internet of Things

2021 ◽  
Vol 17 (3) ◽  
pp. 1-23
Author(s):  
Ning Chen ◽  
Tie Qiu ◽  
Mahmoud Daneshmand ◽  
Dapeng Oliver Wu
Keyword(s):  
Internet Of Things ◽  
Smart Cities ◽  
Evolutionary Process ◽  
Small Error ◽  
Cyber Attacks ◽  
Communication Capacity ◽  
Error Ratio ◽  
The Internet Of Things ◽  
Evolution Learning

The Internet of Things (IoT) has been extensively deployed in smart cities. However, with the expanding scale of networking, the failure of some nodes in the network severely affects the communication capacity of IoT applications. Therefore, researchers pay attention to improving communication capacity caused by network failures for applications that require high quality of services (QoS). Furthermore, the robustness of network topology is an important metric to measure the network communication capacity and the ability to resist the cyber-attacks induced by some failed nodes. While some algorithms have been proposed to enhance the robustness of IoT topologies, they are characterized by large computation overhead, and lacking a lightweight topology optimization model. To address this problem, we first propose a novel robustness optimization using evolution learning (ROEL) with a neural network. ROEL dynamically optimizes the IoT topology and intelligently prospects the robust degree in the process of evolutionary optimization. The experimental results demonstrate that ROEL can represent the evolutionary process of IoT topologies, and the prediction accuracy of network robustness is satisfactory with a small error ratio. Our algorithm has a better tolerance capacity in terms of resistance to random attacks and malicious attacks compared with other algorithms.

scholarly journals A Middleware with Comprehensive Quality of Context Support for the Internet of Things Applications

Sensors ◽  
2017 ◽  
Vol 17 (12) ◽  
pp. 2853 ◽  
Author(s):  
Berto Gomes ◽  
Luiz Muniz ◽  
Francisco da Silva e Silva ◽  
Davi dos Santos ◽  
Rafael Lopes ◽  
...  
Keyword(s):  
Internet Of Things ◽  
The Internet ◽  
Quality Of Context ◽  
The Internet Of Things
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