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Suvankar Barai

Bio: Suvankar Barai is an academic researcher from Jadavpur University. The author has contributed to research in topics: Wireless sensor network & Wireless. The author has an hindex of 1, co-authored 8 publications receiving 52 citations.

Papers
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Proceedings ArticleDOI
01 Dec 2017
TL;DR: This work used two NodeMCU (ESP8266 WiFi module) which is a easily programmable which acts itself as a sensor node and used RSSI technique to determine the distance between two nodes.
Abstract: The use of WiFi is now a part of each human life. Airports, railways, bus-stand, home, markets everywhere now people uses WiFi because its reliability and low-cost. WiFi is also applicable in future tech of IoT (Internet of Things). In this work we used two NodeMCU (ESP8266 WiFi module) which is a easily programmable. NodeMCU which acts itself as a sensor node can be used as Access Point (AP) or as a STAtion (STA). We used one node as a AP and another as a STA. To locate a device distance measurement one of most important issue. There are lots of technique to find out the distance between two nodes (e.g. Time of Arrival (TOA), Time Difference of Arrival (TDOA) or Received Signal Strength (RSS) algorithms etc). In this work we use RSSI technique to determine the distance. First, we take around 300 sample data (RSSI values) and find the standard deviation to calculate how much the RSSI values are spread out and use curve fitting technique to find suitable equation for estimate distance. Then, we compared the estimated distance with actual distance to find the error level in percentage. We are success to reduce the average error level up to 8.32%.

79 citations

Proceedings ArticleDOI
11 Feb 2021
TL;DR: In this article, a new localization scenario on distributing the coverage area based on the received signal strength indicator (RSSI) was investigated, where the base station (BS) will track the target vehicles.
Abstract: The physical position of the vehicles is vital information for the tracking operation. The vehicles localization have several benefits and support for safety, comfort, and reliability in future transportation systems. Thus the vehicular localization has investigated, where the base station (BS) will track the target vehicles. This paper mainly addresses a new localization scenario on distributing the coverage area based on the received signal strength indicator (RSSI). The RSSI measured in regular operation and consume minimum energy. However, wireless RSSI suffers from various interference in dynamic environments. For solving these issues, several methods have been proposed in the literature, including the signal intensity attenuation model (SIAM). This paper incorporates the fact that the motion of vehicles satisfies environmental constraints to improve the accuracy of RSSI-based localization by a new model, namely the gaussian signal attenuation model (GSAM) using most likely RSSIs. Numerical results demonstrate that the proposed method considerably outperforms the existing methods in terms of dynamic positioning accuracy.

9 citations

Proceedings ArticleDOI
18 Dec 2020
TL;DR: Experimental results demonstrate that CFT provides a better estimation than other existing models, and proposes a method by curve fitting technique (CFT), which reduce error to an extreme limit.
Abstract: In our modern society, WiFi (wireless fidelity) is an essential part of human life because every smartphone builds with WiFi facilities. Due to the requirement for WiFi, the wireless sensor networks (WSNs) gaining its maturity and the data traffic demand has also increased. Therefore, the problem of network localization became more challenging. For any uncertain position localization by the signal strength, the received signal strength indicator (RSSI) is a crucial benchmark. The localization process using RSSI is simple as well as cheap than any other existing methods. At the time of the internet of things (IoT) applications, WiFi can use as a leading access technology. Thus we focused on the localization using the cheapest WiFi module NodeMCU (ESP8266), which is easily programmable and operate on 2.4 GHz frequency bands. For position estimation, two modules have used, one as an access point (AP) and another as a station (STA). In the present literature, a lot of techniques have discussed to reduce the error level. However, most of them cannot achieve significant accuracy. Therefore, in this work, we proposed a method by curve fitting technique (CFT), which reduce error to an extreme limit. Moreover, we observed the error in three circumstances, i.e. outdoor, corridor and indoor. Experimental results demonstrate that CFT provides a better estimation than other existing models.

3 citations

Proceedings ArticleDOI
16 Dec 2020
TL;DR: In this article, the authors proposed an RSSI-based localization scheme that considers the trend of RSSIs obtain from the AP to estimate the positions of stations (STAs), i.e., sensors.
Abstract: The sensors’ geographical position is vital information in wireless sensor networks (WSNs) required for tracking, target detection, controlling, and monitoring systems. Thus 10-calization is an essential matter in WSNs. Localization methods are classified into two categories, range-based and range-free. Range-based localization achieves higher accuracy using received signal strength indicator (RSSI) values compare to range-free approaches, which obtain lower efficiency. RSSI produces a convenient process to find sensors’ positions because of the energy constraints of hardware in sensors. However, for the channel noise, fading, and shadowing, it is not possible to calculate the actual locations. In this paper, we introduce an RSSI-based localization scenario that considers the trend of RSSIs obtain from the Wi-Fi access point (AP) to estimate the positions of stations (STAs), i.e., sensors. Through applying the curve fitting technique (CFT), we estimate the relationship and choosing the best perfect curve the relation is established. The error level can significantly reduce by selecting the best accurate curve. Simulation demonstrates that the proposed localization scheme achieves higher positioning accuracy compared to a broad range of localization approaches.

3 citations

Proceedings ArticleDOI
27 Nov 2020
TL;DR: In this paper, the authors proposed a new angle localization method using a rotating object, where the objective is to find angles in the indoor environment with the most reliable RSSI values.
Abstract: Indoor localization with wireless fidelity (Wi-Fi) based received signal strength indicator (RSSI) has the advantages of low cost and easy installation compared to a broad range of other localization approaches. However, RSSI suffers from multiple interferences (catalyst factors) in indoor dynamic environments, which results in a significant error in RSSI measurements. To handle this issue, several methods have been proposed in the literature. These methods are classified into two categories: the trilateration method and location fingerprint positioning method. The trilateration method is based on the intersecting distance, and the fingerprinting method is based on specific geometric or probabilistic methods to calculate the unknown positions. This paper presents a new idea for angle localization using a rotating object. The objective is to find angles in the indoor environment with the most reliable RSSI values. The result demonstrates that the proposed method significantly locates the position of angles and successfully identify the best RSSI values for the perfect localization.

3 citations


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Journal ArticleDOI
TL;DR: The surveying of recent research in this area can support a better understanding of smart-city solutions based on popular platforms such as Raspberry Pi, BeagleBoard and Arduino, as presented in this article.
Abstract: With the increasing availability of affordable open-source embedded hardware platforms, the development of low-cost programmable devices for uncountable tasks has accelerated in recent years. In this sense, the large development community that is being created around popular platforms is also contributing to the construction of Internet of Things applications, which can ultimately support the maturation of the smart-cities era. Popular platforms such as Raspberry Pi, BeagleBoard and Arduino come as single-board open-source platforms that have enough computational power for different types of smart-city applications, while keeping affordable prices and encompassing many programming libraries and useful hardware extensions. As a result, smart-city solutions based on such platforms are becoming common and the surveying of recent research in this area can support a better understanding of this scenario, as presented in this article. Moreover, discussions about the continuous developments in these platforms can also indicate promising perspectives when using these boards as key elements to build smart cities.

40 citations

Journal ArticleDOI
TL;DR: This work presents the People Mobility Analytics (PmA) solution, which collects probe requests generated by Wi-Fi devices when scanning the radio channels to detect Access Points and processes the collected data to extract key insights on the people mobility.

16 citations

Journal ArticleDOI
01 Nov 2019
TL;DR: A system was proposed to reduce the level of fraud in filling the attendance list and effectiveness of student data processing using a system of applying the concept of the Internet of Things (IoT) with the fingerprint presence method.
Abstract: Lecture attendance data at universities is a reference in showing the credibility of each student used by lecturers as data for student grades as well as an evaluation material for the success of teaching and learning activities in lectures, but there are several examples of cases related to student attendance data currently prevalent in the world of education or lectures is the phenomenon of "Leave Absence" or better known as TA. In addition, other problems also arise from lecturers and administrative staff, namely difficulties in monitoring student attendance and efforts to validate attendance data because of the large amount of student data. Therefore in this study a system was proposed to reduce the level of fraud in filling the attendance list and effectiveness of student data processing using a system of applying the concept of the Internet of Things (IoT) with the fingerprint presence method. Existing system modeling results are expected to be able to support the service of processing academic data automatically and produce accurate and accurate statistical data and be able to reduce data manipulation factors from irresponsible parties.

14 citations

Journal ArticleDOI
TL;DR: This paper describes the development of a system to detect the presence of an object and monitor it, based on four NodeMcu modules programmed under Arduino IDE and communicating between them via the HTTP protocol.
Abstract: In recent years, wireless sensors networks (WSNs) have been imposed as an effective means of interconnection with simultaneous communication and information processing. They allow operating with sensors at low cost and low power consumption in various application areas such as ecosystem monitoring, detection and monitoring of objects and smart cities, etc.This paper describes the development of a system to detect the presence of an object and monitor it. This prototype is based on four NodeMcu modules (a static access point that provides the WIFI network, a server, a client and a mobile access point attached to the remote surveillance object) programmed under Arduino IDE and communicating between them via the HTTP protocol. The remote monitoring of the object for a linear disposition of the nodes used is based on the existence of the mobile access point in the HTTP client field.

10 citations

Proceedings ArticleDOI
30 Nov 2020
TL;DR: The preliminary results suggest that the proposed solution may be a viable alternative to ensure social distancing as a practice to face the pandemic caused by COVID-19.
Abstract: This paper presents the prototype of a compact and low-cost wearable electronic device that, based on the reading of the Wi-Fi signal strength emitted by other wearable devices of the same type, estimates the proximity between users and issues a notification (an audible and visual alarm) when the distance between them is less than a reference value. Our preliminary results suggest that the proposed solution may be a viable alternative to ensure social distancing as a practice to face the pandemic caused by COVID-19.

8 citations