Intruder Detection System - A LoRa Based Approach

C, Mallikarjun B.; Kiranmayi, K J; Lavanya, N.; Prateeksha, K H; Sushmitha, J

doi:10.1109/icces48766.2020.9137923

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…(iii) Intruders: Security of applications against intruders and intruder detection [102] is an immense and dynamic problem in several domains including IoT scenarios. In the context of SARA/SNAPS, it is important to strike an operational balance between the infinite breadth of this problem versus a feasible inclusion of a verification system which can authenticate and authorize users accessing various parts of the SNAPS network/application.…”

Section: Decision Support Application-cybersecuritymentioning

confidence: 99%

Sense–Analyze–Respond–Actuate (SARA) Paradigm: Proof of Concept System Spanning Nanoscale and Macroscale Actuation for Detection of Escherichia coli in Aqueous Media

et al. 2020

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Foodborne pathogens are a major concern for public health. We demonstrate for the first time a partially automated sensing system for rapid (~17 min), label-free impedimetric detection of Escherichia coli spp. in food samples (vegetable broth) and hydroponic media (aeroponic lettuce system) based on temperature-responsive poly(N-isopropylacrylamide) (PNIPAAm) nanobrushes. This proof of concept (PoC) for the Sense-Analyze-Respond-Actuate (SARA) paradigm uses a biomimetic nanostructure that is analyzed and actuated with a smartphone. The bio-inspired soft material and sensing mechanism is inspired by binary symbiotic systems found in nature, where low concentrations of bacteria are captured from complex matrices by brush actuation driven by concentration gradients at the tissue surface. To mimic this natural actuation system, carbon-metal nanohybrid sensors were fabricated as the transducer layer, and coated with PNIPAAm nanobrushes. The most effective coating and actuation protocol for E. coli detection at various temperatures above/below the critical solution temperature of PNIPAAm was determined using a series of electrochemical experiments. After analyzing nanobrush actuation in stagnant media, we developed a flow through system using a series of pumps that are triggered by electrochemical events at the surface of the biosensor. SARA PoC may be viewed as a cyber-physical system that actuates nanomaterials using smartphone-based electroanalytical testing of samples. This study demonstrates thermal actuation of polymer nanobrushes to detect (sense) bacteria using a cyber-physical systems (CPS) approach. This PoC may catalyze the development of smart sensors capable of actuation at the nanoscale (stimulus-response polymer) and macroscale (non-microfluidic pumping).

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Section: Decision Support Application-cybersecuritymentioning

confidence: 99%

Sense–Analyze–Respond–Actuate (SARA) Paradigm: Proof of Concept System Spanning Nanoscale and Macroscale Actuation for Detection of Escherichia coli in Aqueous Media

et al. 2020

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“…Mallikarjun et al [21] proposed an automated NLP-based text processing approach. Their approach can successfully convert an excel datasheet into a DBMS.…”

Section: Related Workmentioning

confidence: 99%

BERT Model-based Natural Language to NoSQL Query Conversion using Deep Learning Approach

Hossen¹,

Uddin²,

Arefin³

et al. 2023

IJACSA

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Databases are commonly used to store complex and distinct information. With the advancement of the database system, non-relational databases have been used to store a vast amount of data as traditional databases are not sufficient for making queries on a wide range of massive data. However, storing data in a database is always challenging for non-expert users. We propose a conversion technique that enables non-expert users to access and filter data as close to human language as possible from the NoSQL database. Researchers have already explored a variety of technologies in order to develop more precise conversion procedures. This paper proposed a generic NoSQL query conversion learning method to generate a Non-Structured Query Language from natural language. The proposed system includes natural language processing-based text preprocessing and the Levenshtein distance algorithm to extract the collection and attributes if there were any spelling errors. The analysis of the result shows that our suggested approach is more efficient and accurate than other state-of-the-art methods in terms of bilingual understudy scoring with the WikiSQL dataset. Additionally, the proposed method outperforms the existing approaches because our method utilizes a bidirectional encoder representation from a transformer multi-text classifier. The classifier process extracts database operations that might increase the accuracy. The model achieves state-of-the-art performance on WikiSQL, obtaining 88.76% average accuracy.

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“…The system gives flexibility to the farmers for guaranteeing full protection of their farmlands from any suspicious activities or attacks. Mallikarjun et al [15] implemented an intruder detection system based on long range (LoRa) approach, which marks the usages of PIR sensor to sense the intruder and their nearby environment. They use the LoRa approach to send the information to the control system for further processing, to make an appropriate decision.…”

Section: Related Workmentioning

confidence: 99%

FSAS: An IoT-Based Security System for Crop Field Storage

Prakash

Barthwal

Avikal

et al. 2023

International Journal of Distributed Sensor Networks

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Internet of Things abstracts the ability to remotely associate and observe things or objects over the Internet. When it comes to agriculture, this idea has been incorporated to make agriculture-related tasks smart, secure, and automated. Agriculture is vital for economic growth and also for the survival of humans. Farmers living in rural areas of India face a common problem of the theft of equipment like induction motors from small storage houses meant for storing commodities in crop fields. In this study, we present a remote security management framework for monitoring the crop field storage house, known as the farm security alert system (FSAS). FSAS is a small, energy efficient, low cost, and accurate security management system that uses microcontroller-based passive infrared (PIR) sensor and global system for mobile communication (GSM) module to generate an alert to the farm owner if there is an intrusion event at the crop field store. The microcontroller board utilized in the proposed model is the Arduino Uno, and PIR motion sensor is used to recognize the intruder. In addition, FSAS also can be used for monitoring of induction motor by utilizing a similar arrangement of sensors. The sensor signal is transmitted to the cloud whenever the intruder is within the sensing range of the sensor node. Naive Bayes’ prediction model is used to identify the level of encroachment as no (green), mild (yellow), or high (red) threat. The status and the alarms can be received by the farm owners, either on their smartphones as application alerts or as a short message/phone call, at any distance, and independent of whether their cell phones are connected to the Internet.

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Intruder Detection System - A LoRa Based Approach

Cited by 5 publications

References 7 publications

Sense–Analyze–Respond–Actuate (SARA) Paradigm: Proof of Concept System Spanning Nanoscale and Macroscale Actuation for Detection of Escherichia coli in Aqueous Media

Sense–Analyze–Respond–Actuate (SARA) Paradigm: Proof of Concept System Spanning Nanoscale and Macroscale Actuation for Detection of Escherichia coli in Aqueous Media

BERT Model-based Natural Language to NoSQL Query Conversion using Deep Learning Approach

FSAS: An IoT-Based Security System for Crop Field Storage

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