Agent based system for home automation, monitoring and security

Bordencea, Daniela; Vălean, Honoriu; Folea, Silviu; Dobircau, Ancuta

doi:10.1109/tsp.2011.6043750

Cited by 10 publications

(4 citation statements)

References 2 publications

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“…The papers [46]- [47] are studies on physical and MAC layer protocols built on LoRa. [48] is a study on forest fires and different factors effecting it. The studies [49] and [50] show thepracticality of utilizing Long Range (LoRa) transmission technology in conjunction with theLoRa Wide Area Network (LoRaWAN) protocol for underground monitoring applications.So, the literature review motivated to propose a solution to design a system for early detection and prevention system of fire at landfill sites…”

Section: Block Diagram and Proposed Architecturementioning

confidence: 99%

An Intelligent IoT Based Landfill Fire Prediction And Prevention System

SAKYA,

Yadav,

Shukla

et al. 2023

Preprint

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This work focuses on India’s tallest heap of garbage at Ghazipur landfill site in New Delhi. About 2000 tonnes of garbage is being dumped at Ghazipur every day and it causes poisonous gases. Fire, sparked by methane gas coming from the garbage and takes days to extinguish. Methane belching from garbage can cause even more toxic gases. The dumping of garbage needs to be stopped for the betterment of people and the environment. The research helps detect the gases released from garbage and the importance of the project increasing due to the growth of solid waste. The detection of the risk of fire and the communication of this situation to the people could reduce the number of fires. The system also helps with automatic detection of fire in garbage-dumped areas and alerts to the authorities. This research work presents a low cost network based on Long Range(LoRa) network which is able to assess automatically the level of risk of catching fire in the solid waste. The system consists of several LoRa nodes with sensors to scale temperature, relative humidity and increase in methane level. The data coming from LoRa nodes is stored and processed in the server that is The Things Network and sends data finally to a website or an application for the analysis of the collected data using machine learning . This helps in reducing environmental pollution which results in health hazards for the people residing in the vicinity.The Simulation of the Lora Network is done in NS-3 first ,for finding the throughput and latency with different scenarios of lora nodes and gateways. The deployin stretegies are fixed after ns-3 simulation. The ns-3 simulation is done in ns-3.36.1 using lorawan module.Thereafter the prototype model is prepared using LoRA hardware and different sensors. The real time data is collected on the server for data analysis. The work have used machine learning algorithms to analyse real-time sensor data received on a network server through the LoRa Gateway to detect abnormal patterns indicative of a potential fire outbreak and to justify the ―intelligence‖ of our system. The wireless LoRa communication eliminates extensive wiring, while intelligent data analysis enhances accuracy and minimizes false alarms. Experimental evaluations demonstrate the system's effectiveness in timely fire detection and response, minimizing environmental impact and ensuring community safety. The proposed system presents a promising solution for efficient landfill fire early detection, prevention, and mitigation.

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Section: Block Diagram and Proposed Architecturementioning

confidence: 99%

An Intelligent IoT Based Landfill Fire Prediction And Prevention System

SAKYA,

Yadav,

Shukla

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…It is considered the best in control, but its price is very high, and the data transfer rate is low [32]. X10 is used for communication between electronic appliances, but it is very slow [33]. This paper presents the HAMS, where Z-Wave technology was used for wireless control of the automation system by integrating Raspberry pi with LabVIEW technology and a graphical user interface (GUI) design to control the entire system and to monitor and control home appliances efficiently.…”

Section: Motivations and Problem Statementmentioning

confidence: 99%

Design and Implementation of a Real-Time Smart Home Management System Considering Energy Saving

et al. 2022

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One of the most challenging problems related to the operation of smart microgrids is the optimal home energy management scheme with multiple and conflicting objectives. Moreover, there is a noticeable increase in homes equipped with renewable energy sources (RESs), where the coordination of loads and generation can achieve extra savings and minimize peak loads. In this paper, a solar-powered smart home with optimal energy management is designed in an affordable and secure manner, allowing the owner to control the home from remote and local sites using their smartphones and PCs. The Raspberry Pi 4 B is used as the brain of the proposed smart home automation management system (HAMS). It is used to collect the data from the existing sensors and store them, and then take the decision. The home is monitored using a graphical interface that monitors room temperature, humidity, smoke, and lighting through a set of sensors, as well as PIR sensors to monitor the people movement. This action enables remote control of all home appliances in a safe and emission-free manner. This target is reached using Cayenne, which is an IoT platform, in addition to building some codes related to some appliances and sensors not supported in Cayenne from scratch. Convenience for people with disabilities is considered by using the Amazon Echo Dot (Alexa) to control home appliances and the charging point by voice, implementing the associated code for connecting the Raspberry pi with Alexa from scratch, and simulating the system on LabVIEW. To reach the optimal operation and reduce the operating costs, an optimization framework for the home energy management system (HEMS) is proposed. The operating costs for the day amounted to approximately 16.039 €. There is a decrease in the operating costs by about 23.13%. The consumption decreased after using the smart HAMS by 18.161 kWh. The results of the optimization also show that the least area that can be used to install solar panels to produce the desired energy with the lowest cost is about 118.1039 m2, which is about 23.62% of the total surface area of the home in which the study was conducted. The obtained results prove the effectiveness of the proposed system in terms of automation, security, safety, and low operating costs.

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“…In [79], the researchers developed a software agent based system providing adaptation and fault tolerance allowing a system to continue to function in presence of access point failure or defective sensors. Their solution allocate sensors to specific access points, providing virtual redundancy, therefore increasing reliability.…”

Section: Network and Data In Transit Defencesmentioning

confidence: 99%

The Security of Smart Buildings: a Systematic Literature Review

Ciholas,

Lennie,

Sadigova

et al. 2019

Preprint

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Smart Buildings are networks of connected devices and software in charge of automatically managing and controlling several building functions such as HVAC, fire alarms, lighting, shading and more. These systems evolved from mostly electronic and mechanical elements to complex systems relying on IT and wireless technologies and networks. This exposes smart buildings to new risks and threats that need to be enumerated and addressed. Research efforts have been done in several areas related to security in smart buildings but a clear overview of the research field is missing. In this paper, we present the results of a systematic literature review that provides a thorough understanding of the state of the art in research on the security of smart buildings. We found that the field of smart buildings security is growing significantly in complexity due to the many protocols introduced recently and that the research community is already studying. We also found an important lack of empirical evaluations, though evaluations on testbeds and real systems seems to be growing. Finally, we found an almost complete lack of consideration of non-technical aspects, such as social, organisational, and human factors, which are crucial in this type of systems, where ownership and liability is not always clear.

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Agent based system for home automation, monitoring and security

Cited by 10 publications

References 2 publications

An Intelligent IoT Based Landfill Fire Prediction And Prevention System

An Intelligent IoT Based Landfill Fire Prediction And Prevention System

Design and Implementation of a Real-Time Smart Home Management System Considering Energy Saving

The Security of Smart Buildings: a Systematic Literature Review

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