Intelligent greenhouse monitoring and control scheme: An arrangement of Sensors, Raspberry Pi based Embedded System and IoT platform

Arshad, Jehangir

doi:10.17485/ijst/v13i27.311

Cited by 34 publications

(17 citation statements)

References 29 publications

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“…In further, [14] proposed a deep RL algorithm based on near-end Actor-Critic for feedback control applications. A prototype model is also designed to monitor the climatic parameters and light intensity in a greenhouse environment that comprises of a sensor network-based (SN) node and a Raspberry Pi-based embedded system (ES) [15]. A framework to optimize the training process and performance improvement has been proposed in [16,17] using m-out-of-n bootstrapping and aggregating multiple DDPGs for experimental verification.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Deterministic Policy Gradient to Regulate Feedback Control Systems Using Reinforcement Learning

Al‐Bawri¹,

Islam²,

Singh³

et al. 2022

Computers, Materials &Amp; Continua

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Controlling feedback control systems in continuous action spaces has always been a challenging problem. Nevertheless, reinforcement learning is mainly an area of artificial intelligence (AI) because it has been used in process control for more than a decade. However, the existing algorithms are unable to provide satisfactory results. Therefore, this research uses a reinforcement learning (RL) algorithm to manage the control system. We propose an adaptive speed control of the motor system based on depth deterministic strategy gradient (DDPG). The actor-critic scenario using DDPG is implemented to build the RL agent. In addition, a framework has been created for traditional feedback control systems to make RL implementation easier for control systems. The RL algorithms are robust and proficient in using trial and error to search for the best strategy. Our proposed algorithm is a deep deterministic policy gradient, in which a large amount of training data trains the agent. Once the system is trained, the agent can automatically adjust the control parameters. The algorithm has been developed using Python 3.6 and the simulation results are evaluated in the MATLAB/Simulink environment. The performance of the proposed RL algorithm is compared with a proportional integral derivative (PID) controller and a linear quadratic regulator (LQR) controller. The simulation results of the proposed scheme are promising for the feedback control problems.

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Section: Literature Reviewmentioning

confidence: 99%

Deep Deterministic Policy Gradient to Regulate Feedback Control Systems Using Reinforcement Learning

Al‐Bawri¹,

Islam²,

Singh³

et al. 2022

Computers, Materials &Amp; Continua

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“…As a result, these data captured by Raspberry pi3 to be sent to the application that allows to interface with the greenhouse controller. In [11], the authors created a model that was comprising a sensor arrange (SN) based node. They also used Raspberry Pi-based inserted framework (ES) to screen the climatic parameters with temperature, soil moisture, carbon dioxide, and lightly concentrated inside a greenhouse environment.…”

Section: Related Workmentioning

confidence: 99%

Application of Self-Managing System in Greenhouse with Wireless Sensor Network

Khudhur¹,

Croock²

2021

IJCCCE

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It is well known that the Wireless Sensor Network (WSN) is a part of different fields of modern life (self-managing life). The automation of operating the WSN without any need for human efforts develops the technique used in this network in terms of power consumption, costs, and so on. In this paper, the WSN application that adopts the self-managing property is presented as well as applying this property in a greenhouse as a case study. It uses remote controlling technology for data exchanging in a multi-layer WSN. Different hardware and software equipment have been employed in the application based on self-managing techniques. Such hardware includes (NodeMCU ESP8266) as a microcontroller, Raspberry pi3 as a base station that captures the sensor reading from the node. Additionally, it was utilized two types of sensors (DS18B20, soil moisture) to sense the environmental parameters such as temperature and soil moisture and two actuators (LED1, LED2). The results show the proper performance of the presented WSN in terms of the self-managing side by checking the received data in real-time mode. These results are achieved for the threshold values of 25ᵒC temperature and 500 soil moisture as an upper level for operating the actuators. Index Terms—WSN, MQTT protocol, NodeMCU ESP8266, Raspberry pi3, sensors.

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“…is issue can be solved by edge computing. Computation offloading is formulated as an optimization problem to minimize offloading cost and provide performance guarantees [14][15][16][17][18][19]. Cloud computing resources are located at distant data centres due to which communication latency and network bandwidth become serious problems.…”

Section: Introductionmentioning

confidence: 99%

Edge Computing for IoT-Enabled Smart Grid

Mehmood

Oad

Abrar

et al. 2021

Security and Communication Networks

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Smart grid is a new vision of the conventional power grid to integrate green and renewable technologies. Smart grid (SG) has become a hot research topic with the development of new technologies, such as IoT, edge computing, artificial intelligence, big data, 5G, and so on. The efficiency of SG will be increased by smart embedded devices that have intelligent decision-making ability. Various types of sensors and data sources will collect data of high resolution. One of the vital challenges for IoT is to manage a large amount of data produced by sensors. Sending this massive amount of data directly to the cloud will create problems of latency, security, privacy, and high bandwidth utilization. This issue is addressed by edge computing (EC). In EC, the data are processed at the edge of the network that is near the embedded gadgets. This paper provides a comprehensive review of the smart grid systems, based on IoT and EC. The development in the rising technologies, the framework for EC-IoT-based SG, and requirements to implement the EC-IoT-based SG system are highlighted in the paper. Framework for EC-IoT-based SG is examined, and important requirements to implement the EC-IoT-based SG system are outlined. Finally, some critical issues and challenges faced in the implementation of EC-IoT-based SG systems are identified. Some important open research issues are also identified.

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Intelligent greenhouse monitoring and control scheme: An arrangement of Sensors, Raspberry Pi based Embedded System and IoT platform

Cited by 34 publications

References 29 publications

Deep Deterministic Policy Gradient to Regulate Feedback Control Systems Using Reinforcement Learning

Deep Deterministic Policy Gradient to Regulate Feedback Control Systems Using Reinforcement Learning

Application of Self-Managing System in Greenhouse with Wireless Sensor Network

Edge Computing for IoT-Enabled Smart Grid

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