Internet of Things (IoT) for measuring and monitoring sensors data of water surface platform

Kafli, Nurliyana; Isa, Khairunesa

doi:10.1109/usys.2017.8309441

Cited by 26 publications

(12 citation statements)

References 5 publications

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“…This approach may reduce the potential damage that often occurs to the shaft of the Pico hydro power plant due to the unmonitored water flow speed. The monitoring system built in this study equips with one water flow sensor only that may reduce the complexity of the construction of the hardware system as faced in [10] and [11]. The use of one sensor in this study also suppresses the cost of development and gives faster data sending without ignoring the main objective of the system in monitoring the water flow speed.…”

Section: Research Results and Discussionmentioning

confidence: 99%

Design and Development of IoT Based Water Flow Monitoring for Pico Hydro Power Plant

Yuniarti

Hariyanto

Yatmono

et al. 2021

Int. J. Interact. Mob. Technol.

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A Pico hydro power plant is one good solution to support energy independence for rural areas in Indonesia. The problem that normally emerged is the uncertainty of water debit. This may cause the damage of the Pico hydro power plant equipment. The main objective of this study is to design a water flow monitoring (WAFLOW-MT) device based on the Internet of Things (IoT). This device may help the technicians at the Pico hydro power plant in monitoring the speed of water flow at the river so that the water debit is recorded all the time. The design and development process of the WAFLOW-MT device was done through 4 stages: 1) need analysis; 2) system design; 3) system development, and 4) system testing. The data was collected from the water flow sensor and sent via IoT to the webserver thingspeak.com by 6 days duration of testing. According to the testing result, it can be concluded that the WAFLOW-MT device successfully monitored the water flow of the river.

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Section: Research Results and Discussionmentioning

confidence: 99%

Design and Development of IoT Based Water Flow Monitoring for Pico Hydro Power Plant

Yuniarti

Hariyanto

Yatmono

et al. 2021

Int. J. Interact. Mob. Technol.

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“…In [72], the authors also proposed IoT-based monitoring. Their prototype takes precise measurements about air and water quality parameters.…”

Section: A On Water Reuse and Monitoring Water Pollutionmentioning

confidence: 99%

Water Management in Agriculture: A Survey on Current Challenges and Technological Solutions

2020

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Water plays a crucial role in the agricultural field for food production and raising livestock. Given the current trends in world population growth, the urgent food demand that must be answered by agriculture highly depends on our ability to efficiently exploit the available water resources. Among critical issues, there is water management. Recently, innovative technologies have improved water management and monitoring in agriculture. Internet of Things, Wireless Sensor Networks and Cloud Computing, have been used in diverse contexts in agriculture. By focusing on the water management challenge in general, existing approaches are aiming at optimizing water usage, and improving the quality and quantity of agricultural crops, while minimizing the need for direct human intervention. This is achieved by smoothing the water monitoring process, by applying the right automation level, and allowing farmers getting connected anywhere and anytime to their farms. There are plenty of challenges in agriculture involving water: water pollution monitoring, water reuse, monitoring water pipeline distribution network for irrigation, drinking water for livestock, etc. Several studies have been devoted to these questions in the recent decade. Therefore, this paper presents a survey on recent works dealing with water management and monitoring in agriculture, supported by advanced technologies. It also discusses some open challenges based on which relevant research directions can be drawn in the future, regarding the use of modern smart concepts and tools for water management and monitoring in the agriculture domain. INDEX TERMS Data analytics, internet of things, smart agriculture, water management, water monitoring, wireless sensor networks. II. FOUR CHALLENGES RELATED TO WATER MANAGEMENT IN AGRICULTURE We discuss four important challenges in water use in the agriculture domain (see Fig. 1). Each challenge motivates a significant shift from traditional water management solutions to smarter ones for higher efficiency, via the integration of modern technologies, e.g., WSN and IoT.

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“…Choose a state from the state set as the initial state S(t); while t is less than the user's termination iterations do Choose an action from the action set {G, H, I} by e-greedy strategy according to the definition of action in Part C of Section 3; Execute the selected action on the current state S(t) to jump to the next state S(t + 1); Perform crossover operation with global variable on the features contained in state S(t + 1); Calculate the fitness of the multidimensional data discretization scheme after crossover operation using equation 5; Measure the corresponding reward using equation (11) according to the definition of reward in Part C of Section 3; Update crossover Q-Table using equation 6; if the fitness of the multidimensional data discretization scheme > local variable do Update local variable with the fitness of the multidimensional data discretization scheme; end Perform crossover operation in P(t); Calculate the fitness of each individual in P(t) using equation 8; Update global variable with the optimal individual fitness value in P(t); S(t) � S(t + 1); Choose an action from the action set {G, H, I} by e-greedy strategy according to the definition of action in Part C of Section 3; Execute the selected action on the current state S(t) to jump to the next state S(t + 1); Perform mutation operation on the features contained in state S(t + 1) Calculate the fitness of the multidimensional data discretization scheme after mutation operation using equation 5; Measure the corresponding reward using equation (11) according to the definition of reward in Part C of Section 3; Update mutation Q-Table using equation (6); if the fitness of the multidimensional data discretization scheme > local variable do Update local variable with the fitness of the multidimensional data discretization scheme; end Perform mutation operation in P(t); Calculate the fitness of each individual in P(t) using equation (8); Update global variable with the optimal individual fitness value in P(t); t � t + 1; end Return Max(global variable, local variable); end ALGORITHM 2: RLGA algorithm process.…”

Section: Configuration Of Experimentalmentioning

confidence: 99%

“…e rapid development of the internet of things has produced massive amounts of large-scale data [1][2][3][4][5]. ese are mainly from various types of sensors, with high-dimensional, incomplete, random, fuzzy, and strong interference and other characteristics [6]. Despite the growing body of artificial intelligence research, how to extract and analyze valuable information from these massive amounts of complex sensor data is still a huge challenge in the field of artificial intelligence [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-Based Genetic Algorithm in Optimizing Multidimensional Data Discretization Scheme

Chen

Huang

et al. 2020

Mathematical Problems in Engineering

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Feature discretization can reduce the complexity of data and improve the efficiency of data mining and machine learning. However, in the process of multidimensional data discretization, limited by the complex correlation among features and the performance bottleneck of traditional discretization criteria, the schemes obtained by most algorithms are not optimal in specific application scenarios and can even fail to meet the accuracy requirements of the system. Although some swarm intelligence algorithms can achieve better results, it is difficult to formulate appropriate strategies without prior knowledge, which will make the search in multidimensional space inefficient, consume many computing resources, and easily fall into local optima. To solve these problems, this paper proposes a genetic algorithm based on reinforcement learning to optimize the discretization scheme of multidimensional data. We use rough sets to construct the individual fitness function, and we design the control function to dynamically adjust population diversity. In addition, we introduce a reinforcement learning mechanism to crossover and mutation to determine the crossover fragments and mutation points of the discretization scheme to be optimized. We conduct simulation experiments on Landsat 8 and Gaofen-2 images, and we compare our method to the traditional genetic algorithm and state-of-the-art discretization methods. Experimental results show that the proposed optimization method can further reduce the number of intervals and simplify the multidimensional dataset without decreasing the data consistency and classification accuracy of discretization.

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Internet of Things (IoT) for measuring and monitoring sensors data of water surface platform

Cited by 26 publications

References 5 publications

Design and Development of IoT Based Water Flow Monitoring for Pico Hydro Power Plant

Design and Development of IoT Based Water Flow Monitoring for Pico Hydro Power Plant

Water Management in Agriculture: A Survey on Current Challenges and Technological Solutions

Reinforcement Learning-Based Genetic Algorithm in Optimizing Multidimensional Data Discretization Scheme

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