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Citations
... ireless sensor node's life cycle and performance, as well as communication channels, are critical. A sensor node is made up of four major components: a sensing unit, a transceiver unit, a processing unit, and a power unit, as well as supplementary application-specific components including a mobilizer, location detecting system, and power generator [Kaur et. al., 2019]. ...
... Embedded Intelligent System (EI) is a nascent project domain, combining machine learning algorithms such as ("machine learning and neural networks, deep learning, expert systems, fuzzy intelligence, swarm intelligence, self-organizing map and extreme learning") and smart resolution-produce abilities into movable and implanted devices or systems [5][31] [32]. IoT is the contraction of appliances, software, sensors, operators, and physical objects are embedded in the wireless sensor network(WSN), vehicles, house devices, and other outputs that aid these objects to transmissions and data sharing [23] [24] 25] [26]. IoT is rapidly growing with the recent evolutions in wireless technology and embedded devices, with lower energy Microcontrollers that have been evaluated that are typical for IoT's remotely located in separated areas to bind and operate for the large period that need not repairing [5][6] [14]. ...
This paper produces a predictor called Zero to Max Energy Predictor Model based on Deep Embedded Intelligence Techniques (ZME-DEI) to predict Dc-power which is the maximum energy generation from renewable resources that does not cause environmental pollution. ZME-DEI model consists of many stages which flow sequentially in stepwise style; the first stage presents collecting data from sensors of weather and solar plant (i.e., the sensors give a stream of data that contain multi-features). The second stage is preprocessing which contains multi-steps such as (a) Merging between two datasets. (b) Using correlation to the new dataset. (c) Splitting readings into intervals, and deleting duplication intervals. In the third stage, the ZME-DEI model is constructed based on adopting gradient boosting techniques through replacing its kernel (i.e., Decision Tree function) with multi parameters optimization functions. The stage begins with dividing the dataset into two sets using five cross-validation methods, the training dataset is used to construct the ZME-DEI model, while the testing dataset is for evaluating in the final stage. Finally, the fourth ZME-DEI stage is used for evaluation results of the testing dataset based on three measures such as coefficient of determination (R2), Root Mean Square Error (RMSE), and Accuracy(A). the results explain the robust of ZME-DEI and reduce the computation complexity while the best results get from the optimization objective function that based on three parameters Irritation, AC-Power and Temperature.
... ireless sensor node's life cycle and performance, as well as communication channels, are critical. A sensor node is made up of four major components: a sensing unit, a transceiver unit, a processing unit, and a power unit, as well as supplementary application-specific components including a mobilizer, location detecting system, and power generator [Kaur et. al., 2019]. ...
Due to the technological development that occurred in different areas of life, the world and the environment became more vulnerable to different types of pollution, such as the increase in the emission of carbon dioxide and gases that contain different toxicity rates, especially in industrial areas and dense residential areas. Therefore, Renewable energy is the best solution to this problem. In general; Renewable Energy (RE) is infinite and natural energy which can be called also environmentally friendly, clean energy, or green energy that meets a clean, pollution-free climate, and supplies people with a novel cost-efficient kind of energy. There are multi types of Renewable Energy Resources (RER), biomass energy (bioenergy) it’s the transformation of biomass into the interesting format, like heat, electricity, and liquid fuels. Geothermal energy is an efficient extraction of the earth renewable energy. In hydropower, falling water transformed into energy by utilization turbines. Marine energy (ocean) extract from six roots: waves, tidal range, tidal currents, ocean thermal energy conversion, and salinity gradients, each has the various root and needs various technics for transformation. Solar energy extraction requires, utilization of sun's energy to fit hot water by solar thermal systems or electricity by solar photovoltaic (PV) and concentrating solar power (CSP) system. this paper designs an integration model called “Zero to Max Energy Predictor Model Based on Deep Embedded Intelligence Techniques (ZME_ DEI)” based on the concept of deep embedded intelligent techniques. To determine the main rules for each unit that are effective in generating the maximum electrical energy based on the nature of each dataset.
ZME-DEI model contains multi-stages, First stage includes many steps; first step captures data in real-time from multi-sensors. The second step makes merging between solar plant and weather datasets. Then the third step checks missing values. While; second stage conseired of pre-processing contains four steps such as deleting duplicate, adding some features that are useful in prediction, split capture datasets after merging into multi-intervals each interval containing the reading through 15 minutes. In the third stage, the ZME- DEI model creates concerning knowledge constraints and adopts gradient boosting techniques through replace the kernel of GBM represent by DT by new kernel based on multi-objective functions. The dataset divides into two subsets using ten cross-validation methods, training used to construct the ZME-DEI model, and a testing set for evaluating it. The final stage includes evaluation results based on three measures such as coefficient of determination (R2), Root Mean Square Error (RMSE), and Mean Error (ME), furthermore Max Energy Generation (MEG).
... It has several key characteristics; among these, the most important is the low power consumption of the sensor node. The latter is not powered by the electrical network but supported by a renewable energy source like solar harvesting [51][52][53]. In addition, it can also be powered by a multisource harvesting technique [54][55][56][57]. ...
The development of Internet of Things (IoT) systems is a rapidly evolving scenario, thanks also to newly available low-power wide area network (LPWAN) technologies that are utilized for environmental monitoring purposes and to prevent potentially dangerous situations with smaller and less expensive physical structures. This paper presents the design, implementation and test results of a flood-monitoring system based on LoRa technology, tested in a real-world scenario. The entire system is designed in a modular perspective, in order to have the capability to interface different types of sensors without the need for making significant hardware changes to the proposed node architecture. The information is stored through a device equipped with sensors and a microcontroller, connected to a LoRa wireless module for sending data, which are then processed and stored through a web structure where the alarm function is implemented in case of flooding.
Eddy current testing (ECT) is one of the most extensively used non-destructive techniques for inspecting electrically conductive materials at very high speeds that does not require any contact between the test piece and the sensor. However, the characterization of a crack is not easy to obtain, and for this purpose, other eddy current evaluation methods are still under investigation.
In Wireless Sensor Networks, the Sensor Nodes (SNs) need to operate for a long period of time without any kind of intervention in order to achieve dedicated tasks such as surveillance, automation, monitoring, control and many others. SNs are equipped with non-changeable batteries for power supply. Due to the small dimension of an SN, the energy supply attached to the SN battery has to be very limited in size. The lifetime of the sensor nodes and thus of the overall network greatly depends on these batteries. Because SNs generally operate in harsh conditions, the replacement of batteries is impossible. Hence, we make use of natural sources of renewable energies such as wind, sun, vibrations and alike to provide SNs with permanent harvested power supply. In this paper, we adopt an approach which is relatively poorly investigated in the literature by presenting, to the best of our knowledge, a new Generalized Stochastic Petri Net (GSPN) model to an SN with Energy Harvesting capability. The proposed model allows to determine performance parameters and to extract some experimental results to predict the energy consumption of a sensor node.
