NERGY-EFFICIENT IOT SENSOR NETWORK FOR AGRICULTURE MONITORING

Abstract

The present invention relates to an energy-efficient IoT sensor network designed for agriculture monitoring, aiming to optimize resource use and reduce operational costs. The system incorporates low-power environmental sensors, adaptive sensing strategies, edge computing for local data processing, and energy-harvesting modules such as solar panels to power the sensor nodes. By using predictive scheduling and dynamic sleep/wake algorithms, the network adjusts its data collection frequency based on environmental conditions, minimizing energy consumption while ensuring accurate, real-time monitoring. The invention enables scalable, sustainable agricultural practices by providing timely, actionable insights for efficient crop management and enhanced productivity.

Specification

Description:In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.

The ensuing description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

The word "exemplary" and/or "demonstrative" is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as "exemplary" and/or "demonstrative" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms "includes," "has," "contains," and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term "comprising" as an open transition word without precluding any additional or other elements.

Reference throughout this specification to "one embodiment" or "an embodiment" or "an instance" or "one instance" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
The present invention provides an energy-efficient IoT sensor network for monitoring agricultural parameters to optimize crop management. The system is designed to address the limitations of existing solutions, particularly in terms of energy consumption, adaptability, and scalability. The invention integrates a combination of low-power sensors, adaptive sensing strategies, edge computing, energy-harvesting modules, and dynamic sleep/wake scheduling to create a robust, sustainable monitoring network for agricultural applications.

The architecture of the system includes a plurality of sensor nodes distributed across the agricultural field. Each sensor node is equipped with environmental sensors (e.g., soil moisture, temperature, humidity, light intensity), a microcontroller for data processing, a communication module (e.g., LoRa, ZigBee, NB-IoT), and an energy-harvesting unit, typically a solar panel. The sensor nodes are designed to operate autonomously with minimal maintenance, leveraging energy harvested from the environment to sustain their activities over extended periods.

The adaptive sensing mechanism is a core feature of the invention, allowing the system to adjust the frequency of data collection based on real-time environmental conditions. For instance, during periods of stable weather, the system reduces the sensing intervals to conserve energy, while during periods of rapid change, such as during rainfall or temperature spikes, it increases the frequency of data collection. This adaptability is achieved through a machine learning model that continuously analyzes incoming data and predicts optimal sensing schedules based on historical trends and current conditions.

The sensor nodes also feature edge computing capabilities, where preliminary data processing is performed locally before transmission. This reduces the volume of data sent to the central gateway, saving both bandwidth and energy. For example, anomaly detection algorithms may be implemented at the sensor node level to identify and filter out erroneous readings, transmitting only meaningful data to the central system. This helps in reducing unnecessary communication, which is a major contributor to power consumption in conventional IoT networks.

Energy-harvesting modules integrated into each sensor node provide a sustainable power source. The invention primarily uses solar panels, but other methods such as piezoelectric or thermoelectric generators could be employed depending on the environmental conditions. The harvested energy is stored in rechargeable batteries, which power the sensor node components. The power management circuit within the node ensures that the energy is utilized efficiently, prioritizing critical functions like data sensing and transmission.

The centralized gateway receives the processed data from all sensor nodes and aggregates it for comprehensive analysis. The gateway can be connected to a cloud-based platform where advanced analytics and machine learning algorithms are applied to generate insights. The platform supports real-time visualization of agricultural parameters, alert generation for potential issues (e.g., low soil moisture), and recommendations for irrigation or fertilization. This real-time feedback helps farmers make data-driven decisions to enhance crop yield and resource efficiency.

In the first embodiment, the invention incorporates a machine learning model trained on historical environmental data to predict optimal sensing intervals. The model analyzes parameters such as soil moisture, temperature, and humidity patterns over time. For instance, if the model detects that soil moisture levels remain stable during certain weather conditions, it will reduce the frequency of soil moisture sensing, thus conserving energy.

Conversely, if the model predicts a potential weather change that could impact crop health (e.g., an upcoming heatwave), it increases the sensing frequency to closely monitor soil moisture and temperature changes. This predictive scheduling significantly enhances the energy efficiency of the sensor network while ensuring critical data is not missed during key agricultural events.

The embodiment also includes an anomaly detection module at the sensor node level. Using edge computing, the node processes incoming sensor data and identifies outliers that deviate significantly from expected patterns. For example, if a sudden drop in soil moisture is detected that does not correlate with other environmental factors, the anomaly detection algorithm filters out the erroneous reading before transmitting the data to the central gateway. This reduces unnecessary communication and prevents false alarms, further optimizing power consumption.

The second embodiment focuses on an energy-efficient sensor network powered by integrated solar panels. Each sensor node includes a solar panel connected to a power management circuit and a rechargeable battery. The solar panel charges the battery during daylight hours, allowing the sensor node to operate continuously even during low-light conditions, such as at night or on cloudy days.

This embodiment employs a dynamic sleep/wake scheduling algorithm that optimizes the sensor node's power usage based on battery charge levels and environmental conditions. The algorithm monitors the current charge status of the battery and adjusts the activity of the sensor node accordingly. For example, if the battery level is high, the node will remain active for longer periods, collecting and transmitting data frequently. However, if the battery charge is low, the node enters a low-power sleep mode for extended periods, reducing energy consumption until the battery can be recharged.

Additionally, this embodiment utilizes low-power communication protocols such as LoRa or ZigBee, which are optimized for long-range, low-energy data transmission. These protocols allow the sensor nodes to communicate effectively over large agricultural fields without requiring a direct line of sight or extensive infrastructure. The use of solar power, combined with dynamic sleep/wake scheduling and efficient communication, enables the sensor network to operate sustainably with minimal maintenance, making it ideal for deployment in remote or large-scale agricultural settings.

Overall, both embodiments showcase the versatility and adaptability of the invention, highlighting its ability to provide real-time, energy-efficient monitoring solutions for various agricultural applications. The system's innovative features make it a valuable tool for precision agriculture, helping farmers optimize resource use, reduce costs, and improve crop yields.

While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the invention and not as limitation. , Claims:1.A method for energy-efficient agriculture monitoring using an IoT sensor network, the method comprising:
Collecting environmental data using a plurality of low-power sensor nodes distributed across an agricultural field;
Processing and filtering the collected data locally at each sensor node using edge computing;
Adjusting the frequency of data collection based on detected environmental stability;
Transmitting filtered data to a centralized gateway using a low-power communication protocol;
Entering a low-power sleep mode when data transmission is not required;
Harvesting energy using solar panels or other energy-harvesting modules to power the sensor nodes.

2.The method of claim 1, further comprising the step of dynamically adjusting the sleep duration of sensor nodes based on available battery charge levels.

3.The method of claim 1, further including performing data aggregation at the sensor nodes before transmission to reduce bandwidth usage.

4.The method of claim 1, wherein the edge computing module performs anomaly detection on the collected data to filter outliers before transmission.

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