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Despite advances in these types of networks, sensor nodes still rely on low-power batteries to provide their own power due to their large number, small size, and adaptive location method. It is also usually not possible to recharge or replace
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Despite advances in these types of networks, sensor nodes still rely on low-power batteries to provide their own power due to their large number, small size, and adaptive location method. It is also usually not possible to recharge or replace sensor nodes due to the use of these types of networks in harsh and inaccessible environments. Therefore, one of the most important issues in wireless sensor networks is the issue of severe power limitation. Also, since the performance of the sensor networks is highly dependent on the network lifetime and its network coverage, therefore it is crucial to consider energy storage algorithms in the design of long-life sensor networks. Today, dynamic power management methods that reduce the energy consumption of sensor networks after their design and deployment are of paramount importance. In recent years, there has been a significant boom in smart power and empowerment tools such as neural networks for dynamic power management. A neural network is a large system consisting of parallel or distributed processing elements that are interconnected in a graph topology. Data is not stored separately from processing because data is interconnected per se. Neural networks are mathematical algorithms that are capable of learning the mappings between input (s) and output (s) through supervised training or are able to categorize input information in a non-supervised way. All of these capabilities are used in ways to reduce power consumption in wireless sensor networks. The unique features and capabilities of neural networks in reducing dimensionality of input data, classification and prediction of sensor data have particular adaptation to the characteristics and needs of wireless sensor networks. Therefore, neural networks can be a good tool for application in sensor networks and by reducing the need for wireless communication, they have a significant effect on reducing the energy consumption of sensor networks and increasing their lifespan. The purpose of this study is to provide an optimal way to reduce energy consumption in sensor networks by applying neural network capabilities.
In order to arrive at a coherent and appropriate structure for research, the remainder of this chapter will address the most important principles and answer the key questions of a scientific research.
1-2. Problem definition and main research questions
In order to reduce energy consumption using neural networks the following questions are raised:
What are the reasons for the loss of energy in wireless sensor networks?
What are some ways to reduce energy consumption in sensor networks?
How can a compromise be made between energy consumption and the efficiency of sensor networks?
In what applications is the optimal energy consumption of the sensor network important?
How effective is the use of neural networks in reducing energy consumption in wireless sensor networks?
Given the proven capabilities of neural networks in related fields, we hypothesize that neural networks can be used as appropriate tools in energy conservation approaches.
Using neighboring energy levels to cluster the sensor nodes can create balanced clusters in terms of energy level and balance the energy consumption in the network, preventing premature death of the nodes, thereby increasing the lifetime of the node.
Combining different criteria for selecting a beacon, taking advantage of each, can balance the beacon load and increase the lifetime of the network.
1-4. Research Objectives
The main purpose of this research is to reduce the energy consumption of wireless sensor networks, which will be pursued in order to achieve the following goals.
Optimized Algorithm for Reducing Energy Consumption in Wireless Sensor Networks Using Neural Network Capabilities
Increase the lifetime of the wireless sensor network
Maintain maximum network coverage over the lifetime of the sensor network
Due to the novelty of the subject and the lack of sufficient research in the country in this field of knowledge, one of the objectives of this study was to conquer Bobby for applying neural network capabilities to wireless sensor networks and to conduct further research.
1-5. research method
The research method will be historical by studying the different designs and methods available to the research subject and also comparatively by comparing it with the existing methods.
1-6. Research steps
Identification of the complete problem (meaning the problem of energy consumption reduction in wireless sensor networks)
Investigating Progressive Activities in Problem Solving
Provide a framework for evaluation and evaluation
Evaluate existing methods
Utilizing different sales points obtained through the application of the evaluation framework (in order to create efficient and effective methods).
Introducing a New Method for Reducing Energy Consumption in Wireless Sensor Network Using Neural Network Method
Simulation of the mentioned method according to the specific scenario and presenting the simulation results
Comparison of Statistical Results of Simulation of New Proposed Method with Results of Previous Methods
Evaluation of results and conclusions
1-7. Thesis structure
The second chapter will review different designs for reducing energy consumption in wireless sensor networks, presenting the categorization and role of neural networks in each of the existing designs based on the review of different research papers. Chapter 3, while giving a classification for Ro
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