A NN-ML based Energy-Efficient Routing Approach for IoT-WSN Systems

Problem Definition

After conducting a thorough literature review, it becomes evident that the lifespan and data delivery of Wireless Sensor Networks (WSN) have been a focal point of research in recent years. While various approaches have been proposed to address these issues, a common limitation that arises is the utilization of probabilistic methods for Cluster Head (CH) selection. These methods often lead to unequal energy distribution among nodes, resulting in premature node failure and ultimately reducing the lifespan of the entire network. This uneven distribution of energy poses a significant challenge in WSN networks, as it can impact the overall performance and efficiency of data delivery. Therefore, there is a pressing need to develop a more effective approach that can overcome the limitations associated with existing models and enhance the longevity of WSN networks.

By addressing these key issues, it is possible to optimize the performance and reliability of WSN networks, ultimately maximizing their potential impact and utility in various applications.

Objective

The objective of this work is to address the limitations in Wireless Sensor Networks (WSN) related to Cluster Head (CH) selection, uneven energy distribution, and reduced network lifespan. The proposed model aims to improve network performance and longevity by implementing modifications in CH selection, route formation, and communication phases. By utilizing energy evaluation and a Neural Network (NN) based ML model for route selection, the goal is to optimize energy utilization, enhance data delivery efficiency, and ultimately maximize the impact and utility of WSN networks in various applications.

Proposed Work

This work presents a successful and productive routing strategy to address the constraints imposed by current WSN strategies. The suggested model's primary goal is to efficiently choose CHs and create routes in the network to improve overall performance and model longevity. To achieve this objective, modifications have been done in CH selection, route formation, and communication phase. As mentioned earlier, that conventional models were using probabilistic techniques for selecting the CH in the network which resulted in uneven energy distribution and reduced network lifespan. To overcome this issue, the proposed model evaluates the energy present in each node of the cluster.

By using the given equation, the energy present in each node is calculated, and the node with the highest energy rating is selected as CH in that cluster. In the second phase of the work, an effective route needs to be selected for effective working and less energy dissipation while transferring data to the sink node. To do so, the proposed model utilizes Neural Network (NN) based ML model which determines the route for transferring data from sensor nodes to CH to the sink node. NNs are helpful in route selection because they are trained from historical data collected from WSNs to learn patterns and relationships among different nodes, transmission conditions, and resulting data transmission performance. By analyzing this data, the NN effectively identifies routes based on the characteristics of the network and current conditions.

The proposed NN-based route selection model determines the route for CHs in the network rather than considering routes for every node in the network. As per the literature analysis, CH is considered as one of the easiest and earliest next hops for nodes within a network. This means, by effectively selecting the CH in the network, the nodes would exclusively transmit their data to their particular CH which in turn passes this data to the next CH of another cluster and then reaches the sink node. This results in the effective utilization of node energy which in turn will result in an enhanced network lifespan. Once the route is determined, the communication phase begins wherein an energy model is considered for starting the communication.

The suggested model undergoes several phases before the data reaches the sink node while conserving energy.

Application Area for Industry

This project's proposed solutions can be applied in various industrial sectors that utilize Wireless Sensor Networks (WSNs) for data collection and monitoring. Industries such as agriculture, manufacturing, healthcare, and environmental monitoring can benefit from the improved CH selection and routing strategy offered by this project. In agriculture, for example, WSNs are used for monitoring soil conditions, crop growth, and irrigation systems. By implementing the proposed model, the energy efficiency of the network can be enhanced, leading to longer lifespan of the network and more reliable data delivery. Similarly, in healthcare, where WSNs are used for patient monitoring and tracking, the proposed neural network-based route selection can optimize data transmission and conserve energy, ensuring continuous and accurate data collection.

The challenges that these industries face, such as uneven energy distribution, premature node failure, and reduced network lifespan, can be effectively addressed by the proposed approach. By selecting CHs based on energy levels rather than probabilistic methods, the network can achieve a more balanced energy distribution, reducing the risk of node failures and extending the network's lifespan. The use of neural network-based route selection further optimizes data transmission routes, ensuring that data is efficiently transferred to the sink node with minimal energy dissipation. Overall, the benefits of implementing these solutions include improved network reliability, longer lifespan, and more efficient data delivery, which can positively impact various industrial sectors that rely on WSNs for data collection and monitoring.

Application Area for Academics

The proposed project aims to enrich academic research, education, and training by offering a novel approach to enhance the lifespan and data delivery of WSN networks. By addressing the limitations of existing models through effective CH selection, route formation, and communication strategies, this work contributes to advancing the field of wireless sensor networks. Academically, this project can provide researchers, MTech students, and PhD scholars with valuable insights into innovative research methods, simulations, and data analysis within educational settings. The use of Neural Network (NN) based machine learning (ML) models for route selection offers a cutting-edge approach to improving network performance and energy efficiency. The potential applications of this project extend to various technology and research domains within the field of wireless sensor networks.

Researchers in this field can utilize the code and literature of this project to further their studies on optimizing CH selection, routing strategies, and energy management in WSNs. Overall, the proposed project holds significant relevance for academic research, education, and training in the field of wireless sensor networks. Its innovative approach to addressing the challenges faced by existing models can pave the way for future research and advancements in this area. Reference future scope: Further research could explore the integration of additional ML algorithms, such as Deep Learning models, for route selection and energy management in WSNs. Additionally, the application of the proposed approach in real-world scenarios and experimental validation could provide valuable insights for practical implementations in WSN networks.

Algorithms Used

The work presents a successful routing strategy using FF ANN to address constraints in WSN strategies. The model efficiently selects CHs and creates routes in the network to improve performance and longevity. By evaluating energy levels in each node using a specific equation, the model selects the node with the highest energy rating as the CH in the cluster. Furthermore, a Neural Network based ML model is utilized for route selection to transfer data from sensor nodes to CH and then to the sink node. The NN learns from historical data to identify routes based on network characteristics and conditions.

By selecting routes for CHs instead of every node, the model conserves node energy and prolongs network lifespan. Communication then begins using an energy model, ensuring efficient data transmission to the sink node.

Keywords

SEO-optimized keywords: WSN networks, CH selection, energy distribution, network lifespan, routing strategy, route formation, communication phase, energy calculation, Neural Network, ML model, data transmission, sink node, historical data, route selection, node energy, network characteristics, energy model, IoT wireless sensor networks, communication security, energy efficiency, secure data transmission, network protocols, cryptographic algorithms, sensor node authentication, encryption techniques, energy optimization, network performance, resource allocation, network security, secure protocols, energy consumption optimization, IoT security.

SEO Tags

Problem Definition, Literature Review, WSN Networks, CH Selection, Energy Distribution, Network Lifespan, Proposed Model, Routing Strategy, Route Formation, Communication Phase, Probabilistic Methods, Energy Evaluation, Neural Network, ML Model, Historical Data, Data Transmission, Route Selection, Next Hop, Sink Node, Energy Conservation, IoT Wireless Sensor Networks, Communication Security, Energy Efficiency, Secure Data Transmission, Network Protocols, Cryptographic Algorithms, Sensor Node Authentication, Encryption Techniques, Energy Optimization, Network Performance, Resource Allocation, Network Security, Secure Protocols, Energy Consumption Optimization, IoT Security.