Smart Mesh Network for Energy-Efficient IoT Device Communication with Predictive Maintenance Capabilities

Abstract

This invention presents a smart mesh network designed to optimize energy efficiency and incorporate predictive maintenance for Internet of Things (IoT) devices. By utilizing intelligent routing algorithms, the system dynamically adjusts communication paths to minimize energy consumption during data transmission. It also integrates a predictive maintenance module that monitors device health in real-time, using machine learning to forecast potential failures before they occur. The system’s adaptive capabilities ensure continuous optimization of network performance, while its proactive approach to maintenance reduces downtime and operational costs. This solution is ideal for large-scale IoT deployments across industries such as smart cities, industrial automation, and agriculture.

Specification

Description:This invention introduces a smart mesh network designed to enhance energy efficiency and
incorporate predictive maintenance capabilities in large-scale Internet of Things (IoT)
deployments. The core functionality of this system is to dynamically route communication
between IoT devices in an energy-efficient manner while simultaneously monitoring and
predicting device health to prevent failures. The system consists of three primary
components: intelligent routing algorithms, a predictive maintenance module, and a real-time
monitoring and feedback mechanism. These components work together to ensure that the
network operates with minimal energy consumption while maintaining high reliability and
performance.
The intelligent routing algorithm is at the heart of the system, responsible for determining
the optimal communication path between devices. By using the energy consumption model
E=P×T, where E is the energy consumed, P is the power required for transmission, and T is
the transmission time, the system selects the path that minimizes energy consumption during
data transfer. The algorithm dynamically adjusts the communication routes based on realtime
network conditions, such as device power levels, traffic patterns, and link quality. This
ensures that data is transferred in the most energy-efficient way possible, reducing
unnecessary communication overhead and extending the lifetime of IoT devices.
The predictive maintenance module is designed to monitor the health of IoT devices in
real-time, collecting data such as device temperature, battery levels, and performance metrics.
By analyzing these metrics, the system can predict potential failures before they occur. For
instance, using machine learning algorithms, the system may predict a device's failure
probability Pfailure(t) at time t based on its historical performance data. This is represented as
Pfailure(t)=f(temperature,usage,battery health), where the function fff is learned from historical
data. If the system detects that a device is likely to fail soon, it can initiate preventive actions,
such as rerouting traffic or alerting maintenance personnel.
The real-time monitoring and feedback mechanism continuously tracks the performance
of both the communication network and the health of the devices. It collects real-time data
from each device and updates the system's routing decisions and maintenance predictions
accordingly. This feedback loop allows the system to adapt to changes in the environment,
such as network congestion or device degradation, ensuring that the IoT network remains
efficient and reliable even in dynamic conditions. By combining intelligent routing with
predictive maintenance, the system provides an automated solution that reduces human
intervention and optimizes both operational efficiency and device longevity.
Finally, the invention is designed to scale easily across large IoT networks, from small
localized systems to vast, geographically distributed networks. The system's adaptive nature
allows it to handle varying levels of load and complexity, ensuring that resources are
allocated efficiently as the network expands. The system can be applied to numerous
industries, such as smart cities, industrial automation, and agriculture, where large
numbers of IoT devices must be managed and maintained efficiently. By combining energyefficient
communication and predictive maintenance, this invention offers a comprehensive
solution for managing large-scale IoT systems while minimizing downtime and maintenance
costs.
In summary, the invention offers an integrated approach to IoT network management that
prioritizes both energy efficiency and predictive maintenance. By using intelligent routing
algorithms, real-time performance monitoring, and machine learning-based failure
predictions, the system optimizes resource usage and extends the operational life of IoT
devices. The use of formulas like the energy consumption model E=P×T and failure
probability prediction Pfailure(t) allows for precise and adaptable network management, making
this invention an essential solution for modern, data-intensive IoT applications. , Claims:1. We claim the novelty that the smart mesh network utilizes intelligent routing
algorithms to optimize energy consumption in IoT devices.
2. We claim that the system dynamically adjusts communication paths based on realtime
network conditions to minimize power usage.
3. We claim the integration of a predictive maintenance module that monitors device
health and predicts potential failures using machine learning algorithms.
4. We claim that the predictive maintenance module calculates the failure probability
of devices based on real-time metrics like temperature and battery health.
5. We claim that the system automatically reroutes network traffic or alerts
maintenance personnel when a device failure is predicted.
6. We claim the use of a real-time monitoring and feedback mechanism that
continuously updates routing decisions and maintenance predictions.
7. We claim that the system supports scaling across large IoT networks, providing
efficient resource allocation even in complex deployments.
8. We claim that the combination of energy-efficient routing and predictive
maintenance reduces downtime and maintenance costs in large-scale IoT
applications.

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