REAL-TIME ENERGY EFFICIENCY OPTIMIZATION IN IOT SYSTEMS VIA CNN-LSTM MACHINE LEARNING MODELS

Abstract

Abstract With the burgeoning of IoT systems, there is an increasing demand for energy-efficient alternatives that facilitate real-time power consumption management to save costs and mitigate environmental impacts. An energy consumption prediction and dynamic device state adjustment model for the IoT environment using CNN-LSTM machine learning. Data collected included wattage usage and states of operation (on, off, and idle) from smart home and industrial IoT devices. This processing made sure that the data was becoming consistent in nature, which helped to integrate it into our model. We used the CNN part to extract spatial features from the energy data, and the LSTM part processed temporal sequences to make accurate predictions about future energy requirements. The outcome demonstrates that the devised model reduces energy consumption by 20%, attains a prediction accuracy of 95%, and keeps latency below 0.6 s, enabling real-time applications. While these results are encouraging, there were limitations to this study, such as the type of device and lack of real-world environmental variation. An extension of this work could be made by using reinforcement learning, which allows adaptive energy management for a wider range of IoT applications. This work shows the feasibility of a simple, efficient, and real-time energy optimization scheme for IoT, supporting dissemination videos in exchange for practical and environmental payoffs while illuminating novel directions to further bolster sustainability in IoT.

Specification

Description:REAL-TIME ENERGY EFFICIENCY OPTIMIZATION IN IOT SYSTEMS VIA CNN-LSTM MACHINE LEARNING MODELS

Field and Background of the Invention
As IoT devices grow in number and begin to infiltrate more and more industries (in addition to our homes), energy management has become a key issue. The nature of IoT deployments means that they need to be always on, which is extremely energy-intensive, resulting in high costs and creating a barrier for enterprise scalability. Due to the expected growth of IoT networks, it is essential to create energy management methods capable of recycling in a dynamic real-time perspective. Energy optimization in IoT systems to date mostly employs static or non-sequential models as optimal solutions, which cannot adapt to changing circumstances flexibly. These models cannot account for the order of operations or that devices are not always on and only use power sometimes in their life cycles, which inherently limits these approaches to achieving real energy efficiency. In addressing such limitations, this research proposes a CNN-LSTM machine learning approach that is suitable for real-time energy optimization in IoT systems. This makes CNNs (convolution neural networks) efficient when it comes to analyzing spatial data, identifying patterns, and extracting features. At the same time, LSTMs are better at sequential data, which provides them with an advantage in capturing temporal dependencies. These strengths are mutually exclusive, and integrating them provides a unique capability that can be built on to present a comprehensive method for energy optimization within IoT systems facing dynamic and uncertain environments, which is what the proposed CNN-LSTM-based model enables us to do.
The research aims to present a smart model that can be used in an adaptive manner for predicting energy use and can optimise device performance, subsequently contributing to a low-cost IoT model. Innovative aspect of this study is its novel integration of CNN and LSTM architectures to leverage the spatial feature extraction capability of CNN and sequential learning functionality of LSTM. Recently, the IoT and AI techniques have developed, making this dual-potential model that learns from real-time data iterations on its energy allocation in an online manner. The practical importance of this study is manifold-implemented in smart homes as well as industrial IoT scenarios, where power savings indeed lead to significant value. This work addresses a need and utilises highly sophisticated machine learning techniques to provide significant contributions to both research and application, with a model capable of real-time, scalable, and efficient energy management for IoT networks.
Brief Description of the system
This study employs a CNN-LSTM architecture. It optimizes energy efficiency in IoT systems through real-time data processing and predictive control.
Data Collection and Processing: Multiple IoT environments were used to collect data, such as smart home systems and industrial sensors, recording power measures including wattage consumption, operation time, and states of the devices. Ensuring model robustness: these environments include different data that simulate real-world IoT device usage. Preprocessing encompassed normalization to provide consistency across metrics and maintaining temporal accuracy by time-stamping each entry. In order to minimise latency in prediction, outlier handling and missing data imputation were implemented to increase reliability of data in accordance with the characteristics of real-time processing.
Model Development: More specifically, the CNN-LSTM model also aims to take advantage of each type of network. The CNN part has convolution layers that take the IoT energy data and, with it, extract spatial features to learn what usage patterns there are for a device over time frames and energy consumption. The output of these spatial features is then passed to the LSTM layers, allowing it to capture temporal dependencies and hence the sequential prediction capability, making the model suitable for IoT. The same layered design helps forecasting of future energy consumption quite accurately, as it can help timing-based management of electric loads. Grid search methods were used to perform hyper parameter tuning in learning rates, dropout rates, and sequence length. This stage makes sure that we can run the model accurately and efficiently, which is important when going for real-time applications.
Implementation in Real-Time Environment: In this study, the model was developed and deployed in a real-time IoT environment, using edge devices as well as a server-based backend for low-latency data receiving and processing. Data flow was managed via streaming protocols, in which every data entry causes rapid inference into the model. Prediction intervals were optimised to allow the model to update on the fly without sacrificing responsiveness, repeating predictions as device states or environmental factors changed throughout the day. By reducing the latency involved in transmitting data, edge computing enables prompt action based on energy-saving predictions.
Control Mechanisms for Energy Optimization: Control logic was designed to enable dynamic power setting adjustment of devices based on predicted model performance. These consist of automatically shutting down the devices in situations of inactivity, modulating low power states, or transitioning to a standby state whenever a high load is expected. With predictive analytics, the control system can take preventive action to improve energy savings while allowing a device to operate properly. Such a responsive approach to IoT device management allows the system to not only be operated in an energy-efficient manner but also provides both sustainability and economy of scale.
Summary of the Invention
Hardware and Software Details: The experiment setup analysed IoT hardware components, e.g., smart sensors, energy meters, and edge devices integrated with servers for centralized processing of data. The idea is that the computationally limited edge devices are able to perform real-time data analysis and model inference. TensorFlow was used for deploying the CNN-LSTM model in a way that remains compatible with edge devices and scalability compatible with larger systems. We configured the server with enough CPU and memory resources to accommodate incoming data streams while supporting real-time processing.
Controlled Testing Environment: The specific use cases that were simulated included smart home automation and industrial IoT, and the facilities were designed to mimic real-world scenarios. Scenario types consisted of everyday appliance usage behaviours and different operating conditions on industrial machines. The model could operate in various IoT situations due to the controlled environment, which led to repeatable experiments while changing the conditions.
Evaluation Metrics: Evaluation metrics relevant to IoT energy management were used to track model performance. The main metrics were the fraction of energy saved versus a baseline without optimisation and prediction latency, which measured the time it took from data capture to prediction. Model stability and consistency in a production environment were assessed via system uptime, whereas prediction accuracy was calculated as the accuracy of energy consumption predictions. Together, these metrics provided an assessment of the model's ability to perform real-time energy optimization on multiple different IoT applications and showcase both its practical use-case value as well as computational efficiency.
Results
The reduced the total energy consumption of IoT devices being monitored by the model. These devices consumed on average 110 kWh of monthly pre-optimization. This value was reduced to 88 kWh after applying the CNN-LSTM model, which is a 20% decrease in energy consumption. This reduction highlights how real-time prediction-based adjustments can help avoid futile energy draw during periods of low usage. The CNN-LSTM model displayed an accuracy of 95% on average for the next energy prediction, varying according to device condition. The proximity of predicted consumption to actual consumption with a small deviation indicates the excellent performance of the model for accurate energy demand prediction. In one such period, for instance, the predicted consumption was 107 kWh and that split-point paper actual value was 108 kWh, which means an error of about 1%. Prediction latency, that is, time from data input to model inference, averaged at 0.5 seconds, which enabled timely optimization in energy management. The average response time, or time to run prediction-based optimized energy commands, was 0.6 seconds. Such low latencies demonstrate its potential in real-time applications, where timely, take able actions are critical for successful energy optimization. Breakdown of power consumption by device states showed that 60% of energy is used in the active state, 25% in idle, and 15% in standby. Following optimization, power allocation in the idle and standby states was reduced, so energy during the active state stayed at 60%, but idle and standby fell to 20% and 10%, respectively. This change underscores how the model helps limit needless consumption during idle hours. The model was highly reliable with 99% system uptime, thus providing consistent energy optimization. This high uptime number confirms the CNN-LSTM model resiliency in a live IoT environment. The results demonstrate model efficacy with practical results: 20% leaky energy reduction, 95% prediction accuracy, and average latency below 1 second. Such a real-time, adaptive approach significantly improves IoT system energy efficiency compared to traditional energy management approaches.
Results from this work yield important practical implications for IoT energy management. This provides them with operational cost savings through a 20% reduction in energy consumption and a smaller environmental footprint, which makes for greener IoT solutions. This model can achieve good accuracy with low latency, making it ready for deployment in several IoT scenarios, whether at home or in industry, and showing that scalability is possible. There were a number of hurdles that came through, like prediction latency and model deployment in low-power edge devices. In order to tackle these, we made certain optimizations in the data preprocessing and enabled model configurations that are lightweight so as to take very little time with minimal loss of accuracy. In addition, they made use of edge computing to minimize latency by performing closer processing to data sources. But this study does have its limitations. Future research may expand the range of environments and states since this model was tested in certain types of IoT devices with their respective settings. Adding reinforcement learning could also mean that model can incrementally learn to optimize energy as needed while receiving real-time feedback, making it more responsive and efficient. Thereby potentially allowing for even greater energy management in the various existing and emergent IoT ecosystems.
, Claims:We Claim

1. The CNN-LSTM model also delivers a reduction in energy consumption at runtime, with an average improvement of up to 20% of the energy usage relative to other IoT devices, and highlights its capability for considerable energy savings in real-time applications.
2. By accurately predicting energy demand 95% of the time, this model yields trustworthy predictions that can be leveraged for accurate energy management to minimize waste without affecting device performance.
3. It achieves a low latency of 0.6 seconds, which is important to meet real-time processing requirements for even the best IoT applications where adjustments in changes help in faster responses.
4. This makes it very scalable and appropriate to a variety of IoT ecosystems, as the adaptability of Device Hive with tested enterprise-ready characteristics extends across many environments, from smart homes through cities and into industrial use.
5. This opens up a future path in this research where we integrate reinforcement learning to take the next step towards more complex IoT networks and an even higher level of adaptive energy optimization.

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