Optimized Data Offloading in IoT-Based Wireless Sensor Networks

Abstract

The rapid growth of Internet of Things (IoT) data is driven by the massive increase in IoT devices, leading to an explosion in data generation. However, these devices often face challenges such as limited storage capacity and low energy reserves, making local data storage and processing costly, particularly in Wireless Sensor Networks (WSNs) where additional computational resources are scarce. To address these issues, offloading data to specialized edge devices is a crucial solution. However, determining whether IoT devices should store data locally or offload it to edge devices remains a significant challenge. In this paper, we propose an offloading mechanism that allows resource-constrained IoT devices to transfer their data to edge devices for storage or processing. First, we define the resource constraints of IoT devices that guide the design of our proposed system. Using these constraints, we determine each device’s memory fill time and battery life. Next, we apply k-means clustering to group the IoT devices into constrained and unconstrained groups based on memory fill time and battery life. In the second step, the constrained devices offload their data to edge devices. To enhance the control of the WSN, the edge devices forward the collected data to the base station, where Long Short-Term Memory (LSTM) networks are employed to predict the resource constraints, and the K-Nearest Neighbors (KNN) algorithm is used to classify the devices into constrained and unconstrained group. Simulation results show that our approach achieves higher energy efficiency, with the slowest decrease in residual energy and the fewest sensor failures compared to the two reference methods from the literature. It also maintains greater available memory space, declining only from about 90% to 60% while the other methods drop far lower. Data offloading to the edge devices remains stable between 1000 KB and 1300 KB, demonstrating consistent resource utilization.