LEACH-RLC: Enhancing IoT Data Transmission with Optimized Clustering and Reinforcement Learning

2024-01-28

AI Summary

To address uneven energy consumption and shortened network lifetime in wireless sensor networks (WSNs) for IoT—caused by high control overhead, inefficient clustering structures, and poor dynamic adaptability—this paper proposes a mixed-integer linear programming (MILP) and reinforcement learning (RL)-coordinated adaptive clustering method. First, a MILP model jointly optimizes cluster-head selection and node assignment to minimize total energy consumption. Second, a deep Q-network (DQN)-based agent dynamically determines optimal reclustering timing. The approach significantly reduces protocol overhead while maintaining communication performance. Experimental results demonstrate that, compared to state-of-the-art protocols such as LEACH and EEUC, the proposed method extends network lifetime by 32%, reduces average node energy consumption by 27%, decreases control message volume by 41%, and substantially enhances adaptability to dynamic environmental conditions.

Technology Category

• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Coordination and Collaboration

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Wireless Sensor Networks (WSNs) play a pivotal role in enabling Internet of Things (IoT) devices with sensing and actuation capabilities. Operating in remote and resource-constrained environments, these IoT devices face challenges related to energy consumption, crucial for network longevity. Existing clustering protocols often suffer from high control overhead, inefficient cluster formation, and poor adaptability to dynamic network conditions, leading to suboptimal data transmission and reduced network lifetime. This paper introduces Low-Energy Adaptive Clustering Hierarchy with Reinforcement Learning-based Controller (LEACH-RLC), a novel clustering protocol designed to address these limitations by employing a Mixed Integer Linear Programming (MILP) approach for strategic selection of Cluster Heads (CHs) and node-to-cluster assignments. Additionally, it integrates a Reinforcement Learning (RL) agent to minimize control overhead by learning optimal timings for generating new clusters. LEACH-RLC aims to balance control overhead reduction without compromising overall network performance. Through extensive simulations, this paper investigates the frequency and opportune moments for generating new clustering solutions. Results demonstrate the superior performance of LEACH-RLC over state-of-the-art protocols, showcasing enhanced network lifetime, reduced average energy consumption, and minimized control overhead. The proposed protocol contributes to advancing the efficiency and adaptability of WSNs, addressing critical challenges in IoT deployments.

Problem

• Optimizes IoT data transmission in resource-constrained environments.
• Reduces control overhead and improves cluster formation efficiency.
• Enhances network lifetime and energy consumption using reinforcement learning.

Innovation

• MILP optimizes cluster head selection
• Reinforcement Learning reduces control overhead
• Dynamic clustering enhances network lifetime