A Correlated Data-Driven Collaborative Beamforming Approach for Energy-efficient IoT Data Transmission

TL;DR

This paper introduces a data-driven collaborative beamforming framework for IoT networks that enhances energy efficiency and data transmission by integrating multi-hop routing with intelligent node selection using deep reinforcement learning.

Contribution

It proposes a novel combination of collaborative beamforming with an overlap-based multi-hop routing protocol and a SoftPPO-LSTM algorithm for optimized node selection in IoT data transmission.

Findings

Significant energy savings demonstrated in simulations.

Improved data transmission efficiency over existing protocols.

Effective mitigation of hot spot issues in IoT networks.

Abstract

An expansion of Internet of Things (IoTs) has led to significant challenges in wireless data harvesting, dissemination, and energy management due to the massive volumes of data generated by IoT devices. These challenges are exacerbated by data redundancy arising from spatial and temporal correlations. To address these issues, this paper proposes a novel data-driven collaborative beamforming (CB)-based communication framework for IoT networks. Specifically, the framework integrates CB with an overlap-based multi-hop routing protocol (OMRP) to enhance data transmission efficiency while mitigating energy consumption and addressing hot spot issues in remotely deployed IoT networks. Based on the data aggregation to a specific node by OMRP, we formulate a node selection problem for the CB stage, with the objective of optimizing uplink transmission energy consumption. Given the complexity of the problem, we introduce a softmax-based proximal policy optimization with long short-term memory (SoftPPO-LSTM) algorithm to intelligently select CB nodes for improving transmission efficiency. Simulation results validate the effectiveness of the proposed OMRP and SoftPPO-LSTM methods, demonstrating significant improvements over existing routing protocols and node selection strategies. The results also reveal that the combined OMRP with the SoftPPO-LSTM method effectively mitigates hot spot problems and offers superior performance compared to traditional strategies.