Optimizing Data Aggregation for Uplink Machine-to-Machine Communication Networks

TL;DR

This paper introduces an energy-efficient data aggregation scheme for hierarchical M2M networks, optimizing coverage and energy use to improve uplink communication performance in power-constrained environments.

Contribution

It develops a coverage probability-based optimal data aggregation method that balances energy density and coverage tradeoffs in hierarchical M2M networks.

Findings

Successive and half-duplex parallel schemes outperform full-duplex in coverage.

Uplink coverage characteristics significantly influence energy consumption trends.

Optimal aggregation reduces energy density while maintaining coverage quality.

Abstract

Machine-to-machine (M2M) communication's severe power limitations challenge the interconnectivity, access management, and reliable communication of data. In densely deployed M2M networks, controlling and aggregating the generated data is critical. We propose an energy efficient data aggregation scheme for a hierarchical M2M network. We develop a coverage probability-based optimal data aggregation scheme for M2M devices to minimize the average total energy expenditure per unit area per unit time or simply the {\em energy density} of an M2M communication network. Our analysis exposes the key tradeoffs between the energy density of the M2M network and the coverage characteristics for successive and parallel transmission schemes that can be either half-duplex or full-duplex. Comparing the rate and energy performances of the transmission models, we observe that successive mode and half-duplex parallel mode have better coverage characteristics compared to full-duplex parallel scheme. Simulation results show that the uplink coverage characteristics dominate the trend of the energy consumption for both successive and parallel schemes.