Optimizing energy efficiency and reliability in wireless sensor networks with cooperative 2 2 MIMO techniques

Wireless sensor networks (WSNs) consist of autonomous nodes with limited resources in terms of energy, bandwidth, computing capacity, and memory. In hostile environments, it is often impossible to recharge or replace node batteries, making the development of energy-saving techniques crucial. By adopting cooperative Multiple Input Multiple Output (MIMO) techniques, energy consumption can be reduced and network performance can be improved through the gain in spatio-temporal diversity. This paper proposes the Energy Aware Clustering-MIMO (EAC-MIMO) protocol, which adapts MIMO techniques to the Battery Level Aware Clustering (BLAC) routing protocol. The energy level is combined either with the node degree in the first variant, EAC-bg-MIMO, or with the node density in the second variant, EAC-bs-MIMO. The cooperative node election is based on three criteria: ( i ) the residual energy, ( ii ) the number of hops required to reach the base station, and ( iii ) the distance between the cooperative transmitting nodes of the transmitting cluster and the cooperative receiving nodes of the neighboring cluster. Simulation results, performed using Java, indicate that these versions reduce energy consumption by \(30\%\) compared to the concurrent protocols. Precisely, the clustering and transmission data phase of EAC-bg-MIMO consumes about 112172.797 nJ at 250 meters. Moreover, the average number of clusters formed reaches up to 232 at 25 meters range, and the data reception probability rises to 0.156 at 250 meters for EAC-bs-MIMO, which is higher than that of concurrent protocols. The obtained results show that the EAC-MIMO protocol enhances transmission reliability while extending network lifespan. This advancement represents a significant step in optimizing the performance of wireless sensor networks, particularly under challenging conditions. Finally, the statistical analyses performed, including Standard Deviation (SD) and Standard Error of the Mean (SEM), strengthen the validity and accuracy of the results.