Applying an appropriate stopping criterion in wireless sensor networks (WSNs) is essential for optimizing energy consumption, extending network lifetime, and minimizing latency by halting inefficient operations once the desired precision or goal is achieved. As a result, many researchers have concentrated on this topic over the past few decades. Numerous studies have shown that properly bounded algorithms significantly enhance the performance of WSNs by optimizing various operational aspects. Therefore, determining the optimal configuration for a stopping criterion is one of the most critical design considerations in many WSN-based applications. As shown in the literature, data aggregation is important in WSNs because it reduces the amount of data transmitted between nodes, saving energy, minimizing network congestion, enhancing the overall efficiency and lifetime of the network, etc. As a result, many modern applications in this technology leverage mechanisms to fuse data. This paper presents a novel stopping criterion based on the non-resetting of the counter, designed to bound distributed consensus algorithms for data aggregation. The effectiveness of this criterion is tested on the average consensus algorithm with the Maximum Degree weights, across various initial configurations, in Gilbert graphs with different connectivity, and using two metrics: estimation precision (expressed as the mean square error) and convergence rate (measured by the number of iterations required for the algorithm to complete). Furthermore, the performance of the proposed stopping criterion is compared to the one from which it was derived. The experimental results demonstrate that the novel stopping criterion outperforms the existing one across both examined metrics and nearly all scenarios, thereby optimizing distributed consensus-based data aggregation in WSNs. Additionally, the optimal configuration for the stopping criterion is determined.