## 2 Related Work  
In this section, we deal with papers concerned with distributed consensus gossip-based algorithms published by other authors. We focus our attention on papers (or their parts) where the algorithms that we analyze in this paper are compared. The authors of [23] compared \(BG\) and \(RG\) using the simulator Castalia and by applying several metrics, namely the communication overhead, the latency, the energy consumption, the normalized absolute error, and the standard deviation. In addition, three different sets of the initial states are assumed in the presented research. They identified that \(BG\) in general achieves a greater performance than \(RG\). \(BG\) achieves low performance only in terms of the normalized absolute error. As in the previously discussed manuscript, \(BG\) and \(RG\) are also compared in [24], however, under quantized communication in this case. In this paper, it is identified that \(BG\) outperforms \(RG\) in terms of the time required to achieve the quantized consensus over various network types, namely random geographical spatial networks with the same connectivity radius, small-world networks, and scale-free networks. The authors of [25] also focused their attention on a comparison of \(BG\) with \(RG\) and identified that \(BG\) outperforms \(RG\) in both the convergence time and the number of radio transmissions regardless of the network size. In [26], the authors examined and compared three gossip algorithms (\(BG\), \(RG\), and \(GG\)) by applying several metrics. They identified that \(BG\) outperforms the two other examined algorithms in terms of the per-node variance. It is furthermore concluded in the paper that \(GG\) performs better in terms of the mentioned metric than \(RG\) in large-scale networks. However, in smaller networks, \(RG\) achieves better performance than \(GG\). In the paper, an analysis of the algorithms in terms of the per-node mean square error is also provided. In this case, an interesting phenomenon is identified by the authors: the error of \(BG\) does not drop below a specific threshold in contrast to the two other algorithms, the precision of which is increased with no bound as the number of radio transmissions increases. For fewer radio transmissions, \(BG\) outperforms the two other algorithms, but, for higher values, it is the worst-performing algorithm among the analyzed ones--in this case, \(RG\) achieves the lowest per-node mean square error. The same conclusion regarding the per-node variance as in the previous paper was also reported by [PERSON] [27]. [PERSON] [28] compared \(RG\) and \(GG\) in terms of the error in estimation. They identified that \(GG\) outperforms \(RG\) regardless of the network size except for the scenarios where low energy is spent. Moreover, it is identified in the paper that the more energy is spent, the higher precision of the estimation is achieved. In [29], \(RG\) and \(GG\) are compared in terms of the relative error under various initial inner states over RGGs and grid graphs. In RGGs, \(GG\) outperforms \(RG\) in all the realized scenarios except for low values of the number of transmissions. In grid topologies, \(GG\) is better for each value of the number of the transmission. In this paragraph, we turn our attention to papers dealing with \(PS\) and \(PP\). In [30], the authors compared the mentioned algorithms in terms of the convergence rate expressed as the root mean square error as a function of rounds under asynchronous and potentially faulty environments. They identified that \(PS\) is slightly outperformed by \(PP\) for a lower number of rounds. However, when the round number is equal to approximately 60, \(PS\) achieves higher performance than \(PP\). In [31], very similar research to the one from [30] is carried out. In [31], \(PS\) outperforms \(PP\) also for lower values of iterations. [PERSON] [32] compared \(PP\) and \(PS\) by applying the mean percentage error and the variance.