Distributed Linear Summing in Wireless Sensor Networks with Implemented Stopping Criteria

Abstract

Many real-life applications based on the wireless sensor networks are equipped with data aggregation mechanisms for suppressing or even overcoming negative environmental effects and data redundancy. In this paper, we present an extended analysis of the linear average consensus algorithm for distributed summing with bounded execution over wireless sensor networks. We compare a centralized and a fully-distributed stopping criterion proposed for the wireless sensor networks with a varied initial configuration over random geometric graphs with 200 vertices in order to identify the optimal initial configuration of both analyzed stopping criteria and the algorithm as well as finding out which stopping criterion ensures higher performance of the examined algorithm in terms of the estimation precision and the convergence rate expressed as the iteration number for the consensus.

Applicability of Generalized Metropolis-Hastings Algorithm to Estimating Aggregate Functions in Wireless Sensor Networks

“…For counter threshold = 100, accuracy = 10 −6 , and ensuring the highest performance, the convergence rate of GMH is greater than the convergence rate of the average consensus algorithm by 139.5 iterations in Scenario 2, by 87.3 iterations in Scenario 3, and by 52.5 iterations in Scenario 4. Also, like in the previous analysis, the value of the mixing parameter has only a marginal impact on the convergence rate in contrast to the average consensus algorithm examined in [8,28].…”

“…In this case, the values of MSE are from 3 only MSE for = 0 are provided -see Table 2 for the results obtained for the other values of the mixing parameter ), proving that multiplying the initial inner states with the graph order n ensures higher precision of the algorithm than multiplying the final estimates. As mentioned earlier, we analyze the average consensus algorithm for distributed summing and distributed graph order estimation in our previous work [8,28]. Compared to those results, it can be seen that GMH outperforms the average consensus algorithms for estimating both examined aggregate functions.…”

“…Thus, the experimental section consists of four scenarios. Finally, GMH for distributed summing and distributed graph order estimation is compared to our previous work where the average consensus algorithm is analyzed [8,28].…”

Investigation of Error Probability for Grid and Random Uniform Topologies in a Variable Noise Environment

“…Additionally, it is critical to sustain a high connectivity level with the right nodes density level to enable good coverage and quality. is is important in order to avoid high errors using selected communication channels [3][4][5][6][7].…”