In many modern multi-agent systems, data aggregation mechanisms are applied to process independently measured data from multiple sources that are often deployed in extensive geographical areas. Thus, data aggregation is an essential process not only in technical industries (e.g., wireless sensor networks (WSNs), the Internet of Things (IoT), etc.), but also in other fields, such as the financial sector, investments, travel industry, etc. It is because data aggregation can ensure gathering and expressing a typically large data amount in a summary form appropriate for further processing/analyzing.
However, possessing an unorganized large amount of data can be counterproductive to its owner therefore, the importance of data aggregation is gaining in importance nowadays. information) have been becoming an increasingly valuable commodity in our technologically advanced society. The presented theorems and lemmas are verified over evolving graphs with various parameters, whereby it is demonstrated that our approach ensures the convergence of the average consensus algorithm over mobile wireless sensor networks in spite of no edge reconfiguration.ฤก.1. We identify that the mixing parameter of Best Constant weights of a complete finite graph with an arbitrary order ensures the convergence in time-varying topologies without any reconfiguration of the edge weights. Our contribution results from the theorem stating how the Laplacian spectrum of an undirected simple finite graph changes in the case of adding an arbitrary edge into this graph. In this paper, we present a weight matrix simplifying the average consensus algorithm over mobile wireless sensor networks, thereby prolonging the network lifetime as well as ensuring the proper operation of the algorithm. Over recent years, the average consensus algorithm has found a wide application in this technology. Efficient data aggregation is crucial for mobile wireless sensor networks, as their resources are significantly constrained.