Average Consensus over Mobile Wireless Sensor Networks: Weight Matrix Guaranteeing Convergence without Reconfiguration of Edge Weights

Abstract

<jats:p>Efficient data aggregation is crucial for mobile wireless sensor networks, as their resources are significantly constrained. Over recent years, the average consensus algorithm has found a wide application in this technology. 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. 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. 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. 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.</jats:p>

Consensus based Data Transfer in Wireless Sensor Network for Reliability Interpretation

“…The weight matrix ensures the convergence time matching with the topological requirements. Weight matrix is achieved by considering the best constant of weight which occurs within the convergence time [7]. The purpose of predicting the resources of energy in relation to its reporting time has been done in [11].…”

On Mathematical Modelling of Automated Coverage Optimization in Wireless 5G and beyond Deployments

“…The overview of the recent literature in gNodeB optimization and deployment is shown in the Table 3. Additionally, the literature review of gNodeB deployment is presented in the following surveys in more detail [50][51][52][53][54][55][56][57][58][59][60][61].…”

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

“…Wireless sensor networks (WSNs) are a technology formed by smallsize autonomous low-cost sensor nodes equipped with three basic components -a sensing subsystem, a processing subsystem, and a wireless communication subsystem [2]- [4]. These subsystems enable the sensor nodes to concurrently sense the surrounding area, to process the measured data, and to communicate with each other via wireless transmission channels [2,3].…”

“…In many applications, the sensor nodes are required to operate under inhospitable environmental conditions and without any human interaction; therefore, the design of the algorithms for WSNs has to be affected by these facts [2]. So, WSN-based applications have to be viable and energy-efficient so that the robustness of WSNs to potential threads is high [2]- [4]. Over the last years, WSNs have found the application in various areas such as industrial monitoring, health care, localization, security, structural monitoring, military monitoring, etc.…”

“…[14,22]- [25]. As stated in [4], the algorithms differ from each other in many aspects such as the convergence rate, the robustness to potential threats, the initial configuration, the performance in mobile systems, etc.…”