Cluster subdivision towards power savings for randomly deployed WSNs — An analysis using 2-D spatial poisson process

Abstract

We propose to use a 2-dimensional (2-D for short) spatial Poisson process to model a WSN with randomly deployed sensors, and use the model to analyze a scheme that subdivides the clusters of a WSN to achieve an overall power savings. We assume no knowledge of how the sensors are spread in the sensing area, and hence need a statistical process to describe the distribution of all sensors. Assuming the 2-D spatial Poisson distribution, a comprehensive analysis is performed to estimate the power savings brought about by the proposed subdivision. Using hexagon as the shape of the cluster, the analysis shows that the subdivision scheme can yield significant savings in overall power consumption of sensors in the cluster.

Energy-Efficient Clustering Algorithm for Magnetic Induction-Based Underwater Wireless Sensor Networks

“…As we presented in Section I, in order to balance the overall energy consumption loads in the network (e.g., reducing the remaining energy gaps among sensor nodes), there have been a lot of research works on this issue [6]- [8]. Most of which assume that sensor nodes have the same capabilities such as communication range and initial energy, and the sensor node deployment is homogeneous in some scenarios, while we assume that the sensor nodes follow the Poisson distribution and the whole network is divided into a set of hexagonal clusters.…”

“…Once it becomes a CH, its energy will drain quickly. To prevent the early death of the CH, Dajin Wang [8] used a set of fixed hexagonal clusters. The CH is located in the center of each cluster and equipped with a powerful transmitter that can communicate with the autonomous underwater vehicle (AUV) and other CHs.…”

“…Before the beginning of a new round, the CHs are re-selected based on a fixed cluster radius and the remaining energy of all nodes, where the fixed cluster radius and CHs can determine which nodes are aggregated into one cluster, and these nodes cannot be selected as CHs in the current round. Unlike the aforementioned protocols in [6]- [8], the CHs are selected according to the amount of remaining energy, i.e., the sensor nodes with more energy will have a larger probability to become the CH, and they locate at the centers of the clusters in the HENPC algorithm. Thus, the HENPC algorithm can establish more efficient clusters that can balance the remaining energy of each node.…”

“…However, the deployment strategy for MI waveguides to connect a large number of wireless sensor nodes is still a challenging issue. In terms of topology control, several optimal MI waveguide deployment strategies have been developed in one-dimensional (1D) [2]- [5], two-dimensional (2D) [6]- [8] and three-dimensional (3D) [9]- [13] networks. In fact, the positions of MI waveguides in 1D space are placed along a chain such as underground pipeline systems, while in 2D space they can be placed at any desired positions by following either random or regular distributions.…”