Energy provisioning in wireless rechargeable sensor networks with limited knowledge

Internet of Things in many applications depends on Wireless Sensor Networks where the sensors are battery powered. Recent advances in wireless energy transfer and rechargeable batteries provide a new chance for Wireless Rechargeable Sensor Networks when the mobile chargers (MCs) patrol the network field and replenish the power of sensors. We consider multiple MCs and a few charging stations (CSs) in the network. The MCs lose their power too, so they move toward CSs to replenish the energy of themselves. We propose an approach named Limited Knowledge Charging (LKC) where each CS makes a virtual area by using grid cells. Based on the cell’s information, CSs coordinate among themselves to direct MCs in the network. The main design goal of LKC is to prolong the network lifetime, by using many techniques such as balancing the energy of network areas. LKC reduces movements of MCs too as a second goal. LKC is an online approach that adapts itself with situation changes of the network. Many related studies use global knowledge, which is not always satisfied in practice. Instead, LKC is a local knowledge approach. Using exhaustive simulation, the satisfaction of the design goals of LKC is demonstrated.

     

Analytical solution for a suspension bridge by applying HPM and VIM

In this paper, homotopy perturbation method (HPM) and variational iteration method (VIM) are used to solve the large amplitude torsional oscillations equations in a nonlinearly suspension bridge. This paper compares the HPM and VIM in order to solve the equations of nonlinearly suspension bridge. A comparative study between the HPM and VIM is presented in this work. The achieved results reveal that the HPM and VIM are very effective, convenient and quite accurate to nonlinear partial differential equations. These methods can be easily extended to other strongly nonlinear oscillations and can be found widely applicable in engineering and science. The Laplace transform method is applied to obtaining the Lagrange multiplier in the VIM solution.