Intelligent Reflecting Surfaces (IRS) by reconfiguring the reflecting array elements, provides promising solutions in enhancing the transmission efficiency of IoT nodes to long distances. This paper considers an IoT communication network, where the IoT devices are located at remote places and are wirelessly powered from the base station through IRS. Utilizing the harvested energy, the sensing signal from the IRS nodes are then transmitted back to the base station via IRS based on a hybrid NOMA TDMA approach. The objective is to maximize the transmission rate by optimizing the IRS reflection coefficients and time allocations for energy transfer and information transfer by particularly exploiting a practical IRS reflection model. In order to solve this optimization framework, block coordinate descent method is adopted whereby, the original optimization problem is divided into three subproblems: finding optimal IRS reflection coefficient matrix for downlink energy signal transmission; finding the optimal uplink reflection coefficient matrix for signal transmission and finding optimal transmit time for both the energy transmission and signal transmission. In these subproblems, the parameters other than the optimization parameters are assumed to be fixed and solved independently, then, an iterative procedure based on joint optimization is followed. In the first and second subproblems, the optimal IRS reflection coefficients in the uplink and downlink are found using the proposed alternating optimization method. The third subproblem is of convex optimization type and hence the optimal transmit time for energy and signal transmission is computed in closed form using Lagrange method and KKT conditions. Simulation results presented validates the effectiveness of the proposed algorithm both in terms of spectral efficiency and energy efficiency.