Abstract-Energy harvesting is a promising technology for extending the lifetime of battery-powered sensor networks. Due to time variations of harvested energy, one of the main challenging issues is to maximize the uninterrupted sampling rates of all sensor nodes, which represents the network performance. Most of existing works do not consider the limited capacity of rechargeable battery. In this paper, we are concerned with how to adaptively decide the sampling rate for each rechargeable sensor node with a limited battery capacity to maximize the overall network performance. To solve this problem, we firstly propose an adaptive Energy Allocation sCHeme (EACH) for each sensor node to manage its energy use in an efficient way. Then we develop a Distributed Sampling Rate Control (DSRC) algorithm to obtain the optimal sampling rate. Furthermore, an Improved adaptive Energy Allocation sCHeme (IEACH) is proposed to reduce the impact due to imprecise estimation of harvested energy. Extensive simulations using real experimental data obtained from Baseline Measurement System (BMS) of Solar Radiation Research Laboratory are conducted to demonstrate the efficiency of the proposed algorithms. Index Terms-Rechargeable sensor networks, limited battery capacity, energy allocation, sampling rate control.
In REF , Zhang et al. develop a distributed sampling date control (DSRC) algorithm to decide the sampling rate for each rechargeable sensor node with a limited battery capacity while maximizing the network performance.
Distributed Sampling Rate Control for Rechargeable Sensor Nodes with Limited Battery Capacity
IEEE Transactions on Wireless Communications
IEEE Transactions on Wireless Communications
Mathematics Computer Science