Energy Efficient Data Transmission for Sensors with Wireless Charging

A wireless charger powered the extracellular electron transfer for hydrogen recovery from organics

Herein, a simple wireless charger which provided an alternative to conventional connection for delivering the electricity was employed to power the microbial electrolysis cell (MEC) for hydrogen recovery from organics. The coulombic efficiency of the wireless power transmission (WPT) was 75.37%, which was nearly similar to the average value of the conventional wired power transmission (CWPT) at the same experimental conditions (78.23%). The energy efficiency was 130.58%, it was clearly that the wireless charging (141.57%) slightly resulted in energy losing compared with conductive wire connection. The saving cost of WPT-driven MEC was a promising compensation to the energy loss according to the economic analysis of WPT, i.e., the WPT can be cost-beneficial once the distance between charger and reactor beyond a limited value. Overall, the feasibility of WPT suggests a straightforward way to construct large-scale MES with low cost and comparable performance.

Efficient Energy Supply Using Mobile Charger for Solar-Powered Wireless Sensor Networks

An energy-harvesting wireless sensor network mitigates the energy shortage problems of existing battery-based wireless sensors; however, its hotspot area sensor nodes still experience 3 blackouts, thereby reducing network connectivity. Techniques that transfer energy directly to sensor nodes using wireless power transfer (WPT) have been studied in recent years to address this issue. In this paper, we propose a technique that uses a drone (quadcopter), which is a type of unmanned aerial vehicle (UAV), as a mobile sink. The drone selects and manages anchor nodes that aggregate data temporarily, collects data by visiting the anchor nodes to mitigate the hotspot issue, and then prevents blackouts by supplying energy to low-energy nodes, thereby improving network connectivity. The anchor nodes are carefully selected after considering the energy capacity of the drone, the size of the network, the amount of collected data, and the energy consumed by the nodes to increase the network’s energy efficiency. Furthermore, energy is transferred from the drone to the anchor nodes to support their energy consumption. In our study, this method reduced the blackouts of sensor nodes, including anchor nodes, in hotspot regions, and increased network connectivity, thereby improving the amount of data gathered by the mobile sink.

Design and Analysis of Non-Binary LDPC-CPM System for Hybrid Check Matrix Construction Algorithm of WSN

In order to enhance the reliability and anti-interference performance of wireless sensor network (WSN) data transmission, this paper designs the low power scheme of the WSN from the angle of error correction coding and proposes the hybrid check matrix construction (HC) algorithm based on iterative coding algorithms with linear coding complexity. The algorithm first improves the traditional iterative coding algorithm, making it suitable for non-binary low-density parity check (LDPC) codes. Then, the algorithm applies the backward iteration method to change the coding scheme and uses the check matrix construction method so that the progressive edge growth (PEG) algorithm has a lower triangular structure, which is used as a base matrix. An improved quasi-cyclic LDPC (QC-LDPC) algorithm, with a lower triangular structure, is used to generate a cyclic shift matrix and a finite domain coefficient matrix. Simultaneously, the short loop is eliminated and the optimal check matrix is selected for use in the channel coding process. The non-binary LDPC-CPM system is modeled and simulated. The simulation results show that the non-binary LDPC code constructed by the HC algorithm not only has linear coding and storage complexity but also has strong error correction capability. The design of non-binary LDPC-CPM system parameters can enhance the reliability, anti-jamming capability and reduce the complexity and reduce the complexity of the WSN.