The current revolution in communication and information technology is facilitating the Internet of Things (IoT) infrastructure. Wireless Sensor Networks (WSN) are a broad category of IoT applications. However, power management in WSN poses a significant challenge when the WSN is required to operate for a long duration without the presence of a consistent power source. In this paper, we develop a batteryless, ultra-low-power Wireless Sensor Transmission Unit (WSTx) depending on the solar-energy harvester and LoRa technology. We investigate the feasibility of harvesting ambient indoor light using polycrystalline photovoltaic (PV) cells with a maximum power of 1.4 mW. The study provides comprehensive power management design details and a description of the anticipated challenges. The measured power consumption of the developed WSTx was 0.02109 mW during the sleep mode and 11.1 mW during the operation mode. The harvesting system can harvest energy up to 1.2 mW per second, where the harvested energy can power the WSTx for six hours with a maximum power efficiency of 85.714%.

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Vegetation encroachment along electric power transmission lines is one of the major environmental challenges that can cause power interruption. Many technologies have been used to detect vegetation encroachment, such as light detection and ranging (LiDAR), synthetic aperture radar (SAR), and airborne photogrammetry. These methods are very effective in detecting vegetation encroachment. However, they are expensive with regard to the coverage area. Alternatively, satellite imagery can cover a wide area at a relatively lower cost. In this paper, we describe the statistical moments of the color spaces and the textural features of the satellite imagery to identify the most effective features that can increase the vegetation density classification accuracy of the support vector machine (SVM) algorithm. This method aims to distinguish between high- and low-density vegetation regions along the power line corridor right-of-way (ROW). The results of the study showed that the statistical moments of the color spaces contribute positively to the classification accuracy while some of the gray level co-occurrence matrix (GLCM) features contribute negatively to the classification accuracy. Therefore, a combination of the most effective features was used to achieve a recall accuracy of 98.272%.

A new approach to suppressing the four-wave mixing (FWM) crosstalk by using the pairing combinations of differently linear-polarized optical signals was investigated. The simulation was conducted using a four-channel system, and the total data rate was 40 Gb/s. A comparative study on the suppression of FWM for existing and suggested techniques was conducted by varying the input power from 2 dBm to 14 dBm. The robustness of the proposed technique was examined with two types of optical fiber, namely, single-mode fiber (SMF) and dispersion-shifted fiber (DSF). The FWM power drastically reduced to less than −68 and −25 dBm at an input power of 14 dBm, when the polarization technique was conducted for SMF and DSF, respectively. With the conventional method, the FWM powers were, respectively, −56 and −20 dBm. The system performance greatly improved with the proposed polarization approach, where the bit error rates (BERs) at the first channel were2.57×10-40and3.47×10-29at received powers of −4.90 and −13.84 dBm for SMF and DSF, respectively.