Reconfigurable directional antennas (RDA) bring new opportunities to reduce data collision in wireless sensor networks (WSN). In this letter, a new reconfigurable directional antenna-based receiver-Initiated cycled receiver (RDA-RICER) medium access control (MAC) protocol is proposed for WSN nodes equipped with switched antennas. A low complexity and energy efficient scanning process is embedded in RDA-RICER to identify the direction providing the highest received signal strength Indicator between two nodes. OMNeT++ simulation results for a single hop network show that data collision rate can be drastically reduced compared with related MAC protocols, leading to a significant decrease in energy consumption. Our approach is also validated in the field using WSN platforms equipped with a four-direction RDA, and powered by solar cells.

This letter presents a reconfigurable antenna for mobile terminals with extended band coverage obtained by using a digitally tunable capacitor (DTC). The antenna structure is matched permanently over high frequency bands including DCS/PCS, UMTS, LTE 1800/2600, and 3.5-GHz bands. Concerning the sub-GHz bands, several reconfigurable states enable a full coverage of LTE 600/700 and GSM 850/900 standards, as well as expected spectrum reallocations for 5G communications. With dimensions of 40 ×10 ×6 mm ³ for the antenna and 130 ×70 ×0.8 mm ³ for the whole printed circuit board (PCB), this structure is compatible with any mobile terminal.

This work presents the use of a three-element radiating structure for circularly polarized Low-Power Wide Area Network (LP-WAN) communication with space. The proposed structure has a 72 mm × 72 mm × 12 mm compact size with Right-Handed Circular Polarization (RHCP) and a 120${}^{\circ }$ wide beamwidth radiation pattern. Printed on low-cost FR4 Epoxy substrate, a feeding network circuit based on Quasi Lumped Quadrature Coupler (QLQC), it achieves a −0.6 dB insertion loss and a very compact size. The final structure has a 69% total efficiency and a 3.14 dBic realized gain.

In this paper, the way to design different shapes of miniature antenna for LoRa devices are presented. With dimension of 0.06λ×0.1λ, the proposed antennas are printed on 34×80×0.8 mm ³ FR-4 board. The simulation and experiment results show a good agreement with expectations of the electrical small antennas that operate at 868 MHz frequency band. Moreover, the changeable geometry of the designs while keeping a good performance proves that miniature antennas can be created to be more appealing as logos or figures.

In this paper, the signal propagation measurement of LoRa devices that was realized in the indoor environment is presented. The scenario is setup at 12-floor dormitory B of Vietnam National University HCM. The devices using for measurement are a commercial LoRa gateway and the end nodes that designed by University of Information Technology. By placing the gateway at different locations of 12th floor and setting up the end nodes at 6 locations of 12 floors, the results of LoRa signal propagation are observed.

Fruit classification is required in many smart-farming and industrial applications. In the supermarket, a fruit classification system may be used to help cashiers and customer to identify the fruit species, origin, ripeness, and prices. Some methods, such as image processing and NIRS (near-infrared spectroscopy) are already used to classify fruit. In this paper, we propose a fast and cost-effective method based on a low-cost Vector Network Analyzer (VNA) device augmented by K-nearest neighbor (KNN) and Neural Network model. S-parameters features are selected, which take into account the information on signal amplitude or phase in the frequency domain, including reflection coefficient S₁₁ and transmission coefficient S₂₁. This approach was experimentally tested for two separate datasets of five types of fruits, including Apple, Avocado, Dragon Fruit, Guava, and Mango, for fruit recognition as well as their level of ripeness. The classification accuracy of the Neural Network model was higher than KNN with 98.75% and 99.75% on the first dataset, whereas the KNN was seen to be more effective in classifying ripeness with 98.4% as compared to 96.6% for neural network.