This paper presents a complete design, analysis, and performance evaluation of a novel distributed event-triggered control and estimation strategy for DC microgrids. The primary objective of this work is to efficiently stabilize the grid voltage, and to further balance the energy level of the energy storage (ES) systems. The locally-installed distributed controllers are utilised to reduce the number of transmitted packets and battery usage of the installed sensors, based on a proposed event-triggered communication scheme. Also, to reduce the network traffic, an optimal observer is employed which utilizes a modified Kalman consensus filter (KCF) to estimate the state of the DC microgrid via the distributed sensors. Furthermore, in order to effectively provide an intelligent data exchange mechanism for the proposed event-triggered controller, the publish-subscribe communication model is employed to setup a distributed control infrastructure in industrial wireless sensor networks (WSNs). The performance of the proposed control and estimation strategy is validated via the simulations of a DC microgrid composed of renewable energy sources (RESs). The results confirm the appropriateness of the implemented strategy for the optimal utilization of the advanced industrial network architectures in the smart grids.

This paper presents the design and characterisation of a flexible, inkjet-printed, low-profile, and frequency-reconfigurable antenna, intended for the fifth generation (5G) wireless networks operating at millimetre-wave (mm-wave) frequency bands. The antenna geometry is comprised of a slotted T-shaped radiating patch embedded in a rectangular aperture cut inside the ground plane. The slots are designed with two pairs of switches to reconfigure the radiating area for the desired selection of operating frequency at the K _a -band (26.5–40 GHz). Moreover, a two-element inkjet-printed multiple-input multiple-output (MIMO) antenna assembly is proposed based on a flexible polyethylene terephthalate film. The conformal MIMO antenna is suitable for integration in high-performance flexible 5G front-ends, as well as in next-generation wearable electronic devices and applications.

This paper presents a data collection architecture for situational awareness (SA)-centric microgrids. A prototype has been developed which can provide enormous data collection capabilities from smart meters, in order to realise an adequate SA level in microgrids. A communication framework based on the publish-subscribe model is also proposed and implemented for the communication layer of the SA using the message queuing telemetry transport (MQTT) protocol over two different physical (PHY) layers (i.e., WiFi and GPRS). An Internet of things (IoT) platform (i.e., Thingsboard) is used for the SA visualisation with a customised dashboard. It is shown by using the developed system, an adequate level of SA can be achieved with a minimum installation and hardware cost. Moreover, the Modbus protocol over the RS-485 is applied for the smart meter communication.

This work reports on designing and experimentally evaluating an asymmetric metasurface absorber (MA) for wide-band and polarization-insensitive operation in the C- and partial X-bands. As the core building block of the radio frequency (RF) subsystem, a unit-cell has been proposed, comprised of a dual-cut square-ring resonator (SRR) and a square patch placed above an FR4 substrate backed by a copper plate, inherently anisotropic. This arrangement efficiently converts linearly-polarized waves to cross-polarized reflected waves, achieving an 80% conversion efficiency over a wide bandwidth (BW) of 4.72–8.49 GHz. The reflected cross-polarized waves are also inhibited by strategically incorporating just three resistors on the top surface. Therefore, the polarization converter (PC) is transformed into an MA. In contrast to previously known MAs that depended on structural symmetry to maintain stable absorption performances across all polarization angles, this newly proposed asymmetric MA breaks that constraint. It achieves consistent absorption irrespective of variations in the polarization angle of the normal incident waves. The full-wave simulations have resulted in over 80% absorption, closely resembling the initial PC’s reflected BW. This MA has a compact assembly with a size of 0.22λ × 0.22λ and a thickness of 0.07λ. Absorption outputs are experimentally evaluated, resulting in more than 80% absorption, covering almost the entire C-band. This device can be used in different applications, such as radar cross-section assessments and energy-harvesting front-ends.

This paper presents a study to design and analysis of an RF link budget analysis model with a primary focus on the real urban propagation microcell environment to analyze the mobile signal loss factors during the propagation and to calculate the transmitting signal power at the transmitter end. The link budget analysis prediction system utilizes a combined model that demonstrates appropriateness to reliable link design in order to develop a flexible RF link budget analysis prediction model for mobile communication systems cellular network link planning in urban propagation microcell environment.

Radio-frequency electromagnetic field (RF-EMF) forecasting plays an important role in the evaluation of regulatory compliance, network planning and system optimization. The knowledge of RFEMF levels is essential to ensure compliance with standards and avoid public health concerns, especially with the arrival of new frequencies and scenarios in fifth-generation (5G) and sixth generation (6G) wireless networks. This work provides a comprehensive study on time series forecasting for RF-EMF measured in frequency from 100 kHz -3 GHz. The state-of-the-art deep learning model architectures consist of deep neural network (DNN), convolutional neural network (CNN), long-short term memory (LSTM), and transformer are applied for time series forecasting. The prediction performance is evaluated under three different scenarios -namely single-step input single-step output (SISO), multi-step input single-step output (MISO), and multi-step input multi-step output (MIMO). The findings from the simulation demonstrate that SISO forecasting is inadequate in predicting long-term radio-frequency electromagnetic fields (RF-EMF) data as it lacks accuracy while MISO and MIMO forecasting scenarios offer more precise predictions. Specifically, in these two scenarios where the input width and label width are both set to 20 steps, the LSTM and CNN models exhibit superior performance compared to other models. Nonetheless, as the input width and label width in a MIMO scenario increase, the accuracy of both CNN and LSTM models decline considerably, whereas the transformer model consistently maintains good performance. Additionally, the transformer model continues to deliver accurate predictions as the label width and shift length increase, which is not the case for DNN, CNN, and LSTM models.