A low-profile, compact, four-element, eight-port multiple-input--multiple-output and diversity antenna for 5G Internet of Things and cellular handheld applications is presented in this letter. This antenna structure contains four antenna elements, which are same in its configuration. The antenna ground dimensions are taken similar to a modern mobile handset, i.e., 60 mm × 120 mm. The four antenna elements are situated at four corners of antenna ground structure. Each antenna element has two feeding ports, thus making a total of eight-port antenna. A single antenna element has two feeding plates, which are placed perpendicular to one another so that they become cross polarized, thus exploiting polarization diversity, whereas spatial diversity also exists between different antenna elements. For reducing mutual coupling between different antenna ports, slots are cut in ground plane, including a small strip etched along the entire length of ground plane and rectangular slots cut in ground plane under every antenna element. The minimum bandwidth attained by all eight ports for S ₁₁ < –10 dB is more than 1.4 GHz from 2.4 GHz to more than 3.8 GHz, which testify that it covers majority of frequency bands allocated for 5G sub-6 GHz spectrum. The isolation obtained between different ports has a minimum value of –13 dB, and the maximum value goes below –30 dB. For the covered frequency bands by this antenna structure, the correlation coefficient is found to be below 0.03, whereas the measured peak gain is in the range of 3.2--5 dB.

The radio frequency identification (RFID) has emerged Internet of Things (IoT) into the identification of things. This paper presents, a low-cost smart refrigerator system for future IoT applications. The proposed smart refrigerator is used for automatic billing and restoring of beverage metallic cans. The metallic cans can be restored by generating a product shortage alert message to a nearby retailer. To design a low-cost and low-profile tag antenna for metallic items is very challenging, especially when mass production is required for item-level tagging. Therefore, a novel ultrahigh frequency (UHF) RFID tag antenna is designed for metallic cans by exploiting the metallic structure as the main radiator. Applying characteristics mode analysis, we observed that some characteristic modes associated with the metallic structure could be exploited to radiate more effectively by placing a suitable inductive load. Moreover, a low cost, printed (using conductive ink) small loop integrated with meandered dipole used as an inductive load, which was also connected with RFID chip. The 3-dB bandwidth of the proposed tag covers the whole UHF band ranging from 860 to 960 MHz when embedded with metal cans. The measured read range of the RFID tag is more than 2.5 m in all directions to check the robustness of the proposed solution. To prove the concept, a case study was performed by placing the tagged metallic cans inside a refrigerator for automatic billing, 97.5% tags are read and billed successfully. This paper paves the way for tagging metallic bodies for tracking applications in domains ranging from consumer devices to infotainment solutions, which enlightens a vital aspect for the IoT.

This paper presents a low-cost, inkjet printed radio frequency identification (RFID) tag antenna for remote healthcare applications. The electrically small tag consists of nested-slot configuration and parallel strips. The tag antenna is exploited as a sensor by modifying its equivalent circuit to mitigate the effects of water, blood sample phantom, and the human body. As a result, the proposed RFID tag antenna with compact dimensions of 40 × 14 mm ² , features a conjugate match with Impinj R6 RFID chip ranging from 890 to 937 MHz. Moreover, this tag has a read range of 3 m, 2.5 m, and 1.5 m on the water bottle, intravenous (IV) solution and blood bag, respectively. However, the read range of RFID tag on an empty water bottle or IV solution bag is 0.5 m. By comparing the read range of tag on empty and solution filled IV bags, the proposed tag was used as a water proximity sensor. Experimental testing of the tag is performed for sensing the level of the IV solution. Also, this tag is tested after mounting on the liquid mixture (a mixture of salt and sugar is used as a phantom to mimic the blood) filled plastic bags, which leads to a low-cost solution for blood storage management. Experimental results show a good agreement of the proposed tag toward its use in healthcare applications, which leads to better healthcare facilitation regarding cost, time, and care.

The Internet of Thing (IoT) and fifth-generation mobile communication networks (5G) are leading towards a paradigm shift by proving seamless connectivity to a large number of devices. The applications of IoT in smart cities have further attracted local authorities to adopt technologies such as driverless cars, smart parking and smart waste management. This paper presents a compact base station antenna design with enhanced directivity/gain for ultra-wideband (UWB)/5G embraced real-time location systems (RTLS) based smart parking of driverless cars. The proposed base station antenna is based on image theory to achieve enhanced directivity and narrower beam width without using more array elements to keep smaller dimensions. Moreover, the base station antenna consists of an antipodal dipole printed on the opposite side of Rogers 4350 substrate, and a metal plate carefully designed and placed to produce a mirror image in order to achieve a high value of directivity in a specified direction. The advantage behind the antipodal dipole configuration is to avoid the use of extra balun for impedance matching. The half-power beamwidth of 110° is achieved along with 7 dB gain by placing a reflector plane at the distance of $0.25~\lambda \text{o}$ from the proposed antipodal dipole antenna. Also, this antenna provides a bandwidth ranging from 6 to 7.25 GHz, which can be used for UWB or 5G based RTLS systems. Furthermore, the proposed compact antenna design will help to improve the localization accuracy of ultra- wideband RTLS systems for smart parking applications of autonomous cars.

It is challenging for motorists to manually search for a parking slot in a crowded parking area. Conventional methods for locating vacant or occupied space are regarded as inefficient and unreliable. In this work, an IoT-enabled vacant parking slot detection system is developed and tested using low-cost inkjet printed passive Ultra-High-Frequency (UHF) RFID tags. The working principle of the proposed system is to analyze the back-scattered signals strength from the tags at the receiver side for vehicle presence/absence detection in the parking slots. First of all, We propose a low-cost passive tag optimized to work in UHF RFID-band pasted on plastic material display for parking slots. The data collected by RFID reader from the tags is sent over Message Queuing Telemetry Transport (MQTT) protocol via Scotland 5G-network to MongoDB database using Python module Pymongo for decision making and display of vacant parking slots to the motorists. Python based web-app extracts data from the database and shows it on a web-app display using EJS for each slot. Experimental observations conducted at parking slots in the presence of vehicles indicate a drastic reduction in Received-Signal- Strength-Index (RSSI) value at the receiver end, which implies that RSSI signal drop can be utilized for the detection of vehicle presence in the parking slot. The suggested approach aims to facilitate the economical and effective location of available parking slots. The Test results for the proposed system are recorded and presented, demonstrating its significant performance and showing great accuracy for the tested scenarios.

This paper presents an approach based on radio frequency identification (RFID) and machine learning for contamination sensing of food items and drinks such as soft drinks, alcohol, baby formula milk, etc. We employ sticker-type inkjet printed ultra-high-frequency (UHF) RFID tags for contamination sensing experimentation. The RFID tag antenna was mounted on pure as well as contaminated food products with known contaminant quantity. The received signal strength indicator (RSSI), as well as the phase of the backscattered signal from the RFID tag mounted on the food item, are measured using the Tagformance Pro setup. We used a machine-learning algorithm XGBoost for further training of the model and improving the accuracy of sensing, which is about 90%. Therefore, this research study paves a way for ubiquitous contamination/content sensing using RFID and machine learning technologies that can enlighten their users about the health concerns and safety of their food.