The Internet of Everything (IoE), also called ambient intelligence, is the subject of intense worldwide research with the goal of interconnecting a large number of "things": intelligent sensors, tags, mobile phones-the list is virtually endless. These new smart objects and sensors will be able to produce information about their environment and share it with the Internet via wireless communication [1]. Radio-frequency identification (RFID) technology can have a significant impact in this regard. It is a wireless data-capturing technique that utilizes RF energy for automatically extracting the identity of remotely placed objects. The RFID tag has the potential to replace the barcode because of its manifold benefits, including its long reading range (greater than 10 m) and non-line-of-sight reading capability [2], [3].

This paper presents the concept of a chipless RFID based pervasive crack sensing scheme for structural health monitoring (SHM). This scheme includes the design of a novel “Smart Skin” sensor that can provide contiguous or non-discretized detection of a structural deformation at any point on its surface. The proposed sensor can identify the growth and propagation of cracks in an area of a building structure. Smart skin sensor has a sensitive microwave structure made of cascaded novel split box resonators coupled to a coplanar waveguide (CPW) based transmission line. This enables it to offer an uninterrupted crack detection along with the ability to detect multiple structural perturbations simultaneously. The proposed sensor can detect a crack width of as little as 0.5 mm having any orientation; namely, vertical, horizontal and angled cracks. In addition to crack detection, the sensing tag can also provide distinctive response for moisture ingress into the structure. The paper illustrates the theory behind choosing the split box resonator in this sensing scheme followed by the sensor design. It also provides a thorough analysis of the sensor, based on the simulated and experimentally obtained results. Both these results conform to each other very well which lays the foundation to use machine learning as a future work, in detecting random structural cracks. Such results incorporate many distinguishing features which enables an estimation of crack location and orientations using visual and machine-based classification approaches. The repeatability of the obtained results is also established through the experimental analysis.

Smart farming has the potential to overcome the challenge of 2050 to feed 10 billion people. Both artificial intelligence (AI) and the internet of things (IoT) have become critical prerequisites to smart farming due to their high interoperability, sensors, and cutting-edge technologies. Extending the role of responsible leadership, this paper proposes an AI and IoT based smart farming system in Bangladesh. With a comprehensive literature review, this paper counsels the need to go beyond the simple application of traditional farming and irrigation practices and recommends implementing smart farming enabling responsible leadership to uphold sustainable agriculture. It contributes to the current literature of smart farming in several ways. First, this paper helps to understand the prospect and challenges of both AI and IoT and the requirement of smart farming in a nonwestern context. Second, it clarifies the interventions of responsible leadership into Bangladesh’s agriculture sector and justifies the demand for sustainable smart farming. Third, this paper is a step forward to explore future empirical studies for the effective and efficient use of AI and IoT to adopt smart farming. Finally, this paper will help policymakers to take responsible initiatives to plan and apply smart farming in a developing economy like Bangladesh.

This paper investigates the design and practical implementation of a novel Super Wide Band (SWB) antenna on a flexible substrate. The antenna is designed on the Ultralam 3850 substrate having a compact dimension of $60\times 40\times 0.1$ mm ³ . This antenna has an operating frequency band from 1.74 to 100 GHz with a bandwidth (BW) ratio of approximately 57.47:1. Such an extended frequency coverage makes this antenna operable in a wide variety of wireless application areas, including 5G and the Internet of Things (IoT). The simulated performance of the designed antenna is analyzed here with respect to different antenna parameters, including reflection coefficient, radiation pattern, gain, efficiency, and surface current. The proposed antenna prototype is fabricated, and experimental validation is provided through the measurement using a programmable network analyzer (PNA).

This paper presents the design and analysis of a paper-based chipless RFID sensor for wetness content measurement in plant leaves. The sensor is designed using a passive microwave resonator to measure the ambient physical parameter. The resonator is designed with a rectangular loop inductively coupled with an interdigital capacitor. The experimental analysis of the designed sensor is illustrated here to prove its efficacy. The resonator provides adequate backscattered signal for wireless measurement in the microwave frequency band. When interrogated with a couple of UWB antennas and a VNA, it yields accurate leaf wetness response of the plant. The commercial competence of this sensing device is validated through its experimental analysis on three different types of leaf samples. This sensor is suitable for plant health monitoring in any small-scale gardening applications such as nurseries and greenhouses.

Microwaves are non-ionizing electromagnetic radiation with waves of electrical and magnetic energy transmitted at different frequencies. They are widely used in various industries, including the food industry, telecommunications, weather forecasting, and in the field of medicine. Microwave applications in medicine are relatively a new field of growing interest, with a significant trend in healthcare research and development. The first application of microwaves in medicine dates to the 1980s in the treatment of cancer via ablation therapy; since then, their applications have been expanded. Significant advances have been made in reconstructing microwave data for imaging and sensing applications in the field of healthcare. Artificial intelligence (AI)-enabled microwave systems can be developed to augment healthcare, including clinical decision making, guiding treatment, and increasing resource-efficient facilities. An overview of recent developments in several areas of microwave applications in medicine, namely microwave imaging, dielectric spectroscopy for tissue classification, molecular diagnostics, telemetry, biohazard waste management, diagnostic pathology, biomedical sensor design, drug delivery, ablation treatment, and radiometry, are summarized. In this contribution, we outline the current literature regarding microwave applications and trends across the medical industry and how it sets a platform for creating AI-based microwave solutions for future advancements from both clinical and technical aspects to enhance patient care.