The current statuses and future promises of the Internet of Things (IoT), Internet of Everything (IoE) and Internet of Nano-Things (IoNT) are extensively reviewed and a summarized survey is presented. The analysis clearly distinguishes between IoT and IoE, which are wrongly considered to be the same by many commentators. After evaluating the current trends of advancement in the fields of IoT, IoE and IoNT, this paper identifies the 21 most significant current and future challenges as well as scenarios for the possible future expansion of their applications. Despite possible negative aspects of these developments, there are grounds for general optimism about the coming technologies. Certainly, many tedious tasks can be taken over by IoT devices. However, the dangers of criminal and other nefarious activities, plus those of hardware and software errors, pose major challenges that are a priority for further research. Major specific priority issues for research are identified.

Surrogate models are widely used in antenna design for optimization efficiency improvement. Currently, the targeted antennas often have a small number of design variables and specifications, and the surrogate model training time is short. However, modern antennas become increasingly complex, which needs much more design variables and specifications, making the training time become a new bottleneck, i.e., in some cases, even longer than electromagnetic (EM) simulation time. Therefore, a new method, called training cost reduced surrogate model-assisted hybrid differential evolution for complex antenna optimization (TR-SADEA), is presented in this article. The key innovations include: 1) a self-adaptive Gaussian process surrogate modeling method with a significantly reduced training time while mostly maintaining the antenna performance prediction accuracy and 2) a new hybrid surrogate model-assisted antenna optimization framework that reduces the training time and increases the convergence speed. An indoor base station antenna with 2G to 5G cellular bands (45 design variables and 12 specifications) and a 5G outdoor base station antenna (23 design variables and 18 specifications) are used to demonstrate TR-SADEA. Experimental results show that more than 90% of the training time and about 20% iterations (simulations and surrogate modeling) are reduced compared to a state-of-the-art method while obtaining high antenna performance.

Gaussian process (GP) is a very popular machine learning method for online surrogate model-assisted antenna design optimization. Despite many successes, two improvements are important for GP-based antenna global optimization methods, including (1) the convergence speed (i.e., the number of necessary electromagnetic simulations to obtain a high-performance design), and (2) the GP model training cost when there are several tens of design variables and/or specifications. In both aspects, state-of-the-art GP-based methods show practical but not desirable performance. Therefore, a new method, called self-adaptive Bayesian neural network surrogate model-assisted differential evolution for antenna optimization (SB-SADEA), is presented in this paper. The key innovations include: (1) The introduction of the Bayesian neural network (BNN)-based antenna surrogate modeling method into this research area, replacing GP modeling, and (2) a bespoke self-adaptive lower confidence bound method for antenna design landscape making use of the BNN-based antenna surrogate model. The performance of SB-SADEA is demonstrated by two challenging design cases, showing considerable improvement in terms of both convergence speed and machine learning cost compared to state-of-the-art GP-based antenna global optimization methods.

The issue of asset tracking in dense environ-ments where the performance of the Global Positioning System (GPS) becomes unavailable or unreliable is addressed. The proposed solution uses a low-profile array of antenna elements (sensors) mounted on a finite conducting ground. A compact-size sensor array of six electrically-small dual-band omnidirec-tional spiral antenna elements was designed as a front end of a tracker to operate in the 402 and 837 MHz spectrum bands. For the lower band, a three-element superposition method is applied to support estimation of the Angle of Arrival (AOA), whereas all six sensors are employed for the higher band. A low complexity and accurate AOA determination algorithm is proposed, the Projection Vector (PV), and this is combined with the array mentioned. Orthogonal Frequency Division Multiplexing (OFDM) is integrated with the PV technique to increase the estimation resolution. The system was found to be suitable for installation on the roof of vehicles to localize the position of assets. The proposed system was tested for tracking of non-stationary sources, and then two scenarios were investi-gated using propagation modeling software: outdoor to out-door and outdoor to indoor. The results confirm that the pro-posed tracking system works efficiently with a single snapshot.

A closely packed wideband multiple-input multiple-output (MIMO)/diversity antenna (of two ports) with a small size of less than 18.5 mm by 18.5 mm is proposed for mobile communication applications. The antenna can be orthogonally configured for corner installation or by placing it on a back-to-back structure for compact modules. To enhance the isolation and widen the bandwidth, the antenna is structured with multiple layers having differing dielectric constants. The feeding through a via significantly reduces the ground waves. A multi-fidelity surrogate model-assisted design exploration method is employed to obtain the optimized antenna geometric parameters efficiently. The antenna design was investigated using electromagnetic simulation and a physical realization of the optimal design was then created and subjected to a range of tests. The specific parameters investigated included reflection coefficients, mutual coupling between the input ports, radiation patterns, efficiency and parameters specific to MIMO behavior: envelope correlation coefficient and pattern diversity multiplexing coefficient. It was found that the antenna has an impedance bandwidth of approximately 4 GHz, mutual coupling between input ports of better than −18 dB and an envelope correlation coefficient of less than 0.002 across the operating band. This makes it a good candidate design for many mobile MIMO applications.