Evolving Internet-of-Things (IoT) applications often require the use of sensor-based indoor tracking and positioning, for which the performance is significantly improved by classifying the type of the surrounding indoor environment. This classification is of high importance since it leads to efficient power consumption, when operating the deployed IoT sensors. This paper presents a machine learning approach for indoor environment classification based on real-time measurements of the RF signal in a realistic environment. Several machine learning classification methods are explored including decision trees, support vector machine, and k-Nearest Neighbor using different RF features. Results obtained show that a machine learning approach based on weighted k-Nearest Neighbor method, that utilizes a combination of channel transfer function and frequency coherence function, outperforms the other methods in classifying the type of indoor environment with an accuracy of 99.3%. The predication time was found to be below 10's, which verifies that the adopted algorithm are successful candidates for real-time deployment scenarios.

Human activity recognition from sensor readings has proved to be an effective approach in pervasive computing for smart healthcare. Recent approaches in ambient assisted living (AAL) within the home or community setting offers people the prospect of independent care and improved quality of living. However, most of the available AAL systems are limited by several factors including system complexity and computational cost. In this paper, a simple, novel ambient human activity recognition framework using the multivariate Gaussian is proposed. The classification framework augments prior information from passive RFID tags to obtain more detailed activity profiling. The proposed algorithm based on multivariate Gaussian via maximum likelihood estimation is used to learn the features of the human activity model. Twelve sequential and concurrent experimental evaluations are conducted in a mock apartment environment. The sampled activities are predicted using a new dataset of the same activity and high prediction accuracy established. The proposed framework suits well for the single and multi-dwelling environment and offers pervasive sensing environment for elderly, disabled and carers.

Evolling Internet-of-Things (IoT) applications often require the use of sensor-based indoor tracking and positioning, for which the performance is significantly improved by identifying the type of the surrounding indoor environment. This identification is of high importance since it leads to higher localization accuracy. This letter presents a novel method based on a cascaded two-stage machine learning approach for highly-accurate and robust localization in indoor environments using adaptive selection and combination of RF features. In the proposed method, machine learning is first used to identify the type of the surrounding indoor environment. Then, in the second stage, machine learning is employed to identify the most appropriate selection and combination of RF features that yield the highest localization accuracy. Analysis is based on $k$ -Nearest Neighbor ( $k$ -NN) machine learning algorithm applied on a real dataset generated from practical measurements of the RF signal in realistic indoor environments. Received Signal Strength, Channel Transfer Function, and Frequency Coherence Function are the primary RF features being explored and combined. Numerical investigations demonstrate that prediction based on the concatenation of primary RF features enhanced significantly as the localization accuracy improved by at least 50% to more than 70%.

Efficiency improvement is of great significance for simulation-driven antenna design optimization methods based on evolutionary algorithms (EAs). The two main efficiency enhancement methods exploit data-driven surrogate models and/or multi-fidelity simulation models to assist EAs. However, optimization methods based on the latter either need ad hoc low-fidelity model setup or have difficulties in handling problems with more than a few design variables, which is a main barrier for industrial applications. To address this issue, a generalized three stage multi-fidelity-simulation-model assisted antenna design optimization framework is proposed in this paper. The main ideas include introduction of a novel data mining stage handling the discrepancy between simulation models of different fidelities, and a surrogate-model-assisted combined global and local search stage for efficient high-fidelity simulation model-based optimization. This framework is then applied to SADEA, which is a state-of-the-art surrogate-model-assisted antenna design optimization method, constructing SADEA-II. Experimental results indicate that SADEA-II successfully handles various discrepancy between simulation models and considerably outperforms SADEA in terms of computational efficiency while ensuring improved design quality. Highlights An EFFICIENT antenna design global optimization method for problems requiring very expensive EM simulations. A new multi-fidelity surrogate-model-based optimization framework to perform RELIABLE efficient global optimization A data mining method to address distortions of EM models of different fidelities (bottleneck of multi-fidelity design).

A modified indoor path loss prediction model is presented, namely, effective wall loss model. The modified model is compared to other indoor path loss prediction models using simulation data and real‐time measurements. Different operating frequencies and antenna polarizations are considered to verify the observations. In the simulation part, effective wall loss model shows the best performance among other models as it outperforms 2 times the dual‐slope model, which is the second best performance. Similar observations were recorded from the experimental results. Linear attenuation and one‐slope models have similar behavior, the two models parameters show dependency on operating frequency and antenna polarization.

This paper proposes a new low complexity angle of arrival (AOA) method for signal direction estimation in multi-element smart wireless communication systems. The new method estimates the AOAs of the received signals directly from the received signals with significantly reduced complexity since it does not need to construct the correlation matrix, invert the matrix or apply eigen-decomposition, which are computationally expensive. A mathematical model of the proposed method is illustrated and then verified using extensive computer simulations. Both linear and circular sensors arrays are studied using various numerical examples. The method is systematically compared with other common and recently introduced AOA methods over a wide range of scenarios. The simulated results show that the new method has several advantages in terms of reduced complexity and improved accuracy under the assumptions of correlated signals and limited numbers of snapshots.