Here, we first aim to explain practical considerations to design and implement a reconfigurable intelligent surface (RIS) in the sub-6 GHz band and then, to demonstrate its real-world performance. The wave manipulation procedure is explored with a discussion on relevant electromagnetic (EM) concepts and backgrounds. Based on that, the RIS is designed and fabricated to operate at the center frequency of 3.5 GHz. The surface is composed of 2430 unit cells where the engineered reflecting response is obtained by governing the microscopic characteristics of the conductive patches printed on each unit cell. To achieve this goal, the patches are not only geometrically customized to properly reflect the local waves, but also are equipped with specific varactor diodes to be able to reconfigure their response when it is required. An equivalent circuit model is presented to analytically evaluate the unit cell’s performance with a method to measure the unit cell’s characteristics from the macroscopic response of the RIS. The patches are printed on six standard-size substrates which then placed together to make a relatively big aperture with approximate planar dimensions of 120 W 120 cm2. The manufactured RIS possesses a control unit with a custom-built system that can control the response of the reflecting surface by regulating the performance of the varactor diode on each printed patch across the structure. Furthermore, with an introduction of our test-bed system, the functionality of the developed RIS in an indoor real-world scenario is assessed. Finally, we showcase the capability of the RIS in hand to reconfigure itself in order to anomalously reflect the incoming EM waves toward the direction of interest in which a receiver could be experiencing poor coverage.

The advancement in wireless sensor and information technology has offered enormous healthcare opportunities for wearable healthcare devices and has changed the way of health monitoring. Despite the importance of this technology, limited studies have paid attention for predicting individuals’ influential factors for adoption of wearable healthcare devices. The proposed research aimed at determining the key factors which impact an individual's intention for adopting wearable healthcare devices. The extended technology acceptance model with several external variables was incorporated to propose the research model. A multi-analytical approach, structural equation modelling-neural network, was considered for testing the proposed model. The results obtained from the structural equation modelling showed that the initial trust is considered as the most determinant and influencing factor in the decision of wearable health device adoption followed by health interest, consumer innovativeness, and so on. Moreover, the results obtained from the structural equation modelling applied as an input to the neural network indicated that the perceived ease of use is one of the predictors that are significant for adoption of wearable health devices by consumers. The proposed study explains the wearable health device implementation along with test adoption model, and their outcome will help providers in the manufacturing unit for increasing actual users’ continuous adoption intention and potential users’ intention to use wearable devices.

A wireless sensor network (WSN) is defined as a set of spatially distributed and interconnected sensor nodes. WSNs allow one to monitor and recognize environmental phenomena such as soil moisture, air pollution, and health data. Because of the very limited resources available in sensors, the collected data from WSNs are often characterized as unreliable or uncertain. However, applications using WSNs demand precise readings, and uncertainty in data reading can cause serious damage (e.g., health monitoring data). Therefore, an efficient local/distributed data processing algorithm is needed to ensure: (1) the extraction of precise and reliable values from noisy readings; (2) the detection of anomalies from data reported by sensors; and (3) the identification of outlier sensors in a WSN. Several works have been conducted to achieve these objectives using several techniques such as machine learning algorithms, mathematical modeling, and clustering. The purpose of this paper is to conduct a systematic literature review to report the available works on outlier and anomaly detection in WSNs. The paper highlights works conducted from January 2004 to October 2018. A total of 3520 papers are reviewed in the initial search process. Later, these papers are filtered by title, abstract, and contents, and a total of 117 papers are selected. These papers are examined to answer the defined research questions. The current paper presents an improved taxonomy of outlier detection techniques. This will help researchers and practitioners to find the most relevant and recent studies related to outlier detection in WSNs. Finally, the paper identifies existing gaps that future studies can fill.

In this paper, a mathematical model is proposed to govern the phase distribution on a reconfigurable intelligent surface (RIS) for anomalously reflecting the beam towards the directions of interest. To this end, two operational modes are defined with respect to the reflected pattern. In the first mode, the RIS is configured to form multi-reflected beams toward the directions of interest capable of being controlled independently. The second mode is when the RIS provides a wide reflected beam. Regarding to each mode, a cost function is derived and then, in order to enhance the reflected pattern characteristics, a genetic algorithm (GA) is employed to the model as optimization method. To validate the practicality of the method, the proposed model is applied to a fabricated RIS to assess its performance in a real-world outdoor scenario. In the first mode, an asymmetric dual-beam reflected pattern is obtained and tested with tilt angles of θ ₀ = 60° and θ ₁ = 135°. Furthermore, a wide-reflected beam is generated in the second mode with half-power beamwidth of θ _HPBW = 30° and tilt angle of θ ₀ = 75°. At both modes, the measured data are well aligned with the simulated results.