The aim of this study was to evaluate the absorption in a user’s head of an electromagnetic field (EMF) emitted by the Wi-Fi and/or Bluetooth module of a wearable small Internet of Things (IoT) electronic device (emitting EMF of up to 100 mW), in order to test the hypothesis that EMF has an insignificant influence on humans, and to compare the levels of such EMF absorption in various scenarios when using this device. The modelled EMF source was a meandered inverted-F antenna (MIFA)-type antenna of the ESP32-WROOM-32 radio module used in wearable devices developed within the reported study. To quantify the EMF absorption, the specific energy absorption rate (SAR) values were calculated in a multi-layer ellipsoidal model of the human head (involving skin, fat, skull bones and brain layers). The obtained results show up to 10 times higher values of SAR from the MIFA located in the headband, in comparison to its location on the helmet. Only wearable IoT devices (similar in construction and way of use to the investigated device) emitting at below 3 mW equivalent isotropically radiated power (EIRP) from Wi-Fi/Bluetooth communications modules may be considered environmentally insignificant EMF sources.

Wireless sensor networks constitute the platform of a broad range of applications related to national security, surveillance, military, health care, and environmental monitoring. The coverage problem for Wireless Sensor Network (WSN) has been studied extensively in recent years, especially when combined with connectivity and energy efficiency. This paper focuses on the sensor replacement problem in wireless sensor networks consists of mobile sensors. Mobility equipped sensors are utilized to recover and maintain the overall coverage using the dynamic cluster concept. The proposed fault repair solution does not assume the localization information is available. Mobile sensor nodes make use of simple geometric operation to locate and replace dying nodes to recover or increase the existing coverage and connectivity.

Active noise reduction systems based on a control unit in the form of a finite impulse response filter assume the linearity of every single component. Neural networks, which have so far been seldom used in this field, are a kind of a filter with the ability to project nonlinear characteristics of an active noise reduction system. This paper presents some simulation research studies of active noise reduction systems based on neural networks. Also presented are results of the operation of systems with different levels of complexity as well as the influence of different parameters of a neural network and of the system itself on those results. noise active methods control neural network

Application of active noise reduction (ANR) systems in hearing protectors requires the use of control algorithms to ensure stability of the ANR system and at the same time highly effective active noise reduction. A control algorithm based on NOTCH filters is an example of solutions that meet these criteria. Their disadvantage is operation over a narrow frequency band and a need for prior determination of frequencies to be reduced. This paper presents a solution of the ANR system for hearing protectors which is controlled with the use of modified NOTCH filters with parameters determined by a genetic algorithm. Application of a genetic algorithm allows to change the NOTCH filter reference signal frequency, and thus, adapt the filter to the reduced signal frequency.

The aim was to test the hypothesis that there is an insignificant influence on humans from the absorption of an 2.4 GHz electromagnetic field (EMF) emitted by wearable Internet of Things (IoT) devices (using Meandered Inverted-F Antenna (MIFA) for Wi-Fi and Bluetooth technologies) for monitoring hazards in the work environment. To quantify problem, the specific energy absorption rate (SAR) was calculated in a multi-layer ellipsoidal model of the IoT device user’s head exposed to EMF from MIFA attached to a headband or to a helmet. SAR values may be significant when a modelled IoT wearable device is attached to a headband, but not to a helmet.