# Smart Sensor Technologies for IoT
###### Abstract
EECMR is a depth-based clustering protocol that uses the depth level of the node to select cluster head nodes and forwarder nodes for multi-hop routing. EECMR considers the residual energy of the node, which elects cluster heads in turns. The nodes can change roles as cluster head, cluster member, and cluster relay. The cluster relay node forwards data from a deeper level to the sink. With the aid of a cluster relay, the energy consumption for transmission is decreased, leading to fewer dead nodes. The simulation results showed that EECMR achieves better performance in terms of higher residual energy, longer network lifetime, and higher received packets at the sink. In [9], a new and highly precise system has been presented for electromagnetically scanning large structures. The system combines the range information provided as point clouds by an array of mm-wave radars with the highly accurate positioning data provided by Global Navigation Satellite System-Real-Time Kinematic (GNSS-RTK) modules, forming a sensor-fusion system that enables to merge the point clouds taken from different arbitrary positions. Moreover, communication and control components have been employed to send and receive data from the sensors and manage the system status. As a proof of concept, the system has been tested on a stockpile-like model and in a realistic environment at a seaport with a scaled coal stockpile, obtaining accurate results in both cases. In [10], the solution for a dynamic radio map collection is introduced. The proposed solution is based on simultaneous measurements of Received Signal Strength (RSS) from Wi-Fi networks and the Inertial Measurement Unit (IMU) data collection. The dead reckoning algorithm processes the IMU data with particle filtering, which helps reduce the localization error of the recovered track. The proposed solution was tested in a real-world environment. The mean localization error of the recovered track was less than 0.6 m, with a maximum error of approximately 2.5 m.  
Paper [11] presents a novel human activity recognition system, named WiLiMetaSensing. It realizes location-independent sensing with very few samples in the Wi-Fi environment. Inspired by the idea of meta-learning, the authors endow the system with the ability that can utilize the knowledge acquired from one location for others. The authors proposed a Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) feature representation and metric learning-based human activity recognition system. The model focuses on the common characteristics of different locations and extracts discriminative features for different activities. The performance evaluation is conducted on the comprehensive dataset built by authors. It demonstrates that the WiLiMetaSensing system can achieve an average accuracy of 91.11%, with four locations for training, given only one sample for other testing locations. Paper [12] deals with the impact of content on the perceived video quality evaluated using the subjective Absolute Category Rating (ACR) method. The assessment was conducted on eight types of video sequences with diverse content obtained from the SJTU dataset. The sequences were encoded at five different constant bitrates in the two widely used video compression standards H.264/AVC and H.265/HEVC, at Full HD and Ultra HD resolutions, which means 160 annotated video sequences were created. The evaluation was performed in two laboratories: at the University of Zilina, and at the VSB--Technical University in Ostrava. The results acquired in both laboratories showed a high correlation. The evaluation results concluded that it is unnecessary to use the H.265/HEVC codec for compression of Full HD sequences and the compression efficiency of the H.265 codec by the Ultra HD resolution reaches the compression efficiency of both codecs by the Full HD resolution.