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IoT data collection networks, formerly known as wireless sensor networks, have become one of the most active areas of research in the field of information technology. The deployment of connected objects represents a fundamental phase on the establishment of IoT collection networks. The IoT deployment problem consists in positioning all the sensors constituting the network. This paper proposes an approach maximizing the coverage of a region of interest by the hybridization between the Voronoi diagram and the genetic algorithm. The first algorithm divides the field into cells and generates initial solutions (positions of deployed IoT objects). The latter algorithm is used to improve these positions in order to maximize the overall coverage of the region of interest. Obtained results reveal that the performance of the hybrid algorithm exceeds that of the original algorithms in terms of coverage degree, RSSI, lifetime and number of neighbor of objects.

Low-power wide area networks (LPWANs) has recently emerged as a popular long-range and low-speed radio communication technology as a result of the important growth of the Internet of Things (IoT) market. In fact, LoRa, Sigfox and NB-IoT are the three major LPWAN technologies which compete for IoT deployment. In this manuscript, we analyze and compare these technologies. The latter are efficiently applied to intelligent, autonomous and heterogeneous devices. Within this framework, 5G networks have to be used to guarantee full connectivity in the IoT networks.

This article presents a new text entry method for Visually Impaired people called MoonTouch. This method uses an enhanced version of the Moon system to help and assist visually impaired people to enter text into their touchscreen devices using simple gestures. A clinical tests of the method with a population of visually impaired participants showed that MoonTouch was perceived well by the users and presented a good learning curve.