With the development of the Internet of Things (IoT), Low Data Rate-Personal Area Networks (LR-WPAN) have been deployed for different applications. Now comes the need to integrate these networks in search of greater connectivity, performances, and geographic coverage. This integration is facilitated by the recent deployment of low power wide area networks (LPWAN) in the licensed bands, especially narrowband IoT (NB-IoT) and long-term evolution for machine-type communications (LTE-M), which are standardized technologies that will continue evolving as part of the fifth generation (5G) specifications. This paper proposes a design methodology for combined networks using LR-WPAN and LPWAN technologies. These networks are combined at the device level using a cluster-tree topology. An example is shown here, where an existing IEEE 802.15.4 network is combined with NB-IoT. To this end, new dual nodes are incorporated, acting as cluster heads. The paper discusses the different aspects of formation and operation of the combined network. A dynamic link selection (DLS) algorithm is also proposed, based on which cluster headers dynamically determine the preferred link, depending on link quality and type of traffic. Extensive simulations show that the DLS algorithm significantly increases battery life on dual nodes, which are the nodes with the highest power demands.

This work presents the development and construction of an adaptive street lighting system that improves safety at intersections, which is the result of applying low-power Internet of Things (IoT) techniques to intelligent transportation systems. A set of wireless sensor nodes using the Institute of Electrical and Electronics Engineers (IEEE) 802.15.4 standard with additional internet protocol (IP) connectivity measures both ambient conditions and vehicle transit. These measurements are sent to a coordinator node that collects and passes them to a local controller, which then makes decisions leading to the streetlight being turned on and its illumination level controlled. Streetlights are autonomous, powered by photovoltaic energy, and wirelessly connected, achieving a high degree of energy efficiency. Relevant data are also sent to the highway conservation center, allowing it to maintain up-to-date information for the system, enabling preventive maintenance.

This paper presents a new approach for improving the localization of passive ultra high frequency radio frequency identification (RFID) tags in line-of-sight channels using a received signal strength indicator (RSSI) technique. In practice, the complex propagation in the indoor channels and also the variability of some parameters of the RFID equipment itself introduces significant amount of errors when the operation of localization carries out the RSSI technique. Indeed, as the calculation is based on a trilateration, the incomplete knowledge of the propagation and some parameters of RFID tags leads to estimate distances which are wrong, and therefore the localization cannot be correct. In order to overcome this drawback, the proposed method takes into account the presence of unknown parameters relying on a dichotomous algorithm which includes probabilistic parameters. The presented simulation results are in good agreement with the expected theoretical results. Experimental results show that the proposed method strongly increases the accuracy of the estimated position of tags. Compared to other approaches based on the improvement of the RSSI technique, this method does not require too much complexity in terms of materials (no need for specific architecture or reference tags) and processing (fast and simple algorithm).