Narrowband-IoT (NB-IoT) is part of a novel group of access technologies referred to as Low-Power Wide Area Networks (LPWANs), which provide energy-efficient and long-range network access to IoT devices. Although NB-IoT Release 13 has been deployed by Mobile Network Operators (MNO), detailed Quality of Service (QoS) evaluations in public networks are still rare. In this paper, systematic physical layer measurements are conducted, and the application layer performance is verified. Special consideration is given to the influence of the radio parameters on the application layer QoS. Additionally, NB-IoT is discussed in the context of typical smart metering use cases. The results indicate that NB-IoT meets most theoretical Third Generation Partnership Project (3GPP) design goals in a commercial deployment. NB-IoT provides a wide coverage by using signal repetitions, which improve the receiver sensitivity, but simultaneously increase the system latency. The maximum data rates are consistent over a wide range of coverage situations. Overall, NB-IoT is a reliable and flexible LPWAN technology for sensor applications even under challenging radio conditions. Four smart metering transmission categories are analyzed, and NB-IoT is verified to be appropriate for applications that are not latency sensitive.
This work presents a new approach for collaboration among sensors in Wireless Sensor Networks. These networks are composed of a large number of sensor nodes with constrained resources: limited computational capability, memory, power sources, etc. Nowadays, there is a growing interest in the integration of Soft Computing technologies into Wireless Sensor Networks. However, little attention has been paid to integrating Fuzzy Rule-Based Systems into collaborative Wireless Sensor Networks. The objective of this work is to design a collaborative knowledge-based network, in which each sensor executes an adapted Fuzzy Rule-Based System, which presents significant advantages such as: experts can define interpretable knowledge with uncertainty and imprecision, collaborative knowledge can be separated from control or modeling knowledge and the collaborative approach may support neighbor sensor failures and communication errors. As a real-world application of this approach, we demonstrate a collaborative modeling system for pests, in which an alarm about the development of olive tree fly is inferred. The results show that knowledge-based sensors are suitable for a wide range of applications and that the behavior of a knowledge-based sensor may be modified by inferences and knowledge of neighbor sensors in order to obtain a more accurate and reliable output.
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