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LoRa technology is extensively utilized in the Internet of Things world. It allows a transmission of a low volume of data through small wireless devices. The principle of LoRa networks is to transmit data over the air from sensors with low transmission range, for about tens of kilometers. Those sensors are not expected to be powered by electricity, and they are powered by batteries. We understand that visits to hospitals cannot be eliminated and that visits for full examinations were necessary, but technological progress nowadays could reduce the burden on hospitals thanks to remote controls and treatments in homes using those wireless sensors. So, the use of LoRaWAN protocol could greatly make diagnostic of patients more easily by transmitting data between doctors and patients in a real time manner. The aim of this work is to evaluate the performance of a network that contains numerous mobile sensors. Those sensors connect the doctors, nurse, and patient through a reliable and secure wireless network. Here, we want to evaluate various factors of LoRaWAN protocol that have a big effect on power consumption and data transmission delay.. Moreover, our LoRa-based networking implementation, based on software simulations, appears to be an option that allows for a robust, reliable, and lower overall cost IoT deployment and low bandwidth requirements. With LoRa, we can achieve similar or better link quality to IEEE 802.15.4, with higher data rate and lower costs.

Over the last few years, energy optimization in wireless sensor networks (WSNs) has drawn the attention of both the research community and actual users. Sensor nodes are powered by attached batteries that are considered as a critical aspect of sensor nodes design. Besides, the constraint of the limited battery capacity is associated with the concern on how to reduce the energy consumption of nodes to extend the network lifetime. In this context, the purpose of this study is to implement an adaptive medium access control (MAC) for energy saving and traffic control enhancement. This program was designed to arrange nodes into two priority groups according to their traffic rate and data transmission packet delay. This fuzzy algorithm depends on their queue length where it is implemented into the carrier sense multiple access with collision avoidance (CSMA/CA) algorithm. However, other types of nodes should send their data during the contention-free period with a GTS reallocation scheme. Those nodes are classified as low priority access to the medium, and their data transmission is scheduled using time division multiple access methods. Moreover, this proposed scheme dynamically adjusts the contention access period length to ensure that nodes can complete their data transmission during the same super-frame. Simulation results are done using the network simulator tool (NS-2), and it has improved efficiency regarding the IEEE-802.15.4 standard.

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To rich good accuracy in the 2D area for wireless sensor network (WSN) nodes, a localization method has to be selected. The objective of this paper is first to select which localization technique is required (Received Signal Strength Indicator (RSSI)) or (Time of Arrival (ToA)) against anchors placement in a 2D area. Depending on whether the anchor nodes are spaced or not and inspired by the idea of using the RSSI method for small distances and the ToA method for greater distances, we will show which method should be used for the positioning process which mainly guarantees a minimal localization error. Second, a two-dimensional localization scheme for WSN which is called Combined Advantages of ToA-RSSI (CA ToA-RSSI), hereafter, ranging methods, is designed in this work, to make the accuracy better during the positioning process. Results provided through MATLAB simulations show that our new technique improves considerably the positioning accuracy compared with the traditional RSSI and ToA ranging method. The proposed scheme can be run under Line of Sight and (LOS) and Nonline of Sight (NLOS) conditions taking into account a difference in the measurement error.