Search Results (2)

The advantages of LoRaWAN over conventional networks (GSM, 4G, 5G) in terms of investment and operating costs have been proven for network coverage in urban and rural areas. However, the theoretical coverage compared to the reality on the ground and the quality of service (QoS) provided remain very relative and depend on several technical factors, subject to increased research. Several recent approaches and hardware specifications recommended adding gateways as a solution to improve the LoRaWAN QoS indicators, mainly for high-traffic situations. However, such a solution will not work in all real-life scenarios since many factors must be considered. This article presents a study of the factors impacting the LoRaWAN QoS in the case of the usage of multiple gateways by exploring different scenarios to show how the payload length impacts the whole network’s packet delivery ratio (PDR) and how it interacts when enhancing the GW number with and without confirmed traffic. Based on the simulation results, increasing the number of gateways can negatively impact the network’s ability to support higher payload packets, especially in a high-traffic scenario. More precisely, we can say that for a low number of GWs, it is more appropriate to use a high payload length since we can achieve a high PDR. Nevertheless, with a high number of GWs, it would be more appropriate to use a low payload length to achieve a good PDR. Similarly, our analyses show that increasing the number of gateways ensures a better PDR but with a significant packet loss at the gateways, which is synonymous with higher energy consumption. Full article

The Internet of Things (IoT) is a fact today where a high number of nodes are used for various applications. From small home networks to large-scale networks, the aim is the same: transmitting data from the sensors to the base station. However, these data are susceptible to different factors that may affect the collected data efficiency or the network functioning, and therefore the desired quality of service (QoS). In this context, one of the main issues requiring more research and adapted solutions is the outlier detection problem. The challenge is to detect outliers and classify them as either errors to be ignored, or important events requiring actions to prevent further service degradation. In this paper, we propose a comprehensive literature review of recent outlier detection techniques used in the IoTs context. First, we provide the fundamentals of outlier detection while discussing the different sources of an outlier, the existing approaches, how we can evaluate an outlier detection technique, and the challenges facing designing such techniques. Second, comparison and discussion of the most recent outlier detection techniques are presented and classified into seven main categories, which are: statistical-based, clustering-based, nearest neighbour-based, classification-based, artificial intelligent-based, spectral decomposition-based, and hybrid-based. For each category, available techniques are discussed, while highlighting the advantages and disadvantages of each of them. The related works for each of them are presented. Finally, a comparative study for these techniques is provided. Full article