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.

Healthcare Information Exchange (HIE) is becoming a fundamental operation in current healthcare systems. In such systems, electronic health records (EHRs) are digitally stored inside each medical centers and, sometimes, are required to be shared between various healthcare facilities (HCFs). Indeed, sharing patient information is crucial and might be vulnerable to power outages, data misuse, privacy or security violations, and an audit trail. Hence, researchers have focused recently on cutting-edge technologies to develop secure HIE systems and ensure data privacy during transactions. Among such technologies, blockchain and artificial intelligence (AI) occupy a vital role in researchers’ focuses and efforts to detect risky transactions in HIE systems, thus enhancing their security and privacy. While the blockchain allows HCFs to link to each other without requiring a central authority, AI models offer an additional security layer when sharing patient data between HCFs. This paper presents a survey about HIE systems, and the aim is two-fold: we first present the architecture of HIE systems along with their challenges; then, we categorize and classify the current state-the-art-techniques that show the potential of using blockchain and AI technologies in such systems.

One of the central communication infrastructures of the Internet of Things (IoT) is the IEEE 802.15.4 standard, which defines Low Rate Wireless Personal Area Networks (LR- WPAN). In order to share the medium fairly in a non-beacon-enabled mode, the standard uses Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA). The nature of connected objects with respect to various resource constraints makes them vulnerable to cyber attacks. One of the most aggressive DoS attacks is the greedy behaviour attack which aims to deprive legitimate nodes to access to the communication medium. The greedy or selfish node may violate the proper use of the CSMA/CA protocol, by tampering its parameters, in order to take as much bandwidth as possible on the network, and then monopolize access to the medium by depriving legitimate nodes of communication. Based on the analysis of the difference between parameters of greedy and legitimate nodes, we propose a method based on the threshold mechanism to identify greedy nodes. The simulation results show that the proposed mechanism provides a detection efficiency of 99.5%.

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.