Self-adaptive Architectures in IoT Systems: A Systematic Literature Review
Iván Alfonso, Kelly Garcés, Harold Castro, Jordi Cabot
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Accepted in Journal of Internet Services and Applications (JISA). For its published version refer to the Journal of Internet Services and Applications (JISA)
Over the past few years, the relevance of the Internet of Things (IoT) has grown significantly and is now a key component of many industrial processes and even a transparent participant in various activities performed in our daily life. IoT systems are subjected to changes in the dynamic environments they operate in. These changes (e.g. variations in bandwidth consumption or new devices joining/leaving) may impact the Quality of Service (QoS) of the IoT system. A number of self-adaptation strategies for IoT architectures to better deal with these changes have been proposed in the literature. Nevertheless, they focus on isolated types of changes. We lack a comprehensive view of the trade-offs of each proposal and how they could be combined to cope with simultaneous events of different types.
In this paper, we identify, analyze, and interpret relevant studies related to IoT adaptation and develop a comprehensive and holistic view of the interplay of different dynamic events, their consequences on QoS, and the alternatives for the adaptation. To do so, we have conducted a systematic literature review of existing scientific proposals and defined a research agenda for the near future based on the findings and weaknesses identified in the literature.
The Internet as we know it Today, comprises several fundamental interrelated networks, among which is the Internet of Things (IoT). Despite their versatility, several IoT devices are vulnerable from a security perspective, which renders them as a favorable target for multiple security breaches, especially botnet attacks. In this study, the conceptual frameworks of IoT botnet attacks will be explored, alongside several machinelearning based botnet detection techniques. This study also analyzes and contrasts several botnet Detection techniques based on the Bot-IoT Dataset; a recent realistic IoT…
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University of Murcia（穆尔西亚大学）；European Commission, Joint Research Centre（欧盟委员会联合研究中心）
The application of Machine Learning (ML) techniques to the well-known intrusion detection systems (IDS) is key to cope with increasingly sophisticated cybersecurity attacks through an effective and efficient detection process. In the context of the Internet of Things (IoT), most ML-enabled IDS approaches use centralized approaches where IoT devices share their data with data centers for further analysis. To mitigate privacy concerns associated with centralized approaches, in recent years the use of Federated Learning (FL) has attracted a significant interest in different sectors, including hea…
Recent applications of machine learning, remote sensing, and iot approaches in yield prediction: a critical review
International Water Research Institute (IWRI)（国际水研究所（IWRI））；Mohammed VI Polytechnic University (UM6P)（穆罕默德六世理工大学（UM6P））；Research and Development Unit in Agriculture and Agri-food of Abitibi-Témiscamingue (URDAAT)（阿比蒂比-泰米斯卡曼格农业与农业食品研发部（URDAAT））；Université du Québec en Abitibi-Témiscamingue（魁北克大学阿比蒂比-泰米斯卡曼格分校）
With rapid technological advancements within the domain of Internet of Things (IoT), strong trends have emerged which indicate a rapid growth in the number of smart devices connected to IoT networks and this growth cannot be supported by traditional cloud computing platforms. In response to the increased capacity of data being transferred over networks, the edge and fog computing paradigms have emerged as extremely viable frameworks that shift computational and storage resources towards the edge of the network, thereby migrating processing power from centralized cloud servers to distributed LA…
