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Communication systems and networks are evolving as an integral part of not only of our everyday life but also as a part of the industry, fundamental infrastructures, companies, etc. Current directions and concepts, such as the Internet of Things (IoT), promise the enhanced quality of life, greater business opportunities, cost-effective manufacturing, and efficient operation management through ubiquitous connectivity and deployment of smart physical objects. IoT networks can collect, preprocess, and transmit vast amounts of data. A considerable portion of this data is security- and privacy-critical data, which makes IoT networks a tempting option for attackers. Given that these networks deal with the actual aspects of our lives and fundamental infrastructures (e.g. smart grids), security in such networks is crucial. The large scale of these networks and their unique characteristics and complexity bring further vulnerabilities. In this study, we focus on the IoT application layer, security requirements, threats, and countermeasures in this layer, and some of the open issues and future research lines.

This paper investigates distributed continuous-time fault estimation for multiple devices in the Internet-of-Things (IoT) networks by using a hybrid between cooperative control and state prediction techniques. First, a mode-dependent intermediate temperature matrix is designed, which constructs an intermediate estimator to estimate faulty temperature values obtained by the IoT network. Second, the continuous-time Markov chains transition matrix and output temperatures and the sufficient conditions of stability for auto-correct error of the IoT network temperatures are considered. Moreover, faulty devices are replaced by virtual devices to ensure continuous and robust monitoring of the IoT network, preventing in this way false data collection. Finally, the efficiency of the presented approach is verified with the results obtained in the conducted case study.