As the complexity of Internet of Things (IoT) systems increases, a large variety of tools and technologies for IoT management are making their way into both research setups and the market. IoT management solutions must consider the resource restrictions of embedded devices, as well as their heterogeneity and network dynamics. With these in mind, the Internet Engineering Task Force developed several standards targeting the integration and interoperation of heterogeneous devices, such as the Representational State Transfer Configuration Protocol (RESTCONF) or the Constrained Application Protocol (CoAP) Management Interface. Concurrently, the Open Mobile Alliance developed the Lightweight Machine-to-Machine protocol, for IoT device management. This paper provides a comprehensive, up-to-date overview of IoT management technologies, frameworks and protocols. Also, it proposes a taxonomy for IoT devices management. In addition to presenting the various solutions, the paper provides comparative views, standardization timeline, and market analysis. The presented analysis ranges from traditional network management protocols, such as Simple Network Management Protocol, to the newest IoT management and configuration protocols, such as CoAP Management Interface and Lightweight Machine-to-Machine protocols. Moreover, this survey identifies remaining challenges and solutions offered by recent management protocols, not covered by previous surveys.

The past years have witnessed an increasing interest and concern regarding the development of security monitoring and management mechanisms for Critical Infrastructures, due to their vital role in ensuring the availability of many essential services. This task is not easy due to the specific characteristics of such systems, and the natural resistance of Critical Infrastructures operators against actions implying downtime. Digital Twins, as accurate virtual models of physical objects or processes, can provide a faithful environment for security analysis or evaluation of potential mitigation strategies to be deployed in face of specific situations. Nonetheless, their on-premises deployment can be expensive, implying a significant CAPEX whose return will depend on the ability to plan and deploy a suitable support infrastructure, as well as implementing efficient and scalable data collection and processing mechanisms capable of taking advantage of the acquired resources. This paper presents an off-premises approach to design and deploy Digital Twins to secure critical infrastructures, developed in the scope of the ELEGANT project. Such Digital Twins are built using real-time, high fidelity replicas of Programming Logic Controllers, coupled with scalable and efficient data collection processes, supporting the development and validation of Machine Learning models to mitigate security threats like Denial of Service attacks. The validation approach of ELEGANT, which leveraged from the capabilities of the Fed4Fire federated testbeds evaluated the feasibility of using cloudified Digital Twins, thus converting a significant part of the projected CAPEX for the in-premises model into on-demand, pay-as-you-go OPEX, eventually paving the way for the establishment of a DTaaS (Digital Twin as a Service) paradigm. The achieved results demonstrate that the data pipelines providing support for the ELEGANT Digital Twins have low impact in terms of resource usage in Denial of Service and Distributed Denial of Service attack scenarios, when higher volumes of data are generated.