Towards a Software-Defined Industrial IoT-Edge Network for Next-Generation Offshore Wind Farms: State of the Art, Resilience, and Self-X Network and Service Management

The primary aim of this research is to leverage the Open Network Operating System (ONOS) to develop an efficient and scalable process automation architecture for real-world windfarm operations. The study focuses on enhancing network management, data processing, and operational optimization within the windfarm infrastructure. This research utilizes ONOS as the centralized control platform for managing the windfarm's network infrastructure. The proposed system integrates network virtualization, real-time data processing, predictive analytics using LSTM networks, and optimization algorithms to improve wind turbine efficiency and fault management. MATLAB simulations are conducted to evaluate the performance and reliability of the system. The simulation results demonstrate significant improvements in network performance, including reduced latency and increased throughput. The fault detection and recovery mechanisms show enhanced system reliability. Real-time data processing and predictive analytics lead to optimized turbine settings and increased operational efficiency. The system also exhibits robust security measures and compliance with regulatory standards. The implementation of ONOS in windfarm process automation presents a viable solution for efficient and scalable network management. The findings highlight the potential for improved operational performance, reliability, and security in windfarm operations, contributing to the advancement of renewable energy technologies. The study's results offer valuable insights for future research and practical applications in the field of windfarm automation.

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Resilience in the Internet of Medical Things: A Review and Case Study

Tomer,

Sharma,

Davis

2024

Future Internet

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The Internet of Medical Things (IoMT), an extension of the Internet of Things (IoT), is still in its early stages of development. Challenges that are inherent to IoT, persist in IoMT as well. The major focus is on data transmission within the healthcare domain due to its profound impact on health and public well-being. Issues such as latency, bandwidth constraints, and concerns regarding security and privacy are critical in IoMT owing to the sensitive nature of patient data, including patient identity and health status. Numerous forms of cyber-attacks pose threats to IoMT networks, making the reliable and secure transmission of critical medical data a challenging task. Several other situations, such as natural disasters, war, construction works, etc., can cause IoMT networks to become unavailable and fail to transmit the data. The first step in these situations is to recover from failure as quickly as possible, resume the data transfer, and detect the cause of faults, failures, and errors. Several solutions exist in the literature to make the IoMT resilient to failure. However, no single approach proposed in the literature can simultaneously protect the IoMT networks from various attacks, failures, and faults. This paper begins with a detailed description of IoMT and its applications. It considers the underlying requirements of resilience for IoMT networks, such as monitoring, control, diagnosis, and recovery. This paper comprehensively analyzes existing research efforts to provide IoMT network resilience against diverse causes. After investigating several research proposals, we identify that the combination of software-defined networks (SDNs), machine learning (ML), and microservices architecture (MSA) has the capabilities to fulfill the requirements for achieving resilience in the IoMT networks. It mainly focuses on the analysis of technologies, such as SDN, ML, and MSA, separately, for meeting the resilience requirements in the IoMT networks. SDN can be used for monitoring and control, and ML can be used for anomaly detection and diagnosis, whereas MSA can be used for bringing distributed functionality and recovery into the IoMT networks. This paper provides a case study that describes the remote patient monitoring (RPM) of a heart patient in IoMT networks. It covers the different failure scenarios in IoMT infrastructure. Finally, we provide a proposed methodology that elaborates how distributed functionality can be achieved during these failures using machine learning, software-defined networks, and microservices technologies.

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Towards a Software-Defined Industrial IoT-Edge Network for Next-Generation Offshore Wind Farms: State of the Art, Resilience, and Self-X Network and Service Management

Cited by 3 publications

References 156 publications

Investigating the Dependability of Software-Defined IIoT-Edge Networks for Next-Generation Offshore Wind Farms

Investigating the Dependability of Software-Defined IIoT-Edge Networks for Next-Generation Offshore Wind Farms

Leveraging ONOS for Real-World Windfarm Process Automation Architecture: A Framework for Efficiency and Scalability

Resilience in the Internet of Medical Things: A Review and Case Study

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