Due to resource constraints and working surroundings, many IIoT nodes are easily hacked and turn into zombies from which to launch attacks. It is challenging to detect such networked zombies rooted behind the Internet for any individual defender. In this article, we combine federated learning (FL) and fog/edge computing to combat malicious codes. Our protocol trains a global optimized model based on distributed datasets of collaborators while removing the data and communication constraints. The FL-based detection protocol maximizes the values of distributed data samples, resulting in an accurate model timely. On top of the protocol, we place mitigation intelligence in a distributed and collaborative manner. Our approach improves accuracy, eliminates mitigation time, and enlarges attackers’ expense within a defense alliance. Comprehensive evaluations confirm that the cost incurred is 2.7 times larger, the mitigation response time is 72% lower, and the accuracy is 47% higher on average. Besides, the protocol evaluation shows the detection accuracy is approximately 98% in the FL, which is almost the same as centralized training.

The prosperity of Internet of Things (IoT) and the success of rich Cloud services have expedited the emergence of a new computing paradigm called Fog computing, which promotes the processing of data at the proximity of their sources. Complementary to the Cloud, Fog promises to offer many appealing features, such as low latency, low cost, high multitenancy, high scalability, and to consolidate the IoT ecosystem. Although the Fog concept has been widely adopted in many areas, a comprehensive realization has yet been adequately researched. To address all these issues, in this paper, object virtualization is investigated to overcome obstacles resulting from resource constraints on sensory-level nodes while service virtualization is explored to easily create tailored applications for end users. Moreover, network function virtualization is studied to perform the flexibility of network service provisioning. Grounded on object virtualization, network function virtualization and service virtualization, a layered framework that encompasses smart objects, Fog and Cloud is presented to illustrate the realization of virtual Fog along IoT continuum. This proposed virtual Fog framework is applied to a smart living case for verification, then quantitative analysis is conducted to demonstrate the low latency, low operating expense (OpEx), high multitenancy and scalability, followed by an experimental evaluation to further confirm that delay and jitter can be decreased through virtualization.

Nowadays, smart environments (e.g., smart home, smart city) are built heavily relying on Cloud computing for the coordination and collaboration among smart objects. Cloud is typically centralized but smart objects are ubiquitously distributed, thus, data transmission latency (i.e., end-to-end delay or response time) between Cloud and smart objects is a critical issue especially to the applications that have strict delay requirements. To address the concern, a new Fog computing paradigm is recently proposed by the industry, while the detailed Fog platform is yet to be developed. The key idea is to bring the computing power from the remote Cloud closer to the users, which further enables real-time interaction and location-based services. In particular, the local processing capability of Fog computing significantly scales down the data volume towards the Cloud, and it in turn has great impacts on the entire Internet. In this paper, a data-centered Fog platform is developed to support smart living together with dataflow analysis. Case studies are also conducted to validate and evaluate the proposed platform.

Low latency is critical for delay-sensitive applications such as video surveillance, live streaming, and online data analytics. Fog computing enables the emergence of the latency-sensitive internet of things (IoT) network to support real-time applications. While the distance between sensing and processing is minimized in the fog network, the cross-fog latency is yet to be determined. In this paper, we study the components of network delays and develop a latency estimation framework for fog-based IoT. The proposed framework, in particular, precisely predicts the end-to-end inter-node delay along the cloud-fog-things continuum. We investigate the benefits and use cases based on latency estimated by the proposed framework. A case study is further conducted to illustrate the validation and advantages, followed by future research directions.

The rapid expansion of electric vehicle (EV) infrastructure necessitates advanced solutions for secure and private authentication at EV charging stations. This research introduces a blockchain-based framework enhanced with self-sovereign identity (SSI) features, targeting the improvement of privacy and security in cyber marketplaces for EVs. The inclusion of SSI enables users to maintain full control over their digital identities, a critical advancement for authenti- cation processes at EV charging stations. This system effectively addresses the growing privacy and security challenges within the expanding EV infrastructure. By integrating Zero-knowledge proof with self-sovereign identity, the framework not only ensures robust security but also preserves user privacy by enabling users to prove their identity without exposing sensitive personal information. We propose an efficient and user-friendly solution, showcasing its potential as a pioneering inno- vation in the field of EV charging infrastructure.