An edge intelligence empowered flooding process prediction using Internet of things in smart city

Precision agriculture using IoT data analytics and machine learning

Decentralized federated learning for extended sensing in 6G connected vehicles

FELIDS: Federated learning-based intrusion detection system for agricultural Internet of Things

Building the Internet of Things platform for smart maternal healthcare services with wearable devices and cloud computing

The Internet of Things (IoT) is the latest example of the System of Systems (SoS), demanding for both innovative and evolutionary approaches to tame its multifaceted aspects. Over the years, different IoT methodologies, frameworks, platforms, and tools have been proposed by industry and academia, but the jumbled abundance of such development products have resulted into a high (and disheartening) entry-barrier to IoT system engineering. In this survey, we steer IoT developers by: 1) providing baseline definitions to identify the most suitable class of development products—methodologies, frameworks, platforms, and tools–for their purposes and 2) reviewing seventy relevant products through a comparative and practical approach, based on general SoS engineering features revised in the light of main IoT systems desiderata (i.e., interoperability, scalability, smartness, and autonomy). Indeed, we aim to lessen the confusion related to IoT methodologies, frameworks, platforms, and tools as well as to freeze their current state, for eventually easing the approach towards IoT system engineering.

Internet of Things as System of Systems: A Review of Methodologies, Frameworks, Platforms, and Tools

Collaborative learning with multiple edge devices to build group intelligence is a new trend. Edge artificial intelligence (AI) computing often makes full use of various available data and resources in terminal devices, edge servers, and cloud data centers to achieve collaborative deci-sion-making. However, in order to achieve the goal, it should also guarantee the security of data storage, transmission, and management. Therefore, how to build a distributed trust system for cooperative learning in edge computing is a complex and challenging engineering problem. Block-chain can help realize collaborative learning in a distributed way without the need for third-party audit. Also, the consensus protocol plays a key technology role. In this article, a trusted consensus scheme for multi-party collaborative learning of edge AI is proposed. To improve system security, reputation-based rights are used to support fast removal of abnormal nodes. With the method of reputation rewards and punishments, the reputations of the nodes participating in the consensus process are scored and recorded on the chain according to their behavior. Nodes who commit malicious actions will be punished by reducing their reputation values. Nodes are rated according to their reputation value, and those with higher reputation rates are acknowledged with more credits and rights in consensus. Since the reputation module is loadable on X-BFT protocols, it is easy to enforce in real applications. The experimental results show that the probability of the attacker being chosen as the leader of XR-BFT is 87.5 percent lower than that of the X-BFT protocols, thus enabling our protocol to achieve trust collaborative learning in edge AI computing with more safety and efficiency.

Claudio Savaglio

Papers

Internet of Things as System of Systems: A Review of Methodologies, Frameworks, Platforms, and Tools

A Trusted Consensus Scheme for Collaborative Learning in the Edge AI Computing Domain