Semantic communication allows the receiver to know the intention instead of the bit information itself, which is an emerging technique to support real-time human-machine and machine-to-machine interactions for future wireless communications. In semantic communications, both transmitter and receiver share some common knowledge, which can be used to extract small-size information at the transmitter and recover the original information at the receiver. Due to different design purposes, security issues in semantic communications have two unique features compared to standard bit-wise communications. First, an attacker in semantic communications considers not only the amount of stolen data but also the meanings of stolen data. Second, an attacker in semantic communication systems can attack not only semantic information transmission as done in standard communication systems but also attack machine learning (ML) models used for semantic information extraction since most of semantic information is generated using ML based methods. Due to these unique features, in this paper, we present an overview on the fundamentals and key challenges in the design of secure semantic communication. We first provide various methods to define and extract semantic information. Then, we focus on secure semantic communication techniques in two areas: information security and semantic ML model security. For each area, we identify the main problems and challenges. Then, we will provide a comprehensive treatment of these problems. In a nutshell, this article provides a holistic set of guidelines on how to design secure semantic communication systems over real-world wireless communication networks.

Recently, fog computing has gained increasing attention in processing the computing tasks of the industrial Internet of things (IIoT) with different service popularity. In task-diversified fog computing-enabled IIoT (F-IIoT), the mismatch between expected computing efficiency and partitioned resources on fog nodes (FNs) may pose serious traffic congestion even large-scale industrial service interruptions. The existing works mainly studied offloading which type of computing tasks into FNs, but few studies enabled smart resource partitioning of FNs. In this paper, a service popularity-based smart resources partitioning (SPSRP) scheme is proposed for fog computing-enabled IIoT. We first exploit Zipf's law to model the relationship between popularity ranks and computing costs of IIoT services. Moreover, we propose an implementation architecture of the SPSRP scheme for F-IIoT, which decouples the computing control layer from data processing layer of IIoT through a specified SPSRP controller. Besides, a mobility and heterogeneity-aware partitioning algorithm is presented for extending SPSRP scheme to seamlessly support cross-domain resources partitioning. The simulations demonstrate that the SPSRP scheme can bring notable performance improvements on delay time, successful response rate and fault tolerance for fog computing to deal with the large-scale IIoT services.

Remote health care is currently one of the most promising solutions to ensure a high level of treatment outcome, cost-efficiency and sustainability of the healthcare systems worldwide. Even though research on remote health care can be traced back to the early days of the Internet, the recent COVID-19 has necessitated further improvement in existing health care systems with invigorated research on remote health care technologies. In this article we delve into the state-of-the-art research in latest technologies and technological paradigms that play a vital role in enabling the next generation remote health care and assisted living. First the need of using the latest technological developments in the domain of remote health care is briefly discussed. Then the most important technologies and technological paradigms that are crucial in enabling remote health care and assisted living are emphasised. Henceforth, a detailed survey of existing technologies, potential challenges in those technologies, and possible solutions is conducted. Finally, missing research gaps and important future research directions in each enabling technology are brought forth to motivate further research in remote health care.

Software-defined industrial Internet of things (SD-IIoT) exploits federated learning to process the sensitive data at edges, while adaptive poisoning attacks threat the security of SD-IIoT. To address this problem, this article proposes a multi-tentacle federated learning (MTFL) framework, which is essential to guarantee the trustness of training data in SD-IIoT. In MTFL, participants with similar learning tasks are assigned to the same tentacle group. To identify adaptive poisoning attacks, a tentacle distribution-based efficient poisoning attack detection (TD-EPAD) algorithm is presented. And also, to minimize the impact of adaptive poisoning data, a stochastic tentacle data exchanging (STDE) protocol is also proposed. Simultaneously, to protect the tentacle’s privacy in STDE, all exchanged data will be processed by differential privacy technology. A MTFL prototype system is implemented, which provides extensive ablation experiments and comparison experiments, demonstrating that the accuracy of the global model under attack scenario can be improved with 40%.

Recently, wireless edge networks have realized intelligent operation and management with edge artificial intelligence (AI) techniques (i.e., federated edge learning). However, the trustworthiness and effective incentive mechanisms of federated edge learning (FEL) have not been fully studied. Thus, the current FEL framework will still suffer untrustworthy or low-quality learning parameters from malicious or inactive learners, which undermines the viability and stability of FEL. To address these challenges, the potential social attributes among edge devices and their users can be exploited, while not included in previous works. In this paper, we propose a novel S ocial F ederated E dge L earning framework (SFEL) over wireless networks, which recruits trustworthy social friends as learning partners. First, we build a social graph model to find like-minded friends, comprehensively considering the mutual trust and learning task similarity. Besides, we propose a social effect based incentive mechanism for better personal federated learning behaviors with both complete and incomplete information. Finally, we conduct extensive simulations with the Erdos-Renyi random network, the Facebook network, and the classic MNIST/CIFAR-10 datasets. Simulation results demonstrate our framework could realize trustworthy and efficient federated learning over wireless edge networks, and it is superior to the existing FEL incentive mechanisms that ignore social effects.

Edge computational intelligence, integrating artificial intelligence (AI) and edge computing into Internet of Things (IoT), will generate many scattered knowledge. To enable auditable and delay-sensitive IoT services, these knowledge will be shared among decentralized intelligent network edges (DINEs), end users, and supervisors frequently. Blockchain has a promising ability to provide a traceable, privacy-preserving and tamper-resistant ledger for sharing edge knowledge. However, due to the complicated environments of network edges, knowledge sharing among DINEs still faces many challenges. Firstly, the resource limitation and mobility of DINEs impede the applicability of existing consensus tricks (e.g., Poof of Work, Proof of Stake, and Paxos) of blockchain. Secondly, the adversaries may eavesdrop the content of edge knowledge or entice the blockchain to forks using some attacking models (like man-in-the-middle attack, denial of services, etc.). In this article, an user-centric blockchain (UCB) framework is proposed for preserving edge knowledge sharing in IoT. Significant superiorities of UCB benefit from the proof of popularity (PoP) consensus mechanism, which is more energy-efficient and fast. Security analysis and experiments based on Raspberry Pi 3 Model B demonstrate its feasibility with low block generating delay and complexity.