The integration of semantic communication with Internet of Things (IoT) technologies has advanced the development of Semantic IoT (SIoT), with edge mobile networks playing an increasingly vital role. This paper presents a framework for SIoT-based image retrieval services, focusing on the application in automotive market analysis. Here, semantic information in the form of textual representations is transmitted to users, such as automotive companies, and stored as knowledge graphs, instead of raw imagery. This approach reduces the amount of data transmitted, thereby lowering communication resource usage, and ensures user privacy. We explore potential adversarial attacks that could disrupt image transmission in SIoT and propose a defense mechanism utilizing Generative Artificial Intelligence (GAI), specifically the Generative Diffusion Models (GDMs). Unlike methods that necessitate adversarial training with specifically crafted adversarial example samples, GDMs adopt a strategy of adding and removing noise to negate adversarial perturbations embedded in images, offering a more universally applicable defense strategy. The GDM-based defense aims to protect image transmission in SIoT. Furthermore, considering mobile devices’ resource constraints, we employ GDM to devise resource allocation strategies, optimizing energy use and balancing between image transmission and defense-related energy consumption. Our numerical analysis reveals the efficacy of GDM in reducing energy consumption during adversarial attacks. For instance, in a scenario, GDM-based defense lowers energy consumption by 5.64%, decreasing the number of image retransmissions from 18 to 6, thus underscoring GDM's role in bolstering network security.

Lightweight training and distributed tiny data storage in the local model will lead to the severe challenge of convergence for tiny federated learning (FL). Achieving fast convergence in tiny FL is crucial for many emerging applications in Internet of unmanned aerial vehicles (IUAVs) networks. Excessive information exchange between unmanned aerial vehicles (UAVs) and Internet of Things (IoT) devices could lead to security risks and data breaches, while insufficient information can slow down the learning process and negatively system performance experience due to significant computational and communication constraints in tiny FL hardware system. This article proposes a trusting, low latency, and energy-efficient tiny wireless FL framework with blockchain (TBWFL) for IUAV systems. We develop a quantifiable model to determine the trustworthiness of IoT devices in IUAV networks. This model incorporates the time spent in communication, computation, and block production with a decay function in each round of FL at the UAVs. Then it combines the trust information from different UAVs, considering their credibility of trust recommendation. We formulate the TBWFL as an optimization problem that balances trustworthiness, learning speed, and energy consumption for IoT devices with diverse computing and energy capabilities. We decompose the complex optimization problem into three subproblems for improved local accuracy, fast learning, trust verification, and energy efficiency of IoT devices. Our extensive experiments show that TBWFL offers higher trustworthiness, faster convergence, and lower energy consumption than the existing state-of-the-art FL scheme.

The Location-Based Service has been widely used for mobile communication networks and location systems. However, privacy disclosure for incomplete collection location data in LBS was ignored in most of the existing works. To solve the problem of privacy disclosure, we propose a location privacy method based k-anonymity to prevent privacy disclosure in LBS constrained in incomplete data collection. The proposed scheme can provide effectively location privacy-preserving in the process of constructing the anonymous set, and against background attacks. In this method, we first designed a construct method for anonymous candidate set(ACS) with compressing sensing technology, to solve the problem of incomplete data of collection location. To prevent the privacy disclosure in the process of construct anonymous, we then adapt the differential privacy mechanism to construct the anonymous set(AS) with the ACS. we finally used the optimization method based on the Stackelberg game model to improve the privacy level of AS to against probabilistic attack. As shown in the theoretical analysis and the experimental results, the proposed method can achieve significant improvements in terms of privacy degree and applicability.

Interference management and energy management are two important issues in ultra-dense heterogeneous cellular networks (HetNets). However, load balancing investigated for system capacity on interference coordination is not efficient for energy saving in HetNets. Meanwhile, the maximum energy efficiency configuration leads to the serious unfair problem for users associated with larger power node for the ultra-dense scenario. Thus, in the work we investigate energy consumption jointly together with interference coordination for Ultra-Dense HetNets, and formulate max-min energy-efficient Enhanced Inter-cell Interference Coordination (eICIC) configuration problem. Due to the non-smooth and mixed programming nature of this formulation, we propose a novel iterative and distributed algorithm to solve the problem by using fractional programming and Lagrangian dual theory. The simulation results verified the effectiveness of our proposed algorithm and fairness achieved for energy efficiency of users, it especially identified a new energy efficiency tradeoff between macro user and small cell user with interference coordination in ultra-dense HetNets.

Mobile edge computing (MEC) enabled artificial intelligence-generated content (AIGC) has garnered considerable attention. To support AIGC metaverse applications within MEC in internet of things (IoT) networks, it is effective to offload computation tasks, particularly those involving neural networks generative in AIGC, from mobile devices to edge clouds. Existing solutions typically assume the availability of a dedicated and powerful edge server for each user with single modal data, which can handle the entire AIGC service offloading. However, the practical availability of such dedicated and powerful servers may be limited, necessitating the utilization of less capable alternatives. Thus, we propose the multimodal parallel offloading AIGC framework which partitions multimodal content and offloads partial diffusion tasks to multiple servers. Our proposed scheme accelerates mobile deep vision multimodal metaverse applications through parallel offloading provided by multiple servers. We further utilize generative AI scheme to solve offloading problems to adapt the dynamic and available communication and computing resource in wireless IoT network. Our framework proposed a multimodal parallel diffusion offloading scheme with integrating the recurrent region proposal prediction algorithm to optimize communication and computing resources while minimizing delay. Simulation results show that our approach can significantly reduce delay compared to conventional algorithms.