In recent years, Digital Twin (DT) has emerged as a transformative paradigm for enabling the future of the Internet of Things. By mapping the real-time status of physical entities to their digital counterparts, DTs facilitate the creation of high-fidelity, interactive environments suitable for advanced simulation and deeper insight. One of the key challenges lies in achieving a sufficient level of convergence between Physical Twins (PTs) and their corresponding DTs. To tackle this challenge, we introduce a mobile edge computing environment that enables the coordination between PTs and DTs in Digital Twin Networks (DTNs) by offloading data transmission and processing tasks to the edge. A Hierarchical Deep Reinforcement Learning (HDRL) framework is proposed to improve DT-PT synchronization and enhance coordination effectiveness in optimizing information freshness across multiple action policies within the Digital Twin Communication Infrastructure (DTCI). Our approach is validated through a DTCI simulator, where comprehensive evaluations of age of information performance are conducted. Experimental results show that our HDRL-based solution significantly enhances the information freshness under constrained DTCI resources and across diverse environmental conditions.

Large Language Models (LLMs) have emerged as the cornerstone of various natural language processing activities, enabling everything from chatbots to text classification and summarization. However, using LLMs presents some significant challenges, most notably the threat of intellectual property infringement from the exposure of the entire model and the excessive communication and storage overhead associated with their large size. We propose KDLLM, a novel knowledge distillation-based framework for efficient and compact LLMs to address these challenges. KDLLM transfers the performance of a large teacher LLM to a significantly smaller student model with high performance similarity to its teacher, while obscuring architectural and parameter-level details to protect the intellectual property of the original model. The resulting student model substantially reduces the memory footprint and transmission overhead, making it amenable to deployment in bandwidth-constrained or security-sensitive environments. Comprehensive experiments demonstrate that KDLLM achieves robust performance preservation and boosts copyright protection and communication efficiency.

The ever increasing requirements of data sensing applications result in the usage of IoT networks. These networks are often used for efficient data transfer. Wireless sensors are incorporated in the IoT networks to reduce the deployment and maintenance costs. Designing an energy efficient data aggregation method for sensor equipped IoT to process skyline query, is one of the most critical problems. In this paper, we propose two approximation algorithms to process the skyline query in wireless sensor networks. These two algorithms are uniform sampling-based approximate skyline query and Bernoulli sampling-based approximate skyline query. Solid theoretical proofs are provided to confirm that the proposed algorithms can yield the required query results. Experiments conducted on actual datasets show that the two proposed algorithms have high performance in terms of energy consumption compared to the simple distributed algorithm.

As Internet-of-Things (IoT) networks provide efficient ways to transfer data, they are used widely in data sensing applications. These applications can further include wireless sensor networks. One of the critical problems in sensor-equipped IoT networks is to design energy efficient data aggregation algorithms that address the issues of maximum value and distinct set query. In this paper, we propose an algorithm based on uniform sampling and Bernoulli sampling to address these issues. We have provided logical proofs to show that the proposed algorithms return accurate results with a given probability. Simulation results show that these algorithms have high performance compared with a simple distributed algorithm in terms of energy consumption.

Privacy preserving data releasing is an important problem for reconciling data openness with individual privacy. The state-of-the-art approach for privacy preserving data release is differential privacy, which offers powerful privacy guarantee without confining assumptions about the background knowledge about attackers. For genomic data with huge-dimensional attributes, however, current approaches based on differential privacy are not effective to handle. Specifically, amount of noise is required to be injected to genomic data with tens of million of SNPs (Single Nucleotide Polymorphisms), which would significantly degrade the utility of released data. To address this problem, this paper proposes a differential privacy guaranteed genomic data releasing method. Through executing belief propagation on factor graph, our method can factorize the distribution of sensitive genomic data into a set of local distributions. After injecting differential-privacy noise to these local distributions, synthetic sensitive data can be obtained by sampling on noise distribution. Synthetic sensitive data and factor graph can be further used to construct approximate distribution of non-sensitive data. Finally, non-sensitive genomic data is sampled from the approximate distribution to construct a synthetic genomic dataset.