ABSTRACT High‐volume data transmission in Internet of Things (IoT) networks demands the establishment of high‐quality multi‐hop communication paths at the network layer. To address this challenge, this paper proposes a joint routing and resource allocation framework that simultaneously optimizes relay node selection and transmit power allocation under decentralized manner. The proposed approach leverages deep reinforcement learning (DRL) to enable hop‐by‐hop decision‐making with only local observations. To mitigate the partial observability stemmed from limited perception range of node, a transformer‐based architecture is integrated into the DRL agent to enable the fusion of observations and actions collected along the established path. Specifically, the encoder module aggregates historical and frontier observations along the forwarding path, while the decoder module models temporal dependencies among historical actions. The fused representation is utilized to generate optimal decisions for next‐hop node selection and power allocation. After each action execution, a one‐hot encoded placeholder of the chosen action is fed back into the decoder module for subsequent decisions. Finally, numerical simulations demonstrate that the proposed transformer‐enhanced DRL framework significantly outperforms state‐of‐the‐art baselines in terms of end‐to‐end path quality, and robustness under decentralized IoT network configuration.

Decentralized e-voting enables secure and transparent elections without relying on trusted authorities, with blockchain emerging as a popular platform. It has compelling applications in Decentralized Autonomous Organizations (DAOs), where governance relies on voting with blockchain-issued tokens. Quadratic voting (QV), a mechanism that mitigates the dominance of large token holders, has been adopted by many DAO elections to enhance fairness. However, current QV systems deployed in practice publish voters' choices in plaintext with digital signatures. The open nature of all ballots comprises voter privacy, potentially affecting voters' honest participation. Prior research proposes using cryptographic techniques to encrypt QV ballots, but they work in a centralized setting, relying on a trusted group of tallying authorities to administrate an election. However, in DAO voting, there is no trusted third party.

Zero-shot object navigation (ZSON) allows robots to find target objects in unfamiliar environments using natural language instructions, without relying on pre-built maps or task-specific training. Recent general-purpose models, such as large language models (LLMs) and vision-language models (VLMs), equip agents with semantic reasoning abilities to estimate target object locations in a zero-shot manner. However, these models often greedily select the next goal without maintaining a global understanding of the environment and are fundamentally limited in the spatial reasoning necessary for effective navigation. To overcome these limitations, we propose a novel 3D voxel-based belief map that estimates the target’s prior presence distribution within a voxelized 3D space. This approach enables agents to integrate semantic priors from LLMs and visual embeddings with hierarchical spatial structure, alongside real-time observations, to build a comprehensive 3D global posterior belief of the target’s location. Building on this 3D voxel map, we introduce BeliefMapNav, an efficient navigation system with two key advantages: i) grounding LLM semantic reasoning within the 3D hierarchical semantics voxel space for precise target position estimation, and ii) integrating sequential path planning to enable efficient global navigation decisions. Experiments on HM3D and HSSD benchmarks show that BeliefMapNav achieves state-of-the-art (SOTA) Success Rate (SR) and Success weighted by Path Length (SPL), with a notable 9.7 SPL improvement over the previous best SR method, validating its effectiveness and efficiency.

Abstract Inner product arguments are core building blocks of numerous cryptographic primitives and therefore minimizing their complexity is a central goal in this research area. In this paper, we follow the work of Kim et al. (ASIACRYPT’22) and propose the first inner product argument having sublogarithmic communication complexity and sub-square-root verifier complexity simultaneously. We first devise a new subvector combination method for recursion and utilize an aggregated multi-exponentiation argument to prove some committed group elements are valid. We then modify the commitment keys in inner product arguments to be structured and reduce the verifier complexity by delegating the costly computations to the prover. Compared with the state-of-the-art inner product arguments, our protocol is highly competitive in terms of asymptotic complexity.

Abstract Inner product arguments allow a prover to prove that the inner product of two committed vectors equals a public scalar. They are used to reduce the complexity of many cryptographic primitives, such as range proofs. Range proofs are deployed in numerous applications to prove that a committed value lies in a certain range. As core building blocks, their complexity largely determines the performance of corresponding applications. In this paper, we have optimised the inner product argument with statement including two vector commitments (IPA tvc ) and range proof of Daza et al. (PKC’20), the inner product argument with statement including only one vector commitment (IPA ovc ) of Bünz et al. (S&P′18). For IPA tvc , we reduce the concrete communication complexity by 2 log 2 n field elements, where n is the vector dimension. For range proofs, we reduce the concrete communication and prover complexities by about 2 log 2 m field elements and 11 m field multiplications, respectively, where m is the bit length of range. For IPA ovc , we exponentially reduce the asymptotic verifier complexity from linear to logarithmic. Due to the asymptotic characteristics, our protocols are highly competitive when the vector dimension or bit length of range is large.

Embedding virtual network requests in an underlying physical infrastructure, the so-called virtual network embedding (VNE) problem, has attracted significant research interests already. A realistic scenario might entail embedding multiple VN requests (MVNE) that arrive simultaneously (batch arrivals). The existing heuristic MVNE approaches neither consider the coordination among multiple VNR embeddings nor embed all the arriving VNRs simultaneously considering the available physical resources. This paper considers the MVNE problem in the scenario where the available physical resources may not be sufficient to satisfy the physical resource demands of all the VNRs in the batch. We explore applying genetic algorithm (GA) to handle the MVNE problem. We propose an algorithm to decide which VNRs could be mapped together. Extensive simulations are carried out to evaluate the performance of the proposed algorithms in terms of the VN acceptance ratio and the long-term revenue of the service provider.