Text-to-SQL, as a hotspot in NLP, is a task of converting natural language text into the structured query language SQL, which makes database interaction more intuitive. This paper presents TC-SQL, a novel framework for schema linking optimization enabled by graph database modeling, with a decoupled table completion modules as its core component. Leveraging the open-source LLM Qwen for table retrieval and a supervised fine-tuned CodeLlama for SQL generation, TC-SQL models database tables and columns within a graph database to enhance recall completeness. Experiments show that it achieves an execution accuracy of 79.3% on the Spider test set, outperforming multiple comparative methods. Ablation studies further confirm the effectiveness of the supervised fine-tuning process and the table recall and completion modules. Leveraging open-source models, the method optimizes schema linking while leveraging open-source models to enhance system controllability and deployability, offering an efficient and reliable solution for intelligent data access in industrial scenarios.

Transformer-based methods for monocular 3D human pose estimation (3DHPE) excel at modeling temporal dependencies. However, existing approaches typically employ a single temporal attention model that treats all body joints as an inseparable whole. This homogeneous treatment fails to capture the distinct motion characteristics of different body parts, such as the trunk versus the limbs, and is inconsistent with the heterogeneous nature of human movement. To address this limitation, we propose the Part-Aware Spatio-Temporal Transformer (PAST-Former). Within a standard spatio-temporal Transformer framework, our model introduces a parallel dualstream temporal attention module. This module decouples motion modeling into two paths: a global path captures the smooth trajectory of the entire body, while a local path performs differential dynamic modeling on semantically divided body parts. The features from these two streams are dynamically fused via an adaptive gating mechanism, allowing the model to flexibly balance global and local information based on the action context. Extensive experiments on the Human3.6M and MPI-INF-3DHP benchmark datasets validate the effectiveness and state-of-the-art performance of our proposed method.

In wireless sensor networks (WSNs), enabling nodes to harvest energy from the environment and facilitating energy sharing among nodes through wireless power transfer (WPT) technology, known as energy cooperation, can alleviate energy scarcity issues and effectively prolong the lifespan of WSNs. Although previous research has investigated various forms of energy cooperation, recent developments have underscored the potential of Multicast Energy Cooperation (M-EC) in supporting efficient multinode energy sharing. This approach leverages the broadcast nature of wireless signals, potentially offering greater efficiency compared to traditional point-to-point style Unicast Energy Cooperation (U-EC). In this article, We focus on the M-EC Assisted Data Collection paradigm for energy harvesting-WSNs (EH-WSNs) and investigate the underlying M-EC assisted data collection scheduling (MECADCS) problem, aiming to minimize the data collection completion time by jointly optimizing the schedule decisions for energy cooperation and data collection. We formulate the MECADCS problem as a mixed integer nonlinear programming (MINLP) problem and establish its NP-hardness. We also simplified the MECADCS problem into a mixed integer linear programming (MILP) formulation via piecewise linear approximation, yet solving it using existing mature MILP solvers is still computationally expensive. To promptly return good solutions, we propose an efficient greedy-based data transmission scheduling algorithm (GDTS),heuristically determines energy cooperation and data transmission schedules and achieves a computational speedup of$10^{4}$times compared to exact solvers. Simulation results demonstrate that GDTS significantly reduces the data collection completion time compared to both algorithms without energy cooperation and those utilizing U-EC.

Mobile edge computing enhances the performance of low-capability end devices by offloading tasks to nearby edge servers, enabling timely responses for delay-sensitive, computation-intensive tasks. However, the rapid and continuous growth of such tasks may soon exceed the capacity of the initially deployed edge server system. This calls for deploying new servers while re-using deployed ones for saving investment, leading to the emergence of a novel paradigm named as Edge Server Expansion Deployment (ESED) here. For this ESED paradigm, aiming to simultaneously minimize the average access delay between end devices and edge servers and the workload deviation among servers, we studied the Budget-Constrained ESED (BC-ESED) problem under the condition of a specified budget constraint. We formulate the problem as a multi-objective optimization problem and prove its NP-hardness. We then propose an algorithm, by combining Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), named GA-PSO. GA-PSO utilizes a four-step iteration framework of selection, crossover, mutation, and correction, where a novel three-party globalbest-localbest-individual crossover operation, inspired by PSO, complements the traditional two-party crossover operation in the crossover step. The convergence and time complexity of GA-PSO are established and analyzed. Simulation results, based on realistic network topologies and workload data from the Shanghai Telecom base station dataset, demonstrate that GA-PSO outperforms other benchmark algorithms in terms of average access delay and workload deviation.

In order to avoid broadcast storms in wireless sensor networks and improve the efficiency of information transmission, virtual backbone networks have been widely introduced into network information transmission. In order to combine the practical development needs, this paper devotes to the optimization research on the construction of virtual backbone network, optimizes the three aspects of routing path length, backbone size and fault tolerance respectively, introduces the GOC-CDS construction strategy and the$\alpha \text{MOC-SCDAS}$construction strategy, and designs an approximation algorithm for the construction of the virtual backbone network based on the two strategies. Through theoretical analysis, the algorithm can obtain a$(72\rho^{2} + 24\rho + m + 1)(2\rho + 1)^{2}$-approximate optimal solution, where$\rho = \frac{r_{max}}{r_{min}}$. After comparative experiments, it is verified that the can maintain a smaller routing path length of the network and a better fault tolerance of the virtual backbone network, and at the same time, it can ensure that the size of the backbone network is as small as possible.

Participatory sensing is often used in environmental or personal data monitoring, wherein a number of participants collect data using their mobile intelligent devices for earning the incentives. However, a lot of additional information is submitted along with the data, such as the participant’s location, IP and incentives. This multimodal information implicitly links to the participant’s identity and exposes the participant’s privacy. In order to solve the issue of these multimodal information associating with participants’ identities, this paper proposes a protocol to ensure anonymous data reporting while providing a dynamic incentive mechanism simultaneously. The proposed protocol first establishes a submission schedule by anonymously selecting a slot in a vector by each member where every member and system entities are oblivious of other members’ slots and then uses this schedule to submit the all members’ data in an encoded vector through bulk transfer and multiplayer dining cryptographers networks (DC-nets) . Hence, the link between the data and the member’s identity is broken. The incentive mechanism uses blind signature to anonymously mark the price and complete the micropayments transfer. Finally, the theoretical analysis of the protocol proves the anonymity, integrity, and efficiency of this protocol. We implemented and tested the protocol on Android phones. The experiment results show that the protocol is efficient for low latency tolerable applications, which is the cases with most participatory sensing applications, and they also show the advantage of our optimization over similar anonymous data reporting protocols.

This paper proposed an optimized strategy of the incentive mechanism for participatory sensing network on the basis of Sense Util which is our previous research work. For optimizing, the strategy considered the possibility of that some users actually cannot participate the current sensing task, so in order to avoid unnecessary energy and bandwidth consumption on server end, users were classified into different subsets according to their position, and server distributed the sensing tasks directionally. To evaluate the performance of the strategy, we conducted simulation study, and the results showed that the optimization work is energy efficient.

ABSTRACT Solving wireless packet retransmission problems (WPRTPs) using network coding (NC) approach is increasingly attracting research efforts. However, existing researches are almost all focused on solutions in Galois field GF(2), and consequently, the solutions found by these schemes are usually less optimal. In this paper, we focus on optimal NC‐based scheme for perfect WPRTPs (P‐WPRTPs) where, with respect to each receiver, a packet is either requested by or already known to it. The number of retransmitted packets in optimal NC‐based solutions to P‐WPRTPs is firstly analyzed and proved. Then, random network coding‐based optimal scheme (RNCOPT) is proposed for P‐WRPTPs. RNCOPT is optimal in the sense that it guarantees to obtain a valid solution with minimum number of packet retransmissions. Furthermore, in RNCOPT, each coding vector is generated using a publicly known pseudorandom function with a randomly selected seed. The seed, instead of the coding vector, is used as decoding information to be retransmitted together with the coded packet. Thus, packet overhead of RNCOPT is reduced further. Extensive simulations show that RNCOPT distinctively outperforms some previous typical schemes for P‐WPRTPs in saving the number of retransmitted packets. Copyright © 2011 John Wiley & Sons, Ltd.