Data collection is a crucial task of IoTs. According to the data collection scheme and the required data granularity, data collection in IoTs can be classified into pull-based/push-based data collection as well as node-level/target-level data collection. Thus, there are four scenarios for data collection: Push-based node-level data collection (Push-Node), Push-based target-level data collection (Push-Target), Pull-based node-level data collection (Pull-Node), and Pull-based target-level data collection (Pull-Target). Energy-Harvesting IoT (EH-IoT) is an important component of IoTs and the Age of Information (AoI) minimization problem has been studied extensively for data collection in EH-IoTs. However, existing works only studied the problem under the scenario of Push-Node, Push-Target and Pull-Node. Therefore, this paper investigates the AoI minimization problem for Pull-based Target-level data collection in EH-IoTs (AoI-Pull-Target) for the first time. AoI-Pull-Target is formally defined and proved to be NP-hard. A two-stage dynamic programming-based node scheduling algorithm and a real-time schedule adjustment scheme are proposed to solve the problem. The proposed algorithm is analyzed theoretically. Extensive simulations and real-world testbed experiments verify the high performance of our algorithm.

Energy Harvesting (EH) technology has emerged to prolong the lifetime of Internet of Things (IoT) devices. However, in EH-IoTs, the reliance on external energy sources introduces challenges in maintaining up-to-date information. To quantify data freshness in such systems, researchers have introduced the Age-of-Information (AoI) metric, which measures the time elapsed since the generation of the most up-to-date information received by the user. Consequently, the problem of AoI minimization has been studied extensively in EH-IoTs to ensure timely data delivery. While data aggregation is a fundamental task for IoTs, existing works on AoI minimization in EH-IoTs have only considered scenarios where sensory data is updated by individual source nodes. The problem has not been investigated for data aggregation, in which the sensory data is aggregated from multiple source nodes. In this paper, we study the problem of AoI minimization for Data Aggregation in EHIoTs. To address this problem, we propose an energy-adaptive node scheduling algorithm consisting of both offline scheduling and online adjustment. Extensive simulations and testbed experiments verify the high performance of our algorithm in terms of AoI minimization and energy efficiency.

In Verilog code design, identifying and locating functional bugs is an important yet challenging task. Existing automatic bug localization methods have limited capabilities; they only suggest a set of potential buggy lines rather than precisely identifying the bug. Moreover, they depend on verification tools like testbenches and reference models, which require expert input and are time-consuming to develop. This paper introduces LiK (Location is Key), an open-source Large Language Model (LLM) to precisely locate functional bugs in Verilog code without the need for expert-written verification tools. LiK is developed from the open-source coding LLM Deepseek-Coder-Lite-Base-16B through a threestep training process: continuous pre-training to enhance foundational knowledge, supervised fine-tuning to learn how to output localization results, and reinforcement learning to reduce output errors. Experiment results demonstrate that LiK achieves superior functional bug localization accuracy, outperforming both the SOTA traditional method Strider, and SOTA closed-source LLMs like GPT-o1-preview and Claude-3.5-Sonnet. Moreover, integrating LiK into the SOTA LLM-based Verilog debugging tool significantly boosts its functional bug fixing success rate from $76.47 \%$ to $90.54 \%$. This underscores LiK’s potential to enhance the performance of end-to-end automatic Verilog debugging tools.

SystemVerilog Assertions (SVAs) are essential for verifying Register Transfer Level (RTL) designs, as they can be embedded into key functional paths to detect unintended behaviours. During simulation, assertion failures occur when the design’s behaviour deviates from expectations. Solving these failures, i.e., identifying and fixing the issues causing the deviation, requires analysing complex logical and timing relationships between multiple signals. This process heavily relies on human expertise, and there is currently no automatic tool available to assist with it. Here, we present AssertSolver, an opensource Large Language Model (LLM) specifically designed for solving assertion failures. By leveraging synthetic training data and learning from error responses to challenging cases, AssertSolver achieves a bug-fixing pass@1 metric of 88.54% on our testbench, significantly outperforming OpenAI’s o1-preview by up to $\mathbf{1 1. 9 7 \%}$. We release our model and testbench for public access to encourage further research: https://github.com/SEU-ACAL/reproduce-AssertSolver-DAC-25.

SystemVerilog Assertions (SVAs) are essential for verifying Register Transfer Level (RTL) designs, as they can be embedded into key functional paths to detect unintended behaviours. During simulation, assertion failures occur when the design’s behaviour deviates from expectations. Solving these failures, i.e., identifying and fixing the issues causing the deviation, requires analysing complex logical and timing relationships between multiple signals. This process heavily relies on human expertise, and there is currently no automatic tool available to assist with it. Here, we present AssertSolver, an opensource Large Language Model (LLM) specifically designed for solving assertion failures. By leveraging synthetic training data and learning from error responses to challenging cases, AssertSolver achieves a bug-fixing pass@1 metric of $88.54 \%$ on our testbench, significantly outperforming OpenAI’s o1-preview by up to $\mathbf{1 1. 9 7 \%}$. We release our model and testbench for public access to encourage further research: https://github.com/SEU-ACAL/reproduce-AssertSolver-DAC-25.

Age of Information (AoI) has been proposed to measure the freshness of the sensory data for IoT applications. In Battery-free WSNs (BF-WSNs), The AoI minimization data collection problem has been extensively studied. Apart from data collection, target monitoring is also an important application for BF-WSNs. To capture the freshness of the sensory data, the AoI of the sensory data related to targets (The AoI of targets) needs to be considered. To guarantee the performance of the target monitoring system, the maximum AoI of all targets should be minimized. However, existing works mainly investigated the AoI of the sensory data related to nodes (The AoI of nodes). Also, existing energy models are not practical enough for battery-free nodes. To deal with those problems, in this paper, we first propose a more practical energy model. Then the problem of Maximum AoI minimization for Target monitoring in Battery-free WSNs (MTB) is formally defined based on the proposed energy model. A two-stage algorithm is proposed to solve MTB optimally, in which all nodes in the network are scheduled collaboratively to monitor all the targets in the monitoring field. Extensive simulations and real-world experiments verify the high performance of our algorithm and energy model.

Battery-free wireless sensor networks (BF-WSNs) have captured the interest of research community in recent years. Compared with traditional battery-powered WSNs (BP-WSNs), BF-WSNs can prolong the lifetime of the network by exploiting ambient energy. Many-to-many communication is widely used in many applications of WSNs and the problem of minimum-latency many-to-many communication scheduling in BF-WSNs is of great significance. However, this problem has not been studied yet. The existing algorithms for BP-WSNs and BF-WSNs are not suitable for minimum-latency many-to-many scheduling problem in BF-WSNs. Also, different from BP-WSNs, energy-bottleneck nodes with low recharge rate and high workload in BF-WSNs make the problem more challenging. To address these issues, in this article, we first study the problem of many-to-many scheduling in BF-WSNs with the purpose of minimizing communication latency. The problem is formally defined and proved to be NP-hard. The energy-adaptive and bottleneck-aware scheduling algorithm for many to many in BF-WSNs is proposed. The correctness and average latency of the proposed algorithm are carefully analyzed. Extensive simulations show that our algorithm has high performance, in terms of communication latency and energy usage ratio. Furthermore, we also extend the proposed algorithm to other network models.

Currently, a new type of wireless sensor network (WSN) named as battery-free network (BF-WSN), has been proposed and widely studied. Compared with traditional battery-powered WSN (BP-WSN), nodes in BF-WSN can harvest energy from ambient environment, prolonging the lifetime of the network greatly. Multicast is an important way for data dissemination in WSNs. The problem of minimum latency multicast scheduling (MLMS) that seeks a fast schedule without collision for data multicast has been studied extensively in BP-WSNs. However, existing algorithms are not suitable in BF-WSNs. In this paper we study the MLMS problem in BF-WSNs (BF-MLMS). To reduce latency, we investigate how to compute the end-to-end transmission delay. By considering both energy supply and collision, we propose centralized and distributed algorithms for constructing collision-free multicast trees in BF-WSNs. To the best of our knowledge, this is the first work to consider the BF-MLMS problem. Simulation results verify our protocols have high performance in terms of multicast latency and message volume.