In this paper, we leverage unmanned aerial vehicles (UAVs) to enhance mobile crowd sensing (MCS) by addressing two critical challenges: uncontrollable data quality and inevitable unsensed points of interest (PoIs). We introduce a UAV-assisted method to deal with these challenges. To ensure the accuracy of sensing data contributed by human participants, the proposed truth discovery method utilizes UAV-collected sensing data as few-shot samples to train the truth discovery model, which is then employed to calibrate sensing data solely collected by human participants. Additionally, to meet the sensing coverage requirement, we present a method that predicts data values for unsensed PoIs by utilizing their historical sensing data and the sensed neighboring PoIs information. The method employs a graph neural network to capture spatio-temporal relationships of the sensing data, facilitating accurate estimation of unsensed PoIs. Through extensive simulations, our approaches demonstrate superior performance compared to existing methods, showcasing the potential of UAV-assisted MCS for overcoming challenges and enhancing data collection efficiency in various domains.

Task scheduling and load balancing in heterogeneous computing environments has been a challenge for long, especially when dealing with multiple types of task input batches. In this scenario, existing methods cannot take into account both the high efficiency of task processing and the full utilization of cluster resources. However, the rise of artificial intelligence methods provides a new way to solve this problem. In this paper, we design a type-aware task scheduling method based on deep reinforcement learning to tackle multiple types of tasks in heterogeneous computing environment. First, we adopt prioritized dueling double deep q-learning network to make action decisions for each batch of input tasks. Then we build a task type prediction neural network to predict the task type of the input task, and then use the Monte Carlo algorithm based on reward value to realize the load balancing of the scheduled cluster. To verify the effectiveness of our proposed method, we use a widely used dataset Alibaba cluster trace dataset for our experiments. Experimental results show that our proposed algorithm can significantly shorten the average makespan of task batches and achieve better load balancing effect compared with other existing solutions.

Task scheduling and load balancing problem of heterogeneous computing environment (HCE) is getting more and more attention these days and has become a research hotspot in this field. The task scheduling and load balancing problem of heterogeneous environment, which refers to assigning a set of tasks to a specific set of machines with different hardware and different computing performance with the goal of minimizing task processing time and keeping load balance among machines, has been proved to be an NP-complete problem. The development of artificial intelligence provides new ideas to solve this problem. In this paper, we propose a novel task scheduling and load balancing method based on optimized deep reinforcement learning in HCE. First, we formulate task scheduling problem as a Markov decision process and then adopt a dueling double deep Q-learning network to search the optimal task allocation solution. Then we use two well-known large-scale cluster data sets Google Cloud Jobs data set and Alibaba Cluster Trace data set to validate our approach. The experimental results show that compared with other existing solutions, our proposed method can achieve much shorter task response time and better load balancing effect.

This paper presents the design and performance of a gain-boosted LC voltage-controlled oscillator (VCO) topology for ultra-low-power applications. The proposed topology utilizes a second active gain stage to reduce startup power while maintaining acceptable phase noise for low-data-rate applications. The VCO consumes 167 μW from a 1.2 V supply when operating at 915 MHz and exhibits phase noise of -97.9 dBc/Hz at 1 MHz offset. It has a 4-bit digital tuning range of about 80 MHz and an analog tuning range of at least 22.5 MHz. The VCO also has a binary frequency-shift keying (BFSK) modulating system with 550 kHz shift. The VCO is realized in a 0.13 μm CMOS process and occupies 0.26 mm2of area.