Continuous crowdsensing in Mobile CrowdSensing (MCS) involves ongoing monitoring to gather real-time data over extended periods. A key challenge is determining appropriate intervals between consecutive data samplings to capture temporal variations, especially in unknown dynamic environments. Traditional Age-of-Information (AoI) driven methods maintain data freshness but can be costly and result in data redundancy. To address this, we integrate the AoI metric with information entropy difference to create a novel indicator, Composite Data Value (CDV), balancing data freshness and redundancy. Based on it, we investigate the online adaptive sampling problem for continuous crowdsensing in unknown dynamic environments. This problem is challenging due to the vast space of sensing strategies, difficulty in estimating rewards with unknown distributions and varying rates of change, and the degradation of optimal strategies as the environment evolves. Using a multi-armed bandit framework, we propose AdaScs, an online adaptive sampling optimization approach to maximize long-term CDV performance. First, AdaScs develops compact sensing strategies through limited trials. Then, it adaptively performs online sampling based on evolving reward estimations, identifying optimal strategies, detecting environmental drifts, updating strategies, and adjusting cycle lengths. Our results show that AdaScs outperforms all baselines, with accuracy increasing by 22.6% and reaction time at last improving by 54.6%.

We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge. Ego-Exo4D centers around simultaneously-captured egocentric and exocentric video of skilled human activities (e.g., sports, music, dance, bike repair). 740 participants from 13 cities worldwide performed these activities in 123 different natural scene contexts, yielding long-form captures from 1 to 42 minutes each and 1,286 hours of video combined. The multimodal nature of the dataset is unprecedented: the video is accompanied by multichannel audio, eye gaze, 3D point clouds, camera poses, IMU, and multiple paired language descriptions—including a novel “expert commentary” done by coaches and teachers and tailored to the skilled-activity domain. To push the frontier of first-person video understanding of skilled human activity, we also present a suite of benchmark tasks and their annotations, including fine-grained activity understanding, proficiency estimation, cross-view translation, and 3D hand/body pose. All resources are open sourced to fuel new research in the community. https://ego-exo4d-data.org/

Spatial Crowdsourcing (SC), which outsources location-dependent tasks to workers for physical completion, is gaining popularity. Recently, more complex tasks have emerged that require a group of workers collaborating in a coalition. Several pioneering studies have examined this issue using the server assigned tasks mode from an overall perspective, such as maximizing the total benefits of all workers. Unfortunately, maximizing the overall benefit does not necessarily align with maximizing individual benefits. In practice, crowd workers are often self-interested and autonomous, making decisions based on their personal perspectives. In this article, under the worker selected tasks mode, we investigate an important problem: Selfish Workers Coalition Formation (SWCF) problem in SC. Here, selfish workers autonomously form coalitions to accomplish tasks to maximize their individual benefits. Achieving a stable coalition formation for SWCF problem requires balancing cooperation and competition. First, we transform the SWCF problem into a hedonic coalition formation game using a devised exploited skills-based reward distribution model. Subsequently, we propose a distributed algorithm HCFTA and prove its Nash stability and performance bounds. Additionally, to enhance coalition formation efficiency, we propose a Markov blanket coloring parallel optimization algorithm MCPHCF . Extensive experiments demonstrate the superiority of the proposed methods on both synthetic and real-world datasets.

Integrating Unmanned Aerial Vehicles (UAVs) and autonomous vehicles within the crowdsensing paradigm offers a promising approach to collecting environment-relevant data over large spatial areas, particularly in disaster-stricken or high-risk regions. However, deploying crowdsensing systems in emergency response scenarios presents substantial challenges. The lack of prior environmental knowledge complicates the selection of optimal sensing locations and strategy optimization, often relying on costly trial-and-error methods. Additionally, realtime decision-making is critical in such scenarios, requiring the rapid identification of optimal deployment strategies. Yet, the absence of prior knowledge further complicates the assessment of the optimality of these strategies. This gap remains inadequately addressed in existing research. To address this, we present the first framework that frames these challenges as a rapid online strategy optimization problem for mobile agent-based crowdsensing systems operating in unknown environments during emergency response scenarios. We propose DGap-UCB, a novel approach within the multi-armed bandit (MAB) framework, which efficiently identifies the optimal sensing strategy with highconfidence guarantees. Leveraging the Upper-Confidence Bound (UCB) technique, DGap-UCB iteratively refines strategy selection based on reward feedback. To accelerate learning, we introduce a gap-confidence pair (Δt, δt)-based Quick Stopping Criterion, enabling rapid and high-confidence identification of the optimal strategy. Extensive experiments on both synthetic and real-world datasets demonstrate the superiority of DGap-UCB over stateof-the-art techniques

We present Ego-Exo4D, a diverse, large-scale multi-modal multiview video dataset and benchmark challenge. Ego-Exo4D centers around simultaneously-captured ego-centric and exocentric video of skilled human activities (e.g., sports, music, dance, bike repair). 740 participants from 13 cities worldwide performed these activities in 123 different natural scene contexts, yielding long-form captures from 1 to 42 minutes each and 1,286 hours of video combined. The multimodal nature of the dataset is un-precedented: the video is accompanied by multichannel audio, eye gaze, 3D point clouds, camera poses, IMU, and multiple paired language descriptions-including a novel “expert commentary” done by coaches and teachers and tailored to the skilled-activity domain. To push the frontier of first-person video understanding of skilled human activity, we also present a suite of benchmark tasks and their annotations, including fine-grained activity understanding, proficiency estimation, cross-view translation, and 3D hand/body pose. All resources are open sourced to fuel new research in the community.

Recently, a novel Mobile Agent-based Mobile Crowdsensing (MA-MCS) paradigm has emerged, which utilizes unmanned aerial vehicles, pilotless automobiles, etc., to implement data sensing from surroundings. Practically, in time-sensitive applications the freshness of collected data is of critical importance. However, restricted by the intermittent bandwidth of wireless channels, the generated data might not be successfully transferred to the application server for further processing. Fortunately, benefit from the hosted edge computing capability, it is feasible to locally process the sensing data and then transfer the concise result. In this paper, we study a novel and practical problem in the Age-of-Information (AoI) driven MA-MCS systems in wireless edge computing. Specifically, restricted by the issues of battery recharging, etc., we strive to schedule the mobile agents and make transmission/computing decisions in a collaborative manner, aiming at minimizing the total AoI threshold violation. To this end, we propose a two-phase deep reinforcement learning-based solution, namely KCDDQN, including spatiotemporal clustering and decision-making learning. Finally, extensive simulations are carried out to demonstrate the effectiveness of our proposed KCDDQN approach.

High‐efficiency video coding (HEVC), which is the latest video coding standard, is expected to have a dominant position in the market in the near future. However, most video resources are now encoded using the H.264/AVC standard. Consequently, there is a growing need for fast H.264/AVC to HEVC transcoders to facilitate the migration to the updated standard. This paper proposes a fast H.264/AVC to HEVC transcoding scheme, which constructs a three‐level classifier using an optimised tree‐augmented Naive Bayesian approach to predict the HEVC coding unit depth. A feature selection method is then proposed to improve prediction accuracy. A motion vector (MV) calculation method is also proposed to reduce the complexity of MV prediction in HEVC by reusing MVs from H.264/AVC. Experimental results show that, compared with other state‐of‐the‐art transcoding algorithms, the proposed algorithm considerably reduces coding complexity while causing only negligible rate‐distortion degradation.

This paper presents a method to predict the future movements (location and gaze direction) of basketball players as a whole from their first person videos. The predicted behaviors reflect an individual physical space that affords to take the next actions while conforming to social behaviors by engaging to joint attention. Our key innovation is to use the 3D reconstruction of multiple first person cameras to automatically annotate each others visual semantics of social configurations. We leverage two learning signals uniquely embedded in first person videos. Individually, a first person video records the visual semantics of a spatial and social layout around a person that allows associating with past similar situations. Collectively, first person videos follow joint attention that can link the individuals to a group. We learn the egocentric visual semantics of group movements using a Siamese neural network to retrieve future trajectories. We consolidate the retrieved trajectories from all players by maximizing a measure of social compatibility-the gaze alignment towards joint attention predicted by their social formation, where the dynamics of joint attention is learned by a long-term recurrent convolutional network. This allows us to characterize which social configuration is more plausible and predict future group trajectories.

Energy harvesting sensor nodes based on real nonvolatile processors are demonstrated to show the desirable characteristics of those systems, such as no battery, zero stand-by power, microsecond-scale sleep and wake-up time, high resilience to random power failures and fine-grained power management. Furthermore, we show its applications to a distributed moving object detection system, one of novel nonvolatile computing systems.