Low-Earth-Orbit (LEO) satellite constellations are becoming integral to 6G infrastructure, but increasing in-orbit computation accelerates battery degradation and raises sustainability concerns. Meanwhile, renewable-heavy regions worldwide experience persistent energy curtailment due to transmission bottlenecks, leaving substantial clean energy stranded near generation sites. We identify a satellite-grid co-design opportunity: adaptively offloading task-critical data from satellite to data centers co-located with renewable power plants. However, realizing this vision requires jointly considering intermittent and capacity-limited communication windows, as well as time-varying electricity budgets. In this paper, we propose SQSO, a Sustainable and QoS-aware Satellite Offloading framework that models per-interval task offloading as a constrained optimization over dynamic topology and electricity prices. Under this framework, we design $\text{AO}^2$, an adaptive offloading orchestration algorithm to solve the formulated optimization problem. Using Starlink-scale simulations and real-world electricity price traces, $\text{AO}^2$ reduces energy consumption by up to 76.03% and battery life consumption by up to 76.85% compared to state-of-the-art schemes, while also lowering task delay. This work highlights that sustainable scaling of LEO constellations requires co-design of space networking and renewable energy infrastructure, while our solution promotes renewable-aware task offloading and cross-domain collaboration for space-energy integration in the 6G era.

Real-time imaging sonar is crucial for underwater monitoring where optical sensing fails, but its use is limited by low uplink bandwidth and severe sonar-specific artifacts (speckle, motion blur, reverberation, acoustic shadows) affecting up to 98% of frames. We present SCOPE, a self-supervised framework that jointly performs compression and artifact correction without clean–noise pairs or synthetic assumptions. SCOPE combines (i) Adaptive Codebook Compression (ACC), which learns frequency-encoded latent representations tailored to imaging sonar, with (ii) Frequency-Aware Multiscale Segmentation (FAMS), which decomposes frames into low-frequency structure and sparse high-frequency dynamics while suppressing rapidly fluctuating artifacts. A hedging training strategy further guides frequency-aware learning using low-pass proxy pairs generated without labels. Evaluated on months of in-situ ARIS sonar data, SCOPE achieves a structural similarity index (SSIM) of 0.77, representing a 40% improvement over prior self-supervised denoising baselines, at bitrates down to ≤ 0.0118 bpp. It reduces uplink bandwidth by more than 80% while improving downstream detection. The system runs in real time, with 3.1 ms encoding on an embedded GPU and 97 ms full multi-layer decoding on the server end. SCOPE has been deployed for months in three Pacific Northwest rivers to support real-time salmon enumeration and environmental monitoring in the wild. Results demonstrate that learning frequency-structured latents enables practical, low-bitrate sonar streaming with preserved signal details under real-world deployment conditions.

Low Earth Orbit (LEO) satellite networks such as Starlink and Project Kuiper are increasingly integrated with cloud infrastructures, forming an important internet backbone for global web services. By extending connectivity to remote regions, oceans, and disaster zones, these networks enable reliable access to applications ranging from real-time WebRTC communication to emergency response portals. Yet the resilience of these web services is threatened by space radiation: it degrades hardware, drains batteries, and disrupts continuity, even if the space-cloud integrated providers use machine learning to analyze space weather and radiation data. Specifically, conventional fixes like altitude adjustments and thermal annealing consume energy; neglecting this energy use results in deep discharge and faster battery aging, whereas sleep modes risk abrupt web session interruptions. Efficient network-layer mitigation remains a critical gap. We propose RALT (Radiation-Aware LEO Transmission), a control-plane solution that dynamically reroutes traffic during radiation events, accounting for energy constraints to minimize battery degradation and sustain service performance. Our work shows that unlocking space-based web services' full potential for global reliable connectivity requires rethinking resilience through the lens of the space environment itself.

Wild salmon are essential to the ecological, economic, and cultural sustainability of the North Pacific Rim. Yet climate variability, habitat loss, and data limitations in remote ecosystems that lack basic infrastructure support pose significant challenges to effective fisheries management. This project explores the integration of multimodal foundation AI and expert-in-the-loop frameworks to enhance wild salmon monitoring and sustainable fisheries management in Indigenous rivers across Pacific Northwest. By leveraging video and sonar-based monitoring, we develop AI-powered tools for automated species identification, counting, and length measurement, reducing manual effort, expediting delivery of results, and improving decision-making accuracy. Expert validation and active learning frameworks ensure ecological relevance while reducing annotation burdens. To address unique technical and societal challenges, we bring together a cross-domain, interdisciplinary team of university researchers, fisheries biologists, Indigenous stewardship practitioners, government agencies, and conservation organizations. Through these collaborations, our research fosters ethical AI co-development, open data sharing, and culturally informed fisheries management.

Recently, Low Earth orbit Satellite Networks (LSNs) have been suggested as a critical and promising component toward high-bandwidth and low-latency global coverage in the upcoming 6G communication infrastructure. SpaceX’s Starlink is arguably the largest and most operational LSN to date. There have been practical uses of Starlink across diverse networked applications, including those with stringent demands, such as multimedia applications. Given the mixed and inconsistent feedback from end users, it remains unclear whether today’s LSNs, in particular Starlink, are ready for realtime multimedia. In this article, we present a systematic measurement study on realtime multimedia services over Starlink, seeking insights into their operations and performance in this new generation of networking. Our findings demonstrate that Starlink can handle most video-on-demand (VoD) and live-streaming services with properly configured buffers but suffers from video pauses or audio cut-offs during interactive videoconferencing. We identify the key factors that impact the performance of LSN, particularly for multimedia services, including satellite switching, routing strategies, and weather conditions. Our findings offer valuable hints into future enhancements for multimedia services over LSNs. Specifically, we further propose a Weather Aware Buffer Based Rate Adaption algorithm based on our observations on weather impacts, which is capable of maximizing the quality of experience for VoD applications with seamless integration of dynamic weather conditions.

Inter-Satellite Links (ISLs) are pivotal for delivering global connectivity services and optimizing resource utilization in 6 G and beyond. However, delivering effective topology control services through ISL provisioning faces critical challenges insustainabilityandreliability. Reducing ISLs can conserve energy and extend satellite battery life for Low-Earth-Orbit (LEO) satellites where replacing batteries is impractical. Conversely, increasing ISLs can enhance service reliability but may lead to uneven traffic distribution, overloading nodes, and accelerating battery degradation, ultimately degrading the quality of 6 G services. To tackle this dilemma, we propose TASRI—a service-oriented framework forTraffic-Aware, Sustainable, and Reliable ISL provisioning. TASRI provides a dynamic topology control service by partitioning network topologies into logical zones, enabling flexible ISL activation and deactivation to adapt to varying service demands, ensuring efficient resource utilization and dynamic service orchestration. Using a sustainability-oriented weight model, we formulate the topology control service optimization problem and introduce a scalable on-demand topology evolving algorithm with a bounded approximation ratio. Extensive real-world deployment-based simulation results show that, compared to the state-of-the-art, our TASRI can substantially reduce battery life consumption, while achieving comparable reliability and excellent scalability with considerably fewer ISLs or ISL handovers.

Inter-Satellite Links (ISLs) are key for worldwide communication and efficient use of space networks in the future 6G network. However, they face challenges in sustainability and reliability. Reducing ISLs saves energy and extends battery life, which is critical since satellite batteries are hard to replace. More ISLs, however, can make the system more reliable but at the cost of higher energy use, especially problematic when traffic is uneven, speeding up battery wear. To tackle this dilemma, we for the first time develop a Traffic-Aware, Sustainable and Reliable ISL provisioning (TASRI) framework for LEO satellite constellation networks. In TASRI, ISLs can be flexibly switched on and off to better accommodate various traffic conditions as well as reliability and sustainability. We formulate the ISL provisioning problem based on the sustainability-oriented weight model and then propose an on-demand topology evolving algorithm. Extensive real-world deployment-based simulation results show that, compared to the state-of-the-art, our TASRI can substantially reduce battery life consumption, while achieving comparable reliability with considerably fewer ISLs.

Live streaming has experienced significant growth recently. Yet this rise in popularity contrasts with the reality that a substantial segment of the global population still lacks Internet access. The emergence of Low Earth orbit Satellite Networks (LSNs), such as SpaceX's Starlink and Amazon's Project Kuiper, presents a promising solution to fill this gap. Nevertheless, our measurement study reveals that existing live streaming platforms may not be able to deliver a smooth viewing experience on LSNs due to frequent satellite handovers, which lead to frequent video rebuffering events. Current state-of-the-art learning-based Adaptive Bitrate (ABR) algorithms, even when trained on LSNs' network traces, fail to manage the abrupt network variations associated with satellite handovers effectively. To address these challenges, for the first time, we introduce Satellite-Aware Rate Adaptation (SARA), a versatile and lightweight middleware that can seamlessly integrate with various ABR algorithms to enhance the performance of live streaming over LSNs. SARA intelligently modulates video playback speed and furnishes ABR algorithms with insights derived from the distinctive network characteristics of LSNs, thereby aiding ABR algorithms in making informed bitrate selections and effectively minimizing rebuffering events that occur during satellite handovers. Our extensive evaluation shows that SARA can effectively reduce the rebuffering time by an average of 39.41% and slightly improve latency by 0.65% while only introducing an overall loss in bitrate by 0.13%.

Sonar radar captures visual representations of underwater objects and structures using sound wave reflections, making it essential for exploration, mapping, and continuous surveillance in wild ecosystems. Real-time analysis of sonar data is crucial for time-sensitive applications, including environmental anomaly detection and in-season fishery management, where rapid decision-making is needed. However, the lack of both relevant datasets andpre-trained DNN models, coupled with resource limitations in wild environments, hinders the effective deployment and continuous operation of live sonar analytics.

Recently, Low Earth orbit Satellite Networking (LSN) has been suggested as a critical and promising component toward high-bandwidth and low-latency global coverage in the upcoming 6G communication infrastructure. SpaceX's Starlink is arguably the largest and most operable LSN to date. There have been practical uses of Starlink with diverse networked applications, including multimedia applications of stringent demands. Given the mixed and inconsistent feedbacks from end users, it remains unclear whether today's LSNs, in particular, Starlink, have been ready for realtime multimedia. In this paper, we present a systematic measurement study on realtime multimedia services over Starlink, seeking insights into their operations and performance in this new generation networking. Our findings demonstrate that Starlink can effectively handle most video-on-demand (VoD) and live-streaming services with properly configured buffers, but suffer from video pauses or audio cut-offs during interactive video conferencing, especially in extreme weather. We also examine the impact of satellite switching and evolution of satellite routing strategies, offering hints into the future enhancements for multimedia services and for LSNs.