In this work, a flexible and extensive digital platform for Smart Homes is presented, exploiting the most advanced technologies of the Internet of Things, such as Radio Frequency Identification, wearable electronics, Wireless Sensor Networks, and Artificial Intelligence. Thus, the main novelty of the paper is the system-level description of the platform flexibility allowing the interoperability of different smart devices. This research was developed within the framework of the operative project HABITAT (Home Assistance Based on the Internet of Things for the Autonomy of Everybody), aiming at developing smart devices to support elderly people both in their own houses and in retirement homes, and embedding them in everyday life objects, thus reducing the expenses for healthcare due to the lower need for personal assistance, and providing a better life quality to the elderly users.

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Space industry upstarts are deploying thousands of satellites to offer global Internet service. These plans promise large improvements in coverage and latency, and could fundamentally transform the Internet. But what if this transformation extends beyond network transit into a new type of computing service? What if each satellite, in addition to serving as a network router, also offers cloud-like compute, making the new constellations not just global Internet service providers, but at the same time, a new breed of cloud providers offering "compute where you need it"? We examine, qualitatively and quantitatively, the opportunities and challenges of such in-orbit computing. Several applications could benefit from it, including content distribution and edge computing; multiuser gaming, co-immersion, and collaborative music; and processing space-native data. Adding computing hardware to a satellite does not seem prohibitive in terms of weight, volume, and space hardening, but the required power draw could be substantial. Another challenge stems from the dynamics of low Earth orbit: a specific satellite is only visible to a ground station for minutes at a time, thus requiring care in managing stateful applications. Our exploration of these trade-offs suggests that this "outlandish" proposition should not be casually dismissed, and may merit deeper engagement from the research community. CCS CONCEPTS • Networks → Cloud computing.

While video streaming algorithms are a hot research area, with interesting new approaches proposed every few months, little is known about the behavior of the streaming algorithms deployed across large online streaming platforms that account for a substantial fraction of Internet traffic. We thus study adaptive bitrate streaming algorithms in use at 10 such video platforms with diverse target audiences. We collect traces of each video player's response to controlled variations in network bandwidth, and examine the algorithmic behavior: how risk averse is an algorithm in terms of target buffer; how long does it takes to reach a stable state after startup; how reactive is it in attempting to match bandwidth versus operating stably; how efficiently does it use the available network bandwidth; etc. We find that deployed algorithms exhibit a wide spectrum of behaviors across these axes, indicating the lack of a consensus one-size-fitsall solution. We also find evidence that most deployed algorithms are tuned towards stable behavior rather than fast adaptation to bandwidth variations, some are tuned towards a visual perception metric rather than a bitrate-based metric, and many leave a surprisingly large amount of the available bandwidth unused.

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We identify new opportunities in video streaming, involving the joint consideration of offline video chunking and online rate adaptation. We observe that due to a video's complexity varying over time, certain parts are more likely to cause performance impairments during playback with a particular rate adaptation algorithm. To address this, we propose careful use of variable-length video segments, and augmentation of certain segments with additional bitrate tracks. The key novelty of SEGUE is in making these decisions based on the video's time-varying complexity and the expected rate adaptation behavior over time. We propose and implement several methods for such adaptation-aware chunking. Our results show that SEGUE substantially reduces rebuffering and quality fluctuations, while maintaining video quality delivered; SEGUE improves QoE by 9% on average, and by 22% in low-bandwidth conditions. Beyond our specific approach, we view our problem framing as a first step in a new thread on algorithmic and design innovation in video streaming, and leave the reader with several interesting open questions.