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As Point Clouds (PCs) gain popularity in processing millions of data points for 3D rendering in many applications, efficient data compression becomes a critical issue. This is because compression is the primary bottleneck in minimizing the latency and energy consumption of existing PC pipelines. Data compression becomes even more critical as PC processing is pushed to edge devices with limited compute and power budgets. In this paper, we propose and evaluate two complementary schemes, intra-frame compression and inter-frame compression, to speed up the PC compression, without losing much quality or compression efficiency. Unlike existing techniques that use sequential algorithms, our first design, intra-frame compression, exploits parallelism for boosting the performance of both geometry and attribute compression. The proposed parallelism brings around 43.7× performance improvement and 96.6% energy savings at a cost of 1.01× larger compressed data size. To further improve the compression efficiency, our second scheme, inter-frame compression, considers the temporal similarity among the video frames and reuses the attribute data from the previous frame for the current frame. We implement our designs on an NVIDIA Jetson AGX Xavier edge GPU board. Experimental results with six videos show that the combined compression schemes provide 34.0× speedup compared to a state-of-the-art scheme, with minimal impact on quality and compression ratio. 

IoT devices can be used to complete a wide array of physical tasks, but due to factors such as low computational resources and distributed physical deployment, they are susceptible to a wide array of faulty behaviors. Many devices deployed in homes, vehicles, industrial sites, and hospitals carry a great risk of damage to property, harm to a person, or breach of security if they behave faultily. We propose a general fault handling system named IoTRepair, which shows promising results for effectiveness with limited latency and power overhead in an IoT environment. IoTRepair dynamically organizes and customizes fault-handling techniques to address the unique problems associated with heterogeneous IoT deployments. We evaluate IoTRepair by creating a physical implementation mirroring a typical home environment to motivate the effectiveness of this system. Our evaluation showed that each of our fault-handling functions could be completed within 100 milliseconds after fault identification, which is a fraction of the time that state-of-the-art fault-identification methods take (measured in minutes). The power overhead is equally small, with the computation and device action consuming less than 30 milliwatts. This evaluation shows that IoTRepair not only can be deployed in a physical system, but offers significant benefits at a low overhead. 

With the advent of 5G, supporting high-quality game streaming applications on edge devices has become a reality. This is evidenced by a recent surge in cloud gaming applications on mobile devices. In contrast to video streaming applications, interactive games require much more compute power for supporting improved rendering (such as 4K streaming) with the stipulated frames-per second (FPS) constraints. This in turn consumes more battery power in a power-constrained mobile device. Thus, the state-of-the-art gaming applications suffer from lower video quality (QoS) and/or energy efficiency. While there has been a plethora of recent works on optimizing game streaming applications, to our knowledge, there is no study that systematically investigates the design pairs on the end-to-end game streaming pipeline across the cloud, network, and edge devices to understand the individual contributions of the different stages of the pipeline for improving the overall QoS and energy efficiency. In this context, this paper presents a comprehensive performance and power analysis of the entire game streaming pipeline consisting of the server/cloud side, network, and edge. Through extensive measurements with a high-end workstation mimicking the cloud end, an open-source platform (Moonlight-GameStreaming) emulating the edge device/mobile platform, and two network settings (WiFi and 5G) we conduct a detailed measurement-based study with seven representative games with different characteristics. We characterize the performance in terms of frame latency, QoS, bitrate, and energy consumption for different stages of the gaming pipeline. Our study shows that the rendering stage and the encoding stage at the cloud end are the bottlenecks to support 4K streaming. While 5G is certainly more suitable for supporting enhanced video quality with 4K streaming, it is more expensive in terms of power consumption compared to WiFi. Further, fluctuations in 5G network quality can lead to huge frame drops thus affecting QoS, which needs to be addressed by a coordinated design between the edge device and the server. Finally, the network interface and the decoder units in a mobile platform need more energy-efficient design to support high quality games at a lower cost. These observations should help in designing more cost-effective future cloud gaming platforms.