With the rapid growth of mobile devices and applications, a prodigious number of spatio-temporal data are generated constantly. To process these data for applications like traffic forecasting, existing spatio-temporal systems rely on the move-data-to-computation paradigm. However, this approach incurs significant data movement overhead between hosts and storage devices, particularly when a spatio-temporal query is executed on a non-preferred data layout or when the query has a small result size due to its inherent nature. To address this issue, this work introduces Groundhog, an efficient in-storage computing technique designed specifically for spatio-temporal queries, aimed at reducing unnecessary data movement and computations. Groundhog introduces three key designs for efficient in-storage computing: (i) a self-contained and segment-based storage model, which is lightweight for in-storage computing and enables fine-grained pruning for spatio-temporal queries; (ii) a set of fine-grained techniques to optimize query processing inside storage devices for spatio-temporal queries; and (iii) an in-storage-computing-aware query planner, which offloads spatio-temporal queries in a fine-grained manner using a cost-based approach. We implemented Groundhog on real hardware and demonstrated how to apply fine-grained techniques to accelerate various spatio-temporal queries. Extensive experiments conducted on real-world datasets demonstrate that Groundhog achieves significant performance improvements, with latency reductions of up to$81\%$for widely used spatio-temporal queries compared to host computing solutions.

Massive spatio-temporal data are continuously generated by various moving objects. To process these data for applications such as traffic forecasting, existing spatio-temporal systems all employ the move-data-to-computation paradigm. However, this approach suffers from significant data movement overhead between hosts and drives. To address this issue, this work introduces Groundhog, an efficient in-storage computing technique designed specifically for spatio-temporal queries, aimed at reducing unnecessary data movement and computations. Groundhog introduces three key designs for efficient in-storage computing: (i) a self-contained and segment-based storage model, which is lightweight for in-storage computing and enables fine-grained pruning for spatio-temporal queries; (ii) a set of fine-grained techniques to optimize query processing inside storage devices for spatio-temporal queries; and (iii) an in-storage-computing-aware query planner, which offloads spatio-temporal queries in a fine-grained manner using a cost-based approach. We implemented Groundhog on a real hardware board. Extensive experiments conducted on real-world datasets demonstrate that Groundhog achieves significant performance improvements, with latency reductions of up to 81 % for widely used spatio-temporal queries compared to host computing solutions.

Mobile devices have gained immense popularity in recent years and have become an integral part of people’s daily lives. This surge in usage presents new challenges for energy conservation, particularly concerning the screens of mobile phones. Not only are screens being used for longer durations, but also their adjustment needs to be dynamically conducted during runtime. Existing approaches for display energy optimization mainly focus on the display content while disregarding user interactions. This limitation prevents the full exploitation of opportunities to reduce screen energy consumption during runtime. Detection-based display energy optimization approaches incorporate user interactions to some extent. However, these approaches require additional hardware and offer relatively coarse-grained control. To address these challenges, we propose QLEO, a Q-learning-based runtime display energy optimization scheme for Android systems. QLEO takes both the display and user interaction features into account. By dynamically classifying the user state into active and idle states based on these features, we enable adaptive screen brightness adjustments according to the user’s current state. We formulate display energy optimization as a constrained dynamic optimization problem and propose an integrated Q-learning model to effectively solve it. QLEO is designed as a module within the hardware abstraction layer, capable of obtaining display and user interaction features from the upper Android framework, and directly controlling the screen brightness through the underlying Linux kernel. We have implemented QLEO on real hardware and released the source code for public access. The experimental results demonstrate that QLEO achieves significant energy reduction without compromising the user experience.

Winternitz one-time signature (WOTS) is a quantum-resistant signature mechanism that has been widely used in direct acyclic graph (DAG)-based blockchain systems. However, it needs to generate a unique private/public key pair for each transaction, which severely limits blockchain performance by performing a time-consuming unique address checking process when generating transactions. This article proposes a bloom-filter-based approach, called ABACUS, to optimize the unique address checking process in WOTS. In ABACUS, we separate the large address space into multiple small subspaces and apply bloom filters to perform uniqueness checking for all addresses in one subspace. Specifically, we propose a two-level address space mechanism to strike a balance between the checking efficiency and the memory/storage space overhead of the bloom filter design. A bucket-based scalable bloom filter (SBF) design is proposed to match the growth of used addresses with efficient I/O access by storing all sub-bloom-filters together in one bucket. To further reduce disk I/Os, ABACUS incorporates an in-memory write buffer and a read-only cache. To reclaim wasted addresses, we also propose a layered address recycling mechanism by efficiently checking and reusing unused addresses that were identified as used due to false positives of bloom filters. We have implemented a fully functional prototype of ABACUS and integrated it into IOTA, a widely used DAG-based blockchain system. A series of experiments have been conducted on a private IOTA system. Experimental results show that ABACUS can significantly reduce the transaction generation time by up to four orders of magnitude, and achieve up to$3\times $boost on the system throughput with more than 80% space-saving. We have released the open-source code of ABACUS for public access.

Mobile devices often face runtime configuration changes, such as screen orientation changes, screen resizing, and language switching. The current Android design adopts a restarting-based solution to load the corresponding resources according to the new configuration. Therefore, application developers must explicitly deal with state preservation and restoration brought about by the activity restarting. Otherwise, the runtime change will cause state loss and even app crash issues. To solve the runtime change handling issues, we propose RCHDroid, a transparent runtime change handling approach for apps at the Android system level. When a configuration change occurs, we do not restart the activity, but instead, create a new activity according to the new configuration and migrate states from the old one to the new one, while putting the old activity into an inactive mode. We propose a lazy-migration scheme to handle asynchronous tasks that remain working on the old activity, which migrates the result events when the asynchronous tasks return. We have implemented a prototype of RCHDroid with real hardware and released the source code for public access. Overall, using RCHDroid, existing apps can handle runtime configuration changes without any modifications and save the runtime change handling time by 25.46%.

Multidisplay Android systems are emerging and require new display management. The current Android design exposes and offloads the multiscreen display management to app developers. As a consequence, apps cannot utilize multiscreen without redevelopment. This article proposes MSA, a novel Android app development framework for transparent multiscreen support. MSA cooperates with existing Android system services to map the single-screen views provided by apps to multiple screens and maps input events backward correspondingly. Following the new framework, the app development of single-screen Android systems and multiscreen Android systems are unified. Thus, both existing apps and future app development can directly utilize multiple screens with single-screen-based techniques, such as multiwindow and resizable-activity, instead of redevelopment through multiscreen-dedicated new methods such as presentation classes. We have implemented an MSA prototype with real hardware and released the source code for public access. Experimental results show that using MSA, without any modifications, existing apps can directly run and fully exploit multiple screens with better performance and less overhead compared with the state-of-the-art multiscreen Android system.

Dual-screen foldable Android smartphones such as Microsoft Surface Duo are emerging. However, due to its internal design, the Android framework cannot support combined mode, by which two screens can be integrated into one, without modifications, thus requiring new display management. Android introduces a new dual-screen display method, called presentation class, to handle this problem; however, existing apps need to be redesigned based on the new class. In this paper, we propose Dual-Screen Android (DSA), a semantics-aware display scheme for dual-screen foldable Android smartphones. DSA is transparent to apps, thus requiring no changes for existing apps, and incurs minimum modification to the Android framework. Specifically, inside Android, DSA duplicates and maps single-screen views provided by apps to dual screens and maps input events backward correspondingly, thus being transparent to apps. DSA also opens the door to store other hardware states (e.g., screen brightness, screen-touch events, etc.), which can be utilized to bridge other software/hardware semantic gaps for further system optimization. To demonstrate this, we design an effective Q-learning energy optimization scheme within DSA to control screen brightness based on predicted users' behaviors. We have implemented DSA based on Android 10 with real hardware and conducted a series of experiments. Experimental results show that based on DSA, without any modifications, existing apps can directly run and fully exploit dual screens with negligible time and memory overheads, and our energy optimization scheme can effectively reduce energy. We have released the open-source code of DSA for public access.