Mobile applications have been seamlessly integrated into our daily lives. When using mobile devices, the energy efficiency of these applications plays a pivotal role in enhancing the user experience. However, it is noteworthy that incorporating power conservation strategies into the toolkit of user-interface (or UI) developers for mobile applications receives almost none research attention. To address the unique requirements for UI developers, this manuscript studies the fusion of power conservation techniques and UI guidance principles to formulate an innovative framework aimed at conserving power consumption within UI. The power conservation framework begins with the extraction of displayed component configuration, drawing from UI previews without depending on any development environment and deployment equipment, during the development phase. Subsequently, we evaluate the UI guidance of the displayed components, taking into consideration the human visual systems. To recommend a power-saving configuration to developers, the final step generates a power-saving configuration that not only curtails power consumption but also preserves the global and local guidance. To validate the efficacy of our framework, we conducted evaluations using eight distinct UI previews, including light and dark modes, on a commercial smart phone. The results obtained from these evaluations are very promising.

As mobile applications become more ingrained in our daily routines, there is a noticeable gap in incorporating power-saving strategies into the toolkit of user-interface (UI) designers. This paper explores the fusion of power reduction techniques and UI guidance principles to craft an innovative power-saving design. The method initiates by extracting visible element layouts and assessing UI guidance with human visual systems. Then, a power-saving design is created, designed to uphold global and local UI guidance. Evaluation results conducted using four distinct UI previews on a commercial smartphone are very promising.

This paper proposes an Intelligent Evaluation Agent (IEA) with knowledge graph construction based on the Large Language Model (LLM) and Trustworthy AI Dialogue Engine (TAIDE) for personalized Human-Machine Interactive Learning (HMIL). The intelligent agent will deal with multitasks such as learning data preparation and the learner’s data generation, preprocessing, analysis, and evaluation. Multi-modal data is collected from human-machine interactive activities and processed by an IEA to generate structured data stored in human learning repositories. The intelligent agent focuses on various temporal learning periods, such as macro, meso, and micro-level assessments by integrating Human Intelligence (HI) and Machine Intelligence (MI) results, with the MI-based Genetic Algorithm and Neural Network (GANN) learning mechanism employed to optimize the intelligent evaluation model. The learning data evaluation phase aims to identify a model that best fits the group’s learning behavior through HI-based evaluation and to train it further using MI, ensuring that the trained GANN-IEA model closely approximates the HI-based model. An LLM-based knowledge graph agent also supports the evaluation process by helping teachers analyze and visualize students’ learning progress. Experimental results demonstrate that students who study diligently gain knowledge and exhibit increased interest in learning through HMIL. However, the evidence also suggests that some students who excessively rely on Generative AI (GAI) to reproduce learning content without modification become less inclined to engage in diligent study. Additionally, the proposed IEA effectively reduces teachers’ workload in assessing students’ learning status at the end of the semester and supports personalized learning through the designed HMIL model.

Designing intelligent, tiny devices with limited memory is immensely challenging, exacerbated by the additional memory requirement of residual connections in deep neural networks. In contrast to existing approaches that eliminate residuals to reduce peak memory usage at the cost of significant accuracy degradation, this paper presents DERO, which reorganizes residual connections by leveraging insights into the types and interdependencies of operations across residual connections. Evaluations were conducted across diverse model architectures designed for common computer vision applications. DERO consistently achieves peak memory usage comparable to plain-style models without residuals, while closely matching the accuracy of the original models with residuals.

With the growing appeal of real-time interactions between physical views and virtual objects in augmented reality (AR) applications among contemporary users, optimizing power consumption is crucial for extending the battery life of mobile devices. This paper delves into methods for reducing power consumption specifically in mobile OLED devices when running AR applications, depending on user behaviors. Initially, we introduce a detection algorithm designed to accurately identify user status using cost-effective sensors. Subsequently, we present two dynamic configurations aimed at adjusting the display of physical views and virtual objects based on user visual attention of AR applications. The results of extensive experiments conducted on a commercial smartphone using an open-source AR application to assess the performance of the proposed power-saving design are highly promising.

Energy harvesting creates an emerging intermittent computing paradigm but poses new challenges for sophisticated applications such as intermittent deep neural network (DNN) inference. Although model compression has adapted DNNs to resource-constrained devices, under intermittent power, compressed models will still experience multiple power failures during a single inference. Footprint-based approaches enable hardware-accelerated intermittent DNN inference by tracking footprints, independent of model computations, to indicate accelerator progress across power cycles. However, we observe that the extra overhead required to preserve progress indicators can severely offset the computation progress accumulated by intermittent DNN inference. This work proposes the concept of model augmentation to adapt DNNs to intermittent devices. Our middleware stack, JAPARI, appends extra neural network components into a given DNN, to enable the accelerator to intrinsically integrate progress indicators into the inference process, without affecting model accuracy. Their specific positions allow progress indicator preservation to be piggybacked onto output feature preservation to amortize the extra overhead, and their assigned values ensure uniquely distinguishable progress indicators for correct inference recovery upon power resumption. Evaluations on a Texas Instruments device under various DNN models, capacitor sizes, and progress preservation granularities show that JAPARI can speed up intermittent DNN inference by 3× over the state of the art, for common convolutional neural architectures that require heavy acceleration.

The creation of context-aware services in pervasive computing environments has driven the wide development of wireless local area network (WLAN)-based indoor positioning systems. One of the main challenges in WLAN-based indoor positioning is the severe fluctuation of received signal strength (RSS), which may cause the RSS patterns to be mismatched and the positioning to be inaccurate. In this paper, an indoor positioning algorithm that combines the fingerprint scheme with mobility prediction is proposed. Since the mobility prediction is performed according to the moving speed and direction of the mobile client, the resulting location estimation is more stable compared to the use of RSS alone. Experimental results show that the proposed positioning algorithm can mitigate the impact of the RSS fluctuation and has better positioning accuracy and stability than previous fingerprint-based approaches.

Current peripheral execution approaches for intermittently powered systems require full access to the internal hardware state for checkpointing or rely on application-level energy estimation for task partitioning to make correct forward progress. Both requirements present significant practical challenges for energy-harvesting, intelligent edge Internet-of-Things devices, which perform hardware-accelerated deep neural network (DNN) inference. Sophisticated compute peripherals may have an inaccessible internal state, and the complexity of DNN models makes it difficult for programmers to partition the application into suitably sized tasks that fit within an estimated energy budget. This article presents the concept of inference footprinting for intermittent DNN inference, where accelerator progress is accumulatively preserved across power cycles. Our middleware stack, HAWAII, tracks and restores inference footprints efficiently and transparently to make inference forward progress, without requiring access to the accelerator internal state and application-level energy estimation. Evaluations were carried out on a Texas Instruments device, under varied energy budgets and network workloads. Compared to a variety of task-based intermittent approaches, HAWAII improves the inference throughput by 5.7%-95.7%, particularly achieving higher performance on heavily accelerated DNNs.

As people increase their dependency on mobile applications and services, saving power consumption of mobile devices becomes an important challenge for supporting video streaming applications. This paper investigates how to minimize the power consumption of an organic light-emitting diode (OLED) display when displaying a video under consideration for the user's visual experience. We first model the minimization problem as an OLED video scaling optimization problem. We then propose an algorithm to solve the optimization problem and prove that the algorithm is optimal in terms of power savings. We next introduce an energy-saving cloud service based on the algorithm. Finally, the results of the extensive experiments conducted on a commercial mobile smartphone with four real-world videos to evaluate the performance of the proposed algorithm are very encouraging.

As modern people become highly dependent on mobile applications and services, saving the power consumption of browser applications plays an important role for the battery lifetime of mobile devices. This paper investigates how to reduce the power consumption of mobile OLED displays under scrolling operations when displaying web pages. We first propose a content-based analysis to efficiently determine the visual appeal in a web page. Then, two algorithms are presented to dynamically generate power-saving transformation programs for a web page. We next propose a scrolling operation detection to activate the transformation without reducing the user experience. Finally, the results of the extensive experiments conducted on a commercial smart-phone with four real-world web pages to evaluate the performance of the proposed design are very encouraging.

In the future, mobile systems will increasingly feature more advanced organic light-emitting diode (OLED) displays. The power consumption of these displays is highly dependent on the image content. However, existing OLED power-saving techniques either change the visual experience of users or degrade the visual quality of images in exchange for a reduction in the power consumption. Some techniques attempt to enhance the image quality by employing a compound objective function. In this article, we present a win-win scheme that always enhances the image quality while simultaneously reducing the power consumption. We define metrics to assess the benefits and cost for potential image enhancement and power reduction. We then introduce algorithms that ensure the transformation of images into their quality-enhanced power-saving versions. Next, the win-win scheme is extended to process videos at a justifiable computational cost. All the proposed algorithms are shown to possess the win-win property without assuming accurate OLED power models. Finally, the proposed scheme is realized through a practical camera application and a video camcorder on mobile devices. The results of experiments conducted on a commercial tablet with a popular image database and on a smartphone with real-world videos are very encouraging and provide valuable insights for future research and practices.

Self-powered intermittent systems featuring nonvolatile processors (NVPs) allow for accumulative execution in unstable power environments. However, frequent power failures may cause incorrect NVP execution results due to invalid data generated intermittently. This paper presents a HW/SW co-design, called HomeRun, to guarantee atomicity by ensuring that an uninterruptible program section can be run through at one execution. We design a HW module to ensure that a power pulse is sufficient for an atomic section, and develop a SW mechanism for programmers to protect atomic sections. The proposed design is validated through the development of a prototype pattern locking system. Experimental results demonstrate that the proposed design can completely guarantee atomicity and significantly improve the energy utilization of self-powered intermittent systems.

Network coding is a promising technology proven to improve the performance of wireless networks. To successfully design a quality-of-service (QoS)-satisfied routing protocol with network coding, the bandwidth consumption of a coding host should be determined. Furthermore, coding opportunities should be increased to improve network capacity. Nevertheless, it is challenging to determine whether a host can be a coding host and to determine the bandwidth consumption of a coding host in a mobile ad hoc network (MANET). In this paper, we first present and define the coding conditions to identify a coding host. The bandwidth consumption of a coding host is then estimated under the contention-based wireless networks with a random access mechanism. Finally, we propose a bandwidth-satisfied and coding-aware multicast routing protocol (BCMRP). By taking into account the residual bandwidth of the carrier-sense neighbors of the forwarders, the proposed protocol can satisfy the bandwidth requirements of the requested flow and other ongoing flows. As a consequence of considering coding opportunities in multicast tree construction, the proposed multicast protocol can reduce the total bandwidth consumption. The simulation results show that BCMRP outperforms the prior multicast routing protocols in receiving ratio, admission ratio, and total bandwidth consumption.

We present a code- and input-sensitive sanitization synthesis approach for repairing string vulnerabilities that are common in web applications. The synthesized sanitization patch modifies the user input in an optimal way while guaranteeing that the repaired web application is not vulnerable. Given a web application, an input pattern and an attack pattern, we use automata-based static string analysis techniques to compute a sanitization signature that characterizes safe input values that obey the given input pattern and are safe with respect to the given attack pattern. Using the sanitization signature, we synthesize an optimal sanitization patch that converts malicious user inputs to benign ones with minimal editing. When the generated patch is added to the web application, it is guaranteed that the repaired web application is no longer vulnerable. We present refinements to previous sanitization synthesis algorithms that reduce the runtime sanitization cost significantly. We evaluate our approach on open source web applications using common input and attack patterns, demonstrating the effectiveness of our approach.

Organic Light-Emitting Diode (OLED) technology is regarded as a promising alternative to mobile displays. In this article, we introduce the design, algorithm, and implementation of a novel framework called CURA for quality-retaining power saving on mobile OLED displays. First, we link human visual attention to OLED power saving and model the OLED image scaling optimization problem. The objective is to minimize the power required to display an image without adversely impacting the user’s visual experience. Then, we present the algorithm used to solve the modeled problem, and prove its optimality even without an accurate power model. Finally, based on the framework, we implement two practical applications on a commercial OLED mobile tablet. The results of experiments conducted on the tablet with real images demonstrate that CURA can reduce significant OLED power consumption while retaining the visual quality of images.

Mobile systems will increasingly feature emerging OLED displays, whose power consumption is highly dependent on the image content. Existing OLED power-saving techniques change users' visual experience or degrade images' visual quality in exchange for power reduction, or seek a chance to also enhance image quality by employing a compound objective function. This paper presents a win-win scheme that always enhances image quality and reduces power consumption simultaneously. We define metrics to assess the profit and the cost for potential image enhancement and power reduction. Then, we propose algorithms that ensure the transformation of images into their quality-enhanced power-saving versions. Finally, the proposed scheme is realized as a practical camera application on mobile devices. The results of experiments conducted on a commercial tablet with a popular image database are very encouraging and provide valuable insights for future research and practices.

Mobile devices, which are inherently of limited computing capabilities, face a growing demand to support increasingly complex applications. Computation offloading addresses this issue by enabling mobile devices to offload computations to a remote server. Advancing on previous work, this paper presents a cloud-based offloading service, which models an optimization problem with the objective of minimizing the operation cost of the service provider while achieving the agreed quality of service (QoS) for subscribers. The problem is shown to be NP-hard. We propose a pseudo-polynomial-time optimal algorithm for the offline scenario, as well as an efficient online algorithm that has a provable QoS guarantee and allows practical implementations. To evaluate our algorithms, we synthesize remotable tasks and conduct extensive simulations based on real mobile user traces and application workload patterns. Our results demonstrate that our online algorithm could achieve comparable performance to the optimal offline algorithm, in terms of both the required cloud cost and the provided user benefit.

Organic light-emitting diode (OLED) technology is considered as a promising alternative to mobile displays. This paper explores how to reduce the OLED power consumption by exploiting visual attention. First, we model the problem of OLED image scaling optimization, with the objective of minimizing the power required to display an image without adversely impacting the user's visual experience. Then, we propose an algorithm to solve the fundamental problem, and prove its optimality even without the accurate power model. Finally, based on the algorithm, we consider implementation issues and realize two application scenarios on a commercial OLED mobile tablet. The results of experiments conducted on the tablet with real images demonstrate that the proposed methodology can achieve significant power savings while retaining the visual quality.

With the increasing variety of mobile applications, reducing the energy consumption of mobile devices is a major challenge in sustaining multimedia streaming applications. This paper explores how to minimize the energy consumption of the backlight when displaying a video stream without adversely impacting the user's visual experience. First, we model the problem as a dynamic backlight scaling optimization problem. Then, we propose algorithms to solve the fundamental problem and prove the optimality in terms of energy savings. Finally, based on the algorithms, we present a cloud-based energy-saving service. We have also developed a prototype implementation integrated with existing video streaming applications to validate the practicability of the approach. The results of experiments conducted to evaluate the efficacy of the proposed approach are very encouraging and show energy savings of 15-49 percent on commercial mobile devices.

Sensor networks may be deployed to provide external location references to correct configuration errors in indoor robot navigation. Previous work on sensor deployment has considered only sensor and environment models. This paper shows that considering also target models can greatly reduce the deployment cost. We first show how to derive target models for robot navigation and then show that the problem of finding the minimum-cost deployment of a sensor network is NP-hard. The presented algorithms were evaluated through extensive simulations.