Self-powered intermittent systems enable accumulative executions in unstable power environments, where checkpointing is often adopted as a means to achieve data consistency and system recovery under power failures. However, existing approaches based on the checkpointing paradigm normally require system suspension and logging at runtime. This paper presents a design which enables failure-resilient intermittently-powered systems without runtime checkpointing. Our design enforces the consistency and serializability of concurrent data access while maximizing computation progress, as well as allows instant system recovery after power resumption, by leveraging the characteristics of data accessed in hybrid memory. We integrated the design into FreeRTOS running on a Texas Instruments device. Experimental results show that our design achieves up to 11.8 times the computation progress achieved by checkpointing-based approaches, while reducing the recovery time by nearly 90%.

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Perceptual similarity measurement allows mobile applications to eliminate unnecessary computations without compromising visual experience. Existing pixel-wise measures incur significant overhead with increasing display resolutions and frame rates. This paper presents an ultra lightweight similarity measure called LSIM, which assesses the similarity between frames based on the transformation matrices of graphics objects. To evaluate its efficacy, we integrate LSIM into the Open Graphics Library and conduct experiments on an Android smartphone with various mobile 3D games. The results show that LSIM is highly correlated with the most widely used pixel-wise measure SSIM, yet three to five orders of magnitude faster. We also apply LSIM to a CPU-GPU governor to suppress the rendering of similar frames, thereby further reducing computation energy consumption by up to 27.3% while maintaining satisfactory visual quality.

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For rate optimization in interference limited network, improper Gaussian signaling has shown its capability to outperform the conventional proper Gaussian signaling. In this work, we study a weighted sum-rate maximization problem with improper Gaussian signaling for the multiple-input multiple-output interference broadcast channel (MIMO-IBC). To solve this nonconvex and NP-hard problem, we propose an effective separate covariance and pseudo-covariance matrices optimization algorithm. In the covariance optimization, a weighted minimum mean square error (WMMSE) algorithm is adopted, and, in the pseudo-covariance optimization, an alternating optimization (AO) algorithm is proposed, which guarantees convergence to a stationary solution and ensures a sum-rate improvement over proper Gaussian signaling. An alternating direction method of multipliers (ADMM)-based multi-agent distributed algorithm is proposed to solve an AO subproblem with the globally optimal solution in a parallel and scalable fashion. The proposed scheme exhibits favorable convergence, optimality, and complexity properties for future large-scale networks. Simulation results demonstrate the superior sum-rate performance of the proposed algorithm as compared to existing schemes with proper as well as improper Gaussian signaling under various network configurations.

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In this paper, multi-stream transmission in interference networks aided by multiple amplify-and-forward (AF) relays in the presence of direct links is considered. The objective is to minimize the sum power of transmitters and relays by beamforming optimization under the stream signal-to-interference-plus-noise-ratio (SINR) constraints. For transmit beamforming optimization, the problem is a well-known non-convex quadratically constrained quadratic program (QCQP) that is NP-hard to solve. After semi-denite relaxation (SDR), the problem can be optimally solved via alternating direction method of multipliers (ADMM) algorithm for distributed implementation. Analytical and extensive numerical analyses demonstrate that the proposed ADMM solution converges to the optimal centralized solution. The convergence rate, computational complexity, and message exchange load of the proposed algorithm outperforms the existing solutions. Furthermore, by SINR approximation at the relay side, distributed joint transmit and relay beamforming optimization is also proposed that further improves the total power saving at the cost of increased complexity.

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Most existing or currently developing Internet of Things (IoT) communication standards are based on the assumption that the IoT services only require low data rate transmission and therefore can be supported by limited resources such as narrow-band channels. This assumption rules out those IoT services with burst traffic, critical missions, and low latency requirements. In this paper, we propose to utilize the idle devices in mission-critical IoT networks to boost the transmission data rate for critical tasks through multiple concurrent transmissions. This approach virtually expands the existing narrow-band IoT protocols to break the bandwidth limitation in order to provide low latency services for critical tasks. In this approach, we propose the task-balance method and the first-link descending order to determine the relay order and data partition in a given relay set. We theoretically prove that the optimal relay configuration that minimizes the uploading latency in single source scenario can be derived by the proposed algorithms in polynomial time when we have sufficient number of available channels. We also propose a greedy algorithm to approximate the optimal solution within a 1/2 performance lower bound in general scenarios. The simulation results shows that the proposed approach can reduce the latency of critical tasks up to 76% comparing with traditional approaches.

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Wireless body area networks (WBANs) have emerged recently to provide health monitoring for chronic patients. In a WBAN, the patient's smartphone is deemed an appropriate sink to help forward the sensing data to back-end servers. Through a real-world case study, we observe that temporary disconnection between sensors and the associated smartphone can happen frequently due to postural changes, causing a significant amount of data to be lost forever. In this paper, we propose a scheme to parasitize the data in surrounding Wi-Fi networks whenever temporary disconnection occurs. Specifically, we model data parasitizing as an optimization problem, with the objective of maximizing the system lifetime without any data loss. Then, we propose an optimal offline algorithm to solve the problem, as well as an online algorithm that allows practical implementations. We have also implemented a prototype system, where the online algorithm serves as the underlying technique, based on Arduino. To evaluate our scheme, we conduct a series of experiments with the prototype system in controlled and real-world environments. The results show that the lifetime is prolonged by 100 times, and it could be further doubled if the health monitoring application permits a few packet losses.

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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 paper, 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.

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Real-time computing provides insightful ways to explore the optimization in resource usages, especially from the time point of view. Nevertheless, real-time task scheduling is recognized by its high complexity when there are non-preemptive shared resources and multiple processors. When more and more practical factors in system designs are considered, such as energy consumption and memory allocation, even some sub-problems in real-time task scheduling become intractable. Although people often criticize various artificial assumptions in real-time task scheduling, they have to admit that ideas in real-time computing and their extensions, such as tradeoff in cost, performance, energy, and even the quality of service, can be applied to multi-dimensional optimization in system designs. In this direction, we witness the rapid development of the embedded system industry and join the task force in system designs, especially mobile devices and non-volatile memory systems. Resource management on mobile devices, with a special emphasis on user experience, should not only consider the response time but also the visual perception of users. Non-volatile memory has also blurred the boundary between the memory and the storage. It enables certain unified considerations of the main memory and storage and also in-memory computing. It shows the ways to break the boundaries between hardware and software layers and have better integration of computing and memory/storage units. The advances in mobile systems and memory innovations inspire the evolution of embedded system designs and have also brought us insights to solutions regarding how systems should be restructured and how computing should be done. They might also provide their feedback to real-time computing and even shape the future direction of real-time computing in various innovative ways.

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Owing to high cell density caused by the advanced manufacturing process, the reliability of flash drives turns out to be rather challenging in flash system designs. To enhance the reliability of flash drives, error-correcting code (ECC) has been widely utilized in flash drives to correct error bits during programming/reading data to/from flash drives. Although ECC can effectively enhance the reliability of flash drives by correcting error bits, the capability of ECC would degrade while the program/erase (P/E) cycles of flash blocks is increased. Finally, ECC could not correct a flash page, because a flash page contains too many error bits. As a result, reducing error bits is an effective solution to further improve the reliability of flash drives when a specific ECC is adopted in the flash drive. This work focuses on how to reduce the probability of producing error bits in a flash page. Thus, we propose a pattern-aware write strategy for flash reliability enhancement. The proposed write strategy considers both the P/E cycle of blocks and the pattern of written data while a flash block is allocated to store the written data. Since the proposed write strategy allocates young blocks (respectively, old blocks) for hot data (respectively, cold data) and flips the bit pattern of the written data to the appropriate bit pattern, the proposed strategy can effectively improve the reliability of flash drives. The experimental results show that the proposed strategy can reduce the number of error pages by up to 50%, compared with the well-known DFTL solution. Moreover, the proposed strategy is orthogonal with all ECC mechanisms so that the reliability of the flash drives with ECC mechanisms can be further improved by the proposed strategy.

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Users roaming cellular signal coverage with their mobile devices essentially form a mobile cyber-physical system (CPS). By modeling cyber human mentality and physical signal coverage, as well as their interplay, user mobility can be leveraged to improve users' mobile experience with limited wireless bandwidth. Through a real-world case study, we observed that numerous ``null zones''  and ``hot zones'' exist in cellular signal coverage areas, where mobile devices cannot obtain sufficiently high data rates for delay-sensitive applications. Over one-third of the locations in a crowded area could have weak signal coverage and low bandwidth shares, resulting in poor mobile connectivity experience. This paper considers the practicality of a mobile CPS called Oasis, which guides users to leave those zones and move to nearby locations with better mobile experience. To realize the system, we model and maximize a user's willingness to travel to another location, where the willingness involves the compound impact of the travel distance and the improved perceptual quality. We also develop a prototype system that creates a feedback control loop to allow self-adaptation to users' needs. To evaluate the efficacy, we conducted a series of experiments based on the real data collected in downtown Taipei. The results demonstrate that our mobile CPS can further reduce the average distance per unit of quality improvement achieved with OpenSignalMaps by about 80\\%, and motivate further research.

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