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.
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.
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.
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.
A catastrophe equity put (CatEPut) is constructed to recapitalize an insurance company that suffers huge compensation payouts due to catastrophic events (CEs). The company can exercise its CatEPut to sell its stock to the counterparty at a predetermined price when its accumulated loss due to CEs exceeds a predetermined threshold and its own stock price falls below the strike price. Much literature considers the evaluations of a CatEPut that can only be exercised at maturity; however, most CatEPuts can be exercised early so the company can receive timely funding. This paper adopts lattice approaches to evaluate CatEPuts with early exercise features. To solve the combinatorial exposition problem due to the trigger of CatEPuts’ accumulated loss, our method reduces the possible number of accumulated losses by taking advantage of the closeness of integral additions. We also identify and alleviate a new type of nonlinearity error that yields unstable numerical pricing results by adjusting the lattice structure. We provide a rigorous mathematical proof to show how the proposed lattice can be constructed under a mild condition. Comprehensive numerical experiments are also given to demonstrate the robustness and efficiency of our lattice.
Recommender systems are vital ingredients for many e-commerce services. In the literature, two of the most popular approaches are based on factorization and graph-based models; the former approach captures user preferences by factorizing the observed direct interactions between users and items, and the latter extracts indirect preferences from the graphs constructed by user-item interactions. In this paper we present HOP-Rec , a uni ed and e cient method that incorporates the two approaches. The proposed method in- volves random sur ng on a graph to harvest high-order information among neighborhood items for each user. Instead of factorizing a transition matrix, our method introduces a con dence weighting parameter to simulate all high-order information simultaneously, for which we maintain a sparse user-item interaction matrix and enrich the matrix for each user using random walks. Experimental results show that our approach signi cantly outperforms the state of the art on a range of large-scale real-world datasets.
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.
Making sense of the surrounding context and ongoing events through not only the visual inputs but also acoustic cues is critical for various AI applications. This paper presents an attempt to learn a neural network model that recognizes more than 500 different sound events from the audio part of user generated videos (UGV). Aside from the large number of categories and the diverse recording conditions found in UGV, the task is challenging because a sound event may occur only for a short period of time in a video clip. Our model specifically tackles this issue by combining a main subnet that aggregates information from the entire clip to make clip-level predictions, and a supplementary subnet that examines each short segment of the clip for segment-level predictions. As the labeled data available for model training are typically on the clip level, the latter subnet learns to pay attention to segments selectively to facilitate attentional segmentlevel supervision. We call our model the M&mnet, for it leverages both “M”acro (clip-level) supervision and “m”icro (segment-level) supervision derived from the macro one. Our experiments show that M&mnet works remarkably well for recognizing sound events, establishing a new state-of-theart for DCASE17 and AudioSet data sets. Qualitative analysis suggests that our model exhibits strong gains for short events. In addition, we show that the micro subnet is computationally light and we can use multiple micro subnets to better exploit information in different temporal scales.
With the large expected demand of wireless communication, Device-to-Device (D2D) communication has been proposed as a promising technology to enhance network performance. Nevertheless, the selfish nature of potential D2D users may impale the performance of D2D-enabled network. In this paper, we propose a D2D-enabled cellular network framework, which support a novel group D2D mode under overlay D2D communication. The group-based design is derived from the discussions of two common D2D modes, divided and shared D2D modes, regarded as special cases. The proposed framework provides a pricing-based dynamic Stackelberg game for optimal mode selection and spectrum partitioning. We propose the incentive compatible pricing strategy to provide proper incentive for these selfish potential D2D pairs to make optimal choices in mode selection. Our results show that the pricing and spectrum partition strategy effectively prevents selfish potential D2D users from harming the system performance while fully exploits the potential of D2D communication.
This letter investigates the source and relay precoder design for full-duplex multiple-input multiple-output relay systems, where simultaneous wireless information and power transfer is enabled at the destination. The objective is to design the precoders such that the end-to-end performance can be optimized. Different from existing schemes, a novel dual-objective function is adopted in this work. The proposed precoders yield closed-form solutions and avoid iterative algorithms. Moreover, our design is applicable when the system suffers from the residual loop-interference. Simulations show that the proposed scheme enables an efcient way to optimize information-decoding and energy-harvesting performances.