Key-value solid-state drives (KV-SSDs) are considered as a potential storage solution for large-scale key-value (KV) store applications. Unfortunately, the existing KV-SSD designs are tuned for a specific type of workload, namely, those in which the size of the values are much larger than the size of the keys. Interestingly, there also exists another type of workload, in practice, in which the sizes of keys are relatively large. We re-evaluate the current KV-SSD designs using such unexplored workloads and document their significantly-degraded performance. Observing that the performance problem stems from the increased size of the metadata, we subsequently propose a novel KV-SSD design, called AnyKey, which prevents the size of the metadata from increasing under varying sizes of keys. Our detailed evaluation using a wide range of real-life workloads indicates that AnyKey outperforms the state-of-the-art KV-SSD design under different types of workloads with varying sizes of keys and values.

The rapid proliferation of Internet of Things (IoT) devices has led to a significant increase in the number of network users, prompting advancements in security mechanisms. Consequently, traditional attacks targeting specific vulnerabilities have become less effective due to these enhanced defense systems, leading attackers to increasingly adopt phishing strategies as a primary means of bypassing security measures. Among these, phishing websites have been increasing rapidly, exploiting the carelessness of countless users. In response, numerous phishing website detection methods have been investigated, with machine learning-based approaches emerging as a leading strategy. However, these machine learning-based classification methods require substantial computational resources, posing challenges for their direct application in the already widespread IoT environment. To address these challenges, we propose an efficient phishing website detection method based on HTML tag sequences, the core structural elements of websites, by leveraging encoder models known for their effectiveness in classifying sequential data. Our approach also incorporates a customized tokenizer and dictionary specifically tailored for HTML tags. Experiments conducted on publicly available datasets demonstrate that the proposed method achieves over 95% accuracy across key performance metrics. Furthermore, comparative analyses highlight several advantages of our method, including reduced model size and faster detection times compared to existing approaches.

Paralfetchspeeds up application launches on personal computing/communication devices, by means of: 1) accurate collection of launch-related disk read requests, 2) pre-scheduling of these requests to improve I/O throughput during prefetching, and 3) overlapping application execution with disk prefetching for hiding disk access time from the execution of the application. We implementedParalfetchunder Linux kernels on a desktop/laptop PC, a Raspberry Pi 3 board, and an Android smartphone. Tests with popular applications show thatParalfetchsignificantly reduces application launch times on flash-based drives and hard disk drives, and it outperformsGSoC Prefetch[18] andFAST[21], which are representative application prefetchers available for Linux-based systems.

When an SSD is used for executing multiple work-loads, its internal resources should be allocated to prevent the competing workloads from interfering with each other. While channel-based allocation strategies turn out to be quite effective in offering performance isolation, questions like “what is the optimal allocation?” and “how can one efficiently search for the optimal allocation?” remain unaddressed. To this end, we explore the channel allocation problem in SSDs and employ a reinforcement learning-based approach to address the problem. Specifically, we present an autonomic channel allocating SSD, called AutoAlloc, which can seek near-optimal channel allocation in a self-learning fashion for a given set of co-running workloads. The salient features of AutoAlloc include the following: (i) the optimal allocation can change depending on the user-defined optimization metrics; (ii) the search process takes place in an online setting without any need of extra workload profiling or performance estimation; and, (iii) the search process is fully-automated without requiring any user intervention. We implement AutoAlloc in LightNVM (the Linux subsystem) as part of the FTL, which operates with an emulated Open-Channel SSD. Our extensive experiments using various user-defined optimization metrics and workload execution scenarios indicate that AutoAlloc can find a near-optimal allocation after examining only a very limited number of candidate allocations.

Efficient use of caching space is crucial in block I/O caching schemes due to the high cost of caching media. This paper introduces EximCache, a novel scheme that prioritizes blocks not promptly managed by the OS readahead framework, addressing the performance criticality of block I/Os. Experimental results demonstrate that EximCache reduces caching space usage significantly while imposing minimal performance overhead during application launches and Linux boot.

The muscles around the knee deteriorate with age, putting more strain on the knee joint. It is important to strengthen the knee muscles through exercises such as squats even after knee surgery. However, an unstable squatting posture can lead to lower back and knee injuries; thus, it is important to maintain the correct posture. This study proposes the correct squat posture to strengthen the knee muscles using a cost-effective and easily applicable method based on previous research. The squat posture that protects the knee joint involves activating the quadriceps femoris muscle, which is the main muscle in the thigh area that supports weight from below the pelvis, passing through the knee ligaments and reaching the tibia. This method involves maintaining the squat posture while keeping the center of gravity of the body shifted backward and to the sides. We examine general squat and backward and side pressure (BSP) squat muscle recruitment strategies using ten participants. We attach electromyography sensors to the rectus femoris muscle and use foot pressure sensors to confirm that the participants performed both types of squats with precision. The results showed that the specific squat method had a 24% to 31% (paired t-test p=0.0029) increase in muscle strength for the half squat and a 23% to 27% (paired t-test p=0.0475) increase for the full squat, compared to the general squat, in significant intervals in the electromyography sensor data. These results suggest that maintaining a posture that maximizes activation of the rectus femoris muscle is useful for designing training methods that aim to strengthen and protect knee muscles during squats. This study proposes a new statistical analysis method using the k-means algorithm of machine learning after saving the signals from the time series analog as comma separated value (CSV) files.

This paper focuses on the challenges of vehicle collisions in wireless network-based vehicle driving systems. The identified issues include: 1) data imbalance and control signal loss/delay due to communication, and 2) scheduling failures. In this study, we propose an relatively critical approach upon signal power and controllability to address these problems. Our method includes statistical observations related to communication bottlenecks and analyzes the relationship between communication performance and collision risk. We also refine the existing theo-retical framework for collision prediction to ensure compatibility with current protocols. To evaluate our proposed approach, we set up an environment with two vehicles operating at a fixed distance from each other and analyzed the correlation between communication strength and real-time deadline criticality in relation to collision risk.

Accurate task execution time estimation is vital for efficient and dependable operation of safety-critical systems. However, modern automotive functions' complexity challenges conventional estimation methods. To address this, we propose a novel technique that combines execution time and job sequence data using a multi-layer perceptron (MLP) neural network. Leveraging MLP's capabilities, our approach achieves impressive 99.7% prediction accuracy with a mere 38.33 μs latency. Integrating our technique into safety-critical systems optimizes resource allocation and scheduling, enhancing performance and reliability. Importantly, our method extends beyond automotive systems, finding potential in diverse safety-critical domains. By precisely estimating task execution time, we enhance operational efficiency and decision-making in complex systems.

Electrocardiogram (ECG) based user identification has received considerable attention with the advent of wearable devices. It provides emerging applications including personal healthcare a convenient way to authenticate users as the process can be performed at the moment the user makes contact with the device. However, a recent study discovered that injecting noise into the signal transmitted from the user to an application can effectively hinder the classification process. Many efforts have been made to deal with this noise injection attack, but most approaches have focused on noise removal. In contrast, this paper proposes Defensive Adversarial Training (DAT), which involves training a model with various noisy data to enhance the robustness of deep learning-based identification algorithms. We used two types of noise, Gaussian and Laplacian, to create noisy data. In addition, a sliding-window technique was used to effectively extract useful features and to achieve better accuracy. Our simulation results demonstrate that the proposed approach is highly robust to noise injection attacks and even against random noise. A comparative analysis with noise removal schemes also shows that the proposed DAT significantly enhances the robustness of ECG-based user identification.

Obstructive sleep apnea (OSA) is a common sleeping issue that makes it difficult to breathe while you sleep and is linked to a number of other disorders, including cardiovascular conditions, such as hypertension and coronary heart disease. Nocturnal polysomnography (PSG) is one of the clinical diagnostic criteria for OSA, which is a painful and expensive form of diagnosis as it requires manual interpretation by experts and takes a lot of time. ECG-based techniques for diagnosing OSA have been introduced to alleviate these problems, but the most of solutions that have been put up thus far rely on feature engineering, which requires substantial specialist knowledge and expertise. In this study, we present a novel approach for classifying OSA based on a single-lead ECG signal conversion and a composite deep convolutional neural network model. The ECG signal is transformed into scalogram images with heart rate variability (HRV) characteristics and Gramian Angular Field (GAF) matrix images with temporal characteristics, incorporating the temporal properties of the ECG, to create the hybrid image dataset. The composite model contains three sub-convolutional neural networks, two of which utilize fine-tuned AlexNet and ResNet models, the third is a convolutional neural network with five residual blocks that are evaluated by a voting mechanism. The PhysioNet Apnea-ECG database was used to train and evaluate the proposed model. The results show that the proposed classifier achieved 90.93% accuracy, 83.86% sensitivity, 95.29% specificity, and 0.89 AUC on hybrid image datasets.

Imprecise computing has emerged as an approach for designing energy-efficient digital systems as it can save computing resources. Hot-patching is one of the techniques used to implement it. Hot-patching can add or delete application functions without restarting the application. In this study, we implemented a hot-patching tool for a Linux application and applied it to imprecise computing. We measured the CPU cycle and instruction counts based on square root calculation applications. The experimental results confirmed that CPU resource utilization reduced by 20% when applying a patch with an error range of approximately 2%. Hence, we confirmed that imprecise computing can be performed through hot-patching.

The electronic control unit (ECU), considered the brain of a vehicle, suffers from a design problem called single point of failure (SPOF), which can induce system malfunctions. This problem can be addressed via redundancy, which increases the reliability of a mission-critical system by allowing multiple ECUs to perform a single function. However, this solution requires additional ECU and maintenance costs incurred by the redundant ECUs. A cost-effective approach for improving safety is to utilize the network connectivity between existing ECUs. In this paper, we propose a method that migrates critical tasks residing in an infeasible ECU to a replaceable ECU by using the network connection between them. Furthermore, to demonstrate the feasibility of the method, we implemented a task migration method on a Lego vehicle composed of three ECUs to prevent sudden unintended acceleration accidents caused by faults in an ECU managing the acceleration task.

Redfish is a next-generation API standard for the management of data center infrastructures. This rich API can flexibly obtain data using a query string from the client side. However, this feature is optional and not fully supported by many services. We implemented a prototype Redfish query processing module on Nginx, a well-known open source web server. The Redfish query processing module can run with a proxy module and work with any server-side or client-side applications. Additionally, our prototype implementation can be configured to properly utilize queries, which are supported on a backend server, and improve performance. Our implementation was evaluated on an OpenBMC server and a mockup server and showed potential for performance improvement.

For realizing safe autonomous driving, the end-to-end delays of real-time object detection systems should be thoroughly analyzed and minimized. However, despite recent development of neural networks with minimized inference delays, surprisingly little attention has been paid to their end-to-end delays from an object's appearance until its detection is reported. With this motivation, this paper aims to provide more comprehensive understanding of the end-to-end delay, through which precise best- and worst-case delay predictions are formulated, and three optimization methods are implemented: (i) on-demand capture, (ii) zero-slack pipeline, and (iii) contention-free pipeline. Our experimental results show a 76% reduction in the end-to-end delay of Darknet YOLO (You Only Look Once) v3 (from 1070 ms to 261 ms), thereby demonstrating the great potential of exploiting the end-to-end delay analysis for autonomous driving. Furthermore, as we only modify the system architecture and do not change the neural network architecture itself, our approach incurs no penalty on the detection accuracy.

Many prediction models have been proposed to improve the effectiveness of web prefetching for reducing the response time perceived by users when browsing the web. Most of these models are based on structure learning and are applied at the client side. Currently, considerable attention is being paid to proxy-based prefetching because it is more effective and accurate in predicting the correlated pages of many websites of similar interest for more homogeneous users. Compared with client-based prefetching, more complex prediction tasks must run in the proxy, which implies that a more powerful prediction model is required. Thus, based on the time-series characteristics of browsing records, we proposed the intentionality-related long short-term memory (Ir-LSTM) model, which combines both the Skip-Gram embedding method and the LSTM model while expanding the input features with user information. We also propose a novel dynamic allocation module for detecting real-time traffic bursts and correspondingly adjusting the correlation coefficient of the model's output to achieve higher server-side resource utilization while fully maximizing hit ratio.

A cyber–physical system (CPS) consists of physical devices and operations that are closely controlled and monitored by computational processes. This concrete connection involves the real-time actuation of physical devices, real-time sensing of physical quantities, and modeling and control of the overall system. A CPS may be connected to the Internet of Things (IoT) and, if so, should be considered in that context; the IoT is essential to realize a vision of future CPSs, where numerous devices are connected over the Internet, allowing them to collect information about the real world in real time, and share it with other systems and physical devices.

By overlapping disk accesses with computation-intensive operations, prefetching can reduce delays in launching an application and in loading significant amounts of data while the application is running. The key to effective prefetching is making the tradeoff between the mining accuracy of selecting relevant blocks, and the time to decide those blocks. To address this problem, we propose a new prefetcher called ClusterFetch. In its learning mode, ClusterFetch detects periods of intensive disk accesses by monitoring the speed at which read requests are queued; it re-organizes these reads and locates the file opened by the application just before each such period. During subsequent runs of the same application, ClusterFetch prefetches the data associated with the opening of a “trigger” file. Our experimental results show that ClusterFetch implemented in Linux can reduce the application launch time by up to 41.3 percent and the loading time by up to 38.2 percent, while taking up less than 200 KB of main memory.

In cloud services, deduplication technology is commonly used to reduce the space and bandwidth requirements services by eliminating redundant data and storing only single copy. Deduplication is most effective when multiple users outsource the same data to the cloud storage, but raises issues relating to security and ownership. Proof-of-ownership schemes allow any owner of the same data to prove to the cloud storage server that he owns the data in a robust way. However, if encrypted data is outsourced into the cloud storage and the ownership changes dynamically, deduplication would be hampered. Thus, we propose a secure deduplication scheme that supports dynamic ownership management based on randomized convergent encryption in this study.

This study introduces a novel conservative modulation and coding scheme to minimize and stabilize the delay incurred during the process of electrocardiogram (ECG) transmission over a wireless medium, while maintaining the desired level of the ECG pattern quality required for improving the chance of its interpretation. A machine-type communication system is adopted for the delivery of ECG data to benefit from its inherent reliability, pervasiveness, security, and performance of 4G long-term evolution technologies with reduced cost and enhanced coverage. Extensive evaluations indicate that the proposed system provides a sufficient level of service for medical-grade instantaneous ECG monitoring even under significantly deteriorated channel conditions.

Current designs of battery-powered uninterruptible power supplies only provide power for a short time, and offer few control options. We combined a compact synchronous generator with a battery with 10% of the capacity of a UPS of the same rating to produce an uninterruptible generator supply (UGS). The controller of this UGS runs FreeRTOS, which enables it to respond quickly to a power outage. A wifi module provides connectivity to a web server for real-time monitoring and management on a remote PC or cellphone.

When integrated with push notifications, a live migration function can be used to ensure that systems have a high reliability in cases of hardware failures. However, the dependencies for a large range of hardware devices need to be addressed before realizing emergency live migration. Our platform introduces container-based light virtualization and an automated build function to isolate an application so that it can be deployed on different devices such as Edison, Raspberry Pi model B, BeagleBone Black, and Odroid XU3.

Software often requires frequent updates to improve performance and reliability. Typically, a general update process is performed after terminating a program although this is not applicable to applications that require non-disruptive services such as networks and satellites. In order to address this issue, network service providers often provide a technology termed as an in-service software upgrade that performs continual updates without stopping the services. However, it requires additional devices or facilities, and the system structure in this case becomes complicated and additional economic costs are incurred. In this study, the design and implementation of a functional-level hot-patching platform are presented for executable and linkable format binary program, based on an ARM and an Intel processor to add or update functions necessary to provide nonstop services. The proposed platform was validated on devices using both Raspberry Pi and a general x86_64 personal computer. Experiments with an iPerf3 server and a drone simulator on each device demonstrated the effectiveness of the proposed hot-patching platform in achieving non-disruptive services with a negligible latency of less than 10 ms.

This paper proposes a novel hybrid model that integrates the synergy of two superior classifiers for functional magnetic resonance imaging (fMRI) recognition, namely, convolutional neural networks (CNNs) and support vector machines (SVMs), both of which have proven results in the field of image recognition. In the proposed model, the CNN functions as a trainable feature extractor and the SVM functions as a recognizer. This hybrid model extracts features from raw images and generates predictions for fMRI recognition. We conducted experiments on Haxby's 2001 fMRI dataset. Comparisons with Haxby's study using the same database indicated that the proposed fusion achieved superior recognition accuracy of 99.5% compared to the Haxby's approach. Further, when the CNN was used as a feature extractor, the SVM classifier was demonstrated to be the best combining counterpart, providing the best synergy effect in terms of accuracy. This is compared with other classifiers based on learning algorithms such as decision tree, neural network, K-nearest neighbor, random forest, and AdaBoost.

TrapFetch is trained by monitoring the read requests issued by an application. It detects bursts of disk reads, determines the appropriate addresses at which breakpoints should be inserted in the application and library codes prior to the bursts of reads, and then logs this information with the data requested during the interval between each consecutive pair of breakpoints. When the application and library codes are loaded from the disk into memory, TrapFetch inserts breakpoints at the designated addresses based on the logs. During subsequent runs, TrapFetch is invoked at each breakpoint when it prefetches the corresponding data into the page cache. This approach is effective during both launch and run-time. TrapFetch operates at the user level, thus avoiding interference with other applications. In experiments on five popular applications (FlightGear, SpeedDreams 2, Pillars of Eternity, Eclipse, and VegaStrike), TrapFetch reduced the time for launch by up to 39.7% and time for run-time data-loading by up to 63.7%.

In cloud storage services, deduplication technology is commonly used to reduce the space and bandwidth requirements of services by eliminating redundant data and storing only a single copy of them. Deduplication is most effective when multiple users outsource the same data to the cloud storage, but it raises issues relating to security and ownership. Proof-of-ownership schemes allow any owner of the same data to prove to the cloud storage server that he owns the data in a robust way. However, many users are likely to encrypt their data before outsourcing them to the cloud storage to preserve privacy, but this hampers deduplication because of the randomization property of encryption. Recently, several deduplication schemes have been proposed to solve this problem by allowing each owner to share the same encryption key for the same data. However, most of the schemes suffer from security flaws, since they do not consider the dynamic changes in the ownership of outsourced data that occur frequently in a practical cloud storage service. In this paper, we propose a novel server-side deduplication scheme for encrypted data. It allows the cloud server to control access to outsourced data even when the ownership changes dynamically by exploiting randomized convergent encryption and secure ownership group key distribution. This prevents data leakage not only to revoked users even though they previously owned that data, but also to an honest-but-curious cloud storage server. In addition, the proposed scheme guarantees data integrity against any tag inconsistency attack. Thus, security is enhanced in the proposed scheme. The efficiency analysis results demonstrate that the proposed scheme is almost as efficient as the previous schemes, while the additional computational overhead is negligible.

We propose a method of arrhythmia detection based on beat morphology, which offers a new set of features for heartbeat classification. This can be performed by nearest-neighbor search, which we applied to heartbeats from the MIT-BIH arrhythmia database. Our classifier achieved an overall accuracy of 98.18% on 103,923 heartbeats.

Application loading times can be reduced by prefetching disk blocks into the buffer cache. Existing prefetching schemes for general workloads suffer from significant overheads and low accuracy. ClusterFetch is a lightweight prefetcher that identifies continuous sequences of I/O requests and identifies the files that trigger them. The next time that the same files are opened, the corresponding disk blocks are prefetched. In experiments, ClusterFetch reduced the launch time, by which we refer to the latency that first occurs when a program runs, by 15.2 to 30.9%, and loading times, meaning the delays that are incurred while additional data is loaded from the disk during program execution, by 15.9%.

Predictable and guaranteed response is an essential characteristic of safety-critical real-time systems. Thus we need to guarantee that the end-to-end latency of data exchange within such a system is always bounded and controlled, so that the overall system behavior is predictable and safe. In this paper, we propose a method for determining the worst-case delay when a packet traverses a network that consists of real-time crossbar switches. These switches guarantee that any feasible traffic can be switched in two clock periods by adopting an optimal clearance-time switching policy together with clock-driven scheduling. We also propose a switching architecture that introduces a delay buffer and virtual output queues to meet the one-shot traffic assumption required for optimal clearance-time switching. By removing the impractical assumptions made in previous works, we are able to derive a more accurate delay bound for real-world scenarios.

An electrocardiogram is a record of the electrical activity of the heart. There has been considerable research on associated monitoring techniques and on the automated analysis of the resulting data. We integrate these processes within an intelligent heart monitoring system. Designed to fit in with the daily life of a subject, it uses smartphone and web applications to link to a monitoring station via a decision support server. This server analyzes heart activity using the well-known Pan-Tompkins algorithm to detect heartbeats and a decision tree to classify them. We have simulated these analyses with data from the MIT-BIH Arrhythmia Database, and found that the server software had a 97% success rate in heartbeat detection and almost 97% accuracy in heartbeat classification.

Long-term ECG data for a patient can be acquired by integrating a Holter monitor with a mobile device. We use similar patient search to create an abstraction of a patient's typical heartbeat pattern, in the form of a regular expression, by learning heartbeat patterns. We have simulated these processes on data from the MIT-BIH arrhythmia database, and shown how we can search the database for patients with similar heartbeat patterns.

Application launch time is an important performance metric to user experience in desktop environment. The launch time mostly depends on the performance of secondary storage. There is a cost-performance trade-off in using hard disk drive (HDD) or solid-state drive (SSD). Thus, application launch times can be reduced by utilizing SSDs as caches for slow HDDs. We propose a new SSD caching scheme which migrates data blocks from HDDs to SSDs. Since our scheme operates entirely in the file system level and does not require an extra layer for mapping SSD-cached data, which is essential in most other schemes, our scheme does not incur mapping overheads that cause significant burdens on main memory, CPU, and SSD cache itself. Experimental results demonstrate our scheme yields 56% of performance gain in application launch.

Hard disks are one of the largest sources of power consumption in large-scale storage systems. The disk spin up/down technique has been shown to be an effective solution to this problem. Accordingly, the Open Compute Project (OCP) proposed a storage server for cold data, known as Cold Storage, to reduce power consumption using the spin-down technique in large-scale storage systems. With the aim of making effective use of Cold Storage, we characterize the power consumed by a hard disk in its various modes of operation. We then analyze the workload of an instant messaging service. These our contributions should provide guidelines for the implementation of a stable and energy-efficient distributed file system on a Cold Storage server, and to establish a spin-down policy that is power-proportional and promotes reliability.

No abstract available.

New features are often added incrementally to avionics systems to minimize the need for redesign and recertification. However, it then becomes necessary to check that the timing constraints of existing as well as new applications are met. We facilitate these checks by introducing a new data switch that bounds the latency of end-to-end communications across a network. This switch runs a clock-driven switching algorithm that is throughput-optimal with a bounded worst-case delay for all feasible traffic. We propose associated heuristics that determine whether the timing constraints of an integrated modular avionics (IMA) system network that uses this switch are met, even if new features have caused traffic to increase, and then search for alternative network configurations if necessary. Virtual integration is used to make a combined analysis of the worst-case delay in the network and the local buses of individual computing modules. This analysis considers the shared network topology, local hardware architectures, and specified IMA configurations. Our approach can be used by a system architect as an effective method for quickly determining which possible system architectures should be pursued to meet timing constraints, and it allows the cascading effects of changes to be tracked and managed. We demonstrate how these heuristics work through an example in which changes are made to an environmental monitoring facility within an avionics system that uses our switch.

Mobile nodes in military environments such as a battlefield or a hostile region are likely to suffer from intermittent network connectivity and frequent partitions. Disruption-tolerant network (DTN) technologies are becoming successful solutions that allow wireless devices carried by soldiers to communicate with each other and access the confidential information or command reliably by exploiting external storage nodes. Some of the most challenging issues in this scenario are the enforcement of authorization policies and the policies update for secure data retrieval. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising cryptographic solution to the access control issues. However, the problem of applying CP-ABE in decentralized DTNs introduces several security and privacy challenges with regard to the attribute revocation, key escrow, and coordination of attributes issued from different authorities. In this paper, we propose a secure data retrieval scheme using CP-ABE for decentralized DTNs where multiple key authorities manage their attributes independently. We demonstrate how to apply the proposed mechanism to securely and efficiently manage the confidential data distributed in the disruption-tolerant military network.

Quality of service (QoS), and in particular reliability and low latency, are essential qualities of safety-critical wireless systems for medical applications. However, wireless links are typically prone to bursts of errors, with characteristics which vary over time. We propose a wireless system suitable for real-time remote patient monitoring in which both the necessary reliability and a bounded end-to-end delay are achieved by an efficient error control scheme. We have paired an example electrocardiography (ECG) application to this wireless system. We also developed a tool chain to assess various combinations of system parameters: we can determine the QoS in terms of the packet-delivery ratio and the end-to-end latency, and also the size of jitter buffer required for seamless ECG monitoring services. A realistic assessment, based on data from the MIT-BIT arrhythmia database, shows that the proposed wireless system can achieve an appropriate level of QoS for real-time ECG monitoring if link-level error control is appropriately implemented.

Automatic interpretation of electrocardiography provides a non-invasive and inexpensive technique to analyze the heart activity for different cardiac conditions. The emergence of smartphones and wireless networks has made it possible to perform continuous Holter monitoring on patients or potential patients. Recently, much attention has been paid to the development of the monitoring methodologies of heart activity, which include both the detection of heartbeats in electrocardiography and the classification of types of heartbeats. However, many studies have focused on classifying limited types of heartbeats. We propose a system for classification into 17 types of heartbeats. This system consists of two parts, the detection and classification of heartbeats. The system detects heartbeats through repetitive features and classifies them using a A-nearest neighbor algorithm. Features such as the QRS complex and P wave were accurately extracted using the Pan-Tompkins algorithm. For the classifier, the distance metric is an adaptation of locally weighted regression. The system was validated with the MIT-BIH Arrhythmia Database. The system achieved a sensitivity of 97.22 % and a specificity of 97.4 % for heartbeat detection. The system also achieved a sensitivity of 97.1 % and a specificity of 96.9 % for classification.

Quality of service (QoS) and, in particular, reliability and a bounded low latency are essential attributes of safety-critical wireless systems for medical applications. However, wireless links are typically prone to bursts of errors, with characteristics which vary over time.We propose a wireless system suitable for real-time remote patient monitoring in which the necessary reliability and guaranteed latency are both achieved by an efficient error control scheme. We have paired an example remote electrocardiography application to this wireless system. We also developed a tool chain that uses a formal description of the proposed wireless medical system architecture in the architecture analysis and design language to assess various combinations of system parameters: we can determine the QoS in terms of packet-delivery ratio and the service latency, and also the size of jitter buffer required for seamless ECG monitoring. A realistic assessment, based on data from the MIT-BIT arrhythmia database, shows that the proposed wireless system can achieve an appropriate level of QoS for real-time ECG monitoring if link-level error control is correctly implemented. Additionally, we present guidelines for the design of energy-efficient link-level error control, derived from energy data, obtained from simulations.

We propose a model-based description and analysis framework for the design of wireless system architectures. Its aim is to address the shortcomings of existing approaches to system verification and the tracking of anomalies in safety-critical wireless systems. We use Architecture Analysis and Description Language (AADL) to describe an analysis-oriented architecture model with highly modular components. We also develop the cooperative tool chains required to analyze the performance of a wireless system by simulation. We show how this framework can support a detailed and largely automated analysis of a complicated, networked wireless system using examples from wireless healthcare and video broadcasting.

Reliability is an essential quality of safety-critical wireless systems for medical applications. However, wireless links are typically prone to bursts of errors, with characteristics which vary over time. We propose a wireless system suitable for realtime remote patient monitoring in which the necessary reliability is achieved by an efficient error control in the link layer. We have paired an example electrocardiography application to this wireless system. We also developed a tool chain to determine the reliability, in terms of the packet-delivery ratio, for various combinations of system parameters. A realistic case study, based on data from the MIT-BIT arrhythmia database, shows that the proposed wireless system can achieve an appropriate level of reliability for electrocardiogram monitoring if link-level error control is correctly implemented.

With the advance of hardware technology, more features are incrementally added to already existing networked systems. Avionics has a stronger tendency to use preexisting applications due to its complexity and scale. As resource sharing becomes intense among the network and the computing modules, it has become a difficult task for the system designer to make confident architectural decisions even for incremental changes. Providing a tailored environment to model and analyze incremental changes requires a combination of software tools and hardware support. We have built a virtual integration tool called ASIIST which can provide a worst-case end-to-end latency of data that is sent through a network and the internal bus architecture of the end-systems. Also, we have devised a new real-time switching algorithm which guarantees the worst-case network delay of preexisting network traffic under feasible conditions. With the real-time switch support, ASIIST can provide an early modularized analysis of the end-to-end latency to make architectural design choices and incremental changes easier for the user.

Scalable video transmission over a network is easily adaptable to different types of mobile experiencing different network conditions. However the transmission of differentiated video packets in an error-prone wireless environment remains problematic. We propose and analyze a cross-layer error control scheme that exploits priority-aware block interleaving (PBI) in the MAC layer for video broadcasting in CDMA2000 systems. The PBI scheme allocates a higher priority to protecting the data which are more critical to the decoding of a video stream, and therefore has more effect on picture quality in the application layer. The use of Reed-Solomon coding in conjunction with PBI in the MAC layer can handle error bursts more effectively if its implementation takes account of underlying error distributions in the physical layer, and differentiates between different types of video packets in the application layer. We also calculate the maximum jitter from the variability of the Reed-Solomon decoding delay and determine the size of jitter buffer needed to prevent interruptions due to buffer underrun. Simulations demonstrate the extent to which we can improve the perceived quality of scalable video.

We explore the use of IEEE 802.11 wireless technology to support health monitoring application, by providing ubiquitous connectivity while allowing patients greater mobility. The acquisition of electrocardiogram (ECG) is used as an example to highlight the engineering choices, such as packetization and access control rules, that must be made in designing a wireless transport. The error-prone nature of wireless channels, together with the unpredictable delay and jitter caused by the non-deterministic nature of access to the wireless medium, need to be considered. We discuss how our design choice with respect to each issue affects the performance of the network in a high-quality ECG monitoring service with bounded delay. We evaluate our schemes using simulations with non-compressed 2-lead ECG data from the MIT-BIH arrhythmia database.

New features are often added incrementally to avionics systems. This avoids redesign and recertification but still requires verifying the timing constraints of both new and existing applications. We introduce a new switch that facilitates this verification by bounding the latency of end-to-end communication across a network. Our clock-driven real-time switching algorithm is throughput-optimal with a bounded worst-case delay for all feasible traffic. Associated heuristics can verify whether the timing constraints of an avionics network are met, after new features have caused traffic to increase, and then search for alternative network configurations if necessary. We show how these heuristics cope with changes to an example environmental monitoring architecture within an avionics system that incorporates our switch. Our approach to analysis can be used to determine, quickly but rigorously, which system architecture meet timing constraints, and it allows the system architect to manage the cascading effects of component changes in a comprehensive manner.

Delay-sensitive real-time applications require average latency to be reduced as far as possible, while maintaining the minimum necessary level of service quality. However, the interaction of these two performance metrics has not received much attention in the current standard air specification of CDMA2000 broadcast services. The authors analyse the performance of the forward error correction employed at the MAC layer of these broadcast services, with respect to the conditions of a fading wireless channel and the size of the error control block (ECB), which is a buffer in the form of a two-dimensional array. As a result, the authors establish the tradeoff between service reliability and delay, and show how it depends on the size of the ECB. The authors go on to propose a novel adaptive error-recovery scheme which dynamically adjusts the size of the ECB to suit the wireless environment of the mobile nodes, using either a moving-average or a weighted-average method. Reducing the service delay as far as possible necessarily compromises the reliability of the service, but the authors scheme still ensures the required minimum reliability. The authors have shown the effectiveness of their scheme through extensive simulations using a detailed threshold model, which accurately describes the statistics of the block error process in a fading channel. The authors also suggest a service implementation scenario to aid in the design.

In telecardiology, electrocardiogram (ECG) signals from a patient are acquired by sensors and transmitted in real time to medical personnel across a wireless network. The use of IEEE 802.11 wireless LANs (WLANs), which are already deployed in many hospitals, can provide ubiquitous connectivity and thus allow cardiology patients greater mobility. However, engineering issues, including the error-prone nature of wireless channels and the unpredictable delay and jitter due to the nondeterministic nature of access to the wireless medium, need to be addressed before telecardiology can be safely realized. We propose a medical-grade WLAN architecture for remote ECG monitoring, which employs the point-coordination function (PCF) for medium access control and Reed-Solomon coding for error control. Realistic simulations with uncompressed two-lead ECG data from the MIT-BIH arrhythmia database demonstrate reliable wireless ECG monitoring; the reliability of ECG transmission exceeds 99.99% with the initial buffering delay of only 2.4 s.

Providing high-quality broadcast services for soft real-time applications over wireless networks such as CDMA2000, which have high bit error rates, requires the control of errors that occur during data transmission. Reed-Solomon (RS) forward error correction (FEC) in the medium access control (MAC) layer performs this role in 3G broadcast services. We propose new analytic models for predicting the performance of RS coding and its execution time, which take into account the memory property of a fading channel, different channel conditions, and a variable level of block interleaving. We identify RS decoding as a significant cause of variability in execution time, taking the form of jitter, which depends on the channel conditions. We analyze the size of buffer required to absorb the jitter under different channel conditions. We then formulate a trade-off between the performance of RS coding and the delay that it causes in transmitting a fixed amount of data with different levels of block interleaving. Finally, we show how to balance the quality with which content is presented against an acceptable buffering delay, which is very important to soft real-time applications, by using an adequate level of block interleaving. This study offers a guide for the provision of efficient broadcast services in real time with stochastically guaranteed quality.

The recent development of high-speed data transmission over wireless cellular networks has enabled the delivery of multimedia broadcasting services to mobile users. These services involve a range of interactions among different system components, including the wireless channel, the network, and mobile devices, making it crucial for the service provider to verify the model, design, and behavior of a new service before it is deployed. However, previous studies have largely relied on network simulations or scaled experiments, and there has been little work on the sort of unified framework for quality-of-service (QoS) assessment, which considers the interactions between components, that we propose in this paper. Accurate models of the wireless channel, the network, and the data processing that takes place on an embedded system of a mobile client, are integrated within our framework, and allow us to predict several key system metrics and the quality of the video stream as it is perceived by users. Furthermore, different models of system components can be easily plugged in to extend this framework. As an example application, we analyze the performance of the process of decoding scalable videos on ARM-based mobile embedded systems in CDMA2000 wireless cellular networks.

Broadcast and multicast services (BCMCS) in CDMA2000 wireless telecommunications networks restrict high- quality multimedia services to their intended recipients by encrypting content. The air specification of BCMCS includes the advance encryption standard (AES) block cipher in the security layer of the broadcast protocol suite. We surveyed the AES block cipher algorithm and characterized the time it requires. We also profiled the execution time of each transformation that comprises the AES cipher. Drawing on these results, we proposed a analytic model that can predict the time required to decrypt encrypted contents at a mobile by considering cross-layer information, such as the characteristics of error control using Reed-Solomon coding in the MAC layer and varying conditions of the fading channel in the physical layer. This model will enable us to estimate the buffer size required to ensure seamless multimedia services.

Using solar power in wireless sensor networks (WSNs) requires a different approach to energy consumption from networks with battery-based nodes. Since solar energy can be harvested periodically, our aim is to use this harvested energy efficiently for specific network-wide performance goals, not just to minimize the energy consumption to sustain the network lifetime. Therefore, we propose a flow control algorithm to maximize the amount of data collected by the network while cooperating with our energy allocation scheme in order to efficiently use the available solar energy which varies from time to time. Our algorithm runs on each node in a distributed manner using only information about its neighbors. We demonstrate the efficacy of our approach on a real solar-powered WSN.

The recent development of high-speed data transmission over wireless networks enables multimedia broadcasting service to mobile users. Multimedia broadcasting service involves interactions among different system and network components, so it is crucial for the service provider to verify the correctness of system/service model and design, and their behaviors before a new type of service is deployed. However, due to limitations of using network simulations or scaled experimental testbeds, there has been none of research on such verification and simulation framework in 3G broadcasting networks. Therefore, we propose a simulation and analysis framework for multimedia broadcasting service over wireless networks. With concrete modeling of wireless physical channel, network, and data processing on a client device, it enables the prediction of various interesting system parameters and perceived quality of multimedia streams to users. Different models of system and network components can be plugged easily in our simulation framework for further extensions. Using this framework, we analyze the processing performance for decoding scalable videos on mobile devices in CDMA2000 wireless networks.

BCMCS (broadcast and multicast services) greatly increase the capacity of CDMA2000 mobile networks for multimedia broadcasting, but only require minor changes to existing radio and core network protocols. To provide high-quality multimedia services over error-prone wireless networks, we need to control delay, because it is one of the most important QoS parameters for multimedia applications. We identified error control using Reed-Solomon (RS) forward error correction (FEC) in the medium access control (MAC) layer as a significant cause of delay variation, and have proposed a novel analytic model that can predict the time required for RS decoding at a mobile, under different levels of block interleaving and for varying conditions of the Rayleigh fading channel at that mobile. This model enables us to estimate the buffer size required to ensure seamless multimedia services.

Superposition interference cancellation (SIC) has been highlighted due to its high spatial reuse efficiency in wireless networks. It exploits superposition aspect of multiple signals to cancel interferences. We present three basic SIC schemes, which are (1) superposition demodulation scheme, (2) superposition decoding scheme and (3) iterative superposition decoding scheme in this paper. When selecting an SIC algorithm among them, the computational overhead and cancellation performance have a trade-off relationship. In a fair communication environment, the receiver can save energy with little loss of cancellation performance by adopting a simple SIC algorithm, such as superposition demodulation. If the network condition looks bad, the node should apply a sophisticated SIC scheme, like iterative superposition decoding, to accommodate near-optimal performance of cancellation. Selecting a different SIC scheme in terms of network condition is called adaptive interference cancellation. The adaptive cancellation scheme draws fairly good interference cancellation performance with relatively low mean computational overhead to save energy consumption.

In cdma2000 1xEV-DO Broadcast and Multicast Services (BCMCS), Reed-Solomon coding is applied to the layers above the turbo code, and is particularly effective in correcting long bursts of errors. An additional block interleaving mechanism improves the performance of RS coding by increasing the time diversity, to an extent controlled by the width of the error control block (ECB), which converts error bursts into random patterns. We propose and evaluate a performance model of RS coding under varying conditions of Rayleigh fading on the broadcast channel, within the levels of block interleaving that the BCMCS specification allows. Simulation results show that our model predicts the performance of RS coding accurately. This evaluation will allow a service provider to determine the most efficient RS code for their target service quality, that maximizes the effective data-rate and minimizes the energy required for the RS decoding process.

In CDMA2000 1xEV-DO broadcast and multicast services (BCMCS), Reed-Solomon (RS) coding is applied to the layers above the turbo code, which is effective in correcting long bursts of errors when an additional block interleaving mechanism is used to increase the time diversity. This is controlled by changing the width of the error control block (ECB), which converts error bursts into random patterns. We propose and evaluate an energy model of the RS decoding process under varying conditions of Rayleigh fading on the broadcast channel, with different levels of block interleaving within those permitted by the BCMCS specification. Simulation results show that our model accurately predicts the energy consumption of the RS decoding process, and is thus able to contribute to the development of energy-efficient broadcast services.

Recent advances in communication and mobile computing have made it possible for cellular networks and digital broadcasting systems to converge on the basis of their common multimedia applications. We analyze the real-time characteristics of a cdma2000 1xEV-DO broadcast and multicast service (BCMCS) system, and propose a system architecture to provide seamless multimedia services. We found that jitter in the processing time of the Reed-Solomon decoder can lead to a discontinuity in multimedia streaming, and we therefore estimated the Reed-Solomon decoding time on a common mobile platform for a given bit error-rate or fading margin in the channel. Next we evaluated the jitter bound and the best cache size for real-time applications in BCMCS. This analysis allows us to suggest a system architecture based on the jitter-EDD model to ensure the continuity of real-time services for multimedia applications over a 3G cellular broadcast network, and to provide a better quality of service.

1xEV-DO is the first wireless standard to support the delivery of broadcast and multicast services (BCMCS). Complementing unicast, which sends content to subscribers individually (e.g. video-on-demand), BCMCS transmits multimedia content from a single source to multiple users simultaneously. In BCMCS, Reed-Solomon coding is applied to the layers above the turbo code, and is particularly effective in correcting long bursts of errors. An additional block interleaving mechanism improves the performance of RS coding by converting the error bursts into a random pattern by increasing the width of the error control block (ECB) to increase the time diversity. We propose and evaluate a performance model of RS coding for varying conditions of Rayleigh fading on the broadcast channel with the maximum amount of block interleaving that the BCMCS specification allows. The fitness of the proposed performance model is verified by comparing the results from the analytic model with a simulation. Our evaluation will allow a service provider to exploit the most efficient RS code for their target service quality.

Abstract The error‐prone nature of the radio channel is the major challenge in servicing video streams over cdma2000 broadcast networks. The MAC protocol for broadcast and multicast services (BCMCS) in cdma2000 specifies forward error correction using Reed–Solomon coding, which is effective in recovering from bursts of errors. However, its performance degrades significantly when channel conditions are bad, a frequent occurrence at the edge of the coverage area, reducing the availability of high‐speed broadcasts. We propose an error recovery scheme and a scheduling algorithm based on the use of slots saved by changing to a Reed–Solomon code with a lower parity overhead. Within the fixed transmission budget thus created, corrupted packets are retransmitted in a priority order determined by a utility function that is derived from the map of the error control block at each mobile, and which also considers the number of mobiles that did not receive each lost packet. Simulation results show the effectiveness of the proposed scheme in improving the quality of high‐data‐rate MPEG‐4 video streams over a range of channel conditions. Copyright © 2007 John Wiley & Sons, Ltd.

Multimedia services over a CDMA2000 broadcast network face a challenge from the unreliable and error-prone nature of the radio channel. Reed–Solomon (RS) coding, integrated with the MAC protocol, is used to cope with this problem. However, performance analysis of RS coding under varying channel conditions shows that it is not always effective, especially for slow-moving nodes which experience relatively long error bursts. Therefore a more efficient scheme is proposed that uses a RS code with reduced parity overhead, and freeing bandwidth can be used flexibly for retransmission. The packets to be retransmitted are prioritised by a utility function derived from the map of the error control block at each mobile node and the number of mobile nodes that require the lost packet. Simulation results show that the gain of retransmission exceeds the loss incurred by reducing the parity, leading to an improvement in the playback quality of MPEG-4 video streams. As a result, service area for high-quality multimedia can be expanded.

The error-prone nature of the radio channel is the major challenge in servicing video streams over cdma2000 1/spl times/EV-DO broadcast networks. Forward error correction is commonly used when broadcasting a video stream over a lossy network: in BCMCS, the MAC protocol uses Reed-Solomon coding. We analyze the performance of Reed-Solomon error recovery with various parameter settings, and prove the correctness of our analysis through experiment. We also propose a hybrid-ARQ scheme to improve error recovery capacity in the BCMCS environment. Simulation results show a significant reduction in packet loss while quantitatively validating our approach.

We analyze the execution time of MAC-layer Reed-Solomon decoding in cdma2000 1xEV-DO broadcast and multicast services (BCMCS), using different cache sizes and a simulated air channel. MPEG-4 video is widely used for wireless multimedia services; however, it requires successive frames within a specific time interval, which can be missed by a MAC-layer Reed-Solomon decoder due to its computation time. We propose static and dynamic proactive Reed-Solomon bypass (PRSB) schemes to alleviate this problem in mobile nodes. Our first scheme bypasses the Reed-Solomon decoding process to satisfy the MPEG-4 time constraint statically, despite varying channel conditions. The second, dynamic scheme corrects errors in a best-effort manner within this same time constraint, leading to a further quality improvement. Extensive simulation results show a dramatic quality improvement in video quality

We analyze the performance of MAC-layer Reed-Solomon error recovery in the CDMA2000 1xEV-DO broadcast and multicast services (BCMCS) environment, with respect to the size of the error control block (ECB) and the air-channel condition, and establish the relationship between ECB size, error-recovery capacity and energy consumption. Real-time traffic, such as voice and video streaming, is very sensitive to delay, but can stand a certain level of packet loss. We therefore propose an energy-efficient size of the ECB to reduce the average energy consumption during error recovery while minimizing the reduction in service quality for real-time multimedia applications. Extensive simulation results suggest that a significant amount of energy can be saved with negligible performance degradation by selecting the appropriate ECB size for the bit error-rate of the forward traffic channel, instead of always choosing the largest possible ECB, with the sole aim of increasing error recovery performance

3GPP2 has recently introduced the Broadcast and Multicast Services (BCMCS) architecture for cdma2000 lxEV-DO wireless networks to enable service providers to broadcast multimedia content such as MPEG-4 video. For MAC-layer forward error correction the BCMCS scheme uses the Reed-Solomon (RS) decoding process, which consumes a considerable amount of energy on mobile phones. To address this problem, we first characterize the energy consumption of the decoder with respect to its components: the data decoder, error locator and erasure decoder. Based on this detailed energy characterization we propose an analytic energy model which takes account of different levels of bit error rate in the forward traffic channel. This model is then verified experimentally on an ARM microprocessor-based testbed. Our results will help design energy-efficient BCMCS systems for 3G cellular networks

We analyze the execution time of MAC-layer Reed-Solomon error recovery in cdma2000 1xEV-DO broadcast and multicast services (BCMCS), with respect to cache size and air channel condition. We observe that a deteriorating channel condition often causes an intolerable delay for real-time tasks because of the error recovery process, even when sufficient cache memory is available to increase CPU throughput. To overcome this problem, we propose proactive Reed-Solomon bypass (PRSB), which determines whether the Reed-Solomon scheme should be used to correct errors in the real-time communication environment. The proposed scheme utilizes the cache effectively, thus preventing reset of the entire decoding process and allowing real-time multimedia applications to meet their deadline constraints. Simulation results show that the PRSB greatly improves the overall playback quality of a video.

cdma2000 1/spl times/EV-DO, one of the prominent 3G mobile communication systems, provides broadcast and multicast services (BCMCS) to meet increasing demand for multimedia data services. Currently, 1/spl times/EV-DO schedules broadcast streams using a slot-based static algorithm, which fails to support dynamic environments where broadcast content is added or removed on-line. We propose a dynamic packet-scheduling algorithm that works with a retransmission scheme for the BCMCS scheduler. Integrated with earliest deadline first (EDF) real-time scheduling, the proposed algorithm not only adapts to dynamic contexts efficiently but also satisfies the real-time requirements of broadcast streams. Furthermore, by exploiting the fine granular scalable (FGS) characteristics of the MPEG-4 part 2 standard, our scheme can avoid abrupt degradation of playback quality by protecting the most important data. Extensive simulations have quantitatively validated the efficiency of our approach.