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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jss.2006.09.029 Byline: Tian He, John A. Stankovic, Michael Marley, Chenyang Lu, Ying Lu, Tarek Abdelzaher, Sang Son, Gang Tao Keywords: Real-time; Feedback control; Quality of service; Scheduling Abstract: The resource management in distributed real-time systems becomes increasingly unpredictable with the proliferation of data-driven applications. Therefore, it is inefficient to allocate the resources statically to handle a set of highly dynamic tasks whose resource requirements (e.g., execution time) are unknown a prior. In this paper, we build a distributed real-time system based on the control theory, focusing on the computational resource management. Specifically, this work makes three important contributions. First, it allows the designer to specify the desired temporal behavior of system adaptation, such as the speed of convergence. This is in contrast to previous literature, specifying only steady-state metrics, e.g. the deadline miss ratio. Second, unlike QoS optimization approaches, our solution meets performance guarantees with no accurate knowledge of task execution parameters - a key advantage in a poorly modeled environment. Last, in contrast to ad hoc algorithms based on intuition and testing, we rigorously prove that our approach not only has excellent steady state behavior, but also meets stability, overshoot, and settling time requirements. Author Affiliation: Department of Computer Science, University of Virginia, 151 Engineer's Way, Charlottesville, VA 22904-4740, United States

The design and implementation of an adaptive Web server architecture are presented to provide relative and absolute connection delay guarantees for different service classes. The adaptive architecture based on feedback control loops and the theoretic techniques used to suitably model and design the feedback loops are described, and experimental results demonstrate that the adaptive server provides robust delay guarantees even when workload varies significantly.

A spatiotemporal communication protocol for sensor networks called SPEED is presented. End-to-end soft real-time communication is achieved by maintaining a desired delivery speed across the sensor network through a novel combination of feedback control and nondeterministic geographic forwarding.

The demand for real-time data services is increasing in many applications including e-commerce, agile manufacturing, and telecommunications network management. In these applications, it is desirable to execute transactions within their deadlines, i.e., before the real-world status changes, using fresh (temporally consistent) data. However, meeting these fundamental requirements is challenging due to dynamic workloads and data access patterns in these applications. Further, transaction timeliness and data freshness requirements may conflict. In this paper, we define average/transient deadline miss ratio and new data freshness metrics to let a database administrator specify the desired quality of real-time data services for a specific application. We also present a novel QoS management architecture for real-time databases to support the desired QoS even in the presence of unpredictable workloads and access patterns. To prevent overload and support the desired QoS, the presented architecture applies feedback control, admission control, and flexible freshness management schemes. A simulation study shows that our QoS-aware approach can achieve a near zero miss ratio and perfect freshness, meeting basic requirements for real-time transaction processing. In contrast, baseline approaches fail to support the desired miss ratio and/or freshness in the presence of unpredictable workloads and data access patterns. Index Terms--Real-time database, deadline miss ratio, sensor data freshness, QoS management.

In this paper, issues involved in the design of a real-time database which maintains data temporal consistency are discussed. The concept of data-deadline is introduced and time cognizant transaction scheduling policies are proposed. Informally, data-deadline is a deadline assigned to a transaction due to the temporal constraints of the data accessed by the transaction. Further, two time cognizant forced wait policies which improve performance significantly by forcing a transaction to delay further execution until a new version of sensor data becomes available are proposed. A way to exploit temporal data similarity to improve performance is also proposed. Finally, these policies are evaluated through detailed simulation experiments. The simulation results show that taking advantage of temporal data semantics in transaction scheduling can significantly improve the performance of user transactions in real-time database systems. In particular, it is demonstrated that under the forced wait policy, the performance can be improved significantly. Further improvements result by exploiting data similarity. Index Terms--Real-time database systems, temporal consistency, earliest deadline first, least slack first, data-deadline, transaction processing.

COVID-19 has caused many disruptions in conducting smart health research. Both in-lab sessions and in-home deployments had to be delayed or canceled because in-person meetings were no longer allowed. Our research project on "in-home monitoring with personalized recommendations to reduce the stress of caregivers of Alzheimer's patients" was affected. To enable continued research without any person-to-person contact, we created an out-of-the-box deployment solution. The solution is multifaceted and deals with everything from technical adjustments, deployment documentation, EMA additions, additional monitoring software, use of videos, Zoom and TeamViewer, budget changes, new logistics, and changes to IRBs. This article briefly describes the purpose and design of the original system and then articulates the necessitated changes. We also provide lessons learned and an initial evaluation of the effectiveness of the solutions after the changes. The evaluation surveys the opinions of seven people that assembled, initialized, and deployed our system in home environments. We believe that the various solutions we developed can be applied to other similar projects, and will be helpful to new projects even when personal contact returns. [ABSTRACT FROM AUTHOR]

Healthcare cognitive assistants (HCAs) are intelligent systems or agents that interact with users in a context-aware and adaptive manner to improve their health outcomes by augmenting their cognitive abilities or complementing a cognitive impairment. They assist a wide variety of users ranging from patients to their healthcare providers (e.g., general practitioner, specialist, surgeon) in several situations (e.g., remote patient monitoring, emergency response, robotic surgery). While HCAs are critical to ensure personalized, scalable, and efficient healthcare, there exists a knowledge gap in finding the emerging trends, key challenges, design guidelines, and state-of-the-art technologies suitable for developing HCAs. This survey aims to bridge this gap for researchers from multiple domains, including but not limited to cyber-physical systems, artificial intelligence, human-computer interaction, robotics, and smart health. It provides a comprehensive definition of HCAs and outlines a novel, practical categorization of existing HCAs according to their target user role and the underlying application goals. This survey summarizes and assorts existing HCAs based on their characteristic features (i.e., interactive, context-aware, and adaptive) and enabling technological aspects (i.e., sensing, actuation, control, and computation). Finally, it identifies critical research questions and design recommendations to accelerate the development of the next generation of cognitive assistants for healthcare. [ABSTRACT FROM AUTHOR]

1. INTRODUCTION Real-time computing is an enabling technology for many important application areas, including process control, nuclear power plants, agile manufacturing, intelligent vehicle highway systems, avionics, air-traffic control, telecommunications (the […]

How can the advantages of formal methods be brought to emerging smart cities? We discuss several core challenges and our recent efforts as the first step toward developing novel formal methods to ensure safety and performance in smart cities. [ABSTRACT FROM AUTHOR]

Predictive monitoring--making predictions about future states and monitoring if the predicted states satisfy requirements--offers a promising paradigm in supporting the decision making of Cyber-Physical Systems (CPS). Existing works of predictive monitoring mostly focus on monitoring individual predictions rather than sequential predictions. We develop a novel approach for monitoring sequential predictions generated from Bayesian Recurrent Neural Networks (RNNs) that can capture the inherent uncertainty in CPS, drawing on insights from our study of real-world CPS datasets. We propose a new logic named Signal Temporal Logic with Uncertainty (STL-U) to monitor a flowpipe containing an infinite set of uncertain sequences predicted by Bayesian RNNs. We define STL-U strong and weak satisfaction semantics based on whether all or some sequences contained in a flowpipe satisfy the requirement. We also develop methods to compute the range of confidence levels under which a flowpipe is guaranteed to strongly (weakly) satisfy an STL-U formula. Furthermore, we develop novel criteria that leverage STL-U monitoring results to calibrate the uncertainty estimation in Bayesian RNNs. Finally, we evaluate the proposed approach via experiments with real-world CPS datasets and a simulated smart city case study, which show very encouraging results of STL-U based predictive monitoring approach outperforming baselines. [ABSTRACT FROM AUTHOR]

Basic Reclaiming and Reclaiming with Early Start, two resource reclaiming algorithms, are correct and simple. Both these algorithms have been implemented to demonstrate that the algorithms are applicable in the Spring Kernel. Resource reclaiming is very important and very effective in dynamic real time multiprocessor environments.

Aim: The aim of this study is to develop a Smarthealth system of monitoring, modelling, and interactive recommendation solutions (for caregivers) for in‐home dementia patient care that focuses on caregiver–patient relationships. Design: This descriptive study employs a single‐group, non‐randomized trial to examine functionality, effectiveness, feasibility, and acceptability of the novel Smarthealth system. Methods: Thirty persons with Alzheimer's Disease or related dementia and their family caregivers (N = 30 dyads) will receive and install Smarthealth technology in their home. There will be a 1‐month observation phase for collecting baseline mood states and a 2‐month implementation phase when caregivers will receive stress management techniques for each detected, negative mood state. Caregivers will report technique implementation and usefulness, sent via Ecological Momentary Assessment system to the study‐provided smartphone. Caregivers will provide daily, self‐reported mood and health ratings. Instruments measuring caregiver assessment of disruptive behaviours and their effect on caregivers; caregiver depressive symptoms, anxiety and stress; caregiver strain; and family functioning will be completed at baseline and 3 months. The study received funding in 2018 and ethics board approval in 2019. Discussion: This study will develop and test novel in‐home technology to improve family caregiving relationships. Results from this study will help develop and improve the Smarthealth recommendation system and determine its usefulness, feasibility, and acceptability for persons with dementia and their family caregiver. Impact: The Smarthealth technology discussed will provide in‐home stress reduction resources at a time when older adults may be experiencing increasingly high rates of isolation and anxiety and caregiver dyads may be experiencing high levels of relationship strain. Trial Registration: This study was registered with Clinical Trials.gov (Identifier NCT04536701). [ABSTRACT FROM AUTHOR]