ACM TCPS

ACM Transactions on

Cyber-Physical Systems (TCPS)

the premier journal for the publication of high-quality original research papers and survey papers that have scientific and technological understanding of the interactions of information processing, networking and physical processes

Latest Articles

Introduction

Tei Kuo

Research Directions for Cyber Physical Systems in Wireless and Mobile Healthcare

John A. Stankovic

Wireless and mobile healthcare systems are proliferating. Many of these systems are Cyber Physical Systems (CPS). However, the complexities involved... (more)

Design and Realization of WInternet

Jianjia Wu, Wei Zhao

In recent years, Internet of Things (IoT) has attracted great attention from academia, industry, and government. IoT is considered to be a networking infrastructure that can connect enormous physical objects and has great potential to extend mankind's capabilities in monitoring, analyzing, and controlling the physical space using cyber... (more)

Fundamental Limits of Cyber-Physical Systems Modeling

Edward A. Lee

This article examines the role of modeling in the engineering of cyber-physical systems. It argues that the role that models play in engineering is... (more)

Heterogeneous Model Integration for Multi-Source Urban Infrastructure Data

Desheng Zhang, Juanjuan Zhao, Fan Zhang, Tian He, Haengju Lee, Sang H. Son

Data-driven modeling usually suffers from data sparsity, especially for large-scale modeling for urban phenomena based on single-source... (more)

NEWS

CFP: Special Issue on Dependability in Cyber Physical Systems and Applications
This special issue focuses on bringing together current research ideas and techniques from researchers and practitioners belonging to a myriad of research areas, with the final goal of sharing their specific challenges and solutions for CPS dependability. More specifically, contributions related to dependability aspects of CPS applications/systems in practice are of interest. For more information, visit the Special Issue webpage.

CFP: Special Issue on Medical Cyber-Physical Systems

This special issue seeks papers describing significant research contributions in the domain of medical cyber-physical systems; each paper should show enough evidence of contributions to medical cyber-physical systems applications and systems in practice. For more information, visit the Special Issue webpage.

CFP: Special Issue on Internet of Things

This special issue focuses on the technical issues we face when designing, engineering, deploying, and maintaining the IoT. We seek high-quality and unpublished papers that push research in all the facets of the IoT. Contributions may present and solve open technical problems, integrate novel solutions efficiently, and focus on the performance evaluation and comparison with existing standards. Both theoretical and experimental studies are welcome. For more information, visit the Special Issue webpage.

CFP: Special Issue on Smart Homes, Buildings, and Infrastructures

The purpose of this special issue is to present the state-of-the-art CPS research for building efficient smart homes, buildings, and infrastructures. The submissions should address the above challenges with a system perspective that includes both cyber and physical aspects, and should articulate how proposed approaches may be applied in practical CPS systems. For more information, visit the Special Issue webpage.

About TCPS

Cyber-Physical Systems (CPS) has emerged as a unifying name for systems where the cyber parts, i.e., the computing and communication parts, and the physical parts are tightly integrated, both at the design time and during operation. Such systems use computations and communication deeply embedded in and interacting with physical processes to add new capabilities to physical systems. These cyber-physical systems range from miniscule (pace makers) to large-scale (a national power-grid). There is an emerging consensus that new methodologies and tools need to be developed to support cyber-physical systems. READ MORE

Forthcoming Articles

A Cloud-based Black Box Solar Predictor for Smart Homes Mr. Srinivasan Iyengar(University of Massachusetts Amherst); Dr. Navin Sharma(University of Massachusetts Amherst); Mr. David Irwin(University of Massachusetts Amherst); Mr. Prashant Shenoy(University of Massachusetts Amherst); Prof. Krithi Ramamritham(Indian Institute of Technology Bombay)

The popularity of rooftop solar for homes is rapidly growing. However, accurately forecasting solar generation is critical to fully exploiting the benefits of locally-generated solar energy. In this paper, we present two machine learning techniques to predict solar power from publicly-available weather forecasts. We use these techniques to develop SolarCast, a cloud-based web service, which automatically generates models that provide customized site-specific predictions of solar generation. SolarCast utilizes a ``black box'' approach that requires only i) a site's geographic location and ii) a minimal amount of historical generation data. Since we intend SolarCast for small rooftop deployments, it does not require detailed site- and panel-specific information, which owners may not know, but instead automatically learns these parameters for each site. We evaluate the accuracy of SolarCast's different algorithms on two publicly available datasets, each containing over one hundred rooftop deployments with a variety of attributes, e.g., climate, tilt, orientation, etc. We show that SolarCast learns a more accurate model using much less data (~1 month) than prior SVM-based approaches, which require ~3 months of data. SolarCast also provides a programmatic API, enabling developers to integrate its predictions into energy-efficiency applications. Finally, we present two case studies of using SolarCast to demonstrate how real-world applications can leverage its predictions. We first evaluate a ``sunny" load scheduler, which schedules a dryer's energy usage to maximally align with a home's solar generation. We then evaluate a smart solar-powered charging station, which can optimally charge the maximum number of electric vehicles (EVs) on a given day. Our results indicate that a representative home is capable of reducing its grid demand up to 40% by providing a modest amount of flexibility (of ~5 hours) in the dryer's start time with opportunistic load scheduling. Further, our charging station uses SolarCast to provide EV owners the amount of energy they can expect to receive from solar energy sources.

Demand Response and Smart Buildings: A Survey of Control, Communication, and Cyber-Physical Security Dr. Junjian Qi(Argonne National Laboratory); Dr. Youngjin Kim(Argonne National Laboratory); Dr. Chen Chen(Argonne National Laboratory); Dr. Xiaonan Lu(Argonne National Laboratory); Dr. Jianhui Wang(Argonne National Laboratory)

In this paper, we perform a comprehensive survey to the technical aspects related to the implementation of demand response and smart buildings. Specifically, we discuss various smart loads such as heating, ventilating, and air-conditioning (HVAC) systems and plug-in electric vehicles (PEVs), the power architecture with multi-bus characteristics, different control algorithms such as the hybrid centralized and decentralized control and the distributed coordination among buildings, the communication technologies and network architectures, and the potential cyber-physical security issues and possible mechanisms for enhancing the system security at both cyber and physical layers. The current status of the demand response in United States, Europe, Japan, and China is reviewed, and the benefits, costs, and challenges of implementing and operating demand response and smart buildings are also discussed.