Topic
Cyber-physical system
About: Cyber-physical system is a research topic. Over the lifetime, 11096 publications have been published within this topic receiving 162489 citations. The topic is also known as: CPS.
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TL;DR: Predictive control methods can provide a basis to tackle the appearing challenges: the efficient and easy implementation of predictive control on omnipresent embedded computation hardware, the question of resource and network aware control, as well as control on the network level of systems of systems.
64 citations
TL;DR: This paper addresses the aspect of data, aiming at addressing that power laws may yet be a universality of data in CPNSs and discusses that power-law-type data may be governed by stochastically differential equations of fractional order.
Abstract: Cyber-physical networking systems (CPNSs) are made up of various physical systems that are heterogeneous in nature. Therefore, exploring universalities in CPNSs for either data or systems is desired in its fundamental theory. This paper is in the aspect of data, aiming at addressing that power laws may yet be a universality of data in CPNSs. The contributions of this paper are in triple folds. First, we provide a short tutorial about power laws. Then, we address the power laws related to some physical systems. Finally, we discuss that power-law-type data may be governed by stochastically differential equations of fractional order. As a side product, we present the point of view that the upper bound of data flow at large-time scaling and the small one also follows power laws.
64 citations
TL;DR: A novel incentive scheme based on the reputation of social users to encourage users to contribute sourcing data in CPSS and an auction game model is developed to help CPSS select the optimal social user to obtain the needed data.
Abstract: Cyber-physical social system (CPSS) has emerged as a new paradigm to help social users share and exchange data by the close association with the cyberspace and physical world. To further improve the performance of CPSS, the incentive computing scheme to provide efficient crowd sourcing in the CPPS becomes a challenge. Therefore, in this paper we propose a novel incentive scheme for CPSS based on the reputation of social users. First, we present a framework to provide crowd sourcing service in CPSS by dividing social users into three types, which are malicious users, speculative users and honest users, respectively. Second, based on the reputation of social users, an incentive scheme is proposed to encourage users to contribute sourcing data. Next, an auction game model is developed to help CPSS select the optimal social user to obtain the needed data. Finally, simulation results show that the proposal can obtain a lower cost and higher data accuracy than other conventional methods.
64 citations
01 Jan 2020
TL;DR: This chapter presents an analysis of the impact that WBAN has on health care and provides some definitions of Medical Cyber-Physical Systems (MCPSs) and Digital Twins along with technological enablers such as cloud and IoT.
Abstract: Cyber-Physical Systems and Digital Twins are commonly used today in the industrial sector, and the healthcare sector is keen to implement these technological solutions to enhance their capabilities and offer better services for patient care provision. In fact, the adoption of Wireless Body Area Networks (WBAN) based on IoT along with cloud computing systems has led to the development of new methodologies to monitor and treat patients. However, the adoption of the new technologies comes with several challenges in terms of performance and security. Considering that, WBAN can be wearable or implanted under the skin, and the overall concept leads to several cybersecurity challenges that would require deeper investigation. This chapter presents an analysis of the impact that WBAN has on health care. It also provides some definitions of Medical Cyber-Physical Systems (MCPSs) and Digital Twins along with technological enablers such as cloud and IoT.
63 citations
Posted Content•
TL;DR: This paper studies the scheduling of sensor transmissions to estimate the states of multiple remote, dynamic processes using a Deep Q-Network, a recent deep reinforcement learning algorithm that is at once scalable and model-free.
Abstract: In many Cyber-Physical Systems, we encounter the problem of remote state estimation of geographically distributed and remote physical processes This paper studies the scheduling of sensor transmissions to estimate the states of multiple remote, dynamic processes Information from the different sensors have to be transmitted to a central gateway over a wireless network for monitoring purposes, where typically fewer wireless channels are available than there are processes to be monitored For effective estimation at the gateway, the sensors need to be scheduled appropriately, ie, at each time instant one needs to decide which sensors have network access and which ones do not To address this scheduling problem, we formulate an associated Markov decision process (MDP) This MDP is then solved using a Deep Q-Network, a recent deep reinforcement learning algorithm that is at once scalable and model-free We compare our scheduling algorithm to popular scheduling algorithms such as round-robin and reduced-waiting-time, among others Our algorithm is shown to significantly outperform these algorithms for many example scenarios
63 citations