scholarly journals In-network generalized trustworthy data collection for event detection in cyber-physical systems

2021 ◽  
Vol 7 ◽  
pp. e504
Author(s):  
Hafiz Ur Rahman ◽  
Guojun Wang ◽  
Md Zakirul Alam Bhuiyan ◽  
Jianer Chen
Keyword(s):  
Data Collection ◽  
Event Detection ◽  
Resource Constraints ◽  
Main Idea ◽  
Region Of Interest ◽  
Gray Code ◽  
Cyber Physical Systems ◽  
Logic Device ◽  
Physical Systems ◽  
Faulty Sensors

Sensors in Cyber-Physical Systems (CPS) are typically used to collect various aspects of the region of interest and transmit the data towards upstream nodes for further processing. However, data collection in CPS is often unreliable due to severe resource constraints (e.g., bandwidth and energy), environmental impacts (e.g., equipment faults and noises), and security concerns. Besides, detecting an event through the aggregation in CPS can be intricate and untrustworthy if the sensor's data is not validated during data acquisition, before transmission, and before aggregation. This paper introduces In-network Generalized Trustworthy Data Collection (IGTDC) framework for event detection in CPS. This framework facilitates reliable data for aggregation at the edge of CPS. The main idea of IGTDC is to enable a sensor's module to examine locally whether the event's acquired data is trustworthy before transmitting towards the upstream nodes. It further validates whether the received data can be trusted or not before data aggregation at the sink node. Additionally, IGTDC helps to identify faulty sensors. For reliable event detection, we use collaborative IoT tactics, gate-level modeling with Verilog User Defined Primitive (UDP), and Programmable Logic Device (PLD) to ensure that the event's acquired data is reliable before transmitting towards the upstream nodes. We employ Gray code in gate-level modeling. It helps to ensure that the received data is reliable. Gray code also helps to distinguish a faulty sensor. Through simulation and extensive performance analysis, we demonstrate that the collected data in the IGTDC framework is reliable and can be used in the majority of CPS applications.

scholarly journals On-Demand Mobile Data Collection in Cyber-Physical Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Liang He ◽  
Linghe Kong ◽  
Jun Tao ◽  
Jingdong Xu ◽  
Jianping Pan
Keyword(s):  
Data Collection ◽  
Research Effort ◽  
Large Data ◽  
Cyber Physical Systems ◽  
Data Generation ◽  
Time Data ◽  
Mobile Data ◽  
Mobile Data Collection ◽  
Physical Systems ◽  
On Demand

The collection of sensory data is crucial for cyber-physical systems. Employing mobile agents (MAs) to collect data from sensors offers a new dimension to reduce and balance their energy consumption but leads to large data collection latency due to MAs’ limited velocity. Most existing research effort focuses on the offline mobile data collection (MDC), where the MAs collect data from sensors based on preoptimized tours. However, the efficiency of these offline MDC solutions degrades when the data generation of sensors varies. In this paper, we investigate the on-demand MDC; that is, MAs collect data based on the real-time data collection requests from sensors. Specifically, we construct queuing models to describe the First-Come-First-Serve-based MDC with a single MA and multiple MAs, respectively, laying a theoretical foundation. We also use three examples to show how such analysis guides online MDC in practice.

scholarly journals The Intelligent Space Modeled as a Cyber-Physical System

2019 ◽  
Vol 6 (1.) ◽  
Author(s):  
Csaba Szász
Keyword(s):  
Industry 4.0 ◽  
Technological Development ◽  
Main Idea ◽  
General Rule ◽  
Building Blocks ◽  
Cyber Physical Systems ◽  
Physical Systems ◽  
Intelligent Space ◽  
High Level ◽  
Cps Model

According to a general rule definition, the intelligent space (iSpace) is defined as a location (or space) provided with electronic sensor networks that enable the considered environment with intelligent behaviors. As a result, the considered space will be able to perceive stimulus around them and to understand events that happen its near surrounding. Cyber-physical systems (CPSs) are building blocks in Industry 4.0 that links digital technology and the physical environment in an industrial context. They combine intelligent physical objects and systems on a high level of functions integration. This paper emphasizes the main idea that intelligent spaces may be also modeled as complex cyber-physical systems, as well. This approach has been developed by discussing the theoretical basis of both the iSpaces and CPSs, respectively unfolding a short comparison between their basic behaviors. As a concrete example, the CPS model of a given iSpace framework is presented and discussed widely in the paper. This model has been experimented by using a Field Programmable Gate Array (FPGA) processor-based ready-to-use development systems and software technologies that handles reconfigurable hardware technology. The implementation proves that the developed CPS model is well feasible and expresses in all the main behaviors and functions of iSpaces. It is also mentioned that the actual stage of the technological development terms and scientific areas related to iSpaces and CPSs overlaps. In fact, this is not surprising at all by considering nowadays evidence that iSpaces are widely present and shared components in modern manufactory processes that are an inherent part of Industry 4.0 vision and reality.

scholarly journals Guidelines for Managing Sensors in Cyber Physical Systems with Multiple Sensors

2011 ◽  
Vol 2011 ◽  
pp. 1-15 ◽  
Author(s):  
Pradeepkumar Ashok ◽  
Ganesh Krishnamoorthy ◽  
Delbert Tesar
Keyword(s):  
Cyber Physical Systems ◽  
False Alarms ◽  
Physical Processes ◽  
Physical Systems ◽  
Multiple Sensors ◽  
Relationship Network ◽  
And Performance ◽  
Faulty Sensors ◽  
Integrate Data ◽  
Careful Design

Cyber physical systems (CPSs) typically have numerous sensors monitoring the various physical processes involved. Some sensor failures are inevitable and may have catastrophic effects. The relational nature of the diverse measurands can be very useful in detecting faulty sensors, monitoring the health of the system, and reducing false alarms. This paper provides procedures on how one may integrate data from the various sensors, by careful design of a sensor relationship network. Once such a network has been adopted, choices become available in real time for enhancing the reliability, safety, and performance of the overall system.

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