From sensor to decision – augmented and automated decision-making based on real-time data

Among the new trends in technology that have emerged through the Industry 4.0, Cyber Physical Systems (CPS) and Internet of Things (IoT) are crucial for the real-time data acquisition. This data acquisition, together with its transformation in valuable information, are indispensable for the development of real-time indicators. Moreover, real-time indicators provide companies with a competitive advantage over the competition since they enhance the calculus and speed up the decision-making and failure detection. Our research highlights the advantages of real-time data acquisition for supply chains, developing indicators that would be impossible to achieve with traditional systems, improving the accuracy of the existing ones and enhancing the real-time decision-making. Moreover, it brings out the importance of integrating technologies 4.0 in industry, in this case, CPS and IoT, and establishes the main points for a future research agenda of this topic.

Download Full-text

Towards a Stratified Multi-Criteria Decision-Making in the Real-Time Data Processing

2019 International Conference on Virtual Reality and Visualization (ICVRV) ◽

10.1109/icvrv47840.2019.00042 ◽

2019 ◽

Author(s):

Mario Jose Divan

Keyword(s):

Decision Making ◽

Data Processing ◽

Real Time ◽

Multi Criteria Decision Making ◽

Time Data ◽

The Real ◽

Real Time Data ◽

Real Time Data Processing

Download Full-text

Still normal? Contextualizing real-time data with long-term statistics to monitor anomalies and systematic changes in storm surge activity – Introduction of a prototype web tool storm surge monitor for the German coasts

10.5194/nhess-2021-75 ◽

2021 ◽

Author(s):

Xin Liu ◽

Insa Meinke ◽

Ralf Weisse

Keyword(s):

Decision Making ◽

Real Time ◽

Storm Surge ◽

Storm Surges ◽

Added Value ◽

Time Data ◽

Storm Activity ◽

The Public ◽

Real Time Data

Abstract. Storm surges represent a major threat to many low-lying coastal areas in the world. While most places can cope with or are more or less adapted to present-day risks, future risks may increase from factors such as sea level rise, subsidence, or changes in storm activity. This may require further or alternative adaptation and strategies. For most places, both forecasts and real-time observations are available. However, analyses of long-term changes or recent severe extremes that are important for decision-making are usually only available sporadically or with substantial delay. In this paper, we propose to contextualize real-time data with long-term statistics to make such information publicly available in near real-time. We implement and demonstrate the concept of a ”storm surge monitor” for tide gauges along the German North Sea and Baltic Sea coasts. It provides automated near real-time assessments of the course and severity of the ongoing storm surge season and its single events. The assessment is provided in terms of storm surge height, frequency, duration, and intensity. It is proposed that such near real-time assessments provide added value to the public and decision-making. It is further suggested that the concept is transferable to other coastal regions threatened by storm surges.

Download Full-text

IoT-based system for communication and coordination of football robot team

Internet Research ◽

10.1108/intr-02-2016-0056 ◽

2017 ◽

Vol 27 (2) ◽

pp. 162-181 ◽

Cited By ~ 18

Author(s):

Zhuming Bi ◽

Guoping Wang ◽

Li Da Xu ◽

Matt Thompson ◽

Raihan Mir ◽

...

Keyword(s):

Decision Making ◽

Information System ◽

Sensor Networks ◽

Real Time ◽

System Level ◽

Time Data ◽

Content Type ◽

Iot Applications ◽

Real Time Data ◽

Communication And Coordination

Purpose The purpose of this paper is to develop an information system which is based on the Internet of things (IoT) and used to support the communication and coordination in a cooperative robot team. Design/methodology/approach The architecture of the IoT applications for decision-making activities in a complex system is elaborated, the focus lies on the effective implementation of system interactions at the device-level. A case study is provided to verify system performances. Findings The IoT concept has been introduced in an information system of a football robot team to support the coordination among team players. Various sensors are used to collect data from IoT, and data are processed for the controls of robotic players to achieve the better performance at the system level. The field test has shown the feasibility and effectiveness. Research limitations/implications To investigate how IoT can be utilized in an information system for making complex decisions effectively, the authors use the decision-support system for a football robot team to illustrate the approaches in developing data acquisition infrastructure, processing and utilizing real-time data for the communication and coordination of robot players in a dynamic competing environment. While the presented work has shown the feasibility of an IoT-based information system, more work are needed to integrate advanced sensors within the IoT and develop more intelligent algorithms to replace manually remote control for the operations of robot players. Practical implications The proposed system is specifically for a football robot team; however, the associated approaches are applicable to any decentralized system for developing an information system to support IoT-based communication and coordination within the system in the real-time mode. Originality/value The exploration of IoT applications is still at its early stage, existing relevant work is mostly limited to the development of system architecture, sensor networks, and communication protocols. In this paper, the methods on how to use massive real-time data for decision-making of a decentralized team have been investigated, and the proposed system has its theoretical significance to developing other decentralized wireless sensor networks and decision-making systems.

Download Full-text

Monitoring of Microphysiological Systems: Integrating Sensors and Real-Time Data Analysis toward Autonomous Decision-Making

ACS Sensors ◽

10.1021/acssensors.8b01549 ◽

2019 ◽

Vol 4 (6) ◽

pp. 1454-1464 ◽

Cited By ~ 9

Author(s):

Ashlyn T. Young ◽

Kristina R. Rivera ◽

Patrick D. Erb ◽

Michael A. Daniele

Keyword(s):

Decision Making ◽

Data Analysis ◽

Real Time ◽

Time Data ◽

Autonomous Decision ◽

Microphysiological Systems ◽

Real Time Data

Download Full-text

London Heathrow Airport Uses Real-Time Analytics for Improving Operations

INFORMS Journal on Applied Analytics ◽

10.1287/inte.2020.1044 ◽

2020 ◽

Vol 50 (5) ◽

pp. 325-339

Author(s):

Xiaojia Guo ◽

Yael Grushka-Cockayne ◽

Bert De Reyck

Keyword(s):

Machine Learning ◽

Decision Making ◽

Real Time ◽

Data Sharing ◽

Time Data ◽

Collaborative Decision Making ◽

Staffing Levels ◽

Real Time Data ◽

Passenger Flows ◽

Collaborative Decision

Improving airport collaborative decision making is at the heart of airport operations centers (APOCs) recently established in several major European airports. In this paper, we describe a project commissioned by Eurocontrol, the organization in charge of the safety and seamless flow of European air traffic. The project’s goal was to examine the opportunities offered by the colocation and real-time data sharing in the APOC at London’s Heathrow airport, arguably the most advanced of its type in Europe. We developed and implemented a pilot study of a real-time data-sharing and collaborative decision-making process, selected to improve the efficiency of Heathrow’s operations. In this paper, we describe the process of how we chose the subject of the pilot, namely the improvement of transfer-passenger flows through the airport, and how we helped Heathrow move from its existing legacy system for managing passenger flows to an advanced machine learning–based approach using real-time inputs. The system, which is now in operation at Heathrow, can predict which passengers are likely to miss their connecting flights, reducing the likelihood that departures will incur delays while waiting for delayed passengers. This can be done by off-loading passengers in advance, by expediting passengers through the airport, or by modifying the departure times of aircraft in advance. By aggregating estimated passenger arrival time at various points throughout the airport, the system also improves passenger experiences at the immigration and security desks by enabling modifications to staffing levels in advance of expected surges in arrivals. The nine-stage framework we present here can support the development and implementation of other real-time, data-driven systems. To the best of our knowledge, the proposed system is the first to use machine learning to model passenger flows in an airport.

Download Full-text