LAURA architecture: Towards a simpler way of building situation-aware and business-aware IoT applications

Teixeira, Sergio; Agrizzi, Bruno Alves; Filho, José Gonçalves Pereira; Rossetto, Silvana; Pereira, I. S. A.; Costa, Paolo; Branco, Adriano; Martinelli, Ruan Rocha

doi:10.1016/j.jss.2019.110494

Cited by 16 publications

(11 citation statements)

References 45 publications

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“…Studies related to high level sensor management are reported in [6,7], where a situation aware as well as business aware framework is proposed with improvements reported in homeland security and surveillance. They propose a high-level management approach to sensor control.…”

Section: Related Workmentioning

confidence: 99%

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IFAA: An Intelligent Framework Aware Algorithm to Determine the Boundary of Area under Attack in Military Surveillance and Reconnaissance WSN

Raj¹,

Babu²

2022

RIA

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Wireless sensor networks (WSNs) have proven effective in military applications of surveillance and reconnaissance. Sensors capable of detecting pressure, temperature, movement and presence of specific chemicals are deployed in such applications. Traditionally, sensor data is collected and transferred to a centralized high-capacity node or control station. Analysis of data is carried out at such centralized facilities. Information or intelligence gathered from sensor data after analysis is used to generate control and management commands that are relayed back to sensor nodes. The situation is analogous to an actual wartime scenario where soldiers who are on the field are equivalent to the sensors. Soldiers observe and sense the situation and communicate their observations to the decision maker who is stationed in the control tent. On gathering field information, the decision maker analyses the data and arrives at his decision which is again communicated to the soldiers on the field. Soldiers as well as sensors are not placed illogically or randomly but intentionally and strategically. Observations made on the field ultimately affect how the soldiers or sensors continue to function. Intelligence gained on the field ultimately gets used on the field itself. Our attempt is to observe, analyze and apply intelligence on the field itself. This work proposes an intelligent algorithm that is aware of the sensor network topology, analyses sensor data within the network and uses the network framework to arrive at usable intelligence. Locally generated intelligence avoids communication to and from the command/control and adds value to military surveillance and reconnaissance applications of WSN. Intelligent sensor management allows us to use just the necessary number of sensors while saving resources on otherwise redundant expenditure. In the present work we have designed and applied a dynamic boundary computation algorithm to determine the boundary of the area under attack. We have compared the results of simulation experiments incorporating the proposed algorithm against a control experiment without the algorithm.

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Section: Related Workmentioning

confidence: 99%

“…The research presented in Teixeira et al [6] proposes an abstraction approach to situation awareness in a business environment. The architecture proposed is flexible and enables scaling.…”

Section: Related Workmentioning

confidence: 99%

IFAA: An Intelligent Framework Aware Algorithm to Determine the Boundary of Area under Attack in Military Surveillance and Reconnaissance WSN

Raj¹,

Babu²

2022

RIA

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“…NCs in production may be observed from the viewpoints of many different disciplines, including management, economics, environment, technics or technology (Závadská and Závadský 2018). These facts are derived from a modern industrial organisation's goals, reduce production costs and amount of misused raw materials, and increase public awareness of environmental sustainability (Marques et al 2019, Teixeira et al .2020). Giorgetti et al (2017) introduced NCs reporting through the holistic model based on Root Cause Analysis and TRIZ method.…”

Section: Nonconformity Managementmentioning

confidence: 99%

A novel JavaScript MEAN stack approach for real-time nonconformity management in SMEs within Industry 4.0 and Quality 4.0 concepts

Đorđević¹,

Stefanović²,

Cvetić³

et al. 2021

Preprint

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Nonconformity detection and reporting are evergoing issues in quality management practice and theory. Throughout industrialisation, and consequently, in the Industry 4.0 period, manufacturing organisations strived to reduce nonconformities to the minimum level and reach zero-defect production. Nowadays, it is feasible to overcome this issue by incorporating adequate information and communication technologies and edge devices for nonconformity reporting, and nonconformity classification. These assumptions were a starting point for the research presented in this paper. This paper aims to propose a software solution for nonconformity detection and preventive and corrective actions definition that expands the utilisation of edge devices in compliance with Industry 4.0 and Quality 4.0 paradigms. Presented software solution design is based on JavaScript programming language and its ability to be implemented in all software solution's tiers through interconnected frameworks (MongoDB, Express.js, Angular, and Node.js) in a MEAN stack. The developed solution is implemented in three small and medium enterprises. Initial results show several benefits in increasing nonconformity detection and reporting, increasing employee participation in preventive and corrective actions definition and improved quality management system. The number of corrective and preventive actions was taken based on the assistance of the prediction module. All nonconformities were related to specific sections of ISO 9001:2015 standards so that quality managers and managers in the companies could have insight into the sources of the issues and the foundation for defining different managerial actions. When comparing the newly developed solution and other affordable solutions, it was determined that the new solution has a higher operating velocity if there is a significant increase in the volume of queries towards databases. The paper's main contribution reflects in the software solution's presentation intended for affordable identification and nonconformities massive workload reporting, integrated with other software modules for quality analysis, prediction, and problem-solving. In this way, it is possible to obtain horizontal scalability and richness of the proposed software solution for smart enterprises focused on World Class manufacturing and lean manufacturing. Furthermore, it is possible to achieve a shorter time for nonconformities elimination and better compliance with the ISO 9001: 2015 standard. The presented solution usage contributes to all participants involved in the organisation of production and production itself, through (1) involvement of all employees; (2) digitalisation and improvement of existing nonconformity reporting systems; (3) improvement of nonconformity perception; (4) improved awareness and evaluation of employer's contribution to nonconformity management; (5) making the technology easy to use. The research's novelty lies in presented solution utilisation for real-time nonconformity classification and proactive and corrective measures proposals through communication and prediction modules.

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“…Recent developments in sensor devices, communication technology, ubiquitous computing, artificial intelligence, and wireless sensor network (WSN) gained momentum to the adoption of IoT based applications [14,15]. Internet of things combined with cloud computing and big data analytics is speeding up the advancement of solutions to monitor the mobility of traffic in smart cities [16,17].…”

Section: Literature Reviewmentioning

confidence: 99%

IoT Based Smart Parking System Using Deep Long Short Memory Network

Ali

Irfan

et al. 2020

Electronics

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Traffic congestion is one of the most notable urban transport problems, as it causes high energy consumption and air pollution. Unavailability of free parking spaces is one of the major reasons for traffic jams. Congestion and parking are interrelated because searching for a free parking spot creates additional delays and increase local circulation. In the center of large cities, 10% of the traffic circulation is due to cruising, as drivers nearly spend 20 min searching for free parking space. Therefore, it is necessary to develop a parking space availability prediction system that can inform the drivers in advance about the location-wise, day-wise, and hour-wise occupancy of parking lots. In this paper, we proposed a framework based on a deep long short term memory network to predict the availability of parking space with the integration of Internet of Things (IoT), cloud technology, and sensor networks. We use the Birmingham parking sensors dataset to evaluate the performance of deep long short term memory networks. Three types of experiments are performed to predict the availability of free parking space which is based on location, days of a week, and working hours of a day. The experimental results show that the proposed model outperforms the state-of-the-art prediction models.

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LAURA architecture: Towards a simpler way of building situation-aware and business-aware IoT applications

Cited by 16 publications

References 45 publications

IFAA: An Intelligent Framework Aware Algorithm to Determine the Boundary of Area under Attack in Military Surveillance and Reconnaissance WSN

IFAA: An Intelligent Framework Aware Algorithm to Determine the Boundary of Area under Attack in Military Surveillance and Reconnaissance WSN

A novel JavaScript MEAN stack approach for real-time nonconformity management in SMEs within Industry 4.0 and Quality 4.0 concepts

IoT Based Smart Parking System Using Deep Long Short Memory Network

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