An efficient method for physical fields mapping through crowdsensing

Dardari, Davide; Pasolini, Gianni; Zabini, Flavio

doi:10.1016/j.pmcj.2018.06.001

Cited by 11 publications

(9 citation statements)

References 40 publications

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“…In the future, this research could be extended to not only deriving material flow patterns in smart factories, but also deriving worker movement and patterns in factories using techniques such as crowd sensing [60,61]. In addition, this research could be integrated with numerous Smart X studies, including smart logistics, smart cities and smart streets [62][63][64], beyond smart factory fields.…”

Section: Discussionmentioning

confidence: 99%

Development of Scalable On-Line Anomaly Detection System for Autonomous and Adaptive Manufacturing Processes

2019

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Featured Application: The proposed framework is capable of analyzing the flow of numerous objects in real time on the basis of a new process anomaly detection approach that considers the manufacturing process of an autonomous and adaptive smart factory.Abstract: Factories of the future are foreseen to evolve into smart factories with autonomous and adaptive manufacturing processes. However, the increasing complexity of the network of manufacturing processes is expected to complicate the rapid detection of process anomalies in real time. This paper proposes an architecture framework and method for the implementation of the Scalable On-line Anomaly Detection System (SOADS), which can detect process anomalies via real-time processing and analyze large amounts of process execution data in the context of autonomous and adaptive manufacturing processes. The design of this system architecture framework entailed the derivation of standard subsequence patterns using the PrefixSpan algorithm, a sequential pattern algorithm. The anomalies of the real-time event streams and derived subsequence patterns were scored using the Smith-Waterman algorithm, a sequence alignment algorithm. The excellence of the proposed system was verified by measuring the time for deriving subsequence patterns and by obtaining the anomaly scoring time from large event logs. The proposed system succeeded in large-scale data processing and analysis, one of the requirements for a smart factory, by using Apache Spark streaming and Apache Hbase, and is expected to become the basis of anomaly detection systems of smart factories.

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Section: Discussionmentioning

confidence: 99%

Development of Scalable On-Line Anomaly Detection System for Autonomous and Adaptive Manufacturing Processes

2019

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“…To tackle these issues, the authors in [25] and [26] propose a combined GP-state space method, whose complexity and memory requirements do not depend on the number of observations, in static and dynamic scenarios, respectively. This allows an efficient statistical characterization of the spatial field, which can be easily updated once new data become available, thus making it well-suited for crowd-based learning applications.…”

Section: Inference Methods For Learning Spatial Fieldsmentioning

confidence: 99%

Crowd-Based Learning of Spatial Fields for the Internet of Things: From Harvesting of Data to Inference

Arias-de-Reyna

Closas

Dardari

et al. 2018

IEEE Signal Process. Mag.

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“…In fact, this method is also used for crowd perception. Dardari et al propose a combined Gaussian Process (GP)-State space method for crowd mapping whose complexity and memory requirements for field representation do not depend on the number of data measured [40].…”

Section: Autonomous Drivingmentioning

confidence: 99%

An Efficient Task Scheduling Strategy Utilizing Mobile Edge Computing in Autonomous Driving Environment

Liu

Chen

et al. 2019

Electronics

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With the rapid development of various new types of services, autonomous driving has received extensive attention. Due to the dense traffic flow, the limited battery life and computing power of the vehicles, intelligent vehicles are unable to support some computationally intensive and urgent tasks. Autonomous driving imposes strict requirements on the response time of the task. Due to the strong computing power and proximity to the terminal of mobile edge computing (MEC) and the arrival of 5G, the task can be unloaded to MEC, and data can be exchanged in milliseconds, which can reduce the task execution time. However, the resources of the MEC server are still very limited. Therefore we proposed a scheduling algorithm that takes into account the special task of the autopilot. Tasks will select the appropriate edge cloud execution and schedule the execution sequence on the edge cloud by the scheduling algorithm. At the same time, we take the mobility of high-speed vehicles into consideration. The position of the vehicle can be obtained by the prediction algorithm, and the task results are returned to the vehicle by means of other edge clouds. The experimental results show that with the increase of the task amount, the algorithm can effectively schedule more tasks to be completed within the specified time, and in different time slots; it can also predict the location of the vehicle and return the result to the vehicle.

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An efficient method for physical fields mapping through crowdsensing

Cited by 11 publications

References 40 publications

Development of Scalable On-Line Anomaly Detection System for Autonomous and Adaptive Manufacturing Processes

Development of Scalable On-Line Anomaly Detection System for Autonomous and Adaptive Manufacturing Processes

Crowd-Based Learning of Spatial Fields for the Internet of Things: From Harvesting of Data to Inference

An Efficient Task Scheduling Strategy Utilizing Mobile Edge Computing in Autonomous Driving Environment

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