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Alexandrescu A. Parallel Processing of Sensor Data in a Distributed Rules Engine Environment through Clustering and Data Flow Reconfiguration. SENSORS (BASEL, SWITZERLAND) 2023; 23:1543. [PMID: 36772584 PMCID: PMC9919915 DOI: 10.3390/s23031543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/27/2023] [Accepted: 01/28/2023] [Indexed: 06/18/2023]
Abstract
An emerging reality is the development of smart buildings and cities, which improve residents' comfort. These environments employ multiple sensor networks, whose data must be acquired and processed in real time by multiple rule engines, which trigger events that enable specific actuators. The problem is how to handle those data in a scalable manner by using multiple processing instances to maximize the system throughput. This paper considers the types of sensors that are used in these scenarios and proposes a model for abstracting the information flow as a weighted dependency graph. Two parallel computing methods are then proposed for obtaining an efficient data flow: a variation of the parallel k-means clustering algorithm and a custom genetic algorithm. Simulation results show that the two proposed flow reconfiguration algorithms reduce the rule processing times and provide an efficient solution for increasing the scalability of the considered environment. Another aspect being discussed is using an open-source cloud solution to manage the system and how to use the two algorithms to increase efficiency. These methods allow for a seamless increase in the number of sensors in the environment by making smart use of the available resources.
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Affiliation(s)
- Adrian Alexandrescu
- Department of Computer Science and Engineering, Faculty of Automatic Control and Computer Engineering, Gheorghe Asachi Technical University of Iaşi, Str. Prof. dr. doc. Dimitrie Mangeron, nr. 27, 700050 Iași, Romania
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Huang H, Huang T, Li Z, Lyu S, Hong T. Design of Citrus Fruit Detection System Based on Mobile Platform and Edge Computer Device. SENSORS (BASEL, SWITZERLAND) 2021; 22:s22010059. [PMID: 35009602 PMCID: PMC8747137 DOI: 10.3390/s22010059] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 12/15/2021] [Accepted: 12/20/2021] [Indexed: 05/26/2023]
Abstract
Citrus fruit detection can provide technical support for fine management and yield determination of citrus orchards. Accurate detection of citrus fruits in mountain orchards is challenging because of leaf occlusion and citrus fruit mutual occlusion of different fruits. This paper presents a citrus detection task that combines UAV data collection, AI embedded device, and target detection algorithm. The system used a small unmanned aerial vehicle equipped with a camera to take full-scale pictures of citrus trees; at the same time, we extended the state-of-the-art model target detection algorithm, added the attention mechanism and adaptive fusion feature method, improved the model's performance; to facilitate the deployment of the model, we used the pruning method to reduce the amount of model calculation and parameters. The improved target detection algorithm is ported to the edge computing end to detect the data collected by the unmanned aerial vehicle. The experiment was performed on the self-made citrus dataset, the detection accuracy was 93.32%, and the processing speed at the edge computing device was 180 ms/frame. This method is suitable for citrus detection tasks in the mountainous orchard environment, and it can help fruit growers to estimate their yield.
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Affiliation(s)
- Heqing Huang
- College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China; (H.H.); (T.H.); (S.L.)
- National Research Laboratory of Mechanization of Citrus Industry Technical System, Guangzhou 510642, China
| | - Tongbin Huang
- College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China; (H.H.); (T.H.); (S.L.)
- National Research Laboratory of Mechanization of Citrus Industry Technical System, Guangzhou 510642, China
| | - Zhen Li
- College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China; (H.H.); (T.H.); (S.L.)
- National Research Laboratory of Mechanization of Citrus Industry Technical System, Guangzhou 510642, China
- Guangdong Agricultural Information Monitoring Engineering and Technology Research Center, Guangzhou 510642, China
- Pazhou Lab, Guangzhou 510330, China
| | - Shilei Lyu
- College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China; (H.H.); (T.H.); (S.L.)
- National Research Laboratory of Mechanization of Citrus Industry Technical System, Guangzhou 510642, China
- Pazhou Lab, Guangzhou 510330, China
| | - Tao Hong
- School of Electronic and Information Engineering, Beihang University, Beijing 100190, China;
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Daher AW, Rizik A, Muselli M, Chible H, Caviglia DD. Porting Rulex Software to the Raspberry Pi for Machine Learning Applications on the Edge. SENSORS 2021; 21:s21196526. [PMID: 34640846 PMCID: PMC8512253 DOI: 10.3390/s21196526] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 09/16/2021] [Accepted: 09/27/2021] [Indexed: 11/28/2022]
Abstract
Edge Computing enables to perform measurement and cognitive decisions outside a central server by performing data storage, manipulation, and processing on the Internet of Things (IoT) node. Also, Artificial Intelligence (AI) and Machine Learning applications have become a rudimentary procedure in virtually every industrial or preliminary system. Consequently, the Raspberry Pi is adopted, which is a low-cost computing platform that is profitably applied in the field of IoT. As for the software part, among the plethora of Machine Learning (ML) paradigms reported in the literature, we identified Rulex, as a good ML platform, suitable to be implemented on the Raspberry Pi. In this paper, we present the porting of the Rulex ML platform on the board to perform ML forecasts in an IoT setup. Specifically, we explain the porting Rulex’s libraries on Windows 32 Bits, Ubuntu 64 Bits, and Raspbian 32 Bits. Therefore, with the aim of carrying out an in-depth verification of the application possibilities, we propose to perform forecasts on five unrelated datasets from five different applications, having varying sizes in terms of the number of records, skewness, and dimensionality. These include a small Urban Classification dataset, three larger datasets concerning Human Activity detection, a Biomedical dataset related to mental state, and a Vehicle Activity Recognition dataset. The overall accuracies for the forecasts performed are: 84.13%, 99.29% (for SVM), 95.47% (for SVM), and 95.27% (For KNN) respectively. Finally, an image-based gender classification dataset is employed to perform image classification on the Edge. Moreover, a novel image pre-processing Algorithm was developed that converts images into Time-series by relying on statistical contour-based detection techniques. Even though the dataset contains inconsistent and random images, in terms of subjects and settings, Rulex achieves an overall accuracy of 96.47% while competing with the literature which is dominated by forward-facing and mugshot images. Additionally, power consumption for the Raspberry Pi in a Client/Server setup was compared with an HP laptop, where the board takes more time, but consumes less energy for the same ML task.
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Affiliation(s)
- Ali Walid Daher
- COSMIC Lab, Department of Electrical, Electronic and Telecommunications Engineering and Naval Architecture (DITEN), University of Genoa, 16145 Genoa, Italy; (A.W.D.); (A.R.)
- MECRL Laboratory, Ph.D. School for Sciences and Technology, Lebanese University, Beirut 6573/14, Lebanon;
- Consiglio Nazionale delle Ricerche, Institute of Electronics Computer and Telecommunication Engineering (IEIIT), 16149 Genoa, Italy;
- Rulex Innovation Labs, Rulex Inc., 16122 Genoa, Italy
| | - Ali Rizik
- COSMIC Lab, Department of Electrical, Electronic and Telecommunications Engineering and Naval Architecture (DITEN), University of Genoa, 16145 Genoa, Italy; (A.W.D.); (A.R.)
- MECRL Laboratory, Ph.D. School for Sciences and Technology, Lebanese University, Beirut 6573/14, Lebanon;
| | - Marco Muselli
- Consiglio Nazionale delle Ricerche, Institute of Electronics Computer and Telecommunication Engineering (IEIIT), 16149 Genoa, Italy;
- Rulex Innovation Labs, Rulex Inc., 16122 Genoa, Italy
| | - Hussein Chible
- MECRL Laboratory, Ph.D. School for Sciences and Technology, Lebanese University, Beirut 6573/14, Lebanon;
| | - Daniele D. Caviglia
- COSMIC Lab, Department of Electrical, Electronic and Telecommunications Engineering and Naval Architecture (DITEN), University of Genoa, 16145 Genoa, Italy; (A.W.D.); (A.R.)
- Correspondence: ; Tel.: +39-010-33-56-587
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