Browsing by Subject "Data compression"

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    Context-Aware Collaborative Intelligence With Spatio-Temporal In-Sensor-Analytics for Efficient Communication in a Large-Area IoT Testbed
    (IEEE, 2021) Chatterjee, Baibhab; Seo, Dong-Hyun; Chakraborty, Shramana; Avlani, Shitij; Jiang, Xiaofan; Zhang, Heng; Abdallah, Mustafa; Raghunathan, Nithin; Mousoulis, Charilaos; Shakouri, Ali; Bagchi, Saurabh; Peroulis, Dimitrios; Sen, Shreyas; Electrical and Computer Engineering, Purdue School of Engineering and Technology
    Decades of continuous scaling has reduced the energy of unit computing to virtually zero, while energy-efficient communication has remained the primary bottleneck in achieving fully energy-autonomous Internet-of-Things (IoT) nodes. This article presents and analyzes the tradeoffs between the energies required for communication and computation in a wireless sensor network, deployed in a mesh architecture over a 2400-acre university campus, and is targeted toward multisensor measurement of temperature, humidity and water nitrate concentration for smart agriculture. Several scenarios involving in-sensor analytics (ISA), collaborative intelligence (CI), and context-aware switching (CAS) of the cluster head during CI has been considered. A real-time co-optimization algorithm has been developed for minimizing the energy consumption in the network, hence maximizing the overall battery lifetime. Measurement results show that the proposed ISA consumes ≈ 467× lower energy as compared to traditional Bluetooth low energy (BLE) communication, and ≈ 69500× lower energy as compared with long-range (LoRa) communication. When the ISA is implemented in conjunction with LoRa, the lifetime of the node increases from a mere 4.3 h to 66.6 days with a 230-mAh coin cell battery, while preserving >99% of the total information. The CI and CAS algorithms help in extending the worst case node lifetime by an additional 50%, thereby exhibiting an overall network lifetime of ≈ 104 days, which is >90% of the theoretical limits as posed by the leakage current present in the system, while effectively transferring information sampled every second. A Web-based monitoring system was developed to continuously archive the measured data, and for reporting real-time anomalies.
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    DCMS: A data analytics and management system for molecular simulation
    (SpringerOpen, 2014-11-26) Kumar, Anand; Grupcev, Vladimir; Berrada, Meryem; Fogarty, Joseph C.; Tu, Yi-Cheng; Zhu, Xingquan; Pandit, Sagar A.; Xia, Yuni; Department of Computer and Information Science, School of Science
    Molecular Simulation (MS) is a powerful tool for studying physical/chemical features of large systems and has seen applications in many scientific and engineering domains. During the simulation process, the experiments generate a very large number of atoms and intend to observe their spatial and temporal relationships for scientific analysis. The sheer data volumes and their intensive interactions impose significant challenges for data accessing, managing, and analysis. To date, existing MS software systems fall short on storage and handling of MS data, mainly because of the missing of a platform to support applications that involve intensive data access and analytical process. In this paper, we present the database-centric molecular simulation (DCMS) system our team developed in the past few years. The main idea behind DCMS is to store MS data in a relational database management system (DBMS) to take advantage of the declarative query interface (i.e., SQL), data access methods, query processing, and optimization mechanisms of modern DBMSs. A unique challenge is to handle the analytical queries that are often compute-intensive. For that, we developed novel indexing and query processing strategies (including algorithms running on modern co-processors) as integrated components of the DBMS. As a result, researchers can upload and analyze their data using efficient functions implemented inside the DBMS. Index structures are generated to store analysis results that may be interesting to other users, so that the results are readily available without duplicating the analysis. We have developed a prototype of DCMS based on the PostgreSQL system and experiments using real MS data and workload show that DCMS significantly outperforms existing MS software systems. We also used it as a platform to test other data management issues such as security and compression.