SimTune: bridging the simulator reality gap for resource management in edge-cloud computing

Industries and services are undergoing an Internet of Things centric transformation globally, giving rise to an explosion of multi-modal data generated each second. This, with the requirement of low-latency result delivery, has led to the ubiquitous adoption of edge and cloud computing paradigms. Edge computing follows the data gravity principle, wherein the computational devices move closer to the end-users to minimize data transfer and communication times. However, large-scale computation has exacerbated the problem of efficient resource management in hybrid edge-cloud platforms. In this regard, data-driven models such as deep neural networks (DNNs) have gained popularity to give rise to the notion of edge intelligence. However, DNNs face significant problems of data saturation when fed volatile data. Data saturation is when providing more data does not translate to improvements in performance. To address this issue, prior work has leveraged coupled simulators that, akin to digital twins, generate out-of-distribution training data alleviating the data-saturation problem. However, simulators face the reality-gap problem, which is the inaccuracy in the emulation of real computational infrastructure due to the abstractions in such simulators. To combat this, we develop a framework, SimTune, that tackles this challenge by leveraging a low-fidelity surrogate model of the high-fidelity simulator to update the parameters of the latter, so to increase the simulation accuracy. This further helps co-simulated methods to generalize to edge-cloud configurations for which human encoded parameters are not known apriori. Experiments comparing SimTune against state-of-the-art data-driven resource management solutions on a real edge-cloud platform demonstrate that simulator tuning can improve quality of service metrics such as energy consumption and response time by up to 14.7% and 7.6% respectively

FIWARE Open Source Standard Platform in Smart Farming - A Review

[EN] FIWARE is an open source platform for the deployment of Internet of Things (IoT) applications, driven by European Union and managed by FIWARE Foundation. Recently, FIWARE Foundation has launched his new product Agricolus, which focus on Smart Farming and it uses FIWARE infrastructure. Agricolus manages to bring Hardware and Software together in a decision-making process that support farming activities and offers a "plug and play" interface for precision agriculture. This is encompassed by the phenomenon of Smart Farming, which is a development that take advantage of the use of Information Communication Technologies (ICT) in the daily farm management. This review aims to gain insight into the state-of-the-art of FIWARE in Smart Farming and identify the components of Agricolus in comparison with essential FIWARE architecture.This research has been carried out in the framework of the project "Development of an integrated maturity model for agility, resilience and gender perspective in supply chains (MoMARGE). Application to the agricultural sector." Ref. GV/2017/025 funded by the Generalitat Valenciana.Rodríguez-Sánchez, MDLÁ.; Cuenca, L.; Ortiz Bas, Á. (2018). FIWARE Open Source Standard Platform in Smart Farming - A Review. IFIP Advances in Information and Communication Technology. 534:581-589. https://doi.org/10.1007/978-3-319-99127-6_50S581589534Robert, P.C.: Precision agriculture: research needs and status in the USA. In: Stafford, J.V. (ed.) Proceedings of the 2nd European Conference on Precision Agriculture, Part 1, pp. 19–33. Academic Press, SCI/Sheffield (1999)Ge, Y., Thomasson, J.A., Sui, R.: Remote sensing of soil properties in precision agriculture: a review. Front. Earth Sci. 5(3), 229–238 (2011)Sundmaeker, H., Verdouw, C., Wolfert, S., Pérez Freire L.: Internet of food and farm 2020. In: Vermesan, O., Friess, P. (eds.) Digitising the Industry - Internet of Things Connecting Physical, Digital and Virtual Worlds, pp. 129–151. River Publishers, Gistrup/Delft (2016)Lin, J., Liu, C.: Monitoring system based on wireless sensor network and a SocC platform in precision agriculture. In: Proceedings of the International Conference on Communication Technology (ICCT), Hangzhou, pp. 101–104 (2008)Kaewmard, N., Saiyod, S.: Sensor data collection and irrigation control on vegetable crop using smart phone and wireless sensor networks for smart farm. In: Proceedings of the International Conference on Wireless Sensors (ICWiSE), pp. 106–112 (2014)FIWARE. https://www.fiware.org/Future Internet Private Public Partnership (FI-PPP). https://www.fi-ppp.eu/Agricolus. https://www.agricolus.comFIWARE Generic Enablers. http://edu.fiware.org/FIWARE Catalogue. https://catalogue.fiware.org/enablersKamilaris, A., Gao, F., Prenafeta-Boldu, F.X., Ali, M.I.: Agri-IoT: a semantic framework for Internet of Things-enabled smart farming applications. In: IEEE 3rd World Forum on Internet of Things, WF-IoT 2016, pp. 442–447 (2017)López-Riquelme, J.A., Pavón-Pulido, N., Navarro-Hellín, H., Soto-Valles, F., Torres-Sánchez, R.: A software architecture based on FIWARE cloud for precision agriculture. Agric. Water Manag. 183, 123–135 (2017)Martínez, R., Pastor, J.Á., Álvarez, B., Iborra, A.: A testbed to evaluate the FIWARE-based IoT platform in the domain of precision agriculture. Sensors (Switzerland), 16(11) (2016)Pesonen, L.A., et al.: Cropinfra - an internet-based service infrastructure to support crop production in future farms. Biosys. Eng. 120, 92–101 (2014)Barmpounakis, S., et al.: Management and control applications in agriculture domain via a future internet business-to-business platform. Inf. Process. Agric. 2(1), 51–63 (2015)Kaloxylos, A., et al.: Farm management systems and the future internet era. Comput. Electron. Agric. 89, 130–144 (2012)Kaloxylos, A., et al.: A cloud-based farm management system: architecture and implementation. Comput. Electron. Agric. 100, 168–179 (2014)Ryu, M., Yun, J., Miao, T., Ahn, I.Y., Choi, S.C., Kim, J.: Design and implementation of a connected farm for smart farming system. In: 2015 IEEE SENSORS Proceedings, pp. 1–4 (2015)Layton, A.W., Balmos, A.D., Sabpisal, S., Ault, A., Krogmeier, J.V., Buckmaster, D.: ISOBlue: an open source project to bring agricultural machinery data into the cloud, Montreal, 13 July–16 July 2014. American Society of Agricultural and Biological Engineers (2014)SmartAgriFood. http://smartagrifood.com/FIWARE MarketPlace. https://marketplace.fiware.orgFIWARE iHubs. https://www.fiware.org/community/fiware-ihubs/Agricolus in FIWARE MarketPlace. https://marketplace.fiware.org/pages/solutions/2ec3c741ef4dd8f83bab4e83Implementation example of Agricolus. http://www.libelium.com/increasing-tobacco-crops-quality-by-climatic-conditions-control/FIspace. https://www.fispace.eu/whatisfispace.htm

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea