Inside GNSS Media & Research

MAY-JUN 2018

Issue link:

Contents of this Issue


Page 60 of 67 M A Y / J U N E 2 0 1 8 Inside GNSS 61 1 and 5 ms. The allocation of the jobs generated by the requests of a network of cloud GNSS IoT sensors (Figure 1(b)) is assumed to be optimum in terms of usage time: the computational tasks of a given amount of sensors are sequentially performed in the same EC2 instance, hence reducing the cost per position fix. e monthly cost of the necessary cloud resources (i.e., EC2) for an IoT application that requires one position fix per hour is presented in Table 3 : for $0.51, $0.23 and $0.1 per month, a cloud GNSS IoT sensor (Figure 1(b)) position can be calculated once per hour for on-demand, reserved and spot services, respectively. Notice that in typical IoT positioning applica- tions, a position fix is usually requested from hours up to days. Hence, the cost of the cloud services used by a network of cloud GNSS IoT sensors (Figure 1(b)) should not be a showstopper as it has been demonstrated to be considerably low. Conclusions is article discusses the conventional solutions for IoT positioning, w it h GNSS-based solutions being the most widespread in positioning IoT sensor networks. The architecture of a con- ventional GNSS IoT positioning sensor (Figure 1(a)) has been addressed, togeth- er with the energy consumption of its different components, showing that the GNSS module is the largest consumer if the data to be transferred is not large. To tackle the dilemma of energy con- sumption in IoT positioning sensors, we propose the use of a cloud-based GNSS approach, in which the purpose of sen- sors is just to capture the GNSS signal and send it to a cloud server where it will then be processed. The energ y consu mpt ion of t he proposed cloud GNSS IoT sensor (Fig- ure 1(b)) has also been addressed and compared with state-of-the-art GNSS IoT positioning sensors. Under the con- straint of working with a relatively small signal length, the use of the cloud GNSS receiver achieves a significant savings in the sensor's consumed energy, up to one order of magnitude compared with hot and assisted starts, and up to roughly 2.5 orders of magnitude in contrast with warm and cold starts. Finally, the economic cost implied by the use of cloud services to process the GNSS data and obtain the position of the sensor has been shown to be low, thus ensuring the cloud GNSS receiver as a low-energy and low-cost solution for IoT positioning. Acknowledgements e views presented in this article rep- resent solely the opinion of the authors and not necessarily the view of ESA. is work was partly supported by the European Space Agency (ESA) under contract No. 4000119070/16/NL/GLC and by the Spanish Government under grant TEC2017-89925-R. Manufacturers When the authors address the emer- gence of IoT positioning applications sparking the interest of MM GNSS ven- dors, they note u-blox, alwil, Switzer- land, Telit, London, UK, and Broadcom Corp., Irvine, CA, as examples. In the section discussing the accu- racy performance of the cloud GNSS receiver's experimental test, a synthetic signal was generated using a Spirent (West Sussex, UK) GPS/Galileo signal generator. Furthermore, the cloud GNSS receiver was using the baseline broadcast ephemeris from the IGS service. Additional Resources [1] Anghileri, M., M. Paonni, S. Wallner, J.-Á Ávila-Rodríguez, and B. Eissfeller, "Ready to Navigate! A Methodology for the Estimation of the Time-to-First-Fix," Inside GNSS, Volume: 5, Issue: 2, 2010 [2] Atmel, "SPI Serial EEPROM - ATM25M02," Data Sheet, 2017 [3] Batty, M., K. W. Axhausen, F. Giannotti, A. Pozdnoukhov, A. Bazzani, M. Wachowicz, G. Ouzounis, and Y. Portugali, "Smart Cities of the Future," European Physical Journal Special Topics, Volume: 214, Issue: 1, 2012 [4] Bousquet, F. and u-blox, "Portables: The Challenge of Low Power and Good GNSS Per- formance," White Paper, 2017 [5] Broadcom Corporation, "Top Ten Advan- tages: AGPS Server and Worldwide Reference Network," 2007 [6] Brown, A. and R. Silva, "TIDGET Mayday Sys- tem for Motorists," IEEE Position Location and Navigation Symposium (PLANS), 1994 [7] Curran, J., M. Arizabaleta, T. Pany, and S. Gunawardena, "The Institute of Navigation's GNSS SDR Metadata Standard," Inside GNSS, Volume: 12, Issue: 6, 2017 [8] De Angelis, G., A. De Angelis, V. Pasku, A. Moschitta, and P. Carbone, "A Hybrid Outdoor/ Indoor Positioning System for IoT Applica- tions," Proceedings of the 1st IEEE International Symposium on Systems Engineering (ISSE 2015), 2015 [9] García-Molina, J. A. and J. M. Parro-Jiménez, "Cloud-based GNSS Processing of Distributed Receivers of Opportunity: Techniques, Appli- cations and Data-Collection Strategies," 6th International Colloquium - Scientific Funda- mental Aspects of GNSS/Galileo, 2017 [10] Khan, R., S. U. Khan, R. Zaheer, and S. Khan, "Future Internet: The Internet of Things Architecture, Possible Applications and Key Challenges," Proceedings of the IEEE 10th Inter- national Conference on Frontiers of Information Technology (FIT 2012), 2012 [11] Liu, J., B. Priyantha, T. Hart, Y. Jin, W. Lee, V. Raghunathan, H. S. Ramos, and Q. Wang, "CO-GPS: Energy Efficient GPS Sensing with Cloud Offloading," IEEE Transactions on Mobile Computing, Volume: 15, Issue: 6, 2016 [12] López-Salcedo, J., Y. Capelle, M. Toledo, G. Seco, J. López Vicario, D. Kubrak, M. Monnerat, A. Mark, and D. Jiménez, "DINGPOS: A Hybrid Indoor Navigation Platform for GPS and GALI- LEO," 21st International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2008), 2008 [13] Lucas-Sabola, V., G. Seco-Granados, J. A. López-Salcedo, J. A. García-Molina, and M. Crisci, "Cloud GNSS Receivers: New Advanced Applications Made Possible," Proceedings of the International Conference on Localization and GNSS (ICL-GNSS), 2016 [14] Lucas-Sabola, V., G. Seco-Granados, J. A. López-Salcedo, J. A. García-Molina, and M. Crisci, "Efficiency Analysis of Cloud GNSS Sig- nal Processing for IoT Applications," Proceed- ings of ION GNSS (ION GNSS 2017+), 2017 [15] Seco-Granados, G., J. A. López-Salcedo, D. Jiménez-Baños, and G. López-Risueño, "Chal- lenges in Indoor Global Navigation Satellite Systems," IEEE Signal Processing Magazine, February 2012 [16] Singh, D., G. Tripathi, and A. J. Jara, "A Survey of Internet-of-Things: Future Vision, Architecture, Challenges and Services," 2014 IEEE World Forum Internet of Things ( WF-IoT 2014), 2014 [17] Telit, "K3 Series Power Modes," Application Note, 2017 [18] Texas Instruments, "CC1310 SimpleLink Ultra-Low-Power Sub-1 GHz Wireless MCU," Data Sheet, 2016 [19] u-blox, "Power Management Consider- ations for u-blox 7 and M8 GNSS Receivers," Application Note, 2014 Payment type Monthly cost On-demand instances $0.51 Reserved instances $0.23 Spot instances $0.10 Table 3 Monthly cost (taxes excluded) of cloud ser- vices (c3.xlarge EC2) employed by an IoT application that requires one position fix per hour; signal length set from 1 to 5 m

Articles in this issue

Links on this page

view archives of Inside GNSS Media & Research - MAY-JUN 2018