The Internet of Things and Information Fusion: Who Talks to Who?

References

• Acemoglu D, Bimpikis K, Ozdaglar A (2014a) Dynamics of information exchange in endogenous social networks. Theor. Econom. 9(1):41–97.Crossref, Google Scholar
• Acemoglu D, Mostagir M, Ozdaglar A (2014b) State-dependent opinion dynamics. 2014 IEEE Internat. Conf. Acoustics Speech Signal Processing (ICASSP) (IEEE, Piscataway, NJ), 4773–4777.Google Scholar
• Ashton AH, Ashton RH (1985) Aggregating subjective forecasts: Some empirical results. Management Sci. 31(12):1499–1508.Link, Google Scholar
• Atanasov P, Rescober P, Stone E, Swift SA, Servan-Schreiber E, Tetlock P, Ungar L, Mellers B (2016) Distilling the wisdom of crowds: Prediction markets vs. prediction polls. Management Sci. 63(3):691–706.Link, Google Scholar
• Ayaz E (2014) Autoregressive modeling approach of vibration data for bearing fault diagnosis in electric motors. J. Vibroengineering 16(5):2130–2138.Google Scholar
• Ballotta L, Schenato L, Carlone L (2020) Computation-communication trade-offs and sensor selection in real-time estimation for processing networks. IEEE Trans. Network Sci. Engrg. 7(4):2952–2965.Crossref, Google Scholar
• Bassamboo A, Cui R, Moreno A (2018) The wisdom of crowds in operations: Forecasting using prediction markets. Preprint, submitted October 27, https://dx.doi.org/10.2139/ssrn.2679663.Google Scholar
• Bates JM, Granger CW (1969) The combination of forecasts. J. Oper. Res. Soc. 20(4):451–468.Crossref, Google Scholar
• BehrTech (2020) Five IoT applications for offshore monitoring in oil and gas. Accessed May 21, 2020, https://behrtech.com/blog/5-iot-applications-for-offshore-monitoring-in-oil-and-gas/.Google Scholar
• Bren A, Saghafian S (2019) Data-driven percentile optimization for multi-class queueing systems with model ambiguity: Theory and application. INFORMS J. Optim. 1(4):267–287.Link, Google Scholar
• Chen C, Zhao XL, Li ZH, Zhu ZG, Qian SH, Flewitt AJ (2017) Current and emerging technology for continuous glucose monitoring. Sensors 17(1):182.Crossref, Google Scholar
• Delage E, Iancu DA (2015) Robust multistage decision making. Aleman DM, Thiele AC, eds. The Operations Research Revolution, INFORMS TutORials in Operations Research (INFORMS, Catonsville, MD), 20–46.Link, Google Scholar
• Delage E, Mannor S (2010) Percentile optimization for Markov decision processes with parameter uncertainty. Oper. Res. 58(1):203–213.Link, Google Scholar
• Evans P, Annunziata M (2012) Industrial internet: Pushing the boundaries of minds and machines. Report, General Electric Company, Boston.Google Scholar
• Fisher M, Raman A (1996) Reducing the cost of demand uncertainty through accurate response to early sales. Oper. Res. 44(1):87–99.Link, Google Scholar
• FlukeCorp (2020) An introduction to machinery vibration. Accessed May 21, 2020, https://www.reliableplant.com/Read/24117/introduction-machinery-vibration.Google Scholar
• Gaur V, Kesavan S, Raman A, Fisher ML (2007) Estimating demand uncertainty using judgmental forecasts. Manufacturing Service Oper. Management 9(4):480–491.Link, Google Scholar
• GEPower (2018) Electrical rotating machine APM overview. Accessed June 10, 2020, https://www.ge.com/news/sites/default/files/GEA33604%20Motor_Fleet_APM_Paper_0.pdf.Google Scholar
• Hall DL, Llinas J (1997) An introduction to multisensor data fusion. Proc. IEEE 85(1):6–23.Crossref, Google Scholar
• Han D, Wu J, Zhang H, Shi L (2017) Optimal sensor scheduling for multiple linear dynamical systems. Automatica J. IFAC 75:260–270.Crossref, Google Scholar
• International Electrotechnical Commission (IEC) (2014) Internet of things: Wireless sensor networks. White paper, International Electrotechnical Commission, Geneva, Switzerland.Google Scholar
• IKM (2019) Condition monitoring: Oil & gas. Report, IKM, Larvik, Norway. Accessed June 10, 2020, https://www.ikm.com/getfile.php/1343855-1559300145/IKM20Selskaper/IKM20Instrutek/Nedlastninger/Datablad20og20brosjyrer/Tilstandskontroll/CM20Offshore_low.pdf.Google Scholar
• Jernigan S, Kiron D, Ransbotham S (2016) Data sharing and analytics are driving success with IoT. MIT Sloan Management Rev. 58(1):1–16.Google Scholar
• Khaleghi B, Khamis A, Karray FO, Razavi SN (2013) Multisensor data fusion: A review of the state-of-the-art. Inform. Fusion 14(1):28–44.Crossref, Google Scholar
• Leal Y, Garcia-Gabin W, Bondia J, Esteve E, Ricart W, Fernández-Real JM, Vehí J (2010) Real-time glucose estimation algorithm for continuous glucose monitoring using autoregressive models. J. Diabetes Sci. Tech. 4(2):391–403.Crossref, Google Scholar
• Lennon Olsen T, Tomlin B (2020) Industry 4.0: Opportunities and challenges for operations management. Manufacturing Service Oper. Management 22(1):113–122.Google Scholar
• Liyanage LH, Shanthikumar JG (2005) A practical inventory control policy using operational statistics. Oper. Res. Lett. 33(4):341–348.Crossref, Google Scholar
• Marr B (2016) What everyone must know about industry 4.0. Accessed March 1, 2018, https://www.forbes.com/sites/bernardmarr/2016/06/20/what-everyone-must-know-about-industry-4-0.Google Scholar
• McKinsey (2015) The internet of things: Mapping the value beyond the hype. Report, McKinsey Global Institute.Google Scholar
• Mišić V, Perakis G (2021) Data analytics in operations management: A review. Manufacturing Service Oper. Management. Forthcoming.Google Scholar
• Mitchell HB (2007) Multi-Sensor Data Fusion: An Introduction (Springer Science & Business Media, Berlin).Google Scholar
• Momot R, Belavina E, Girotra K (2020) The use and value of social information in selective selling of exclusive products. Management Sci. 66(6):2610–2627.Link, Google Scholar
• Nemirovski A, Shapiro A (2006) Convex approximations of chance constrained programs. SIAM J. Optim. 17(4):969–996.Crossref, Google Scholar
• Perakis G, Roels G (2008) Regret in the newsvendor model with partial information. Oper. Res. 56(1):188–203.Link, Google Scholar
• Ran X, Chen H, Liu Z, Chen J (2017) Delivering deep learning to mobile devices via offloading. VT/AR Network ’17 Proc. Workshop Virtual Reality Augmented Reality Network (Association for Computing Machinery, New York), 42–47.Google Scholar
• Saghafian S (2018) Ambiguous partially observable Markov decision processes: Structural results and applications. J. Econom. Theory 178(11):1–35.Crossref, Google Scholar
• Saghafian S, Tomlin B (2016) The newsvendor under demand ambiguity: Combining data with moment and tail information. Oper. Res. 64(1):167–185.Link, Google Scholar
• Saghafian S, Tomlin B, Biller S (2018) The Internet of things and informmation fusion: Who talks to who? Harvard Kennedy School Working Paper 18-009, Harvard Kennedy School, Cambridge, MA. Available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3124417.Google Scholar
• Sethi R, Yildiz M (2016) Communication with unknown perspectives. Econometrica 84(6):2029–2069.Crossref, Google Scholar
• Shi L, Zhang H (2012) Scheduling two Gauss–Markov systems: An optimal solution for remote state estimation under bandwidth constraint. IEEE Trans. Signal Processing 60(4):2038–2042.Crossref, Google Scholar
• Simchi-Levi D (2010) Operations Rules: Delivering Customer Value Through Flexible Operations (MIT Press, Cambridge, MA).Google Scholar
• Sparacino G, Zanderigo F, Corazza S, Maran A, Facchinetti A, Cobelli C (2007) Glucose concentration can be predicted ahead in time from continuous glucose monitoring sensor time-series. IEEE Trans. Biomed. Engrg. 54(5):931–937.Crossref, Google Scholar
• Swaminathan JM, Tayur SR (2003) Models for supply chains in e-business. Management Sci. 49(10):1387–1406.Link, Google Scholar
• Syed S, Pai M (2016) Antifriction bearing diagnostics in a manufacturing industry: A case study. J. Mechanical Engrg. Automation 6(5A):58–62.Google Scholar
• Thanagasundram S, Schlindwein FS (2006) Autoregressive order selection for rotating machinery. Internat. J. Acoustics Vibration 11(3):144–154.Crossref, Google Scholar
• Tong J, Feiler D (2017) A behavioral model of forecasting: Naive statistics on mental samples. Management Sci. 63(11):3609–3627.Link, Google Scholar
• Tsoukalas G, Falk BH (2020) Token-weighted crowdsourcing. Management Sci. 66(9):3843–3859.Link, Google Scholar
• Vitus MP, Zhang W, Abate A, Hu J, Tomlin CJ (2012) On efficient sensor scheduling for linear dynamical systems. Automatica J. IFAC 48(10):2482–2493.Crossref, Google Scholar
• Wallis KF (2011) Combining forecasts: Forty years later. Appl. Financial Econom. 21(1–2):33–41.Crossref, Google Scholar
• Winkler RL, Clemen RT (2004) Multiple experts vs. multiple methods: Combining correlation assessments. Decision Anal. 1(3):167–176.Link, Google Scholar
• Winnig L (2016) GE’s big bet on data and analytics. MIT Sloan Management Rev. Reprint 57380:1–16.Google Scholar
• Wu S, Ding K, Cheng P, Shi L (2020) Optimal scheduling of multiple sensors over lossy and bandwidth limited channels. IEEE Trans. Control Network Systems 7(3):1188–1200.Crossref, Google Scholar
• Xiong F, Hipszer BR, Joseph J, Kam M (2011) Improved blood glucose estimation through multi-sensor fusion. 2011 Annu. Internat. Conf. IEEE Engrg. Med. Biol. Society EMBC (IEEE, Piscataway, NJ), 377–380.Google Scholar
• Xu D, Li T, Li Y, Su X, Tarkoma S, Hui P (2020) A survey on edge intelligence. Preprint, submitted March 26, https://arxiv.org/abs/2003.12172.Google Scholar
• Yang W, Chen G, Wang X, Shi L (2014) Stochastic sensor activation for distributed state estimation over a sensor network. Automatica J. IFAC 50(8):2070–2076.Crossref, Google Scholar