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

Problem definition: Autonomous sensors connected through the internet of things (IoT) are deployed by different firms in the same environment. The sensors measure an important operating-condition state variable, but their measurements are noisy, so estimates are imperfect. Sensors can improve their own estimates by soliciting estimates from other sensors. The choice of which sensors to communicate with (target) is challenging because sensors (1) are constrained in the number of sensors they can target and (2) only have partial knowledge of how other sensors operate—that is, they do not know others’ underlying inference algorithms/models. We study the targeting problem, examine the evolution of interfirm sensor communication patterns, and explore what drives the patterns. Academic/practical relevance: Many industries are increasingly using sensors to drive improvements in key performance metrics (e.g., asset uptime) through better information on operating conditions. Sensors will communicate among themselves to improve estimation. This IoT vision will have a major impact on operations management (OM), and OM scholars need to develop and examine models and frameworks to better understand sensor interactions. Methodology: Analytic modeling combining decision-making, estimation, optimization, and learning is used. Results: We show that when selecting its target(s), each sensor needs to consider both the measurement quality of the other sensors and its level of familiarity with their inference models. We establish that the state of the environment plays a key role in mediating quality and familiarity. When sensor qualities are public, we show that each sensor eventually settles on a constant target set, but this long-run target set is sample-path dependent (i.e., dependent on past states) and varies by sensor. The long-run network, however, can be fully defined at time zero as a random directed graph, and hence, one can probabilistically predict it. This prediction can be made perfect (i.e., the network can be identified in a deterministic way) after observing the state values for a limited number of periods. When sensor qualities are private, our results reveal that sensors may not settle on a constant target set but the subset among which it cycles can still be stochastically predicted. Managerial implications: Our work allows managers to predict (and influence) the set of other firms with which their sensors will form information links. Analogous to a manufacturer mapping its supplier base to help manage supply continuity, our work enables a firm to map its sensor-based-information suppliers to help manage information continuity.