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Sensor Measurements Estimation in Internet of Things

In modern IoT deployments for continuous monitoring applications, many inexpensive sensors along with a relatively few expensive high-precision sensors are used to reduce deployment costs. Generally, the low-cost, low-precision sensor nodes have limited memory and processing power. Most techniques for sensor drift detection are not suitable for modern IoT deployment as they do not consider measurement errors/uncertainties present in low-precision sensor measurements. We developed an automatic sensor drift detection and correction technique by leveraging a Bayesian maximum entropy-based estimation method that incorporates measurement errors/uncertainties of low-precision sensors to estimate drift, with Kalman filtering to track and correct the estimated drift from sensor measurements. We implemented the proposed technique in both centralized and distributed frameworks to facilitate in-network sensor drift detection/correction in real-time. We extended this work for both smooth and abrupt drift detection using Interacting Multiple Models. The following figures present the block diagram of the developed sensor drift detection/correction method and demonstrate how it automatically detects/corrects the sensor drift (from Dockland Deployment) in a distributed manner.

    Research Outcome:
  • Rathore, P., Kumar, D., Rajasegarar, S., Palaniswami, M. (2017). Maximum entropy-based auto drift correction using high-and low-precision sensors. ACM Transactions on Sensor Networks (TOSN), 13(3), 24.
  • Rathore, P., Kumar, D., Rajasegarar, S., & Palaniswami, M. (2018, February). Bayesian maximum entropy and interacting multiple model based automatic sensor drift detection and correction in an IoT environment, in IEEE 4th World Forum on Internet of Things (WF-IoT) (pp. 598-603).