Internet of Things resource monitoring through proactive fault prediction

Chowdhury, Abishi; Raut, Shital; Pal, Amrit

doi:10.1016/j.cie.2022.108265

Cited by 7 publications

(2 citation statements)

References 33 publications

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“…Random notification delivery was chosen as the baseline because no previous method provided the optimization of notification timing for forecasted activities with multiple time options. This means that notification delivery for forecasted results in various fields of study [ 16 , 23 , 24 , 25 , 26 ] was solely based on a single-time forecasted result or employed different scheduling for notification delivery times [ 28 , 32 ]. We performed the simulation in which the activity and responses are collected from the EngagementService dataset [ 83 ] and the dataset from the implementation of the system as described in Section 3.2 and Section 4.1 ; this is intended to test our method’s efficacy in user-dependent scenarios.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…However, sending forecasted results in a timely manner continues to be a challenge for this approach. In other studies, forecasting results are sent when forecasting results are obtained or depending on thresholds [ 16 , 21 , 23 , 24 , 25 , 26 , 27 ], while others create a separate schedule for sending forecast results [ 22 , 26 , 28 , 29 , 30 , 31 ]. Using an example of a weather information forecasting system for farmers, the system will send a push notification whenever the readings fall below the threshold [ 21 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Forecasted Activity Notifications with Reinforcement Learning

Fikry

Inoue

2023

Sensors

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In this paper, we propose the notification optimization method by providing multiple alternative times as a reminder for a forecasted activity with and without probabilistic considerations for the activity that needs to be completed and needs notification. It is important to consider various factors when sending notifications to people after obtaining the results of the forecasted activity. We should not send notifications only when we have forecasted results because future daily activities are unpredictable. Therefore, it is important to strike a balance between providing useful reminders and avoiding excessive interruptions, especially for low probabilities of forecasted activity. Our study investigates the impact of the low probability of forecasted activity and optimizes the notification time with reinforcement learning. We also show the gaps between forecasted activities that are useful for self-improvement by people for the balance of important tasks, such as tasks completed as planned and additional tasks to be completed. For evaluation, we utilize two datasets: the existing dataset and data we collected in the field with the technology we have developed. In the data collection, we have 23 activities from six participants. To evaluate the effectiveness of these approaches, we assess the percentage of positive responses, user response rate, and response duration as performance criteria. Our proposed method provides a more effective way to optimize notifications. By incorporating the probability level of activity that needs to be done and needs notification into the state, we achieve a better response rate than the baseline, with the advantage of reaching 27.15%, as well as than the other criteria, which are also improved by using probability.

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