| Title | Battery Life Prediction Model of Sensor Nodes using Merged Data Collecting methods |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Sebestyén, Gergely, Kopják, József |
| Conference Name | 2022 IEEE 20th Jubilee World Symposium on Applied Machine Intelligence and Informatics (SAMI) |
| Date Published | mar |
| Keywords | battery lifetime estimation, composability, energy consumption, flooding routing, FRC, IQRF, MDC, merged data collecting, Metrics, Predictive models, pubcrawl, resilience, Resiliency, Routing, TDMA, time division multiple access, wireless mesh networks, wireless network energy consumption, wireless networks, Wireless sensor networks |
| Abstract | The aim of this paper is to describe the battery lifetime estimation and energy consumption model of the sensor nodes in TDMA wireless mesh sensor using merged data collecting (MDC) methods based on lithium thionyl chloride batteries. Defining the energy consumption of the nodes in wireless mesh networks is crucial for battery lifetime estimation. In this paper, we describe the timing, energy consumption, and battery lifetime estimation of the MDC method in the TDMA mesh sensor networks using flooding routing. For the battery life estimation, we made a semiempirical model that describes the energy consumption of the nodes with a real battery model. In this model, the low-level constraints are based on the measured energy consumption of the sensor nodes in different operation phases. |
| DOI | 10.1109/SAMI54271.2022.9780734 |
| Citation Key | sebestyen_battery_2022 |
Battery Life Prediction Model of Sensor Nodes using Merged Data Collecting methods