aspg-18-4015-1792489400 20261020094320 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2026 18 2 Deden Ardiansyah Doctoral Program of Information System, School of Postgraduate, Universitas Diponegoro, Semarang, Indonesia; Computer Engineering Department, Vocational School, Universitas Pakuan, Bogor, Ind Moestafid Doctoral Program of Information System, School of Postgraduate, Universitas Diponegoro, Semarang, Indonesia Teddy Mantoro Lab-Tech-Computer Science Department, Sampoerna University, Jakarta, Indonesia 16.00 Normal false false false TR X-NONE AR-SA DefSemiHidden="false" DefQFormat="false" DefPriority="99" LatentStyleCount="371"> UnhideWhenUsed="true" QFormat="true" Name="heading 2"/> UnhideWhenUsed="true" QFormat="true" Name="heading 3"/> UnhideWhenUsed="true" QFormat="true" Name="heading 4"/> UnhideWhenUsed="true" QFormat="true" Name="heading 5"/> 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Name="List Table 6 Colorful Accent 4"/> Name="List Table 7 Colorful Accent 4"/> Name="List Table 1 Light Accent 5"/> Name="List Table 6 Colorful Accent 5"/> Name="List Table 7 Colorful Accent 5"/> Name="List Table 1 Light Accent 6"/> Name="List Table 6 Colorful Accent 6"/> Name="List Table 7 Colorful Accent 6"/> /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0in; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial; mso-bidi-theme-font:minor-bidi; mso-ansi-language:TR;} Wireless Sensor Networks (WSNs) support intelligent data acquisition systems across environmental monitoring, industrial automation, and smart cities. As a fundamental enabler of the Internet of Things (IoT), WSNs rely heavily on battery-powered sensor nodes for sustained operation in dynamic and often remote environments. However, predicting battery lifetime in WSNs remains a critical challenge due to the complex interplay between environmental conditions and operational behaviors. Conventional energy models often fail to consider the simultaneous influence of temperature, humidity, and data traffic intensity on battery depletion rates. This study proposes a battery lifetime prediction model based on a Markov framework integrated with an exponential energy consumption function to address this issue. The model incorporates three primary variables—ambient temperature, relative humidity, and data movement to simulate energy usage dynamically. The framework calculates transition probabilities and energy load based on environmental states, enabling accurate forecasting. Additionally, the model evaluates the impact of different battery chemistries (Ni-MH, LiPo, Li-ion, and Alkaline) on lifespan performance across varying environmental scenarios. Simulation results reveal that temperature and humidity significantly influence energy depletion, while data transmission intensity plays a supporting role in high-traffic cases. LiPo and Li-ion batteries demonstrate superior performance and stability, especially under extreme environmental conditions. This study contributes a novel multi-variable model that bridges physical sensing environments with predictive battery analytics. The findings provide a foundation for strategic energy planning and adaptive deployment of WSNs in sustainability-critical applications. 2026 85 98 4015 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.180206 https://www.americaspg.com/journal/18/article/4015 https://www.americaspg.com/storage/11756071790.pdf