Adaptive Scheduling in Cognitive IoT Sensors for Optimizing Network Performance Using Reinforcement Learning
Cognitive sensors are embedded in home appliances and other surrounding devices to create a connected, intelligent environment for providing pervasive and ubiquitous services. These sensors frequently create massive amounts of data with many redundant and repeating bit values. Cognitive sensors are...
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MDPI AG
2025-05-01
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| author | Muhammad Nawaz Khan Sokjoon Lee Mohsin Shah |
| author_facet | Muhammad Nawaz Khan Sokjoon Lee Mohsin Shah |
| author_sort | Muhammad Nawaz Khan |
| collection | DOAJ |
| description | Cognitive sensors are embedded in home appliances and other surrounding devices to create a connected, intelligent environment for providing pervasive and ubiquitous services. These sensors frequently create massive amounts of data with many redundant and repeating bit values. Cognitive sensors are always restricted in resources, and if careful strategy is not applied at the time of deployment, the sensors become disconnected, degrading the system’s performance in terms of energy, reconfiguration, delay, latency, and packet loss. To address these challenges and to establish a connected network, there is always a need for a system to evaluate the contents of detected data values and dynamically switch sensor states based on their function. Here in this article, we propose a reinforcement learning-based mechanism called “Adaptive Scheduling in Cognitive IoT Sensors for Optimizing Network Performance using Reinforcement Learning (ASC-RL)”. For reinforcement learning, the proposed scheme uses three types of parameters: internal parameters (states), environmental parameters (sensing values), and history parameters (energy levels, roles, number of switching states) and derives a function for the state-changing policy. Based on this policy, sensors adjust and adapt to different energy states. These states minimize extensive sensing, reduce costly processing, and lessen frequent communication. The proposed scheme reduces network traffic and optimizes network performance in terms of network energy. The main factors evaluated are joint Gaussian distributions and event correlations, with derived results of signal strengths, noise, prediction accuracy, and energy efficiency with a combined reward score. Through comparative analysis, ASC-RL enhances the overall system’s performance by 3.5% in detection and transition probabilities. The false alarm probabilities are reduced to 25.7%, the transmission success rate is increased by 6.25%, and the energy efficiency and reliability threshold are increased by 35%. |
| format | Article |
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| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
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| spelling | doaj-art-a0155181500141d7b01b2dba265eb3eb2025-08-20T02:33:43ZengMDPI AGApplied Sciences2076-34172025-05-011510557310.3390/app15105573Adaptive Scheduling in Cognitive IoT Sensors for Optimizing Network Performance Using Reinforcement LearningMuhammad Nawaz Khan0Sokjoon Lee1Mohsin Shah2Department of Smart Security, Gachon University, Seongnam-si 13120, Gyeonggi-do, Republic of KoreaDepartment of Smart Security, Gachon University, Seongnam-si 13120, Gyeonggi-do, Republic of KoreaDepartment of Computer Engineering, Gachon University, Seongnam-si 13120, Gyeonggi-do, Republic of KoreaCognitive sensors are embedded in home appliances and other surrounding devices to create a connected, intelligent environment for providing pervasive and ubiquitous services. These sensors frequently create massive amounts of data with many redundant and repeating bit values. Cognitive sensors are always restricted in resources, and if careful strategy is not applied at the time of deployment, the sensors become disconnected, degrading the system’s performance in terms of energy, reconfiguration, delay, latency, and packet loss. To address these challenges and to establish a connected network, there is always a need for a system to evaluate the contents of detected data values and dynamically switch sensor states based on their function. Here in this article, we propose a reinforcement learning-based mechanism called “Adaptive Scheduling in Cognitive IoT Sensors for Optimizing Network Performance using Reinforcement Learning (ASC-RL)”. For reinforcement learning, the proposed scheme uses three types of parameters: internal parameters (states), environmental parameters (sensing values), and history parameters (energy levels, roles, number of switching states) and derives a function for the state-changing policy. Based on this policy, sensors adjust and adapt to different energy states. These states minimize extensive sensing, reduce costly processing, and lessen frequent communication. The proposed scheme reduces network traffic and optimizes network performance in terms of network energy. The main factors evaluated are joint Gaussian distributions and event correlations, with derived results of signal strengths, noise, prediction accuracy, and energy efficiency with a combined reward score. Through comparative analysis, ASC-RL enhances the overall system’s performance by 3.5% in detection and transition probabilities. The false alarm probabilities are reduced to 25.7%, the transmission success rate is increased by 6.25%, and the energy efficiency and reliability threshold are increased by 35%.https://www.mdpi.com/2076-3417/15/10/5573reinforcement learningadaptive schedulingcognative sensorsenergy efficiencyInternet of Thingslatency |
| spellingShingle | Muhammad Nawaz Khan Sokjoon Lee Mohsin Shah Adaptive Scheduling in Cognitive IoT Sensors for Optimizing Network Performance Using Reinforcement Learning Applied Sciences reinforcement learning adaptive scheduling cognative sensors energy efficiency Internet of Things latency |
| title | Adaptive Scheduling in Cognitive IoT Sensors for Optimizing Network Performance Using Reinforcement Learning |
| title_full | Adaptive Scheduling in Cognitive IoT Sensors for Optimizing Network Performance Using Reinforcement Learning |
| title_fullStr | Adaptive Scheduling in Cognitive IoT Sensors for Optimizing Network Performance Using Reinforcement Learning |
| title_full_unstemmed | Adaptive Scheduling in Cognitive IoT Sensors for Optimizing Network Performance Using Reinforcement Learning |
| title_short | Adaptive Scheduling in Cognitive IoT Sensors for Optimizing Network Performance Using Reinforcement Learning |
| title_sort | adaptive scheduling in cognitive iot sensors for optimizing network performance using reinforcement learning |
| topic | reinforcement learning adaptive scheduling cognative sensors energy efficiency Internet of Things latency |
| url | https://www.mdpi.com/2076-3417/15/10/5573 |
| work_keys_str_mv | AT muhammadnawazkhan adaptiveschedulingincognitiveiotsensorsforoptimizingnetworkperformanceusingreinforcementlearning AT sokjoonlee adaptiveschedulingincognitiveiotsensorsforoptimizingnetworkperformanceusingreinforcementlearning AT mohsinshah adaptiveschedulingincognitiveiotsensorsforoptimizingnetworkperformanceusingreinforcementlearning |