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| Title | A Categorized Information Fusion Model For Reliable Services Delivery In Smart Cities |
|---|---|
| ID_Doc | 611 |
| Authors | Anjum M.; Shahab S.; Khan M.A.; Ahmad S. |
| Year | 2024 |
| Published | Applied Soft Computing, 151 |
| DOI | http://dx.doi.org/10.1016/j.asoc.2023.111144 |
| Abstract | Wireless Sensor Network serves as the core source of data in the Internet of Things (IoT) environment. The WSN nodes sense, aggregate, and relay sensed data for the different services in the IoT. The reliability of the aggregated and transmitted information is crucial for the effectiveness of IoT services. The data from multiple sensors at different timestamps need to be fused for efficient data transmission, reducing complexity and improving the reliability of the data being transmitted, improving the quality of IoT services. Therefore, data fusion is an essential aspect of WSN, where multiple sensors collect and combine data to produce a single, more comprehensive data for transmission. This data fusion reduces the amount of data transmission and network energy consumption and increases the accuracy of the result. This paper introduces a Categorised Information Fusion Model (CIFM) to minimise the replication in handling the aggregation of multiple instances of sensor data by categorising the information. The proposed CIFM employs federated learning for distributed verification of different aggregation time frames. This learning identifies the replication of sensor data from different sensors based on time frames and aggregation instances. In the multi-timed aggregation level, the sensed information's interfering distribution is diminished by controlling the relaying instance. The recurrent learning instances diminish the multi-timed information based on the occurrence factor. This recurrent learning improves delivery precision by controlling computation complexity and fusion time. The CIFM model also reduces the communication overhead by reducing the amount of replicated information that needs to be transmitted and processed and providing a consistent delivery ratio. The use of federated learning also allows for privacy-preserving data processing as the raw data is only processed locally, and aggregated results are shared. The performance of the proposed CIFM model is evaluated through simulations and compared with other existing fusion methods. The results show that the proposed model outperforms existing accuracy and communication efficiency methods. This approach helps to simplify the process of aggregating and relaying the data in the WSN to improve the performance and efficiency of IoT services. The goal of CIFM is to provide a more effective way to process and utilise the information from the sensors to deliver accurate and reliable data to the IoT platform. The CIFM improves the fusion rate by 7.77 % and minimize the fusion time up to 13.03 % compared to other methods. © 2023 Elsevier B.V. |
| Author Keywords | Federated learning; Information fusion; Internet of things; Multi-timed aggregation; Wireless sensor network |
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