Smart City Gnosys
Smart city article details
| Title | An Iot-Based Resource Utilization Framework Using Data Fusion For Smart Environments |
|---|---|
| ID_Doc | 8741 |
| Authors | Fawzy D.; Moussa S.M.; Badr N.L. |
| Year | 2023 |
| Published | Internet of Things (Netherlands), 21 |
| DOI | http://dx.doi.org/10.1016/j.iot.2022.100645 |
| Abstract | Nowadays, many communities are emerging towards smart environments, requiring the communication and collaboration of diverse Internet-of-Things (IoT) devices. A smart environment exploits the use of IoT technology to share and process data among such devices for a better living. However, this comes with additional costs, such as the exponential growth of IoT devices, the heterogeneity of IoT use cases, and the new complex features encountered by IoT data, which complicate their processing and analysis using the traditional techniques. This causes a dramatic performance degradation of the used processing resources, which directly affects the overall efficiency and performance of IoT-based systems. Although different studies have presented resource utilization approaches for IoT systems, but they were not evaluated from different resource utilization perspectives. Besides, no efforts have been directed to investigate their effectiveness to process the unprecedented IoT data features that inevitably impact the accuracy and efficiency of resource utilization. In this paper, the Triple Phases Resource Utilized Data Fusion (TPRUDF) framework is proposed as the first IoT-based cost-aware resource utilization using data fusion. It exclusively considers different IoT data features by employing three phases of data fusion: (1) data in – data out, (2) data in – feature out, and (3) feature in – decision out. TPRUDF fuses the raw IoT data by maintaining the complex IoT data features, independent of the IoT domain or the computing model, using the spatiotemporal data fusion (STDF) IoT-based data fusion approach. TPRUDF then fuses the uncorrelated data features via the Principal Component Analysis. Finally, it employs two different resource utilization techniques: (1) Genetic Algorithms and (2) Particle Swarm Optimization, fusing their results using the voting logic fusion technique. A public edge-computing simulator is used to evaluate TPRUDF via three real-world smart cities datasets. The experimental results of the proposed TPRUDF framework indicate that it: (1) achieves an average accuracy level of resource utilization equal to 91%, (2) increases the resource utilization throughput by an average of 40% and eventually minimizes the processing delay, (3) boosts the resource utilization availability by 60%, and (4) decreases the energy consumption by 35%. © 2022 |
| Author Keywords | Data fusion; Data optimization; Features extraction; IoT; Resource utilization; Smart cities |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
4324  | 0.886 | Pourghebleh B.; Hekmati N.; Davoudnia Z.; Sadeghi M. | A Roadmap Towards Energy-Efficient Data Fusion Methods In The Internet Of Things | Concurrency and Computation: Practice and Experience, 34, 15 (2022) |
| 40574 | 0.885 | Liu C.; Wang R.; Zou P.; Gan B. | Optimization And Algorithm Research Of Intelligent Monitoring System Combining Artificial Intelligence And Internet Of Things | Advances in Transdisciplinary Engineering, 70 (2025) |
| 17221 | 0.883 | Alam F.; Mehmood R.; Katib I.; Albogami N.N.; Albeshri A. | Data Fusion And Iot For Smart Ubiquitous Environments: A Survey | IEEE Access, 5 (2017) |
| 25051 | 0.88 | Darabkh K.A.; Al-Akhras M. | Evolutionary Cost Analysis And Computational Intelligence For Energy Efficiency In Internet Of Things-Enabled Smart Cities: Multi-Sensor Data Fusion And Resilience To Link And Device Failures | Smart Cities, 8, 2 (2025) |
| 5243 | 0.873 | Cengiz B.; Adam I.Y.; Ozdem M.; Das R. | A Survey On Data Fusion Approaches In Iot-Based Smart Cities: Smart Applications, Taxonomies, Challenges, And Future Research Directions | Information Fusion, 121 (2025) |
| 22927 | 0.863 | Nizam M.K.; Goyal S.B.; Verma C.; Illés Z. | Empowering Smart Cities With Edge Computing-Based Iot Systems: A Focus On Data Analytics And Machine Learning Techniques | Lecture Notes in Electrical Engineering, 1194 (2024) |
| 8925 | 0.862 | Krishnamurthi R.; Kumar A.; Gopinathan D.; Nayyar A.; Qureshi B. | An Overview Of Iot Sensor Data Processing, Fusion, And Analysis Techniques | Sensors (Switzerland), 20, 21 (2020) |
| 14138 | 0.858 | Wang M.; Perera C.; Jayaraman P.P.; Zhang M.; Strazdins P.; Shyamsundar R.K.; Ranjan R. | City Data Fusion: Sensor Data Fusion In The Internet Of Things | The Internet of Things: Breakthroughs in Research and Practice (2017) |
| 30379 | 0.854 | Lakshmi D.; Jeyarani J.; Suguna R.; Muneeshwari P.; Valantina G.M.; Jayaraman S. | Impact Of Iot Data Integration On Real-Time Analytics For Smart City Management | Proceedings of the 2024 10th International Conference on Communication and Signal Processing, ICCSP 2024 (2024) |
| 40868 | 0.853 | Kumari R.; Sah D.K.; Cengiz K.; Nauman A.; Ivković N.; Mihaljević I. | Optimizing Resource Utilization Using Vector Databases In Green Internet Of Things | 2023 IEEE Globecom Workshops, GC Wkshps 2023 (2023) |