| Autor: |
Guguloth Ravi, M. Swamy Das, Karthik Karmakonda |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Measurement: Sensors, Vol 27, Iss , Pp 100744- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2665-9174 |
| DOI: |
10.1016/j.measen.2023.100744 |
| Popis: |
Background: Several IoT nodes are deployed in the monitoring environment to ensure reliability. In both sensor and sink nodes, the same data is sensed and forwarded. While redundant data maintains reliability, sink nodes waste energy processing the redundant data. Objective: In order to upholdthe compromiseamong energy ingesting and reliability, necessary to eliminate the redundancies in sensed data up to an appropriate level. Data aggregation algorithms currently assign time slots based on data sensing period and program rate, disregarding packet loss and latency. Methodology: In this paper, we suggest a cluster based reliable data aggregation (CRDA) scheme for IoTnetwork which ensures data collection and aggregation in energy efficient manner and transfer to another end very effectively. We first introduce a monarch and sine-cosine (MSC) algorithm to form clusters by grouping the IoT sensors which ensures the effective data transferring. In data aggregation phase, we utilize the multiple design metrics to compute the trust degree of each IoT sensors and design an improved sunflower optimization (ISFO) algorithm to optimize the design constraints. The highest trust degree owned swelling is act as cluster head (CH) of the cluster which ensures data aggregation. A reformative optimal–learning-based deep neural network (ROL-DNN) is then used to compute routes between IoT sensors which ensures reliabledata aggregation and transferring. Results: and analysis: Finally, we validate our proposed routing with the different simulation scenario and their results are compared with the existing routing protocols to prove the effectiveness. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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