Multi-Objective Decision-Making of Cluster Heads Election in Routing Algorithm for Field Observation Instruments Network
Field observation systems are mainly deployed in the harsh natural environment. These systems principally focus on observation and study within the station currently, which leads to problems such as the inability to form combined network observation and quite challenging to answer the scientific que...
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| Vydáno v: | IEEE sensors journal Ročník 21; číslo 22; s. 25796 - 25807 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
IEEE
15.11.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1530-437X, 1558-1748 |
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| Abstract | Field observation systems are mainly deployed in the harsh natural environment. These systems principally focus on observation and study within the station currently, which leads to problems such as the inability to form combined network observation and quite challenging to answer the scientific questions of wider regions and scales. To form Field Observation Instruments Networks (FOINs) and accelerate the general automation rate as well as in real-time data exchange in field observation, a multi-objective decision-making mehod named Entropy-based TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) Clustering routing algorithm (ETC) for FOIN is proposed in this paper. The ETC algorithm can select the optimal cluster head (Optimal-CH) through multi-objective decision-making and mainly solves the problem that some existing multi-objective optimization algorithms cannot dynamically and objectively allocate weights. The ETC algorithm was compared with some latest work and similar kinds of work from network lifespan, the number of CH and energy consumption in the Matlab simulations experiments. The result shows that the ETC algorithm performs well, enhancing energy conservation and extending the existence of FOIN. |
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| AbstractList | Field observation systems are mainly deployed in the harsh natural environment. These systems principally focus on observation and study within the station currently, which leads to problems such as the inability to form combined network observation and quite challenging to answer the scientific questions of wider regions and scales. To form Field Observation Instruments Networks (FOINs) and accelerate the general automation rate as well as in real-time data exchange in field observation, a multi-objective decision-making mehod named Entropy-based TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) Clustering routing algorithm (ETC) for FOIN is proposed in this paper. The ETC algorithm can select the optimal cluster head (Optimal-CH) through multi-objective decision-making and mainly solves the problem that some existing multi-objective optimization algorithms cannot dynamically and objectively allocate weights. The ETC algorithm was compared with some latest work and similar kinds of work from network lifespan, the number of CH and energy consumption in the Matlab simulations experiments. The result shows that the ETC algorithm performs well, enhancing energy conservation and extending the existence of FOIN. |
| Author | Al-Neshmi, Hamzah Murad Mohammed Huo, Jiuyuan Yang, Jiguang |
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| SubjectTerms | Algorithms Clustering Clustering algorithms Clustering routing algorithms Data exchange Decision making Elections Energy consumption entropy field observation instrument network (FOIN) Heuristic algorithms Instruments multi-objective decision-making Multiple objective analysis Optimization Routing Sensors TOPSIS Wireless sensor networks |
| Title | Multi-Objective Decision-Making of Cluster Heads Election in Routing Algorithm for Field Observation Instruments Network |
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