Event Classification and Intensity Discrimination for Forest Fire Inference With IoT
Simultaneously occurring random events are often reported by multiple nodes. However, the accuracy of the event detection at every node is dependent on the node's relative position from the event, and hence, not reliable. Moreover, the factors influencing the event inference are so many, that t...
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| Veröffentlicht in: | IEEE sensors journal 2022-05, Vol.22 (9), p.8869-8880 |
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| container_title | IEEE sensors journal |
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| creator | Singh, Vishal K. Singh, Chhaya Raza, Haider |
| description | Simultaneously occurring random events are often reported by multiple nodes. However, the accuracy of the event detection at every node is dependent on the node's relative position from the event, and hence, not reliable. Moreover, the factors influencing the event inference are so many, that the accuracy of such an event detection is compromised. Targeting the problem of accurate event inference in the detection of priority events, such as forest fire, a fuzzy rule-based method is proposed. Four parameters are identified for which fuzzyfied values are obtained by a membership function for every variable. A set of 256 rules are used to generate different permutations of the fire index with respect to the identified variables. Extensive analysis of the results proves the efficacy of the proposed scheme with a significantly reduced error rate of 2.01% for humidity and an error rate of 1.94% for temperature. |
| doi_str_mv | 10.1109/JSEN.2022.3163155 |
| format | Article |
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However, the accuracy of the event detection at every node is dependent on the node's relative position from the event, and hence, not reliable. Moreover, the factors influencing the event inference are so many, that the accuracy of such an event detection is compromised. Targeting the problem of accurate event inference in the detection of priority events, such as forest fire, a fuzzy rule-based method is proposed. Four parameters are identified for which fuzzyfied values are obtained by a membership function for every variable. A set of 256 rules are used to generate different permutations of the fire index with respect to the identified variables. 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Extensive analysis of the results proves the efficacy of the proposed scheme with a significantly reduced error rate of 2.01% for humidity and an error rate of 1.94% for temperature.</description><subject>Data gathering</subject><subject>energy efficiency</subject><subject>Error reduction</subject><subject>event classification</subject><subject>Event detection</subject><subject>Forest fires</subject><subject>Forestry</subject><subject>Humidity</subject><subject>Inference</subject><subject>intensity discrimination</subject><subject>IoT</subject><subject>knowledge extraction</subject><subject>Parameter identification</subject><subject>Permutations</subject><subject>Sensors</subject><subject>Temperature measurement</subject><subject>Temperature sensors</subject><subject>Wind speed</subject><issn>1530-437X</issn><issn>1558-1748</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><recordid>eNo9UMFOwzAMjRBIjMIHIC6ROHckTVq3RzQ2GJrgwBDcojR1RaaRjqRD2t-TqhMX25Lf8_N7hFxzNuWcVXfPb_OXacaybCp4IXien5BJrGXKQZanwyxYKgV8npOLEDaM8QpymJD1_BddT2dbHYJtrdG97RzVrqFL16MLtj_QBxuMt9_Wjcu283TReQw9XViPEdiiR2eQftj-iy679SU5a_U24NWxJ-R9MV_PntLV6-Nydr9KjRBFnwoDTYsSBKvLrK7zrJSYGdk2YIBLJlByU0FhRCMRsawBojcArVnNdMFBJOR2vLvz3c8-PqQ23d67KKmyIq-EBBbPJISPKOO7EDy2ahfdaH9QnKkhPDWEp4bw1DG8yLkZOTYq_-MrkJLzUvwBKzpqyQ</recordid><startdate>20220501</startdate><enddate>20220501</enddate><creator>Singh, Vishal K.</creator><creator>Singh, Chhaya</creator><creator>Raza, Haider</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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However, the accuracy of the event detection at every node is dependent on the node's relative position from the event, and hence, not reliable. Moreover, the factors influencing the event inference are so many, that the accuracy of such an event detection is compromised. Targeting the problem of accurate event inference in the detection of priority events, such as forest fire, a fuzzy rule-based method is proposed. Four parameters are identified for which fuzzyfied values are obtained by a membership function for every variable. A set of 256 rules are used to generate different permutations of the fire index with respect to the identified variables. 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| source | IEEE Electronic Library (IEL) |
| subjects | Data gathering energy efficiency Error reduction event classification Event detection Forest fires Forestry Humidity Inference intensity discrimination IoT knowledge extraction Parameter identification Permutations Sensors Temperature measurement Temperature sensors Wind speed |
| title | Event Classification and Intensity Discrimination for Forest Fire Inference With IoT |
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