{"title":"MPSARB:通过可重构区块链设计具有安全农业记录存储模型的高效多作物模式预测系统","authors":"Deepali Jawale, Sandeep Malik","doi":"10.1007/s12652-024-04769-z","DOIUrl":null,"url":null,"abstract":"<p>Smart agriculture has become one of the most popular technologies for farmers due to its simplicity, ease of deployment, high efficiency, and low overheads. But due to an exponential increase in smart-farming data generation, it is necessary to design secure storage interfaces, that can be scaled for multiple farms. Existing storage models either showcase high security, or high storage efficiency, but a very few models enhance both these parameter sets. Such models are highly complex, and reduce the scalability when applied to large-scale scenarios. To overcome these limitations, this text proposes design of a highly efficient and secure agriculture-record-storage model via reconfigurable blockchains. The proposed model initially uses a multiple crop pattern prediction system via Binary Cascaded Convolutional Neural Network (BC CNN), and deploys a single chained Proof-of-Trust (PoT) based blockchain, that is tuned w.r.t. context of the farms. The prediction is done via weather conditions and soil types. This assists in identification of different crop types, and selection of high trust miner nodes, that can preserve privacy during communication and storage operations. As the blockchain is scaled, a Grey Wolf Optimization (GWO) based model is deployed, which assists in splitting the underlying chain into multiple sidechains. This split is done based on QoS and Security optimizations, which is estimated via temporal miner performance under different farm types. The GWO Model also assists in estimating long-term and high-capacity storage chains, which can be used for archival operations. Due to which, the proposed model is able to improve mining speed by 9.4%, while reducing the energy consumption by 3.5% for different mining operations. The model also defines an indexing strategy for different shards, which assists in increasing data access speed by 12.8% for long-term data sets. Due to these enhancements, the proposed model is capable of deployment for large-scale scenarios.</p>","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"MPSARB: design of an efficient multiple crop pattern prediction system with secure agriculture-record-storage model via reconfigurable blockchains\",\"authors\":\"Deepali Jawale, Sandeep Malik\",\"doi\":\"10.1007/s12652-024-04769-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Smart agriculture has become one of the most popular technologies for farmers due to its simplicity, ease of deployment, high efficiency, and low overheads. But due to an exponential increase in smart-farming data generation, it is necessary to design secure storage interfaces, that can be scaled for multiple farms. Existing storage models either showcase high security, or high storage efficiency, but a very few models enhance both these parameter sets. Such models are highly complex, and reduce the scalability when applied to large-scale scenarios. To overcome these limitations, this text proposes design of a highly efficient and secure agriculture-record-storage model via reconfigurable blockchains. The proposed model initially uses a multiple crop pattern prediction system via Binary Cascaded Convolutional Neural Network (BC CNN), and deploys a single chained Proof-of-Trust (PoT) based blockchain, that is tuned w.r.t. context of the farms. The prediction is done via weather conditions and soil types. This assists in identification of different crop types, and selection of high trust miner nodes, that can preserve privacy during communication and storage operations. As the blockchain is scaled, a Grey Wolf Optimization (GWO) based model is deployed, which assists in splitting the underlying chain into multiple sidechains. This split is done based on QoS and Security optimizations, which is estimated via temporal miner performance under different farm types. The GWO Model also assists in estimating long-term and high-capacity storage chains, which can be used for archival operations. Due to which, the proposed model is able to improve mining speed by 9.4%, while reducing the energy consumption by 3.5% for different mining operations. The model also defines an indexing strategy for different shards, which assists in increasing data access speed by 12.8% for long-term data sets. Due to these enhancements, the proposed model is capable of deployment for large-scale scenarios.</p>\",\"PeriodicalId\":14959,\"journal\":{\"name\":\"Journal of Ambient Intelligence and Humanized Computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Ambient Intelligence and Humanized Computing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s12652-024-04769-z\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Ambient Intelligence and Humanized Computing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s12652-024-04769-z","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Computer Science","Score":null,"Total":0}
MPSARB: design of an efficient multiple crop pattern prediction system with secure agriculture-record-storage model via reconfigurable blockchains
Smart agriculture has become one of the most popular technologies for farmers due to its simplicity, ease of deployment, high efficiency, and low overheads. But due to an exponential increase in smart-farming data generation, it is necessary to design secure storage interfaces, that can be scaled for multiple farms. Existing storage models either showcase high security, or high storage efficiency, but a very few models enhance both these parameter sets. Such models are highly complex, and reduce the scalability when applied to large-scale scenarios. To overcome these limitations, this text proposes design of a highly efficient and secure agriculture-record-storage model via reconfigurable blockchains. The proposed model initially uses a multiple crop pattern prediction system via Binary Cascaded Convolutional Neural Network (BC CNN), and deploys a single chained Proof-of-Trust (PoT) based blockchain, that is tuned w.r.t. context of the farms. The prediction is done via weather conditions and soil types. This assists in identification of different crop types, and selection of high trust miner nodes, that can preserve privacy during communication and storage operations. As the blockchain is scaled, a Grey Wolf Optimization (GWO) based model is deployed, which assists in splitting the underlying chain into multiple sidechains. This split is done based on QoS and Security optimizations, which is estimated via temporal miner performance under different farm types. The GWO Model also assists in estimating long-term and high-capacity storage chains, which can be used for archival operations. Due to which, the proposed model is able to improve mining speed by 9.4%, while reducing the energy consumption by 3.5% for different mining operations. The model also defines an indexing strategy for different shards, which assists in increasing data access speed by 12.8% for long-term data sets. Due to these enhancements, the proposed model is capable of deployment for large-scale scenarios.
期刊介绍:
The purpose of JAIHC is to provide a high profile, leading edge forum for academics, industrial professionals, educators and policy makers involved in the field to contribute, to disseminate the most innovative researches and developments of all aspects of ambient intelligence and humanized computing, such as intelligent/smart objects, environments/spaces, and systems. The journal discusses various technical, safety, personal, social, physical, political, artistic and economic issues. The research topics covered by the journal are (but not limited to):
Pervasive/Ubiquitous Computing and Applications
Cognitive wireless sensor network
Embedded Systems and Software
Mobile Computing and Wireless Communications
Next Generation Multimedia Systems
Security, Privacy and Trust
Service and Semantic Computing
Advanced Networking Architectures
Dependable, Reliable and Autonomic Computing
Embedded Smart Agents
Context awareness, social sensing and inference
Multi modal interaction design
Ergonomics and product prototyping
Intelligent and self-organizing transportation networks & services
Healthcare Systems
Virtual Humans & Virtual Worlds
Wearables sensors and actuators