Optimizing QoS and security in agriculture IoT deployments: A bioinspired Q-learning model with customized shards
Agriculture Internet of Things (AIoTs) deployments require design of high-efficiency Quality of Service (QoS) & security models that can provide stable network performance even under large-scale communication requests. Existing security models that use blockchains are either highly complex o...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S240584402400255X |
| _version_ | 1797328644356440064 |
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| author | Sonali Mahendra Sonavane G.R. Prashantha Pranjali Deepak Nikam Mayuri A V R Jyoti Chauhan Sountharrajan S Durga Prasad Bavirisetti |
| author_facet | Sonali Mahendra Sonavane G.R. Prashantha Pranjali Deepak Nikam Mayuri A V R Jyoti Chauhan Sountharrajan S Durga Prasad Bavirisetti |
| author_sort | Sonali Mahendra Sonavane |
| collection | DOAJ |
| description | Agriculture Internet of Things (AIoTs) deployments require design of high-efficiency Quality of Service (QoS) & security models that can provide stable network performance even under large-scale communication requests. Existing security models that use blockchains are either highly complex or require large delays & have higher energy consumption for larger networks. Moreover, the efficiency of these models depends directly on consensus-efficiency & miner-efficiency, which restricts their scalability under real-time scenarios. To overcome these limitations, this study proposes the design of an efficient Q-Learning bioinspired model for enhancing QoS of AIoT deployments via customized shards. The model initially collects temporal information about the deployed AIoT Nodes, and continuously updates individual recurring trust metrics. These trust metrics are used by a Q-Learning process for identification of miners that can participate in the block-addition process. The blocks are added via a novel Proof-of-Performance (PoP) based consensus model, which uses a dynamic consensus function that is based on temporal performance of miner nodes. The PoP consensus is facilitated via customized shards, wherein each shard is deployed based on its context of deployment, that decides the shard-length, hashing model used for the shard, and encryption technique used by these shards. This is facilitated by a Mayfly Optimization (MO) Model that uses PoP scores for selecting shard configurations. These shards are further segregated into smaller shards via a Bacterial Foraging Optimization (BFO) Model, which assists in identification of optimal shard length for underlying deployment contexts. Due to these optimizations, the model is able to improve the speed of mining by 4.5%, while reducing energy needed for mining by 10.4%, improving the throughput during AIoT communications by 8.3%, and improving the packet delivery consistency by 2.5% when compared with existing blockchain-based AIoT deployment models under similar scenarios. This performance was observed to be consistent even under large-scale attacks. |
| first_indexed | 2024-03-08T06:55:39Z |
| format | Article |
| id | doaj.art-23c7c10146b242b09a4ef290d0e4eff7 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-08T06:55:39Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-23c7c10146b242b09a4ef290d0e4eff72024-02-03T06:36:37ZengElsevierHeliyon2405-84402024-01-01102e24224Optimizing QoS and security in agriculture IoT deployments: A bioinspired Q-learning model with customized shardsSonali Mahendra Sonavane0G.R. Prashantha1Pranjali Deepak Nikam2Mayuri A V R3Jyoti Chauhan4Sountharrajan S5Durga Prasad Bavirisetti6G H Raisoni College of Engineering and Management, Pune, Maharashtra, IndiaJain Institute of Technology, Davangere, Karnataka, IndiaAnantrao Pawar College of Engineering and Research, Pune, Maharashtra, IndiaSchool of Computing Science and Engineering, VIT Bhopal University, Sehore, Madhya Pradesh, IndiaSchool of Computing Science and Engineering, VIT Bhopal University, Sehore, Madhya Pradesh, IndiaDepartment of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, IndiaDepartment of Computer Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway; Corresponding author.Agriculture Internet of Things (AIoTs) deployments require design of high-efficiency Quality of Service (QoS) & security models that can provide stable network performance even under large-scale communication requests. Existing security models that use blockchains are either highly complex or require large delays & have higher energy consumption for larger networks. Moreover, the efficiency of these models depends directly on consensus-efficiency & miner-efficiency, which restricts their scalability under real-time scenarios. To overcome these limitations, this study proposes the design of an efficient Q-Learning bioinspired model for enhancing QoS of AIoT deployments via customized shards. The model initially collects temporal information about the deployed AIoT Nodes, and continuously updates individual recurring trust metrics. These trust metrics are used by a Q-Learning process for identification of miners that can participate in the block-addition process. The blocks are added via a novel Proof-of-Performance (PoP) based consensus model, which uses a dynamic consensus function that is based on temporal performance of miner nodes. The PoP consensus is facilitated via customized shards, wherein each shard is deployed based on its context of deployment, that decides the shard-length, hashing model used for the shard, and encryption technique used by these shards. This is facilitated by a Mayfly Optimization (MO) Model that uses PoP scores for selecting shard configurations. These shards are further segregated into smaller shards via a Bacterial Foraging Optimization (BFO) Model, which assists in identification of optimal shard length for underlying deployment contexts. Due to these optimizations, the model is able to improve the speed of mining by 4.5%, while reducing energy needed for mining by 10.4%, improving the throughput during AIoT communications by 8.3%, and improving the packet delivery consistency by 2.5% when compared with existing blockchain-based AIoT deployment models under similar scenarios. This performance was observed to be consistent even under large-scale attacks.http://www.sciencedirect.com/science/article/pii/S240584402400255XBlockchainAIoTQoSSecurityShardingCustom |
| spellingShingle | Sonali Mahendra Sonavane G.R. Prashantha Pranjali Deepak Nikam Mayuri A V R Jyoti Chauhan Sountharrajan S Durga Prasad Bavirisetti Optimizing QoS and security in agriculture IoT deployments: A bioinspired Q-learning model with customized shards Heliyon Blockchain AIoT QoS Security Sharding Custom |
| title | Optimizing QoS and security in agriculture IoT deployments: A bioinspired Q-learning model with customized shards |
| title_full | Optimizing QoS and security in agriculture IoT deployments: A bioinspired Q-learning model with customized shards |
| title_fullStr | Optimizing QoS and security in agriculture IoT deployments: A bioinspired Q-learning model with customized shards |
| title_full_unstemmed | Optimizing QoS and security in agriculture IoT deployments: A bioinspired Q-learning model with customized shards |
| title_short | Optimizing QoS and security in agriculture IoT deployments: A bioinspired Q-learning model with customized shards |
| title_sort | optimizing qos and security in agriculture iot deployments a bioinspired q learning model with customized shards |
| topic | Blockchain AIoT QoS Security Sharding Custom |
| url | http://www.sciencedirect.com/science/article/pii/S240584402400255X |
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