Optimizing QoS and security in agriculture IoT deployments: A bioinspired Q-learning model with customized shards

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 or re...

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Bibliographic Details
Published in:Heliyon Vol. 10; no. 2; p. e24224
Main Authors: Sonavane, Sonali Mahendra, Prashantha, G.R., Nikam, Pranjali Deepak, A V R, Mayuri, Chauhan, Jyoti, S, Sountharrajan, Bavirisetti, Durga Prasad
Format: Journal Article
Language:English
Published: England Elsevier Ltd 30-01-2024
Elsevier
Subjects:
AIoT
Attacks
Bacterial
Blockchain
Custom
Delay
Foraging
Mayfly
PoP
QoS
Security
Sharding
Throughput
Sharding
Bacterial
Foraging
Blockchain
Throughput
Custom
Security
Delay
PoP
AIoT
QoS
Mayfly
Attacks
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Summary: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.
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ISSN:2405-8440
2405-8440
DOI:10.1016/j.heliyon.2024.e24224