Smart Agriculture with Quantum Intelligence and Blockchain Security

Abstract

Selecting energy-efficient, trustworthy cluster heads (CHs) remains a key bottleneck in agricultural wireless sensor and Internet of Things (IoT) networks, where heuristic protocols such as LEACH often converge to unstable configurations and cannot verify whether a selected node is trustworthy. This paper proposes a framework that combines a Quantum-Inspired Genetic Algorithm (QIGA) for cluster head selection with a permissioned blockchain trust-verification layer. Unlike prior work that applies quantum classifiers to sensor-data classification, the proposed method encodes candidate cluster head assignments as qubit chromosomes and evolves them through quantum rotation-gate updates, treating cluster formation as a combinatorial optimization problem rather than a classification task. Verified assignments and node reputation scores are recorded as immutable blockchain transactions validated through a lightweight Byzantine fault-tolerant consensus, enabling automatic exclusion of low-trust nodes without a central authority. A discrete-event simulation comparing the proposed framework against LEACH and a classical genetic algorithm baseline shows comparable or improved energy retention and reliable detection of malicious nodes across a range of attack ratios. The results indicate that combining quantum-inspired combinatorial optimization with blockchain-verified trust offers a practical, hardware-independent pathway toward energy-aware, tamper-resistant clustering for precision agriculture.

Introduction

The text presents a smart agriculture IoT framework that combines a Quantum-Inspired Genetic Algorithm (QIGA) with blockchain-based trust verification to improve cluster head selection, energy efficiency, network reliability, and security in agricultural sensor networks.

Background

Smart agriculture uses geographically distributed, battery-powered IoT sensors to monitor soil, crops, and weather conditions. Because sensors have limited energy, they are organized into clusters. Each cluster has a Cluster Head (CH) that collects data from member sensors and forwards aggregated information to the base station.

Selecting suitable cluster heads is difficult because it is a combinatorial and NP-hard problem. Traditional approaches such as LEACH may become inefficient or unstable as the network grows. At the same time, a compromised cluster head can drop, delay, or falsify agricultural data, creating a security and reliability problem.

Main Contribution

The proposed framework combines two technologies:

• Quantum-Inspired Genetic Algorithm (QIGA): Used to optimize cluster head selection.
• Permissioned Blockchain: Used to verify selected cluster heads, maintain reputation scores, and exclude untrusted nodes.

The framework is designed to address both energy-efficient clustering and trust/security, rather than focusing only on sensor-data classification or blockchain-based data storage.

Research Gap

The literature review identifies two major gaps:

1. Quantum-inspired techniques in agricultural IoT have mostly been applied to classification tasks, such as pest or anomaly detection, rather than cluster head selection.
2. Blockchain applications in agricultural IoT have mainly focused on data storage, provenance, and security, rather than verifying the clustering decisions that determine how data are collected.

The proposed study attempts to address both gaps within a single framework.

Proposed Methodology

The system consists of four main layers:

1. Sensing Layer: IoT sensors collect agricultural and environmental data.
2. Optimization Layer: QIGA selects appropriate cluster heads at the base station or edge server.
3. Trust and Verification Layer: A permissioned blockchain records cluster-head decisions and reputation scores.
4. Application Layer: Verified data are used for irrigation, pest detection, and yield-related applications.

Cluster Head Selection

The optimization considers three major factors:

• Energy consumption: Lower transmission energy is preferred.
• Node reputation: High-trust nodes are preferred.
• Load balance: Cluster sizes should be reasonably balanced.

The fitness function therefore rewards low energy cost and high reputation while penalizing uneven cluster loading. The initial weights used are:

• Energy cost: 0.5
• Reputation: 0.3
• Load imbalance: 0.2

Quantum-Inspired Genetic Algorithm

Unlike a conventional genetic algorithm, QIGA represents candidate solutions using qubits. Each qubit represents the probability that a sensor node will be selected as a cluster head.

The algorithm operates through:

• Qubit initialization based on the expected cluster-head density.
• Observation of qubits to generate candidate cluster-head sets.
• Fitness evaluation.
• Rotation-gate updates that move solutions toward the best candidate.
• Quantum mutation to maintain diversity and avoid premature convergence.
• Repetition until a fixed number of generations is reached or the solution stabilizes.

This approach is intended to provide faster convergence and better exploration than classical genetic algorithms while running on ordinary classical computing hardware.

Blockchain-Based Trust Verification

After QIGA selects the candidate cluster heads, the selection is not immediately broadcast.

Instead:

• Nodes with reputation below a predefined threshold are excluded.
• Low-trust nodes are placed in a cooldown period.
• The remaining selection is submitted to blockchain validators.
• Validators use a PBFT-style consensus mechanism to approve the cluster-head assignment.
• The verified decision and reputation information are stored as an immutable blockchain transaction.
• After each round, cluster-head reputation is updated based on data plausibility and forwarding behavior.

This means a node that becomes malicious after being selected can gradually lose reputation and be excluded from future rounds.

Complexity

For:

• P = population size
• m = number of sensor nodes
• G = number of generations

the QIGA optimization has an overall time complexity of approximately:

O(G × P × m)

The blockchain reputation checking requires O(k) operations for k selected cluster heads, while PBFT-style consensus requires approximately O(v²) for v validators. Since the number of validators is relatively small compared with the number of sensors, the blockchain overhead is considered manageable.

Security Analysis

The framework addresses several security threats:

• A compromised node cannot easily become a confirmed cluster head because of reputation filtering and validator consensus.
• A cluster head that starts behaving maliciously after selection can be detected through forwarding-consistency and data-plausibility checks.
• Malicious nodes can subsequently be excluded from future clustering rounds.

However, an important limitation is that data falsification occurring during the current round cannot be corrected retroactively. Real-time anomaly detection could address this issue in future research.

Conclusion

This paper proposed a smart agriculture framework combining a Quantum-Inspired Genetic Algorithm for energy-aware, trust-sensitive cluster head selection with a permissioned blockchain layer for tamper-evident verification of clustering decisions and reputation-based exclusion of malicious nodes. Unlike prior work that applies quantum classifiers to sensor-data classification, the proposed framework applies quantum-inspired representation to the combinatorial cluster-head-selection problem itself, and unlike prior blockchain-secured agriculture systems that focus on data storage, the proposed trust layer verifies the clustering decisions that determine how data is collected. Simulation results indicate that QIGA and a classical genetic algorithm baseline retain more network-wide residual energy than LEACH, that QIGA converges within a small number of generations once qubit probabilities are initialized toward the expected cluster density, and that the blockchain trust layer reliably detects malicious nodes across a range of attack ratios, with a precision/recall trade-off that depends on the reputation threshold. Future work includes evaluating the framework at larger network scales and across multiple random seeds to obtain statistically robust performance estimates; tuning the load-imbalance weight and cool-down mechanism to reduce first-node-death variance; deploying the framework on real IoT hardware with a lightweight permissioned blockchain such as Hyperledger Fabric; and integrating real-time, data-level anomaly detection — of the kind used in the authors\' prior classification-focused system [16] — as a complementary layer to the reactive, election-level reputation mechanism proposed here.

References

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Copyright

Copyright © 2026 Munta Padmavathi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.