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<p>Path planning for cloud based autonomous systems such as smart transportation, Internet of Things (IoT) installations and robot fleets need to be secure, energy efficient, time efficient and fulfil privacy constraints. Current reinforcement learning (RL) techniques mainly consider optimisation of single objective, centralised or loosely secured model updates which are susceptible to data poisoning, privacy breach and adversarial model updates. We present Blockchain enabled Energy and Time efficient Multi Objective Reinforcement Learning (BlockE2T MORL) a new decentralised approach for secure, cloud assisted path planning. BlockE2T MORL has three main components: (i) a dynamic multi objective reward function that reduces energy, travel time and security threat; (ii) a lightweight blockchain inspired trust mechanism that assigns continuous trust values to agents, and is incorporated in the reward function to punish dishonest or malicious agents; and (iii) a hybrid actor critic learning strategy that facilitates exploration and exploitation in dynamic environments. Unlike conventional blockchain systems, our approach incurs low computational overhead (<InlineEquation ID="IEq1"><EquationSource Format="TEX">\(\:O(K\cdot\:D)\)</EquationSource></InlineEquation> instead of <InlineEquation ID="IEq2"><EquationSource Format="TEX">\(\:O({K}^{2}\cdot\:D)\)</EquationSource></InlineEquation> for validation) and operates without heavy consensus protocols. We evaluate BlockE2T-MORL on a simulated grid-based cloud environment with up to 100 agents and varying adversarial ratios. After 200 training episodes (5 independent runs), the proposed framework achieves: (i) energy consumption = 124.3 ± 8.7&#xa0;J (23.4% reduction vs. standard RL, <i>p</i> &lt; 0.01), (ii) latency = 45.2 ± 3.8 ms (17.4% improvement, <i>p</i> &lt; 0.05), and (iii) trust score = 0.87 ± 0.04 (67% improvement, <i>p</i> &lt; 0.001). The framework converges faster (210 ± 25 episodes in the final optimized configuration, compared with ≥ 520 episodes for baseline methods). BlockE2T-MORL offers a scalable, privacy-preserving, and computationally lightweight solution for next-generation intelligent path planning in cloud-based autonomous systems.</p>

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A blockchain-enabled multi-objective reinforcement learning framework for secure energy- and time-efficient smart path planning in cloud environments

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月3日

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  • Revati Raman Dewangan,
  • Deepali Thombre,
  • Vivek Parganiha,
  • Monika Verma,
  • Amit Pimpalkar,
  • Bhupesh Kumar Dewangan,
  • Nilesh Shelke

摘要

Path planning for cloud based autonomous systems such as smart transportation, Internet of Things (IoT) installations and robot fleets need to be secure, energy efficient, time efficient and fulfil privacy constraints. Current reinforcement learning (RL) techniques mainly consider optimisation of single objective, centralised or loosely secured model updates which are susceptible to data poisoning, privacy breach and adversarial model updates. We present Blockchain enabled Energy and Time efficient Multi Objective Reinforcement Learning (BlockE2T MORL) a new decentralised approach for secure, cloud assisted path planning. BlockE2T MORL has three main components: (i) a dynamic multi objective reward function that reduces energy, travel time and security threat; (ii) a lightweight blockchain inspired trust mechanism that assigns continuous trust values to agents, and is incorporated in the reward function to punish dishonest or malicious agents; and (iii) a hybrid actor critic learning strategy that facilitates exploration and exploitation in dynamic environments. Unlike conventional blockchain systems, our approach incurs low computational overhead (\(\:O(K\cdot\:D)\) instead of \(\:O({K}^{2}\cdot\:D)\) for validation) and operates without heavy consensus protocols. We evaluate BlockE2T-MORL on a simulated grid-based cloud environment with up to 100 agents and varying adversarial ratios. After 200 training episodes (5 independent runs), the proposed framework achieves: (i) energy consumption = 124.3 ± 8.7 J (23.4% reduction vs. standard RL, p < 0.01), (ii) latency = 45.2 ± 3.8 ms (17.4% improvement, p < 0.05), and (iii) trust score = 0.87 ± 0.04 (67% improvement, p < 0.001). The framework converges faster (210 ± 25 episodes in the final optimized configuration, compared with ≥ 520 episodes for baseline methods). BlockE2T-MORL offers a scalable, privacy-preserving, and computationally lightweight solution for next-generation intelligent path planning in cloud-based autonomous systems.