Blockchain MCS Offloading With C-PoW and DRL Cost Control
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Solution Overview
Problem
The application of blockchain in Mobile Crowdsensing (MCS) systems is obstructed by the computation-intensive Proof-of-Work (PoW), due to the limited computational capacity of mobile devices (MDs), and traditional centralized transaction mechanisms pose security issues and performance bottlenecks.
Innovation Solution
A consortium blockchain-based framework with a novel Credit-based Proof-of-Work (C-PoW) algorithm and a scalable deep reinforcement learning based computation offloading (DRCO) method, utilizing Proximal Policy Optimization (PPO) and Differentiable Neural Computer (DNC), to handle computation-intensive tasks and secure transactions in MCS systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional Proof-of-Work (PoW) algorithm is used in blockchain, then security and reliability of transactions are improved, but computational complexity increases making it infeasible for mobile devices
Solution Approach 1:
The system segments the blockchain network into consortium blockchain nodes (with full PoW capability) and mobile device participants (with limited capability). Mobile devices can join the consortium blockchain without running full PoW, while consortium nodes maintain security through PoW. This segmentation allows mobile devices to participate in blockchain transactions without bearing the full computational burden.
Solution Approach 2:
Consortium blockchain nodes act as intermediaries between mobile devices and the blockchain network. These intermediary nodes perform the computationally intensive PoW operations on behalf of mobile devices, enabling devices with limited computational capacity to participate in secure blockchain transactions without directly executing PoW algorithms.
2Reliability
If Proof-of-Work (PoW) is used to ensure secure transactions, then transaction reliability is improved, but energy consumption increases due to computation-intensive operations
Solution Approach 1:
The system segments energy consumption responsibilities by designating consortium blockchain nodes to handle energy-intensive PoW operations while mobile devices perform lighter verification tasks. This segmentation enables secure transactions without requiring every participant to consume high energy, making blockchain feasible for mobile devices with limited power supplies.
3Device complexity
If centralized transaction confirmation mechanism is used, then system complexity is reduced, but security issues and performance bottlenecks arise
Solution Approach 1:
The system merges the advantages of both centralized and decentralized approaches by creating a consortium blockchain where a selected group of nodes (consortium members) jointly maintain the blockchain. This hybrid structure provides distributed security through multiple nodes while maintaining manageable complexity through controlled participation, avoiding the single-point failure of centralized systems while preventing the excessive complexity of fully decentralized systems.
4Reliability
If mobile devices perform full PoW operations, then transaction security is maintained, but productivity decreases due to limited computational capacity
Solution Approach 1:
The system segments transaction processing roles between consortium nodes and mobile devices. Consortium nodes handle computationally intensive PoW operations to maintain security, while mobile devices can quickly verify transactions and submit new transactions. This segmentation enables mobile devices to achieve high transaction throughput without compromising security, as the security-critical PoW operations are performed by dedicated consortium nodes with sufficient computational resources.
Data Source
AI summary
A computation offloading approach in blockchain-enabled MCS systems is provided to reach a lower total cost in computation offloading. Firstly, building a consortium blockchain-based framework to guarantee secure transactions in MCS systems. Secondly, designing a novel credit-based proof-of-work (C-PoW) algorithm instead of PoW to confirm transactions and add new blocks to the chain, thereby relieving the complexity of POW while keeping the reliability of blockchain. Thirdly, using a scalable deep reinforcement learning based computation offloading (DRCO) method to handle the computation-intensive tasks of C-PoW; by integrating PPO and DNC, the DRCO executes differentiable read-write operations on structured external memories by following an objective-oriented way; the DRCO uses a clipped surrogate objective to control the update of offloading policy, in order to improve the decision-making efficiency; the DRCO uses the DNNs to address the problem of high-dimensional state space.