Integrating of IOTA-based blockchain with edge computing for task offloading powering the metaverse

The Metaverse is a Virtual Reality world where users engage with a computer-generated environment for immersive experience, which often generate vast amounts of data every second. Processing this large stream data locally is challenging, so Multi-access Edge Computing (MEC) is utilized. The research challenge lies in securely and immutably storing the processed data results for future use by the same or different Metaverse users. To address this issue, we introduce a Blockchain-based edge framework designed to enhance task offloading and secure stored processed results for Metaverse users. To tackle the task offloading problem, we formulated it as a Markov Decision Process (MDP) with a reward function called Multi Task Distribution (MTD). To optimize decision-making within the MTD, we introduce an innovative approach called DQ-LSTM, which combines Deep Q-network (DQN) techniques with Long Short-Term Memory (LSTM) networks. The task offloading results are recorded on the Internet of Things Application (IOTA) Blockchain, a Directed Acyclic Graph (DAG)-based Distributed Ledger Technology (DLT), ensuring a secure and immutable ledger for future reference. Simulation results show that for task offloading, DQ-LSTM outperforms DQN by 8.94%, DDQN by 43.78%, and Q-learning by 12.66% in terms of rewards. Regarding securing results of proposed DQ-LSTM algorithm, IOTA outperforms baseline Hyperledger Fabric by 60% and Ethereum by 6 times in term of latency.