With the increased emphasis on data security in the Internet of Things(IoT), blockchain has received more and more attention.Due to the computing consuming characteristics of blockchain, mobile edge computing(MEC) is ...With the increased emphasis on data security in the Internet of Things(IoT), blockchain has received more and more attention.Due to the computing consuming characteristics of blockchain, mobile edge computing(MEC) is integrated into IoT.However, how to efficiently use edge computing resources to process the computing tasks of blockchain from IoT devices has not been fully studied.In this paper, the MEC and blockchain-enhanced IoT is considered.The transactions recording the data or other application information are generated by the IoT devices, and they are offloaded to the MEC servers to join the blockchain.The practical Byzantine fault tolerance(PBFT) consensus mechanism is used among all the MEC servers which are also the blockchain nodes, and the latency of the consensus process is modeled with the consideration of characteristics of the wireless network.The joint optimization problem of serving base station(BS) selection and wireless transmission resources allocation is modeled as a Markov decision process(MDP), and the long-term system utility is defined based on task reward, credit value, the latency of infrastructure layer and blockchain layer, and computing cost.A double deep Q learning(DQN) based transactions offloading algorithm(DDQN-TOA) is proposed, and simulation results show the advantages of the proposed algorithm in comparison to other methods.展开更多
基金Supported by the National Key Research and Development Program of China(No.2020YFC1807903)the Natural Science Foundation of Beijing Municipality(No.L192002)。
文摘With the increased emphasis on data security in the Internet of Things(IoT), blockchain has received more and more attention.Due to the computing consuming characteristics of blockchain, mobile edge computing(MEC) is integrated into IoT.However, how to efficiently use edge computing resources to process the computing tasks of blockchain from IoT devices has not been fully studied.In this paper, the MEC and blockchain-enhanced IoT is considered.The transactions recording the data or other application information are generated by the IoT devices, and they are offloaded to the MEC servers to join the blockchain.The practical Byzantine fault tolerance(PBFT) consensus mechanism is used among all the MEC servers which are also the blockchain nodes, and the latency of the consensus process is modeled with the consideration of characteristics of the wireless network.The joint optimization problem of serving base station(BS) selection and wireless transmission resources allocation is modeled as a Markov decision process(MDP), and the long-term system utility is defined based on task reward, credit value, the latency of infrastructure layer and blockchain layer, and computing cost.A double deep Q learning(DQN) based transactions offloading algorithm(DDQN-TOA) is proposed, and simulation results show the advantages of the proposed algorithm in comparison to other methods.
