Research On Blockchain Technology Based Edge Crowdsensing Trust Management Mechanism

Traditional cloud-based crowdsensing systems have some problems such as high latency and difficulty in position.While edge computing enables the data to be processed in the edge of networks in order to decrease the latency of crowdsensing services and accurately locate the sensing area.Therefore,edge computing can be used to solve these problems.However,due to the distributed environment and vulnerability of edges,it is difficult for different edges to reach consistency to provide the same service and protect the data from tampering at the same time.To solve these problems,the Blockchain,a credible and natural decentralized technique,is considered as a suitable tool.In this paper,we propose a Blockchain-based edge crowdsensing service system.Specifically,in order to collaborate the different edges and protect the transaction information of task requester and crowdsensing participants,we propose a consensus algorithm for the blockchain,which is named Leader Stable Practical Byzantine Fault Tolerance(LS-PBFT)algorithm based on PBFT algorithm.This algorithm enables all edges to collaboratively maintain an updated,consistent and credible ledger in Blockchain.Furthermore,the data generated in this process are constructed as a multi-transaction,which can be packaged into a block and stored in Blockchain.At the same time,a trustworthy crowdsensing incentive mechanism is presented to guarantee trust management.This mechanism is named trustworthy crowdsensing for edge-platform(TCE).The TCE takes the strategy of reverse auction algorithm for every task that the users request to find a set of winners who can provide corresponding sensing data.Simulation results and theoretical analysis reveal that the proposed system is not only efficient in generating and storing blocks but also feasible in resisting attacks of malicious users and edges.Our experiments also show that the computation and communication latency of edge crowdsensing is significant decreased comparing with the cloud platform.And for consensus time consuming,the result demonstrates that the running time of LS-PBFT is reduced significantly.