TY - GEN

T1 - Incentive Mechanism Design for Uncertain Tasks in Mobile Crowd Sensing Systems Utilizing Smart Contract in Blockchain

AU - Jiang, Xikun

AU - Ying, Chenhao

AU - Yu, Xinchun

AU - Düdder, Boris

AU - Luo, Yuan

N1 - Funding Information: This work is supported by the grant PAPRICAS: Programming technology foundations for Accountability, Privacy-by-design & Robustness in Context-aware Systems. Independent Research Fund Denmark. Publisher Copyright: © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

PY - 2022

Y1 - 2022

N2 - Mobile crowd sensing (MCS) systems recently have been regarded as a newly-emerged sensing paradigm, where the platform receives the requested tasks from requesters and outsources the collection of sensory data to participating workers. However, the centralized structure of the MCS system is vulnerable to a single point of failure, and there is a lack of trust between participants and the platform. Additionally, participating in MCS is often costly. So the paramount problem is how to solve these problems associated with centralized structures and incentivize more participation. Most existing works design the incentive mechanisms only considering static sensing tasks whose information is completely known a priori (e.g., when and which task arrives). Due to the dynamic environment and severe resource constraints, the tasks are usually uncertain, i.e., the information of tasks is incompletely known by the platform. Therefore, in this paper, we design an incentive mechanism, HERALD, for the uncertain tasks in MCS systems by using smart contracts. Specifically, the uncertain tasks are low sensitive to time (that is, tasks do not require real-time information) and arrive according to a probability distribution. HERALD utilizes the decentralized nature of the blockchain to eliminate the system’s reliance on third parties and satisfies truthfulness, individual rationality, as well as low computational complexity and low social cost. The desirable properties of HERALD are validated through both theoretical analysis and extensive simulations.

AB - Mobile crowd sensing (MCS) systems recently have been regarded as a newly-emerged sensing paradigm, where the platform receives the requested tasks from requesters and outsources the collection of sensory data to participating workers. However, the centralized structure of the MCS system is vulnerable to a single point of failure, and there is a lack of trust between participants and the platform. Additionally, participating in MCS is often costly. So the paramount problem is how to solve these problems associated with centralized structures and incentivize more participation. Most existing works design the incentive mechanisms only considering static sensing tasks whose information is completely known a priori (e.g., when and which task arrives). Due to the dynamic environment and severe resource constraints, the tasks are usually uncertain, i.e., the information of tasks is incompletely known by the platform. Therefore, in this paper, we design an incentive mechanism, HERALD, for the uncertain tasks in MCS systems by using smart contracts. Specifically, the uncertain tasks are low sensitive to time (that is, tasks do not require real-time information) and arrive according to a probability distribution. HERALD utilizes the decentralized nature of the blockchain to eliminate the system’s reliance on third parties and satisfies truthfulness, individual rationality, as well as low computational complexity and low social cost. The desirable properties of HERALD are validated through both theoretical analysis and extensive simulations.

KW - Incentive mechanism

KW - Mobile crowd sensing

KW - Smart contract

KW - Uncertain sensing tasks

U2 - 10.1007/978-3-031-24383-7_26

DO - 10.1007/978-3-031-24383-7_26

M3 - Article in proceedings

AN - SCOPUS:85149899529

SN - 9783031243820

T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

SP - 475

EP - 493

BT - Collaborative Computing

A2 - Gao, Honghao

A2 - Wang, Xinheng

A2 - Wei, Wei

A2 - Dagiuklas, Tasos

PB - Springer

T2 - 18th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2022

Y2 - 15 October 2022 through 16 October 2022

ER -