Research Article
Blockchain-Based Reputation Sharing for High-Quality Participant Selection of MCS
| | Input: M: applicants where contains registration information (username, ID, hobbies, historical records hr, and reputation re) | | | T: Perceptual tasks | | | Output: completion results of T. | | (1) | C Ø, FS Ø, B Ø, , | | (2) | Let initialize reputation RE and threshold s for | | (3) | For each applicant in M do: | | (4) | Query reputation information of from | | (5) | If ( ≠ .hr or ≠ ) | | (6) | # is the historical record of the ith element, is the reputation of the ith element. | | (7) | | | (8) | | | (9) | End | | (10) | End | | (11) | For in M do: | | (12) | If ( = Ø and ≥ s) or (status of each item in is completed) then | | (13) | | | (14) | End | | (15) | Calculate the total reputation | | (16) | If ≠ Ø then | | (17) | calculate based on equations (2) and (3) | | (18) | else | | (19) | calculate based on equations (1) and (3) | | (20) | End | | (21) | End | | (22) | FS top-rank (C, N) | | (23) | Store in the blockchain | | (24) | Assign T to | | (25) | Let submit perceptual data to | | (26) | Let process perceptual data and update the by equations (4) and (5) | | (27) | For in B do | | (28) | Announce on the blockchain | | (29) | End | | (30) | Return the completion results of T |
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