Task Allocation Model Based on Worker Friend Relationship for Mobile Crowdsourcing

. 2019 Feb 22;19(4):921. doi: 10.3390/s19040921

Algorithm 2 The

F R S

algorithm.

1:
$L i s t W T \leftarrow$ mix W,T;
2:
shuffle $L i s t W T$ ;
3:
$θ \leftarrow ⌈ln (U_{m a x} + 1)⌉$
4:
$i = 0, 1, \dots, θ - 1, w_{i} = 1, S u m W = \sum_{i} w_{i}, p_{i} = w_{i} / S u m W$
5:
for each $o b j e c t \in L i s t W T$ do
6:
if $o b j e c t$ is worker then
7:
$C a n d i d a t e L i s t$ ← Find a list of workers who are friends with the $o b j e c t$ in the system
8:
if $C a n d i d a t e L i s t$ is not Null then
9:
for each $c a n d i d a t e \in C a n d i d a t e L i s t$ do
10:
Calculate the time relationship strength between $c a n d i d a t e$ and $o b j e c t$
11:
Calculate the geographical relationship strength between $c a n d i d a t e$ and $o b j e c t$
12:
end for
13:
Get the categories of tasks that workers having the strongest relationship with $o b j e c t$ in $C a n d i d a t e L i s t$ have performed and allocate this kind of task to $o b j e c t$
14:
Add to M
15:
else
16:
Randomly choose one as the k value from $0, 1, \dots, θ - 1$ according to the probability $p_{i}$
17:
Allocate tasks that meet all constraints and utilities of the allocation are higher than $e^{k}$
18:
Add to M
19:
$ω_{k} = ω_{k} {(1 + δ)}^{u_{k} / U_{max}}$
20:
Update $S u m W$ and $p_{i}$
21:
end if
22:
else
23:
Append to $A p p e a r T a s k L i s t$
24:
end if
25:
end for