QualityAware Task Allocation for Mobile Crowd Sensing Based on Edge Computing
Abstract
:1. Introduction
 We introduced edge nodes into MCS to perform truth discovery, based on which we can measure the sensed data quality. We formulated the problem of maximizing sensed data quality with the constraints of computing resources.
 We designed a twostage strategy for task allocation in client–edge–cloud MCS. In the first stage, we utilize deep reinforcement learning to make optimal edge node selections that take into account both computing resources and sensed data quality. In the second stage, we introduce a novel greedy selfadaptive stochastic algorithm (GAS) for user recruitment under each specific edge node.
 We conducted extensive experiments to evaluate the performance of our proposed method. Our edge node selection algorithm improved sensed data quality by 2 to 5 times compared with LCBPA (lowcost and balanceparticipating algorithm), MOTA (multiobjective task allocation algorithm), and SMA (stable matching algorithm). The proposed GAS algorithm also significantly improved sensed data quality compared with SMLP and RBR, while it increased spatial coverage by 20% compared with RBR.
2. Related Work
3. System Model and Problem Formulation
3.1. Task Allocation Process Model for MCS with Edge Computing Involved
3.2. Problem of Sensing Quality Maximization
4. TwoStage Task Allocation Strategy for MCS
4.1. Truth Value Discovery Based on Edge Computing
4.1.1. Weight Update
4.1.2. Truth Value Update
4.1.3. Truth Value Aggregation
4.2. Deep Reinforcement LearningBased Task Deployment to Edge Nodes
Algorithm 1: Deep reinforcement learningbased task deployment for edge nodes 
Input: Computing resources for all edge node $R=\left\{{R}_{j}\left(\tau \right)\right\}$, parameters of sensed data quality $W=\left\{{w}_{j,k}\right\}$, task assignment characterization parameters ${s}_{\tau}$, probability $\epsilon $, running epochs $ep$, time slot limitation H Output: Task deployment policy ${\omega}_{s}$

4.3. Greedy SelfAdaptive Stochastic User Recruitment
Algorithm 2: Greedy selfadaptive stochastic user recruitment algorithm 

5. Performance Evaluation
5.1. Experiment Settings
5.2. Simulation Results Analysis
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Notations
T  Set of tasks 
${t}_{i}$  ith task 
N  Set of edge nodes 
${n}_{j}$  jth node 
${R}_{j}$  Remaining computational resources of ${n}_{j}$ 
${\mathcal{R}}_{i}^{\prime}$  Required resource for ${t}_{i}$ 
${\alpha}_{i,j}$  Indicator of task allocation 
${\zeta}_{i,j}$  Indicator of computational resource allocation 
${U}_{j}$  Set of users served by node ${n}_{j}$ 
${u}_{j}^{k}$  kth user served by node ${n}_{j}$ 
${\beta}_{i,j}^{k}$  Indicator of user recruitment 
x  Sensing data value 
${x}^{*}$  Truth value 
$d(\xb7)$  Evaluation function of distance between data and truth value 
$f(\xb7)$  Monotonic descent function 
${w}_{j,k}$  Weight of user ${u}_{k}$ served by ${n}_{j}$ 
$\mathcal{S}$  State space 
$\mathcal{A}$  Action space 
$\mathcal{T}$  State transfer equation 
r  Reward function 
$\pi $  Optimal strategy 
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Li, Z.; Li, Z.; Zhang, W. QualityAware Task Allocation for Mobile Crowd Sensing Based on Edge Computing. Electronics 2023, 12, 960. https://doi.org/10.3390/electronics12040960
Li Z, Li Z, Zhang W. QualityAware Task Allocation for Mobile Crowd Sensing Based on Edge Computing. Electronics. 2023; 12(4):960. https://doi.org/10.3390/electronics12040960
Chicago/Turabian StyleLi, Zhuo, Zecheng Li, and Wei Zhang. 2023. "QualityAware Task Allocation for Mobile Crowd Sensing Based on Edge Computing" Electronics 12, no. 4: 960. https://doi.org/10.3390/electronics12040960