QACM: Quality Aware Crowd Sensing in Mobile Computing
Abstract
:1. Introduction
1.1. Background
1.2. Problem Definition
1.2.1. Objectives
- Determine if our method achieves better QOI in crowd sensing based on application requirements.
- To enhance the overall QOI level in crowd sensing for opportunistic transmission paradigm.
1.2.2. Assumptions
- The opportunistic transmission paradigm is the basis for this crowd sensing.
- Identified/specific groups of mobile users are selected based on incentive mechanism (based on category).
2. Literature Review
3. Methodology
3.1. System Architecture and Mathematical Model
3.2. Reliability Based on Distance—Rbd
3.3. Transmission Rate to Sink–Trt
3.4. Transmission Range to Sink—Tri
3.5. Quality of Sensing of Device—Mqs
Function 1: QOI_ Measurement () |
Inputs: Rbd, Tri, Trt and Mqs for node i to n do calculate Rbd, calculate Tri, calculate Trt calculate Mqs end for for node i to n do if (Rbd > 1 && Tri > 1 && Trt > 1 && Mqs > 1) QOI = high; else if (Rbd == 1 && Tri == 1 && Trt == 1 && Mqs == 1) QOI = medium; else if (Rbd < 1 && Tri < 1 && Trt < 1 && Mqs < 1) QOI = low; end if end if end if end for |
Function 2: Category_Mobile_users () |
Inputs: QOI for each node i to n do if (QOI > high) Insert i to {Mu1} else if (QOI == medium) Insert i to {Mu2} else if (QOI < low) Insert i to {Mu3} end if end if end if end for |
Algorithm 1: QACM Algorithm |
Input: Mobile users participate in crowd sensing no- N nodes Step 1: Random Node Deployment Step 2: Sensing the random events by deployed Nodes N Step 3: QOI_ Measurement () Step 4: Category_Mobile_users () Step 5: (Assign Different categories of mobile sensing group based on data sensitivity level requirements of applications.) for Category Mui i to 3 do if (i = 1) Assign Category Mu1 for high Sensitive Applications: else if (i = 2) Assign Category Mu1 for Medium Sensitive range Applications: else if (i = 3) Assign Category Mu1 for Low range Sensitive Applications: end if end if end if end for |
4. Simulation and Performance Analysis
Performance Metrics
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Parameter | Values |
---|---|
Network size/range | 1000 m ∗ 1000 m |
Number of nodes | 300 |
Node distribution | Random |
Node types | Involves different. level of sensing elements |
Simulation time | 14,000 s |
Simulation Time | SR | TT | ||||
---|---|---|---|---|---|---|
QIMC | QOSA | QACM | QIMC | QOSA | QACM | |
2000 | 0.41 | 0.51 | 0.59 | 0.89 | 0.91 | 0.76 |
4000 | 0.47 | 0.50 | 0.55 | 0.86 | 0.88 | 0.74 |
6000 | 0.49 | 0.52 | 0.59 | 0.84 | 0.86 | 0.72 |
8000 | 0.51 | 0.54 | 0.61 | 0.83 | 0.84 | 0.70 |
10,000 | 0.53 | 0.57 | 0.65 | 0.81 | 0.83 | 0.69 |
12,000 | 0.56 | 0.63 | 0.71 | 0.78 | 0.81 | 0.68 |
14,000 | 0.58 | 0.65 | 0.74 | 0.76 | 0.79 | 0.67 |
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Share and Cite
Thippeswamy, B.M.; Ghouse, M.; Ahmed Jafarabad, S.; Khan Mohammed, M.A.; Adere, K.; B. M., P.P.; B. N., P.K. QACM: Quality Aware Crowd Sensing in Mobile Computing. Appl. Syst. Innov. 2023, 6, 37. https://doi.org/10.3390/asi6020037
Thippeswamy BM, Ghouse M, Ahmed Jafarabad S, Khan Mohammed MA, Adere K, B. M. PP, B. N. PK. QACM: Quality Aware Crowd Sensing in Mobile Computing. Applied System Innovation. 2023; 6(2):37. https://doi.org/10.3390/asi6020037
Chicago/Turabian StyleThippeswamy, B. M., Mohamed Ghouse, Shanawaz Ahmed Jafarabad, Murtuza Ahamed Khan Mohammed, Ketema Adere, Prabhu Prasad B. M., and Pavan Kumar B. N. 2023. "QACM: Quality Aware Crowd Sensing in Mobile Computing" Applied System Innovation 6, no. 2: 37. https://doi.org/10.3390/asi6020037