An Adaptive Data Gathering Algorithm for Minimum Travel Route Planning in WSNs Based on Rendezvous Points
Abstract
:1. Introduction
2. Related Work
3. Preliminaries
- A total of Nsensor nodes is uniformly distributed over the specified area. Each node can be uniquely identified (i.e., it has a unique identity and location information). Furthermore, all the initial energy of sensor nodes is homogeneous. The sensor nodes are powered by a finite energy source.
- The BS is static, and it is located at the center of the deployment field.
- An ME collects data by visiting certain nodes before it eventually returns to the BS.
3.1. Network Model
- A subset of S, denoted by , represents the rendezvous nodes.
- A set of geometric trees is rooted in each rendezvous node . The maximum depth of each geometric tree is constrained by d and .
- The ME tour path U visits each rendezvous node and the BS. The tour path should be shortened to minimize the data gathering latency.
3.2. Limitations of Previous Work
- Some polling nodes may reach only a few sensors since the sensor nodes are randomly deployed.
- The affiliation nodes may be closer to the BS than the polling nodes.
4. The Proposed MCRP Algorithm
Algorithm 1: Minimal constraint rendezvous node (MCRP) algorithm. |
5. Performance Evaluation
5.1. Energy Model
5.2. Simulation Architecture and Assumptions
- The transmission range, deployment field, and the number of sensor nodes are adjustable.
- Each sensor node continually generates a fixed data packet size and sends it to the parent node.
- Communication is symmetric (among nodes), and the power consumption studied is only for the packet transmission and reception. Furthermore, sensing and computation costs for data aggregation are very low, which are considered negligible.
- The ME collects data from a certain number of selected nodes. In addition, ME traverses the deployment field in a linear fashion. It is also assumes that there are no obstacles on the ME’s path. In addition, the ME (i.e., vehicle) can be recharge when it arrives back at the BS or using some sort of solar power. Thus, the power of the ME is not an issue in this paper.
5.3. Rendezvous Node Analysis
5.4. Power Consumption Analysis
5.5. Tour Length Analysis
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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MDG-NL | ZDG-MME | SPT-DGA | LP-RDA | MCRP | |
---|---|---|---|---|---|
Main Objective | To minimize the tour length | To minimize the tour length | To minimize the tour length | To minimize the tour length | tour length tour length |
Technique | Minimize common turning points | Field Segmentation (Inner, Outer) | Minimize polling nodes | Minimize rendezvous nodes | Minimize rendezvous points |
Applications | Delay tolerant | Delay intolerant | Delay tolerant | Delay tolerant | Delay tolerant |
Segmentation Field | No | Yes | No | No | No |
Data Delivery to the BS | Via mobile element | Via mobile and multi-hop | Via mobile element | Via mobile element | Via mobile element |
No. of ME | One | One | One | One | One |
Simulation Parameters | Values |
---|---|
Initial Energy (J) | 0.25 |
Number of Sensor Nodes (N) | 100, 150, 200, 250, 300, 350, 400 |
Transmission Range Tr (m) | 20, 25, 30, 35, 40, 45, 50 |
Relay Hop Count d | 0, 1, 2, 3, 4, 5 |
Mobile Velocity (m/s) | 1 |
Packet Length K (bits) | 640 |
Deployment Area Size L (m) | 150, 200 |
Duty Cycle | 200 |
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Ghaleb, M.; Subramaniam, S.; Ghaleb, S.M. An Adaptive Data Gathering Algorithm for Minimum Travel Route Planning in WSNs Based on Rendezvous Points. Symmetry 2019, 11, 1326. https://doi.org/10.3390/sym11111326
Ghaleb M, Subramaniam S, Ghaleb SM. An Adaptive Data Gathering Algorithm for Minimum Travel Route Planning in WSNs Based on Rendezvous Points. Symmetry. 2019; 11(11):1326. https://doi.org/10.3390/sym11111326
Chicago/Turabian StyleGhaleb, Mukhtar, Shamala Subramaniam, and Safwan M. Ghaleb. 2019. "An Adaptive Data Gathering Algorithm for Minimum Travel Route Planning in WSNs Based on Rendezvous Points" Symmetry 11, no. 11: 1326. https://doi.org/10.3390/sym11111326