Energy-Balanced Cluster-Routing Protocol Based on Particle Swarm Optimization with Five Mutation Operators for Wireless Sensor Networks
<p>Simplified diagram of sensor node clustering: (<b>a</b>) grid-based clustering method, (<b>b</b>) low-energy adaptive clustering hierarchy (LEACH)-based clustering method, and (<b>c</b>) adaptive sensor node clustering method for determining the number of clusters and grouping the number of sensor nodes into clusters evenly.</p> "> Figure 2
<p>The main architecture of sensor nodes comprising four modules: data collection module, data processing module, wireless communication module, and power module.</p> "> Figure 3
<p>Main procedure of the proposed energy-balanced cluster-routing protocol (EBCRP).</p> "> Figure 4
<p>Structure of particles utilized in sensor node clustering optimization.</p> "> Figure 5
<p>Clustering results of sensor nodes based on SPSO and PSO-FMO in terms of different number of sensor nodes 50, 100, 150 and 200. The clusters highlighted by circles have extremely asymmetrical centroids; while the clusters highlighted by square show the imbalance of the number of sensor nodes between clusters, which is the result of SPSO.</p> "> Figure 6
<p>Mean convergence curves of SPSO and PSO-FMO in optimizing sensor node clustering.</p> "> Figure 7
<p>Residual energies of sensor nodes in different rounds.</p> "> Figure 8
<p>Number of dead nodes of different methods in different rounds, and the bottom subfigures detail the the first dead node.</p> "> Figure 9
<p>The curve of energy consumption balance index of different methods.</p> "> Figure 10
<p>Network lifetime of different methods for different numbers of sensor nodes.</p> ">
Abstract
:1. Introduction
- An energy-balanced cluster-routing protocol for WSNs is proposed to balance the energy consumption of sensor nodes and prolong the network lifetime.
- An adaptive sensor node clustering scheme based on PSO is proposed to determine the number of clusters and group sensor nodes into clusters evenly.
- Five mutation operators are specially proposed to improve the performance of PSO in optimizing the clustering of sensor nodes.
2. Related Works
2.1. Clustering Sensor Nodes Based on Non-Computational Intelligence
2.2. Clustering Sensor Nodes Based on Computational Intelligence
3. Network Model
- All sensor nodes have the same communication range and equipment with the same initial energy.
- Sensor nodes can know their location via the GPS.
- The sink node has enough energy; the battery of sensor nodes cannot be replaced or recharged.
- Each sensor node has data of the same size to transmit in each round.
3.1. Energy Dissipation Model of Sensor Nodes
3.2. Lifetime Model of the Network
4. EBCRP: Energy-Balanced Cluster-Routing Protocol
4.1. Clustering Sensor Nodes Based on Particle Swarm Optimization with Five Mutation Operators
4.1.1. Particle Swarm Optimization
4.1.2. Topology of Particles in Sensor Node Clustering
4.1.3. Cost Function in Sensor Node Clustering
4.1.4. Five Mutation Operators
- Reversing probability operator: it is used to reverse a part of the probabilities of sensor nodes. The detail process is shown in Algorithm 1.
- Decreasing probability operator: it is used to decrease the probability of some sensor nodes becoming cluster centroids. The detail process is shown in Algorithm 2.
- Increasing probability operator: it is used to increase the probability of some sensor nodes becoming the cluster centroids. The detail process is shown in Algorithm 3.
- Swapping probability operator: it is used to swap the probabilities of two sensor nodes in a particle. The detail process is shown in Algorithm 4.
- Transforming number operator: it is used to transform the number of clusters in the first dimension of particles. The detail process is shown in Algorithm 5.
Algorithm 1: Reversing probability operator |
Algorithm 2: Decreasing probability operator |
Algorithm 3: Increasing probability operator |
Algorithm 4: Swapping probability operator |
Algorithm 5: Transforming number operator |
4.1.5. Optimization Process of Sensor Node Clustering
Algorithm 6: Clustering sensor nodes based on PSO with five mutation operators |
4.2. Selecting the Cluster Heads Based on Residual Energy
5. Simulations and Results
5.1. The Effectiveness of EBCRP
5.2. Comparison of EBCRP with Other Methods
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Parameter | Value |
---|---|
Initial Energy of Sensor Nodes () | 0.1 J |
Size of Data | 4000 bit |
50 nJ/bit | |
10 pJ/bit | |
1.3 × 10 pJ/bit |
Total Number of Sensor Nodes | ||||
---|---|---|---|---|
Balance index based on SPSO | 0.9615 | 0.9568 | 0.9527 | 0.9542 |
Balance index based on PSO-FMO | 0.9766 | 0.9731 | 0.9715 | 0.9747 |
Number of Sensor Nodes | Round | LEACH | SEP | IICMH | EBCRP |
---|---|---|---|---|---|
30-th | 0.7883 | 0.7918 | 0.4026 | 0.9906 | |
60-th | 0.8939 | 0.8362 | 0.7096 | 0.9954 | |
N = 50 | 90-th | 0.9056 | 0.8485 | 0.9282 | 0.9967 |
120-th | 0.9459 | 0.8270 | 0.9899 | 0.9976 | |
150-th | 0.9712 | 0.8645 | 0.9992 | 0.9997 | |
30-th | 0.7530 | 0.7094 | 0.3935 | 0.9691 | |
60-th | 0.8784 | 0.7623 | 0.6768 | 0.9937 | |
N = 100 | 90-th | 0.8956 | 0.8005 | 0.8010 | 0.9956 |
120-th | 0.9346 | 0.8270 | 0.9712 | 0.9965 | |
150-th | 0.9692 | 0.8719 | 0.9911 | 0.9980 | |
30-th | 0.7894 | 0.7861 | 0.4364 | 0.9722 | |
60-th | 0.8951 | 0.8276 | 0.7299 | 0.9934 | |
N = 150 | 90-th | 0.9042 | 0.8478 | 0.9464 | 0.9960 |
120-th | 0.9470 | 0.8683 | 0.9897 | 0.9970 | |
150-th | 0.9725 | 0.8919 | 0.9973 | 0.9994 | |
30-th | 0.8251 | 0.7858 | 0.4193 | 0.9650 | |
60-th | 0.9185 | 0.8322 | 0.7107 | 0.9890 | |
N = 200 | 90-th | 0.9384 | 0.8402 | 0.9219 | 0.9948 |
120-th | 0.9584 | 0.8479 | 0.9843 | 0.9966 | |
150-th | 0.9758 | 0.8785 | 0.9951 | 0.9991 |
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Han, Y.; Byun, H.; Zhang, L. Energy-Balanced Cluster-Routing Protocol Based on Particle Swarm Optimization with Five Mutation Operators for Wireless Sensor Networks. Sensors 2020, 20, 7217. https://doi.org/10.3390/s20247217
Han Y, Byun H, Zhang L. Energy-Balanced Cluster-Routing Protocol Based on Particle Swarm Optimization with Five Mutation Operators for Wireless Sensor Networks. Sensors. 2020; 20(24):7217. https://doi.org/10.3390/s20247217
Chicago/Turabian StyleHan, Yamin, Heejung Byun, and Liangliang Zhang. 2020. "Energy-Balanced Cluster-Routing Protocol Based on Particle Swarm Optimization with Five Mutation Operators for Wireless Sensor Networks" Sensors 20, no. 24: 7217. https://doi.org/10.3390/s20247217