Dual Cluster Head Optimization of Wireless Sensor Networks Based on Multi-Objective Particle Swarm Optimization
Abstract
:1. Introduction
1.1. Objects
- Fuzzy c-means (FCM) algorithm with the optimal cluster number is utilized to obtain the uniformly distributed first cluster heads (FCHs) for the energy conservation of nodes;
- Based on the FCHs, a multi-objective particle swarm optimization (MOPSO) is applied for the second cluster heads (SCHs) to lessen the load on the FCHs, and its search speed is controlled by the normal distribution decay inertial weight and the sigmoid-based acceleration coefficients to avoid a locally optimal solution;
- With the selected SCHs, the improved ant colony optimization (ACO) is capable of seeking the shortest trajectory of MS for reduced delay;
- The effectiveness of the proposed approach is examined by the comparisons of the lifetime, the death times of the first node and 50% nodes, and the trajectory length of MS.
1.2. Related Work
2. Problem Formulation
2.1. System Model
- The network has n sensor nodes deployed in an F × F area.
- All nodes acknowledge the location of BS.
- The sensor nodes are fixed once they are deployed.
- Each sensor node possesses a unique ID and position awareness.
- The communication range is set as the same between all sensor nodes and MS.
- Nodes should send data to the CH of a cluster in a single hop.
- The speed of MS is fixed as v with infinite energy.
- All sensor nodes are isomorphic, and energy is limited.
2.2. Energy Model
3. Proposed Energy-Efficient Dual CH Approach
3.1. Network Clustering
3.1.1. Number of Clusters
3.1.2. Fuzzy C-Means Clustering
3.2. Selection of FCH
3.3. Selection of SCH
3.3.1. Encoding
3.3.2. Fitness Measure
3.3.3. Archive Management
3.3.4. Leader Selection
3.3.5. Improved MOPSO
3.4. The optimal trajectory of MS
4. Simulation and Analysis
5. Conclusions
- (a)
- The combination of the FCM algorithm and the optimal number of CH could produce a more uniform distribution of the FCHs with a reduction in energy consumption.
- (b)
- The application of the MOPSO for SCHs can effectively reduce the load on FCHs.
- (c)
- In the comparison with PSO-ECHS, PSO-C, PEGASIS, LEACH-C, and LEACH algorithms, for 100 nodes, the F-MOPSO-CH could respectively prolong the FND lifetime by 7.9%, 22.9%, 25.1%, 61%, and 74.4%. As for the HND, its lifetime increased by 27.8%, 34.2%, 98.3%, 213.1%, and 211.2%, respectively. The base station packet reception increased by about 19.3%, 53.5%, 27%, 86.8%, and 181.2%, respectively.
- (d)
- In the F-MOPSO-CH for 100 nodes, the improved ACO could shrink the trajectory of MS by a decrease of 10%.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Nomenclature
ACO | Ant colony optimization algorithm |
allowedq | The set of nodes that ant q has not searched yet |
BAT | BAT algorithm |
BS | Base station |
CH | Cluster head |
c1 | The acceleration coefficient of MOPSO |
c2 | The acceleration coefficient of MOPSO |
cend | cend = 2.5 |
ch | ch = 1, 2,3…k |
cori | cori = 0.5 |
Dis(CNW, CNS) | The distance between a SCH and other SCHs |
Dis(FNS, CNS) | The distance between a FCH and its corresponding SCH |
Dis(SNX, CNS) | The distance from the non-CH node to the SCH |
Dis(SNX, FN) | The distance from the non-CH in the cluster to the FCH |
d | Data transmission distance |
d0 | Threshold value |
dis(b, g) | The distance from Cb to Cg |
dis(g, Init) | The distance from Cg to the initial access node |
dtoBS | Average distance between all nodes and the BS |
ETX | Energy consumption of a node for sending data |
ERX | Energy consumption of a node receiving data |
E(ch) | Residual energy of CH |
E(x) | The residual energy of non-CH nodes in the cluster |
F | Edge length of the sensor square area |
FCH | First CH |
FCM | Fuzzy c-means |
FN | A certain cluster head |
FND | First node death |
fa−1 and fa+1 | The objective function values of adjacent particles |
fre | The current number of ACO iterations |
gbest | Global leader of MOPSO |
HND | 50% nodes death |
hsn | The number of nodes in a cluster |
HND | 50% nodes death |
IOT | Internet of things |
JFCM | Objective function of FCM |
k | The number of cluster heads |
LEACH | Low energy adaptive clustering hierarchy |
LEACH-C | LEACH-C algorithm |
Lq | The length of path from Cb to Cg for Ant q |
MOPSO | Multi-objective particle swarm optimization |
MS | Mobile sink |
m | Fuzzy weighted index |
m1 | The m1 bit of data |
m2 | The number of ants |
n | Number of sensor node |
PEGASIS | PEGASIS algorithm |
PSO | Particle swarm optimization |
pbest | Individual leader of MOPSO |
Q | Pheromone intensity |
q | The ants of ACO |
r | The number of iterations of MOPSO |
r1 | Random numbers between [0, 1] |
r2 | Random numbers between [0, 1] |
SCH | Second CH |
SN | Sensor node |
TSP | Traveling salesman problem |
uij | Membership degree of node |
V | Velocity of particle |
v | Speed of mobile sink |
Cluster center | |
WSN | Wireless sensor networks |
X | Position of particle |
α | The weight factors of ACO |
α0 | The basic factor |
β | The weight factors of ACO |
εfs | Parameter in the power amplifier |
εmp | Parameter in the power amplifier |
ηbg(h) | The heuristic information of ACO |
θ | The degree of data dispersion |
λ | The weight of Formula (16) |
σ | A constant between 0 and 1 |
τbg(h) | The pheromone concentration of ACO |
∆τbg(h, h + H) | The pheromone increment of ACO |
Ψ | The weight of Formula (11) |
Φ | The volatile factor [0, 1] |
ω | The inertial weight of the MOPSO |
χ | Index of data transmission distance |
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Time | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
---|---|---|---|---|---|---|---|---|---|---|
Results | 7.1449 | 7.5578 | 7.5839 | 6.9777 | 7.3939 | 7.5878 | 7.8473 | 8.1677 | 7.9214 | 7.6962 |
Parameters | Values |
---|---|
Network distribution area | 100 × 100 m2 |
Number of nodes (N) | 50–100 |
Location coordinate of the base station | (50,110) |
Initial energy | 0.98 J |
Data packet size | 4000 bits |
Speed of MS | 2 m/s |
Eda | 5 nJ/bit |
Energy consumption on circuit (Eelec) | 50 nJ/bit |
Free-space channel parameter (εfs) | 10 pJ/bit/m2 |
Multi-path channel parameter (εmp) | 0.0013 pJ/bit/m4 |
Parameters | Values |
---|---|
Size of population | 400 |
Maximum iteration | 200 |
Particle position range | [0, 100] |
Particle velocity range | [0, 10] |
r1 and r2 | [0, 1] |
c1 and c2 | [0.5, 2.5] |
ω | [0.4, 0.9] |
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Zheng, A.; Zhang, Z.; Liu, W.; Liu, J.; Xiao, Y.; Li, C. Dual Cluster Head Optimization of Wireless Sensor Networks Based on Multi-Objective Particle Swarm Optimization. Sensors 2023, 23, 231. https://doi.org/10.3390/s23010231
Zheng A, Zhang Z, Liu W, Liu J, Xiao Y, Li C. Dual Cluster Head Optimization of Wireless Sensor Networks Based on Multi-Objective Particle Swarm Optimization. Sensors. 2023; 23(1):231. https://doi.org/10.3390/s23010231
Chicago/Turabian StyleZheng, Aiyun, Zhen Zhang, Weimin Liu, Jiaxin Liu, Yao Xiao, and Chen Li. 2023. "Dual Cluster Head Optimization of Wireless Sensor Networks Based on Multi-Objective Particle Swarm Optimization" Sensors 23, no. 1: 231. https://doi.org/10.3390/s23010231