Wireless sensor network routing method based on improved whale group algorithm

A wireless sensor and swarm algorithm technology, applied in network topology, wireless communication, advanced technology, etc., can solve the problems of the uncertainty of the number of sensor nodes and the uncertainty of the number of design variables.

Active Publication Date: 2018-03-27
HUAZHONG UNIV OF SCI & TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] WSA-IC is used to solve continuous optimization problems, while the wireless sensor network routing problem is a discrete optimization problem, the number of sensor nodes included in the path cannot be determined, that i...

Method used

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  • Wireless sensor network routing method based on improved whale group algorithm
  • Wireless sensor network routing method based on improved whale group algorithm
  • Wireless sensor network routing method based on improved whale group algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0302] In Embodiment 1, the routing method of the present invention operates in the wireless sensor network of Fig. 9(b), Fig. 9(d), Fig. 9(f) and Fig. 9(h), wherein the initial energy of each node is from 6J to 12J And randomly selected in 18J. When the sink node in each wireless sensor network is the source node to calculate the path for the first time, the fitness convergence of the WSA-ICR algorithm and the IHSBEER algorithm is as follows: Figure 10(a) ~ 10(d) shown. In this embodiment, in order to facilitate the comparison of the performance of each algorithm in terms of other evaluation indicators, the performance of routing algorithms in terms of fitness convergence is firstly compared. Since the ACORC algorithm and the EEABR algorithm are distributed routing algorithms based on the ant colony algorithm and do not need to calculate the fitness value, only the WSA-ICR algorithm is compared with the IHSBEER algorithm in terms of fitness convergence. from Figure 10(a)...

Embodiment 2

[0304] In Example 2, for 3 evaluation indicators (average residual energy, residual energy standard deviation, minimum residual energy), the initial energy of each sensor node in 10 networks is initialized to 6J, and all data packets are sent from fixed source nodes , forwarded to the sink node by other nodes, until each sink node receives 6000 data packets, the calculation stops; for the fourth evaluation index (that is, the network life cycle), when the energy of any sensor node in the network is exhausted Just stop counting. Figure 11(a) ~ 11(d) is the experimental result when the initialization energy of each sensor node is the same. Figure 11(a) , 11(b) , 11(c) are the 10 running averages of the average residual energy, standard deviation of residual energy and minimum residual energy of each sensor node in the network after the sink node in each network receives 6000 data packets. Figure 10(d) is the mean value of 10 runs in the network life cycle when the energy of ...

Embodiment 3

[0309] In Example 3, for the three evaluation indicators (average remaining energy, standard deviation of remaining energy, and minimum remaining energy), the initial energy of each sensor node in the 10 networks is initialized to 6J, and the nodes in each scene periodically Send data packets to the sink nodes, and stop the calculation until each sink node receives 26,000 data packets; for the fourth evaluation index (that is, the network life cycle), stop when any sensor node in the network runs out of energy calculate.

[0310] Figure 12(a) ~ 12(d) is the experimental result when the initialization energy of each sensor node is the same. Figure 12(a) , 12(b) , 12(c) are respectively the average residual energy, standard deviation of residual energy and 10 running averages of the minimum residual energy of each sensor node in the network after the sink node in each network receives 26,000 data packets. Figure 12(d) is the mean value of 10 runs in the network life cycle w...

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Abstract

The invention discloses a wireless sensor network routing method based on an improved whale group algorithm. The method comprises the following steps: separately encoding sensor nodes in the network;collecting, by an aggregation node, the information of each sensor to form global information; performing routing calculation according to the global information to obtain an optimal forwarding path of each sensor; sending, by the sensor, a data packet containing the information to the aggregation node according to the optimal forwarding path in a routing table; and receiving, by the aggregation node, the data packet, obtaining the residual energy information of the node, and updating the global information. By adoption of the method in the technical scheme of the invention, the WSA is improved to solve the wireless sensor network routing problem via the excellent performance of multi-peak optimization. By mans of corresponding improvement of the whale group algorithm and the objective function model, the energy consumption of the path and the length of the path are considered, the energy efficiency is effectively improved, and the service life of the whole wireless sensor network is prolonged.

Description

technical field [0001] The invention belongs to the field of wireless sensor network topology control, in particular to a wireless sensor network routing method based on an improved whale swarm algorithm. Background technique [0002] The rapid development of computer system, sensor technology, wireless communication technology and distributed information processing and other technologies has promoted the rapid development of wireless sensor network technology that connects human beings and the real physical world. Wireless sensor network is one of the key technologies in the next generation network and one of the most important emerging technologies in the 21st century. It has been widely used in the military field, economy and life fields to realize the interconnection between the physical world and human society. [0003] In the application scenarios of wireless sensor networks, there are usually dozens to hundreds of sensor nodes arranged in the sensing area. These senso...

Claims

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Application Information

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IPC IPC(8): H04W40/10H04W84/18
CPCH04W40/10H04W84/18Y02D30/00Y02D30/70
Inventor 高亮曾冰张振东程璐瑶李新宇
Owner HUAZHONG UNIV OF SCI & TECH
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