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Wireless sensor node alliance generating method based on improved particle swarm optimization algorithm

A wireless sensor and improved particle swarm technology, applied in wireless communication, instrumentation, computing, etc., can solve the problems of unsupported parallel multi-task environment alliance generation, uncertainty stability, etc., achieve good convergence, expand the search range, The effect of resolving instability

Inactive Publication Date: 2010-07-28
BEIJING UNIV OF POSTS & TELECOMM
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the ant colony algorithm itself is a probabilistic algorithm, which has problems of uncertainty and poor stability. The algorithm evolution does not necessarily move towards the optimal solution. In the direction of the optimal solution, and does not support the coalition generation problem of parallel multi-tasking environment

Method used

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  • Wireless sensor node alliance generating method based on improved particle swarm optimization algorithm
  • Wireless sensor node alliance generating method based on improved particle swarm optimization algorithm
  • Wireless sensor node alliance generating method based on improved particle swarm optimization algorithm

Examples

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Effect test

example 1

[0072] When the intrusion detection task is performed in WSN, multi-node cooperation is required to complete it. This method is used to generate a node alliance and cooperate to complete the intrusion detection task.

[0073] 101. Collect node and task information in the network, specifically as follows:

[0074] Suppose there are 10 nodes in the system, and the node information database maintains the capability vectors of 10 nodes. Assuming that this task needs to consider six parameters, the system first quantifies the collected node parameters to obtain the node capability vector. Similarly, quantify the specific parameter values ​​of the task's capability requirements to obtain the task's capability requirement vector, and set the capability requirement vector for performing the intrusion detection task as T=[10, 35, 25, 41, 23, 42].

[0075] 102-103, input the capability vector of the nodes in the network and the capability requirement of the task. In the current networ...

example 2

[0082] Prove the parallelism of the algorithm in the present invention. Assuming that there are three parallel intrusion detection tasks in the system, it is known that the capability requirements of these three tasks are respectively, t 1 = [10, 35, 35, 41, 23, 42], t 2 = [20, 30, 38, 40, 27, 45], t 3 = [15, 25, 41, 42, 34, 38]. It is known that there are 10 nodes in the system. According to the number of tasks that need to be executed in parallel at this time, the particle swarm is divided into 3 subgroups, and each subgroup is responsible for generating a coalition. Assuming that the number of particles in each subgroup is 5, which is the same as in Example 1, each subgroup searches independently and generates 3 alliances, and the benefit of each alliance gradually tends to the optimal solution during the iterative process.

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Abstract

The invention discloses a wireless sensor node alliance generating method based on an improved particle swarm optimization algorithm, comprising the following steps of: 1, collecting the capacity information and the task information of each node and quantizing the capacity information and the task information of each node by using vectors; 2, dividing a particle swarm into m sub-swarms at t moment according to the number of tasks to be executed, and initializing the current position and setting the maximum iterations for each particle in each sub-swarm; 3, evaluating the benefit value of the current position of each particle by using the following utility functions for m sub-swarms; 4, comparing the benefit values a1 of the current positions of the particles with the preset benefit values a2 of the local optimum positions of the particles and the preset benefit values a3 of the optimum positions of swarm bodies and updating the local optimum positions of the particles and the optimum positions of swarm bodies, wherein the benefit values a1 is obtained in step 3; 5, calculating the particle speed vector and the particle position at the t+1 moment by using the particle swarm optimization algorithm; and 6, repeating step 3-step 5 to obtain the final optimum positions of the swarm bodies. The invention has the advantages of high executing efficiency and stability.

Description

technical field [0001] The invention relates to the field of wireless sensor networks, in particular to a method for generating a wireless sensor node alliance based on an improved particle swarm optimization algorithm. Background technique [0002] Wireless Sensor Network (WSN, Wireless Sensor Network), as a new information acquisition system, has been widely concerned by people for its low power consumption, low cost, distributed and self-organizing characteristics. However, in WSN, due to the characteristics of limited node processing capacity and limited power supply, it often requires multiple nodes to cooperate to solve the task. The nodes in WSN can be regarded as agents, and the agent alliance theory is applied to the multi-node cooperation problem in WSN to solve the alliance generation problem in WSN. [0003] Compared with a single agent, in the cooperative solving process of multiple agents, each member agent only has partial knowledge and incomplete problem-sol...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04W84/18G06N3/00
Inventor 邱雪松熊翱关志丽亓峰杨杨芮兰兰高志鹏
Owner BEIJING UNIV OF POSTS & TELECOMM
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