Wireless sensor network routing energy saving method based on particle swarm

Abstract

The invention discloses a wireless sensor network routing energy saving method based on a particle swarm, and relates to the field of a wireless sensor network. The wireless sensor network routing energy saving method comprises the following steps of: acquiring a fitness function for selecting cluster heads, carrying out iteration on the fitness function by a particle swarm algorithm, searching wireless sensor nodes with large fitness values and using the selected wireless sensor nodes as the cluster heads in the network; broadcasting the selected cluster heads to other wireless sensor nodes, so that other wireless sensor nodes, according to strength of received broadcast signals, sense distances with the cluster heads to select which clusters to add; and enabling the cluster heads to receive environment information data sent by other wireless sensor nodes, carrying out fusion on the environment information data, then sending the fused environment information data to a convergent node, and ending communication. According to the wireless sensor network routing energy saving method based on the particle swarm, residual energy information and position information of the nodes are sufficiently considered, and the suitable cluster heads are selected by particle swarm optimization so as to balance energy consumption of the network and prolong the life cycle.

Description

technical field [0001] The invention relates to the field of wireless sensor networks, in particular to a particle swarm-based wireless sensor network routing energy-saving method. Background technique [0002] Due to its own limitations, wireless sensor networks have very limited energy. Moreover, wireless sensor networks are very different from traditional wireless networks. The primary design goals of traditional wireless networks are to improve application-oriented service quality, reliability and make bandwidth utilization more efficient, and then energy saving is taken into consideration. However, most of the collected areas that use sensor nodes to obtain information are very harsh or uninhabitable areas, such as deserts, forests, and oceans. In this environment, when the sensor network works for a certain period of time and consumes a lot of energy, It is extremely unrealistic to replace the battery artificially. [0003] Therefore, the primary problem in the devel...

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Problems solved by technology

[0005] 1. Early TopDisc clustering algorithm [1] , can make nodes quickly form a cluster structure in a densely deployed sensor network, and establish a tree relationship between cluster heads, but the cluster network formed by this method is not flexible enough, and the algorithm overhead is too large

[0006] 2. Relevant scholars at the University of Southern California proposed a GAF clustering algorithm based on the geographical location of nodes [2] , but the algorithm requires precise geographic location and does not guarantee uniform energy consumption

[0007] 3. LEACH algorithm [3] It is a low-power adaptive hierarchical routing algorithm and the first clustering routing algorithm proposed. This algorithm breaks the idea of ​​fixed cluster heads in the previous clustering algorithm. There is a periodic cycle in the algorithm process. Each cycle It is divided into the cluster establishment phase and the stabilization phase, but the LEACH algorithm does not consider the position information and remaining energy information of the sensor nodes. After clustering, the cluster heads will not appear evenly in the whole network. There is a phenomenon: the node with less residual energy still becomes the cluster head

In this case, the node will die prematurely due to too fast and too large energy consumption, which is not conducive to prolonging the life cycle of the network

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