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A wireless sensor and network coverage technology, applied in the field of three-dimensional wireless sensor network coverage optimization, to achieve the effects of easy implementation, expanding the exploration range, and avoiding invalid searches
Pending Publication Date: 2022-01-11
JIANGXI UNIV OF SCI & TECH
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[0006] The technical problem to be solved by the present invention is to provide two improved multi-objective algorithms, namely the MOSSA-I algorithm and the MOSSA-II algorithm. In addition to deploying wireless sensor nodes on a relatively gentle saddle surface, it also involves complex environments. Peak terrain, while considering the multi-objective coverage controloptimization problem of WSN, including three objectives of coverage, data transmission quantity and network life
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Embodiment 1
[0093] Example 1: MOSSA-I algorithm, input: T: maximum number of iterations, N: population size, Dim: number of sensors, M: number of targets, ∝: proportion of producers, SD: number of sparrows aware of danger, output : non-dominated solution set IP={I 1 , I 2 , I 3 ,..., I N}.
[0094] MOSSA-I algorithm, which comprises the following steps,
[0096] Step 2, use the reverse learning strategy (the following formula) to initialize the population;
[0097]
[0098] In N-dimensional space, let is the individual x i =x i，1 , x i，2 ,...,x i，n The inverse solution of , then da j and db j respectively represent the minimum and maximum values on the jth dimension in the current search space with population;
[0099] Step 3. Update the non-dominated solution set NDS;
[0100] Step 4, start iteration;
[0101] Step 5. Use the following formula to update the location of the producer,
[0102]
[0103] Among them, t rep...
Embodiment 2
[0122] Example 2: MOSSA-II, input: T: maximum number of iterations, N: population size, Dim: number of sensors, M: number of targets, ∝: proportion of producers, SD: number of sparrows aware of danger, output: Non-dominated solution set IP={I 1 , I 2 , I 3 ,..., I N}.
[0123] MOSSA-II algorithm, which includes the following steps,
[0124] Step 1. Initialize the population;
[0125] Step 2. Update the non-dominated solution set NDS;
[0126] Step 3, start iteration;
[0127] Step 4. Use the following formula to update the location of the producer,
[0128]
[0129] Step 5. Use the following formula to update the follower's position,
[0130]
[0131] Step 6. Use the following formula to update the dangerous sparrow position,
[0132]
[0133] The sparrow individual learning rate of change is as follows:
[0134]
[0135] Among them, ε is the minimum constant to prevent the denominator from being 0, is the position information of the i-th sparrow in the...
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Abstract
The invention discloses a three-dimensional wireless sensor network coverage optimization method, which comprises the following steps of: deploying in two different simulation environments, and modeling a coverage control problem of a WSN (Wireless Sensor Network) into a multi-target planning problem by considering three targets, namely maximization of coverage rate, maximization of network service life and maximization of data transmission quantity, two multi-target sparrow search algorithms, namely, an MOSSA-I algorithm and an MOSSA-II algorithm, are provided on the basis of an SSA algorithm, a reverse learning strategy and improved crowdedness degree calculation are mixed in the MOSSA-I algorithm, a learning rate and an external solution set strategy are added in the MOSSA-II algorithm, and a non-dominated sorting method based on a reference point is fused in the MOSSA-II algorithm. The method has the advantages that the MOSSA-I algorithm improves the algorithm diversity and prevents the algorithm from falling into local optimum. According to the MOSSA-II algorithm, the convergence precision is improved through the learning rate, an external solution set strategy is introduced into MOSSA-II, individuals in a population can guide flight of themselves through individuals in an external solution set, and the exploration ability of the algorithm is enriched.
Description
technical field [0001] The invention relates to a three-dimensional wireless sensor network coverage optimization method, which belongs to the technical field of wireless sensor networks. Background technique [0002] Wireless sensor network is a traditional wireless ad hoc network that can collect, integrate and transmit data autonomously. It is a rapidly developing information acquisition technology that can integrate the latest technological achievements, including microelectronics, networking, and communication, making it an important element in fields such as healthcare, environmental monitoring, industrial manufacturing, and smart cities. [0003] Coverage control is one of the crucial issues in WSNs, which mainly involves how sensor networks can monitor target areas through proper node deployment. Usually sensor nodes are deployed in complex areas where the environment is harsh and people cannot reach. Moreover, the initial position of the sensor nodes is not predet...
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