Mobile sensor intelligent track design method based on sustainable data acquisition
A mobile sensor, trajectory design technology, applied in the direction of location-based services, specific environment-based services, electrical components, etc., can solve problems affecting the data throughput of sensor networks, etc.
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Embodiment 1
[0050] This embodiment provides a mobile sensor intelligent trajectory design method based on sustainable data collection, such as figure 1 shown, including steps:
[0051] S1. The mobile sensor collects energy from the surrounding environment and uses the collected energy for its own movement and data transmission;
[0052] S2. Construct a model between energy harvesting and data transmission based on Markov decision;
[0053] S3. Use the deep deterministic strategy gradient algorithm to solve the constructed model, and obtain the optimal joint trajectory movement and power allocation strategy corresponding to the energy.
[0054] In step S1, the mobile sensor collects energy from the surrounding environment, and uses the collected energy for its own movement and data transmission.
[0055] The function of the mobile sensor in this embodiment is to collect energy around the environment, and use the collected energy for its own movement, so that it can collect surrounding data...
Embodiment 2
[0120] The difference between the intelligent trajectory design method for mobile sensors based on sustainable data collection provided in this embodiment and the first embodiment is that:
[0121] This embodiment mainly verifies the effectiveness of the proposed training algorithm.
[0122] Set a 10×10 two-dimensional Cartesian coordinates, where (x,y) represents the position (x, y∈[0,10]) on the two-dimensional plane. Energy 1 and Energy 2 are located at (0,10) and (0,0) respectively. MS can reposition in this area to find a better location for energy collection and data transmission. The moving speed and angle are limited to [0,1], [-π / 2,π / 2], where μ and σ represent energy The mean and variance of the random distribution, δ represents the movement consumption of the mobile sensor (that is, energy δ is consumed every time a certain distance is moved). Next, the effectiveness of the proposed training algorithm (Algorithm 1) is verified by detecting the motion trajectory le...
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