WRSN multi-mobile charger optimal scheduling method based on reinforcement learning

Abstract

The invention discloses a WRSN multi-mobile charger optimal scheduling method based on reinforcement learning, and belongs to the field of Internet of Things wireless rechargeable sensor networks. According to the invention, the neural network and the multi-agent reinforcement learning method are introduced into the charging path planning under the scene of multiple mobile chargers of the wireless rechargeable sensor network, and the multi-agent reinforcement learning is mainly utilized to solve the problems of efficient cooperation and optimal scheduling of the multiple mobile chargers in the wireless rechargeable sensor network. Under the condition that the energy of the mobile chargers and the sensor is limited, the charging paths of the chargers are optimized through mutual cooperation of the multiple mobile chargers, and the sensor node with the low electric quantity is charged in time. On the premise of ensuring that the sensor nodes do not die due to power shortage, the total moving path of each mobile charger is the shortest, and the charging efficiency is optimized on the whole.

Description

technical field [0001] The invention relates to a WRSN multi-mobile charger optimization scheduling method based on reinforcement learning, which belongs to the field of wireless rechargeable sensor networks. Background technique [0002] Wireless sensor network (WSN) is composed of many sensors with limited energy. The sensors can sense the temperature, humidity and pollutant content in the surrounding environment. They are widely used in air quality monitoring, forest fire prevention and control and other fields. But the performance of wireless sensor networks is limited especially by battery capacity. In order to prolong the life of the network as much as possible, using a mobile vehicle equipped with a charging device (called a mobile charger MC) to charge the sensor becomes an effective solution to this problem. [0003] The breakthrough of wireless energy transfer technology in recent years provides a new opportunity to solve the energy constraint problem in wireless ...

AI Technical Summary

Problems solved by technology

In the offline charging scheme, mobile chargers periodically charge nodes along a predetermined path, but such methods are often unable to adapt to dynamic changes in sensor energy consumption, resulting in a large number of node failures

In the online charging scheme, the mobile charger can respond to the charging request sent by the sensor in time, and make a real-time charging decision based on the remaining energy of the sensor node, but this kind of method does not consider the optimization of the charging path as a whole, and lacks the global optimal solution. node failure due to optimality and many unnecessary moves by the mobile charger

[0004] The breakthrough of wireless charging technology provides a solution to the problem of sensor energy limitation in wireless rechargeable sensor networks; when the scale of wireless rechargeable sensor networks is large, a single mobile charger cannot meet the charging needs of nodes in the network. The use of multiple mobile chargers has become a natural choice; however, the existing multi-mobile charger scheduling based on traditional optimization methods has difficulties in problem modeling, solution and implementation, and it is often difficult to obtain an optimized charging scheduling scheme, which leads to its charging efficiency. Low is not suitable for supporting large-scale wireless rechargeable sensor networks

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