In the world of wireless sensor networks(WSNs),optimizing performance and extending network lifetime are critical goals.In this paper,we propose a new model called DTLR-Net(Deep Temporal LSTM Regression Network)that e...In the world of wireless sensor networks(WSNs),optimizing performance and extending network lifetime are critical goals.In this paper,we propose a new model called DTLR-Net(Deep Temporal LSTM Regression Network)that employs long-short-term memory and is effective for long-term dependencies.Mobile sinks can move in arbitrary patterns,so the model employs long short-term memory(LSTM)networks to handle such movements.The parameters were initialized iteratively,and each node updated its position,mobility level,and other important metrics at each turn,with key measurements including active or inactive node ratio,energy consumption per cycle,received packets for each node,contact time,and interconnect time between nodes,among others.These metrics aid in determining whether the model can remain stable under a variety of conditions.Furthermore,in addition to focusing on stability and security,these measurements assist us in predicting future node behaviors as well as how the network operates.The results show that the proposed model outperformed all other models by achieving a lifetime of 493.5 s for a 400-node WSN that persisted through 750 rounds,whereas other models could not reach this value and were significantly lower.This research has many implications,and one way to improve network performance dependability and sustainability is to incorporate deep learning approaches into WSN dynamics.展开更多
文摘In the world of wireless sensor networks(WSNs),optimizing performance and extending network lifetime are critical goals.In this paper,we propose a new model called DTLR-Net(Deep Temporal LSTM Regression Network)that employs long-short-term memory and is effective for long-term dependencies.Mobile sinks can move in arbitrary patterns,so the model employs long short-term memory(LSTM)networks to handle such movements.The parameters were initialized iteratively,and each node updated its position,mobility level,and other important metrics at each turn,with key measurements including active or inactive node ratio,energy consumption per cycle,received packets for each node,contact time,and interconnect time between nodes,among others.These metrics aid in determining whether the model can remain stable under a variety of conditions.Furthermore,in addition to focusing on stability and security,these measurements assist us in predicting future node behaviors as well as how the network operates.The results show that the proposed model outperformed all other models by achieving a lifetime of 493.5 s for a 400-node WSN that persisted through 750 rounds,whereas other models could not reach this value and were significantly lower.This research has many implications,and one way to improve network performance dependability and sustainability is to incorporate deep learning approaches into WSN dynamics.
