The development of the mobile Internet and the success of deep learning in many applications have driven the need to deploy and apply deep learning models on mobile devices under the condition of limited resources. Long Short-Term Memory (LSTM), as a special scheme in deep learning, can learn long-distance dependencies hidden in time series. However, the high computational complexity of LSTM-related structures and the need for a large number of resources for training have become obstacles to their deployment on mobile devices. In order to reduce the resource requirements and computational costs of LSTMs, we use pruning strategies to preserve important connections during the training phase. After training, we reduce the complexity of LSTMs network by sharing weight strategy.Based on these strategies, we propose a sparse connected LSTM with a sharing weight (SCLSTM) model. The experimental results on the real data sets show that SCLSTM with 0.88% neural connections can obtain prediction capabilities comparable to densely connected LSTM. Moreover, SCLSTM can solve the problem of overfitting to some extent. The results of experiments demonstrate that SCLSTM can perform better than the-state-of-arts algorithm on mobile devices of limited resources. INDEX TERMS Deep learning, LSTM, time series, pruning.

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Ad Hoc networks have been widely used in emergency communication tasks. For dynamic characteristics of Ad Hoc networks, problems of node energy limited and unbalanced energy consumption during deployment, we propose a strategy based on game theory and deep reinforcement learning (GDR) to improve the balance of network capabilities and enhance the autonomy of the network topology. The model uses game theory to generate an adaptive topology, adjusts its power according to the average life of the node, helps the node with the shortest life to decrease the power, and prolongs the survival time of the entire network. When the state of the node changes, reinforcement learning is used to automatically generate routing policies to improve the average end-to-end latency of the network. Experiments show that, under the condition of ensuring connectivity, GDR has smaller residual energy variance, longer network lifetime, and lower network delay. The delay of the GDR model is 10.5% higher than that of existing methods on average.