Network connectivity is one of the major design issues in the context of mobile sensor networks. Due to diverse communication patterns, some nodes lying in high-traffic zones may consume more energy and eventually die out resulting in network partitioning. This phenomenon may deprive a large number of alive nodes of sending their important time critical data to the sink. The application of data caching in mobile sensor networks is exponentially increasing as a high-speed data storage layer. This paper presents a deep learning-based beamforming approach to find the optimal transmission strategies for cache-enabled backhaul networks. In the proposed scheme, the sensor nodes in isolated partitions work together to form a directional beam which significantly increases their overall communication range to reach out a distant relay node connected to the main part of the network. The proposed methodology of cooperative beamforming-based partition connectivity works efficiently if an isolated cluster gets partitioned with a favorably large number of nodes. We also present a new cross-layer method for link cost that makes a balance between the energy used by the relay. By directly adding the accessible auxiliary nodes to the set of routing links, the algorithm chooses paths which provide maximum dynamic beamforming usage for the intermediate nodes. The proposed approach is then evaluated through simulation results. The simulation results show that the proposed mechanism achieves up to 30% energy consumption reduction through beamforming as partition healing in addition to guarantee user throughput.

翻译：网络连通性是移动传感器网络设计中的重要问题之一。由于通信模式的多样性，位于高流量区域的节点可能消耗更多能量并最终失效，导致网络分区。这种现象可能使大量存活节点无法向汇聚节点发送关键时间敏感数据。数据缓存作为高速数据存储层在移动传感器网络中的应用正呈指数级增长。本文提出了一种基于深度学习的波束成形方法，用于寻找缓存回程网络的最优传输策略。在所提方案中，孤立分区的传感器节点协同工作形成定向波束，显著提升整体通信范围，从而连接至与网络主干相连的远端中继节点。当孤立簇中具有足够多的节点时，所提出的基于协作波束成形的分区连通性方法能够高效运行。我们还提出了一种新的跨层链路成本方法，能够在中继能耗与路由选择间取得平衡。通过将可访问的辅助节点直接加入路由链路集合，该算法能为中间节点选择提供最大动态波束成形效用的路径。通过仿真对所提方案进行了评估。结果表明，该机制通过波束成形实现分区愈合，在保证用户吞吐量的同时，能耗降低幅度可达30%。