  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.
