A Novel Approach for Establishing Connectivity in Partitioned Mobile Sensor Networks Using Beamforming Techniques
Abbas Mirzaei, Shahram Zandiyan

TL;DR
This paper introduces a deep learning-based beamforming method to enhance connectivity in partitioned mobile sensor networks, reducing energy consumption and maintaining data transmission by forming directional beams among isolated nodes.
Contribution
It proposes a novel cooperative beamforming approach combined with a cross-layer link cost method to improve connectivity and energy efficiency in partitioned sensor networks.
Findings
Achieves up to 30% energy reduction with beamforming.
Effectively restores connectivity in partitioned networks.
Maintains user throughput during partition healing.
Abstract
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…
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Taxonomy
TopicsEnergy Efficient Wireless Sensor Networks · Cooperative Communication and Network Coding · Energy Harvesting in Wireless Networks
