Socially Aware Network Clustering for Throughput Maximization in Mobile Wireless Sensor Networks | IEEE Journals & Magazine | IEEE Xplore

Socially Aware Network Clustering for Throughput Maximization in Mobile Wireless Sensor Networks


Abstract:

Mobile sensors, such as smart wearables, autonomous cars, cognitive healthcare devices, and intelligent drones, draw great attention due to their ability to provide a wid...Show More

Abstract:

Mobile sensors, such as smart wearables, autonomous cars, cognitive healthcare devices, and intelligent drones, draw great attention due to their ability to provide a wide-range of services. Hence, a consistent connection of each sensor node to the access point is a crucial requirement to obtain high data throughput in such mobile wireless sensor networks (MWSN). Data interference among such densely populated mobile sensor nodes (MSN) must also be minimized to enhance the data gathering reliability at individual MSN. In this context, a novel socially aware network clustering and interference management technique for MWSN is proposed in this work. The proposed method considers the current and prediction of future encounters among MSN to compute the social relationship index (SRI)-factor in developing a novel clustering algorithm. Moreover, a frequency-separation (FS) distance between clusters is utilized to form the non-overlapping clusters. The FS distance aids in reducing the network interference. For further interference management and reliable data transfer, beamforming is also utilized over the clustered MWSN. Subsequently, an optimization problem is formulated to maximize the data throughput over the clustered MWSN with respect to antenna downtilt angle while accounting for the high-density mobile behavior of MSN. Finally, experiments are conducted to evaluate the performance of the proposed method over a time-varying MWSN. The obtained results demonstrate the effectiveness of the proposed when compared to the benchmark methods. The results also validate the utilization of the proposed method over medium and large-scale network applications.
Published in: IEEE Transactions on Network and Service Management ( Volume: 21, Issue: 1, February 2024)
Page(s): 838 - 850
Date of Publication: 12 July 2023

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