A Hybrid Trajectory Clustering for Predicting User Navigation

TL;DR

This paper introduces a novel trajectory-based clustering method for wireless sensor networks that improves energy distribution and extends network lifetime by intelligently selecting and rotating cluster heads based on traffic patterns.

Contribution

It presents the first trajectory clustering approach for cluster head selection, addressing the hot spot problem in WSNs and enhancing network longevity.

Findings

Trajectory clustering effectively balances energy consumption.

The method prolongs network lifetime compared to traditional algorithms.

Traffic-based cluster head selection improves energy efficiency.

Abstract

Wireless sensor networks (WSNs) suffers from the hot spot problem where the sensor nodes closest to the base station are need to relay more packet than the nodes farther away from the base station. Thus, lifetime of sensory network depends on these closest nodes. Clustering methods are used to extend the lifetime of a wireless sensor network. However, current clustering algorithms usually utilize two techniques; selecting cluster heads with more residual energy, and rotating cluster heads periodically to distribute the energy consumption among nodes in each cluster and lengthen the network lifetime. Most of the algorithms use random selection for selecting the cluster heads. Here, we propose a novel trajectory clustering technique for selecting the cluster heads in WSNs. Our algorithm selects the cluster heads based on traffic and rotates periodically. It provides the first trajectory based clustering technique for selecting the cluster heads and to extenuate the hot spot problem by prolonging the network lifetime.