Monitoring of dynamic environments that change rapidly with time is the prime application of wireless sensor networks. This change of behaviour is reasoned to either certain external factors or limitation of system designs itself in unpredicted causality. To adapt such conditions, machine learning techniques are deemed to be beneficial in eliminating the need for unnecessary redesign. Moreover, the techniques based on machine learning encourages many practical solutions to maximize usages of resource and thus enhances the lifespan of the sensor network. In this paper, an extensive literature is furnished over machine learning techniques that are used to address the issue of node localization in wireless sensor networks (WSNs). Strengths and weaknesses of each of the proposed algorithm in literature have been analysed and evaluated against the problem it has been developed. A comparative table is also presented to guide future designers in developing machine learning solutions suitable for specific application challenges in localization.

Wireless sensor networks, machine learning, localization, clustering, data aggregation

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