Joint Localization and Data Gathering Over a Small-World WSN With Optimal Data MULE Allocation

Localization of sensor nodes and efficient data gathering over a wireless sensor network (WSN) is vital in applications such as cyber-physical systems, Internet of Things, and context-aware pervasive systems. In WSNs, sensor nodes transfer the data cooperatively using multiple hops over a network. The large number of hops required for data transmission leads to erroneous distance estimation between node pairs, resulting in a large localization error. In this paper, we utilize a recent development in social networks called small-world characteristics for proposing a novel method of joint localization and data gathering over a WSN. A small-world WSN is developed by introducing data mobile ubiquitous LAN extensions (MULEs) into a conventional WSN. A small-world WSN exhibits low average path length and high average clustering coefficient. Such a small-world WSN when designed with novel routing strategies leads to reduced hop counts in sensor data transmission. Additionally, a method for optimal data MULE allocation is also developed. This method minimizes an objective function, which is a normalized weighted sum of network parameters such as bandwidth requirement and localization error. The optimal data MULE allocation method computes both the optimal number of data MULEs and their placement in the network. On the other hand, the joint localization and data gathering method that utilizes a multidimensional-scaling-based cooperative localization method is also developed for this purpose. Experiments are conducted using simulations and real node deployments over a WSN testbed. The performance of the proposed method is evaluated by conducting exhaustive analysis of power consumption, bandwidth required, localization error, data gathering efficiency, and throughput. The obtained experimental results indicate a significant improvement on several evaluation parameters when compared to results obtained on the conventional WSN. The obtained results also illustrate reasonable improvement when compared to results obtained using conventional small-world characteristic introduction and localization methods. The results are motivating enough for the proposed method to be used in large- and medium-scale network applications.

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