دانلود رایگان مقاله الگوریتم توزیع شده برای پارتیشن بندی شبکه Ad hoc بر اساس ورونوی موزاییک کاری

Abstract

This paper presents a data sink node election algorithm for multi-hop Wireless Sensor Networks (WSNs) with multiple data sink nodes. For energy-saving considerations, these nodes should be evenly (spatially) distributed on the network area. To achieve this objective, it proposed a distributed and iterative algorithm, which periodically re-assigns the data sink roles to selected WSN nodes. The main innovation of the algorithm is that, even if it does not need to explicitly compute the Voronoi partition of the WSN at each iteration, it eventually partitions the network according to a Centroidal Voronoid Tessellation, which leads to a spatially well-balanced distribution of the data sink nodes. Analytical proofs as well as simulation results validate the approach.

6. Conclusions

The proposed data sinks election algorithm manages to organize the data sink node migrations in such a way that, during the network lifetime, the data sink nodes are positioned within a network partition which is proved to converge to the Centroidal Voronoi Tessellation. As shown by numerical simulations, the result is a balanced positioning of the data sink nodes within the sensor network area, which is capable of balancing the load of data sink nodes and, especially if coupled with an energy-based metric, of prolonging the lifetime of the nodes. The problem dealt with in the paper is similar to other problems in WSNs, such as the cluster head election, and with some awareness can be regarded as a generic method to perform partitioning in ad-hoc wireless networks. On-going work is devoted at analyzing the impact of energy harvesting approaches (see, e.g., [9]) and the impact of the proposed procedure in presence of all the other network management algorithms of a WSN, as, in particular, the routing algorithm and the data-fusion techniques proposed in the project SWIPE ( [16, 15] and [18, 27], respectively): energy-aware routing algorithms affect the energy depletion of the nodes and may also cause a complex interaction with the data sink election algorithm, and datafusion techniques affect both the computational requirements of the data sink nodes (and therefore their energy consumption) and the traffic load on the network. Moreover, the proposed algorithm is being implemented in the sensor nodes which the SWIPE project will integrate and demonstrate.