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Yayın Optimal deployment in randomly deployed heterogeneous WSNs: A connected coverage approach(Academic Press Ltd- Elsevier Science Ltd, 2014-11) Sevgi, Cüneyt; Koçyiğit, AltanWireless sensor networks (WSNs) are resource-scarce networks and the optimization of the resources is challenging. As far as random deployment is concerned, the optimization of these resources becomes even more difficult In this paper, a novel framework is proposed for solving optimal deployment problems for randomly deployed and clustered WSNs. In several existing approaches to solve these problems, either only partial-coverage is considered or only connectivity is analyzed when full-coverage is assured. Through this study, we aim to contribute to the better understanding of partial connected coverage. For this purpose, we introduce cluster size formulations which provide network designers with estimating partial-coverage easily. While the proposed framework facilitates our cluster size formulations for coverage estimations, it also adopts the percolation theory to analyze the degree of connectivity when the targeted degree of partial-coverage is achieved. As the partial connected coverage approach reflects real-life deployment scenarios, the use of percolation theory results in generic solutions of optimal deployment problems, which indeed makes the solution independent from any routing algorithms. Moreover, a practical optimal deployment problem is formulated to find the cheapest WSN application that satisfies the targeted degree of partial connected coverage. Further, in this paper, the cost effectiveness of the node heterogeneity is investigated through comparing the heterogeneous WSNs with their homogeneous counterparts.Yayın Relocating sensor nodes to maximize cumulative connected coverage in wireless sensor networks(Molecular Diversity Preservation Int, 2008-04) Coşkun, VedatIn order to extend the availability of the wireless sensor network and to extract maximum possible information from the surveillance area, proper usage of the power capacity of the sensor nodes is important. Our work describes a dynamic relocation algorithm called MaxNetLife, which is mainly based on utilizing the remaining power of individual sensor nodes as well as properly relocating sensor nodes so that all sensor nodes can transmit the data they sense to the sink. Hence, the algorithm maximizes total collected information from the surveillance area before the possible death of the sensor network by increasing cumulative connected coverage parameter of the network. A deterministic approach is used to deploy sensor nodes into the sensor field where Hexagonal Grid positioning is used to address and locate each sensor node. Sensor nodes those are not planned to be actively used in the close future in a specific cell are preemptively relocated to the cells those will be in need of additional sensor nodes to improve cumulative connected coverage of the network. MaxNetLife algorithm also includes the details of the relocation activities, which include preemptive migration of the redundant nodes to the cells before any coverage hole occurs because of death of a sensor node. Relocation Model, Data Aggregation Model, and Energy model of the algorithm are studied in detail. MaxNetLife algorithm is proved to be effective, scalable, and applicable through simulations.
