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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 Optimal deployment in randomly deployed heterogeneous WSNs: A connected coverage approach (vol 46, pg 182, 2014)(Academic Press LTD- Elsevier Science LTD, 2015-05) Sevgi, Cüneyt; Koçyiğit, Altan[No abstract available]Yayın On the analysis of expected distance between sensor nodes and the base station in randomly deployed WSNs(Springer Verlag, 2014) Sevgi, Cüneyt; Ali, Syed AmjadIn this study, we focus on the analytical derivation of the expected distance between all sensor nodes and the base station (i.e., E[dtoBS]) in a randomly deployed WSN. Although similar derivations appear in the related literature, to the best of our knowledge, our derivation, which assumes a particular scenario, has not been formulated before. In this specific scenario, the sensing field is a square-shaped region and the base station is located at some arbitrary distance to one of the edges of the square. Having the knowledge of E[dtoBS] value is important because E[dtoBS] provides a network designer with the opportunity to make a decision on whether it is energy-efficient to perform clustering for WSN applications that aim to pursue the clustered architectures. Similarly, a network designer might make use of this expected value during the process of deciding on the modes of communications (i.e., multi-hop or direct communication) after comparing it with the maximum transmission ranges of devices. Last but not least, the use of our derivation is not limited to WSN domain. It can be also exploited in any domain when there is a need for a probabilistic approach to find the average distance between any given number of points which are all assumed to be randomly and uniformly located in any square-shaped region and at a specific point outside this region.Yayın Energy load balancing for fixed clustering in wireless sensor networks(IEEE, 2012-05-07) Ali, Syed Amjad; Sevgi, CüneytClustering can be used as an effective technique to achieve both energy load balancing and an extended lifetime for a wireless sensor network (WSN). This paper presents a novel approach that first creates energy balanced fixed/static clusters, and then, to attain energy load balancing within each fixed cluster, rotates the role of cluster head through uniformly quantized energy levels based approach to prolong the overall network lifetime. The method provided herein, not only provides near-dynamic clustering performance but also reduces the complexity due to the fact that cluster formation phase is implemented once. The presented simulation results clearly show the efficacy of this proposed algorithm and thus, it can be used as a practical approach to obtain maximized network lifetime for energy balanced clusters in fixed clustering environments.
