Current Issue : July - September Volume : 2018 Issue Number : 3 Articles : 5 Articles
Finding the minimum connected dominating set (MCDS) is a key problem in wireless sensor networks, which is\ncrucial for efficient routing and broadcasting. However, the MCDS problem is NP-hard. In this paper, a new\napproximation algorithm with approximation ratio H() + 3 in time O\n\nn2\nis proposed to approach the MCDS\nproblem. The key idea is to divide the sensors in CDS into core sensors and supporting sensors. The core sensors\ndominate the supporting sensors in CDS, while the supporting sensors dominate other sensors that are not in CDS. To\nminimize the number of both the cores and the supporters, a three-phased algorithm is proposed. (1) Finding the\nbase-core sensors by constructing independent set (denoted as S1), in which the sensors who have the largest |N2(v)|\n|N(v)|\n(number of two-hop neighbors over the number of one-hop neighbors) will be selected greedily into S1; (2)\nConnecting all base-core sensors in S1 to form a connected subgraph, the sensors in the subgraph are called cores; (3)\nAdding the one-hop neighbors of the core sensors to the supporter set S2. This guarantees a small number of sensors\ncan be added into CDS, which is a novel scheme for MCDS construction. Extensive simulation results are shown to\nvalidate the performance of our algorithm....
Fingerprint identification and recognition are considered popular technique\nin many security and law enforcement applications. The aim of this paper is to\npresent a proposed authentication system based on fingerprint as biometric\ntype, which is capable of recognizing persons with high level of confidence\nand minimum error rate. The designed system is implemented using Matlab\n2015b and tested on a set of fingerprint images gathered from 90 different\npersons with 8 samples for each using Futronic�s FS80 USB2.0 Fingerprint\nScanner and the ftrScanApiEx.exe program. An efficient image enhancement\nalgorithm is used to improve the clarity (contrast) of the ridge structures in a\nfingerprint. After that core point and candidate core points are extracted for\neach Fingerprint image and feature vector have been extracted for each point\nusing filterbank_based algorithm. Also, for the matching the KNN neural\nnetwork was used. In addition, the matching results were calculated and\ncompared to other papers using some performance evaluation factors. A\nthreshold has been proposed and used to provide the rejection for the fingerprint\nimages that does not belong to the database and the experimental results\nshow that the KNN technique have a recognition rate equal to 93.9683% in a\nthreshold equal to 70%....
Wireless sensor networks (WSNs) have captivated substantial attention from both industrial and academic research in the last few\nyears.Themajor factor behind the research efforts in that field is their vast range of applications which include surveillance systems,\nmilitary operations, health care, environment event monitoring, and human safety. However, sensor nodes are low potential and\nenergy constrained devices; therefore, energy-efficient routing protocol is the foremost concern. In this paper, an energy-efficient\nrouting protocol for wireless sensor networks is proposed. Our protocol consists of a routing algorithm for the transmission of\ndata, cluster head selection algorithm, and a scheme for the formation of clusters. On the basis of energy analysis of the existing\nrouting protocols, a multistage data transmission mechanism is proposed. An efficient cluster head selection algorithm is adopted\nand unnecessary frequency of reclustering is exterminated. Static clustering is used for efficient selection of cluster heads. The\nperformance and energy efficiency of our proposed routing protocol are assessed by the comparison of the existing routing protocols\non a simulation platform. On the basis of simulation results, it is observed that our proposed routing protocol (EE-MRP) has\nperformed well in terms of overall network lifetime, throughput, and energy efficiency....
In wireless sensor networks, sensor nodes are usually powered by battery and thus have very limited energy. Saving\nenergy is an important goal in designing a WSN. It is known that clustering is an effective method to prolong network\nlifetime. Due to the development of big data, there are more sensor nodes and data needed to process. So how to\ncluster sensor nodes cooperatively and achieve an optimal number of clusters in a big data WSN is an open issue. In\nthis paper, we first propose an analytical model to give the optimal number of clusters in a wireless sensor network.\nWe then propose a centralized cluster algorithm based on spectral partitioning method. After that, we present a\ndistributed implementation of the clustering algorithm based on fuzzy C-means method. Finally, we conduct\nextensive simulations, and the results show that the proposed algorithms outperform the hybrid energy-efficient\ndistributed (HEED) clustering algorithm in terms of energy cost and network lifetime....
To solve minimum exposure path (MEP) problem in wireless sensor networks more efficiently, this work proposes an\nalgorithm called target guiding self-avoiding random walk with intersection (TGSARWI), which mimics the behavior of\na group of random walkers that seek path to their destinations in a strange area. Target guiding leads random walkers\nmove toward their end points, while self-avoiding prevents them from taking roundabout routes. Route intersections\nfurther accelerate the speed of seeking connected paths. Dijkstra algorithm (DA) is applied to solve MEP problem in a\nsub-network formed by multiple connected paths that walkers generate (called TGSARWI DA). Simulations show that\nthe path exposure found by TGSARWI DA is very close to that by DA in the global network (Global DA), whereas the\ntime complexity of computation is much lower. Compared with existing heuristic algorithms such as physarum optimization\nalgorithm (POA), our algorithm shows higher generality and efficiency. This algorithm also exhibits good robustness to the\nfluctuations of parameters. Our algorithm could be very useful for the solution to MEP problem in fields with large- or highdensity\nsensors....
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