Current Issue : July - September Volume : 2013 Issue Number : 3 Articles : 4 Articles
Range-free localization algorithm continues to be an important and challenging\r\nresearch topic in anisotropic Wireless Sensor Networks (WSNs). Designing range-free\r\nlocalization algorithms without considering obstacles or holes inside the network area\r\ndoes not reflect the real world conditions. In this paper, we have proposed Detour Path\r\nAngular Information (DPAI) based sensor localization algorithm to accurately estimate the\r\ndistance between an anchor node and a sensor node. We utilized the Euclidean distance and\r\ntransmission path distance among anchor nodes to calculate the angle of the transmission\r\npath between them one by one. Then the estimated hop distance is adjusted by the angle\r\nbetween the anchor pairs. Based on the angle of the detoured path (which is the key factor for\r\naccuracy), our algorithm determines whether the path is straight or detoured by anisotropic\r\nfactors. Our proposed algorithm does not require any global knowledge of network topology\r\nto tolerate the network anisotropy nor require high sensor node density for satisfactory\r\nlocalization accuracy. Extensive simulations are performed and the results are observed to be\r\nin good agreement with the theoretical analysis. DPAI achieved average sensor localization\r\naccuracy better than 0.3r in isotropic network and 0.35r in anisotropic network when the\r\nsensor density is above 8....
The present paper focuses on various aspects regarding Hall Effect sensors�\r\ndesign, integration, and behavior analysis. In order to assess their performance, different\r\nHall Effect geometries were tested for Hall voltage, sensitivity, offset, and temperature\r\ndrift. The residual offset was measured both with an automated measurement setup and by\r\nmanual switching of the individual phases. To predict Hall sensors performance prior to\r\nintegration, three-dimensional physical simulations were performed....
Sensor networks for various event detection applications cannot function\r\neffectively if they are vulnerable to attacks. Malicious nodes can generate incorrect readings\r\nand misleading reports in such a way that event detection accuracy and false alarm rates are\r\nunacceptably low and high, respectively. In this paper, we present a malicious node detection\r\nscheme for wireless sensor networks. Unlike others using a single threshold, the proposed\r\nscheme employs two thresholds to cope with the strong trade-off between event detection\r\naccuracy and false alarm rate, resulting in improved malicious node detection performance.\r\nIn addition, each sensor node maintains the trust values of its neighboring nodes to reflect\r\ntheir behavior in decision-making. Computer simulation shows that the proposed scheme\r\nachieves high malicious node detection accuracy without sacrificing normal sensor nodes\r\nand outperforms the scheme using a single threshold...
In this paper we present a unified comparison of the performance of four\r\ndetection techniques for centralized data-fusion cooperative spectrum sensing in cognitive\r\nradio networks under impulsive noise, namely, the eigenvalue-based generalized likelihood\r\nratio test (GLRT), the maximum-minimum eigenvalue detection (MMED), the maximum\r\neigenvalue detection (MED), and the energy detection (ED). We consider two system\r\nmodels: an implementation-oriented model that includes the most relevant signal processing\r\ntasks realized by a real cognitive radio receiver, and the theoretical model conventionally\r\nadopted in the literature. We show that under the implementation-oriented model, GLRT\r\nand MMED are quite robust under impulsive noise, whereas the performance of MED and\r\nED is drastically degraded. We also show that performance under the conventional model\r\ncan be too pessimistic if impulsive noise is present, whereas it can be too optimistic in the\r\nabsence of this impairment. We also discuss the fact that impulsive noise is not such a severe\r\nproblem when we take into account the more realistic implementation-oriented model....
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