In this paper, recent localization algorithms are analyzed under a common one hop network scenario. The performance of localization is affected by physical parameters in a real wireless environment such as anchor node location and quantity, and error in the measured distance. The numerical analysis presented in this paper can be used to choose among localization algorithms to satisfy practical constraints such as number of anchors, nodes, geometry of anchors and computational efficiency. I INTRODUCTIONLocalization performs a key role in higher level services in current wireless sensor, ad-hoc and cellular networks. Sensor network comprises small size, low power and low cost nodes with various sensors, e.g. temperature, audio, humidity, proximity and others. Typical applications are in surveillance, security applications and monitoring. Location awareness in such networks is crucial due to the position dependent data collection and to reduce overhead for routing data. GPS is not an appropriate solution for localization due to cost, power consumption and its limited application to outdoor environment [1].Popularity of wireless LAN and Bluetooth networks has created security issues and challenges for various location based attacks. Monitoring and tracking the location of wireless nodes reduce these security threats with providing location of attacker [2]. Furthermore, knowledge of location is greatly helpful in routing algorithms and in location critical applications, e.g. first responders and emergency calls.The essential part of localization is range measurement and can be performed by time of arrival (TOA), time difference of arrival (TDOA) or received signal strength indication (RSSI) measurements [3]. Since many devices already have the ability to measure RSSI, we consider ranging using RSSI measurements. A simple radio frequency propagation model is assumed for range measurement and all algorithms are analyzed under this model. We have implemented various algorithms based on multilateration rather than triangulation. These selected algorithms are maximum like- * The work of J. lihood estimation (MLE) [4, 5], modified multidimensional scaling (MMDS) [6], Malguki spring model (MSM) [7] and weighted multidimensional scaling (WMDS) [8].The rest of the paper is organized as follows. In section II the system model is proposed. Section III provides a brief summary of all algorithms to be analyzed. Section IV covers simulation results and discussion regarding the performance of each algorithm. Comparison table is presented in section V. Finally, we conclude with future work in section VI. II SYSTEM MODEL A Propagation ModelIn this paper, the performance of localization algorithms is evaluated under a common scenario. Instead of a very complicated indoor wireless channel model, a simple path loss model is assumed. The signal power in an actual environment decays with the distance [9]. The loss of power depends on various obstacles between transmitter and receiver. The overall effect results in lognormal distribution of...
Over the past years, research has attempted to relate fiber properties with yarn prop erties, and many regression equations have been developed to accomplish this. The complexities of multiple regression equations put limits on their universal acceptance. Neural networks with better nonlinear mapping have also been used to develop such relationships. Our statistical data analysis of a few yarn properties will determine the suitability of neural networks for such textile applications.
Copy move forgery detection in digital images has become a very popular research topic in the area of image forensics. Due to the availability of sophisticated image editing tools and ever increasing hardware capabilities, it has become an easy task to manipulate the digital images. Passive forgery detection techniques are more relevant as they can be applied without the prior information about the image in question. Block based techniques are used to detect copy move forgery, but have limitations of large time complexity and sensitivity against affine operations like rotation and scaling. Keypoint based approaches are used to detect forgery in large images where the possibility of significant post processing operations like rotation and scaling is more. A hybrid approach is proposed using different methods for keypoint detection and description. Speeded Up Robust Features (SURF) are used to detect the keypoints in the image and Binary Robust Invariant Scalable Keypoints (BRISK) features are used to describe features at these keypoints. The proposed method has performed better than the existing forgery detection method using SURF significantly in terms of detection speed and is invariant to post processing operations like rotation and scaling. The proposed method is also invariant to other commonly applied post processing operations like adding Gaussian noise and JPEG compression.
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