Current Issue : July - September Volume : 2013 Issue Number : 3 Articles : 5 Articles
Disasters o?en result in a tremendous cost to our society. Previously, wireless sensor networks have been proposed to provide\r\ninformation for decision making in postdisaster relief operations. ?e existing WSN solutions for postdisaster operations normally\r\nassume that the deployed sensor network can tolerate the damage caused by disasters and maintain its connectivity and coverage,\r\neven though a signi??cant portion of nodes have been physically destroyed. Inspired by the ââ?¬Å?blackboxââ?¬Â technique, we propose that\r\npreserving ââ?¬Å?the last snapshotââ?¬Â of the whole network and transferring those data to a safe zone would be the most logical approach to\r\nprovide necessary information for rescuing lives and control damages. In this paper, we introduce data evacuation (DE), an original\r\nidea that takes advantage of the survival time of the WSN, that is, the gap from the time when the disaster hits and the time when\r\nthe WSN is paralyzed, to transmit critical data to sensor nodes in the safe zone. Numerical investigations reveal the effectiveness of\r\nour proposed DE algorithm....
?is paper deals with the problem of localization of mobile robot in indoor environment with mixed line-of-sight/nonline-ofsight\r\n(LOS/NLOS) conditions. To reduce the NLOS errors, a prior knowledge-based correction strategy (PKCS) is proposed to\r\nlocate the robot. ?is strategy consists of two steps: NLOS identi??cation and mitigation. We propose an NLOS identi??cation\r\nmethod by applying the statistical theory. ?en we correct the NLOS errors by subtracting the expected NLOS errors. Finally,\r\nthe residual weighting algorithm is employed to estimate the location of the robot. Simulation results show that the proposed\r\nstrategy signi??cantly improves the accuracy of localization in mixed LOS/NLOS indoor environment....
Standard classi??cation algorithms are o?en inaccurate when used in a wireless sensor network (WSN), where the observed data\r\noccur in imbalanced classes. ?e imbalanced data classi??cation problem occurs when the number of samples in one class, usually\r\nthe class of interest, is much lower than the number in the other classes. Many classi??cation models have been studied in the datamining\r\nresearch community. However, they all assume that the input data are stationary and bounded in size, so that resampling\r\ntechniques and postad??ustment by measuring the classi??cation cost can be applied. In this paper, we devise a new scheme that\r\nextends a popular stream classi??cation algorithm to the analysis of WSNs for reducing the adverse effects of the imbalanced class\r\nin the data. ?is new scheme is resource light at the algorithm level and does not require any data preprocessing. It uses weighted\r\nna�¯ve Bayes predictors at the decision tree leaves to effectively reduce the impact of imbalanced classes. Experiments show that our\r\nmodi??ed algorithm outperforms the original stream classi??cation algorithm....
Information interaction is a crucial part of modern transportation activities. In this paper, we propose the idea of PASS: a parkinglot-\r\nassisted carpool method over vehicular ad hoc networks (VANETs). PASS aims at optimizing transport utilization by the\r\ncarpooling among car drivers who cover a part of the same traveling route. With wireless device enabled in the vehicle, a user can\r\neasily get matched vehicles information and then express his travel demands via radio queries over VANETs to the corresponding\r\ndriver. ?e driver can decide whether to provide carpooling services or not. We investigate the main challenges in PASS design, via\r\nthe parking lot to collect vehicle trajectories, via accelerator sensor to sense vehicle�s movement, establish a routing tree to delivery\r\nvehicle trajectory information to nearby parking lots, and design a suitable matching scheme to match the target vehicle in VANETs.\r\nFinally, simulation results prove that PASS is effective and efficient in carpooling among vehicle drivers....
Delay tolerant mobile networks feature with intermittent connectivity, huge transmission delay, nodal mobility, and so forth. ?ere\r\nis usually no end-to-end path in the networks and it poses great challenges for routing in DTMNs. In this paper, the architecture\r\nof DTMNs is introduced at ??rst, including the characteristics of DTMNs, routing challenges, and metric and mobility models.\r\nAnd then, the state-of-the-art routing protocols for DTMNs are discussed and analyzed. Routing strategies are classi??ed into three\r\ncategories: nonknowledge-based approach, knowledge-based approach, and social-based approach. Finally, some research issues\r\nabout DTMNs are presented....
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