52,013 research outputs found

    Transparent Location Fingerprinting for Wireless Services

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    Detecting the user location is crucial in a wireless environment, not only for the choice of first-hop communication partners, but also for many auxiliary purposes: Quality of Service (availability of information in the right place for reduced congestion/delay, establishment of the optimal path), energy consumption, automated insertion of location-dependent info into a web query issued by a user (for example a tourist asking informations about a monument or a restaurant, a fireman approaching a disaster area). The technique we propose in our investigation tries to meet two main goals: transparency to the network and independence from the environment. A user entering an environment (for instance a wireless-networked building) shall be able to use his own portable equipment to build a personal map of the environment without the system even noticing it. Preliminary tests allow us to detect position on a map with an average uncertainty of two meters when using information gathered from three IEEE802.11 access points in an indoor environment composed of many rooms on a 625sqm area. Performance is expected to improve when more access points will be exploited in the test area. Implementation of the same techniques on Bluetooth are also being studied

    Trust Based Participant Driven Privacy Control in Participatory Sensing

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    Widespread use of sensors and multisensory personal devices generate a lot of personal information. Sharing this information with others could help in various ways. However, this information may be misused when shared with all. Sharing of information between trusted parties overcomes this problem. This paper describes a model to share information based on interactions and opinions to build trust among peers. It also considers institutional and other controls, which influence the behaviour of the peers. The trust and control build confidence. The computed confidence bespeaks whether to reveal information or not thereby increasing trusted cooperation among peers.Comment: 14 page

    An LSPI based reinforcement learning approach to enable network cooperation in cognitive wireless sensor networks

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    The number of wirelessly communicating devices increases every day, along with the number of communication standards and technologies that they use to exchange data. A relatively new form of research is trying to find a way to make all these co-located devices not only capable of detecting each other's presence, but to go one step further - to make them cooperate. One recently proposed way to tackle this problem is to engage into cooperation by activating 'network services' (such as internet sharing, interference avoidance, etc.) that offer benefits for other co-located networks. This approach reduces the problem to the following research topic: how to determine which network services would be beneficial for all the cooperating networks. In this paper we analyze and propose a conceptual solution for this problem using the reinforcement learning technique known as the Least Square Policy Iteration (LSPI). The proposes solution uses a self-learning entity that negotiates between different independent and co-located networks. First, the reasoning entity uses self-learning techniques to determine which service configuration should be used to optimize the network performance of each single network. Afterwards, this performance is used as a reference point and LSPI is used to deduce if cooperating with other co-located networks can lead to even further performance improvements

    Benchmarking of localization solutions : guidelines for the selection of evaluation points

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    Indoor localization solutions are key enablers for next-generation indoor navigation and track and tracing solutions. As a result, an increasing number of different localization algorithms have been proposed and evaluated in scientific literature. However, many of these publications do not accurately substantiate the used evaluation methods. In particular, many authors utilize a different number of evaluation points, but they do not (i) analyze if the number of used evaluation points is sufficient to accurately evaluate the performance of their solutions and (ii) report on the uncertainty of the published results. To remedy this, this paper evaluates the influence of the selection of evaluation points. Based on statistical parameters such as the standard error of the mean value, an estimator is defined that can be used to quantitatively analyze the impact of the number of used evaluation points on the confidence interval of the mean value of the obtained results. This estimator is used to estimate the uncertainty of the presented accuracy results, and can be used to identify if more evaluations are required. To validate the proposed estimator, two different localization algorithms are evaluated in different testbeds and using different types of technology, showing that the number of required evaluation points does indeed vary significantly depending on the evaluated solution. (C) 2017 Elsevier B.V. All rights reserved
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