Localization based radio model calibration for fault tolerant wireless mesh networks
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Localization based radio model calibration for fault tolerant wireless mesh networks

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To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org

To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org

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    Localization based radio model calibration for fault tolerant wireless mesh networks Localization based radio model calibration for fault tolerant wireless mesh networks Document Transcript

    • GLOBALSOFT TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401 Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmail.com Localization-Based Radio Model Calibration for Fault-Tolerant Wireless Mesh Networks Abstract Wireless mesh networks offer flexibility for industrial automation, but, in these environments with changing propagation conditions, it is challenging to guarantee the radio coverage and connectivity. This paper contributes a new localization-based method for the calibration of radio propagation models. The idea is to find the locations of the mobile stations via localization and to use radio signal strength measurements from them for adjusting the radio model parameters until the model better fits to the real environment. This calibration method is integrated in our previously published fault-tolerance framework for guaranteeing the availability of radio coverage and connectivity of wireless mesh networks. It is used to automatically detect environmental dynamics (errors) at run-time and to propose a network reconfiguration before they lead to service failures. An evaluation in a real industrial scenario shows the practicability of our approach. Existing system The specific network infrastructure, that we consider, are wireless mesh networks.WMNs are similar to the infrastructure wireless networks but promise more flexibility, self-organization, and seamless mobility. This is first because the backbone network requires no cables; instead, the base stations automatically form a Multihop wireless backbone and, second, because the ad hoc communication allows that a mobile station is always connected to the network. The environmental dynamics are unpredictable changes of the radio propagation and radio attenuation properties of the environment new obstacles, movement of obstacles, or increased humidity. They occur due to reconfiguration of the plant layout. Environmental dynamics occur, for
    • instance, in reconfigurable manufacturing systems (RMSs). An RMS is a production system with an adjustable structure that is able to meet the market requirements with respect to capacity, functionality, and cost. Disadvantages This adjustable structure at the system level includes changes in the plant layout, for instance ―adding, removing or modifying machine modules, machines, cells, material handling units and/or complete lines. The environmental dynamics negatively affect the radio coverage (radio signal strength between mobile stations and base stations) and the backbone network connectivity among base stations of a WMN. Proposed System The existing works, our proposed solution includes an automatic radio model calibration by radio signal strength measurements in the operational environment at run-time. In this way, our method detects the environmental dynamics and is able to perform radio coverage assessment and network reconfiguration by base station placement. The proposed method for localization-based model calibration and error detection successfully detects the dynamics of the environment regardless of the attenuation of the initial environment. The localization-based measurements were done during the normal movement of a mobile node with a directional antenna (without various antenna orientations), which means that in general the measured signal strength was lower, which leads to a higher estimation for the path loss exponent. This means that the localization-based radio model calibration provides the mobile station’s view on radio coverage, which can be considered as an advantage. Advantages The presented concept contributes to physical layer availability in a joined research for dependable endto-end communication in wireless mesh networks within our working group. The proposed method for localization-based model calibration and error detection successfully detects the dynamics of the environment regardless of the attenuation of the initial environment. This means that the localization-based radio model calibration provides the mobile station’s view on radio coverage, which can be considered as an advantage.
    • Modules Description Wireless communications The main determining factors for the availability of wireless communications. However, the specifics of the industrial applications (high availability requirements, dynamic propagation environments, personnel constraints) render the radio network planning and engineering, especially providing this service in the operational phase of a factory, a challenging task. Availability of Radio Coverage The loss of radio coverage can only be detected by the mobile stations and the applications, which means interruption of the communication. During the radio coverage repair, the presence of an expert is required for troubleshooting and base station planning. For compensating the dynamics of the environment, the static method uses static radio signal strength redundancy. Localization in Wireless Networks A WLAN-based localization method for radio model calibration. The existing WLAN localization methods base on the radio propagation time and radio signal strength. The propagation time methods are relatively accurate, since they base on precise propagation time measurements of the radio signal. However, they require a wired backbone for time synchronization within a nanosecond range, which is not available in WMNs. The signal strength-based methods use training data, which maps coordinates to some signal strength measurements and location estimation, using a search function of the online signal strength measurements in the training data. Radio Model and Calibration The provide an overview of the radio model and the calibration method, first, because it is a fundamental component of the presented fault-tolerance approach, and, second, this overview is required for understanding the localization-based radio model calibration method. Location Estimation This step estimates the location of each RSS measurement from the RSS measurement time sequence. For this purpose, we use k-nearest neighbors search in signal strength space. The estimated location is the average of the locations of the -nearest training data sets with respect to the similarity of the ARSS of the base stations. Estimation Improvement
    • The estimation improvement is to apply Kalman smoothing. In a typical localization system, the location estimate is used for location tracking. Therefore, the goal of a typical localization system is improving the last location estimate. Flow Diagram
    • CONCLUSION Based on our analysis and the experimental evaluation in industrial scenario, presented in Section V, we conclude that the proposed method for localization-based model calibration and error detection successfully detects the dynamics of the environment regardless of the attenuation of the initial environment. For detailed evaluation results of the localization please. The contribution of this work to the overall flex WARE communications concept is to achieve availability of the wireless medium by radio coverage monitoring and prediction and network engineering. In the flex WARE system, the flex WARE localization component is used for the phases ―Initialization of Localization‖ and ―Location Estimation‖ of the presented approach; the calibration-specific phases ―Estimation Improvement‖ and ―Interpretation‖ remain as described in Section IV. The presented concept contributes to physical layer availability in a joined research for dependable end-to-end communication in wireless mesh networks within our working group. The ongoing works develop concepts for end-to-end quality of service guarantees in WMNs. REFERENCES [1] A. Willig, ―Recent and emerging topics in wireless industrial communications: A selection,‖ IEEE Trans. Ind. Informat., vol. 4, no. 2, pp. 102–121, May 2008. [2] H. A. ElMaraghy, Changeable and ReconfigurableManufacturing Systems, D. T. Pham,Ed. Berlin, Germany: Springer Series in Advanced Manufacturing, 2009. [3] S. Ivanov and E. Nett, ―Fault-tolerant coverage planning in wireless networks,‖ in Proc. 27th IEEE Int. Symp. Reliable Distributed Syst., Napoli, Italy, Oct. 6–8, 2008, pp. 175–184. [4] S. Ivanov, E.Nett, and S. Schemmer, ―Planning availableWLAN in dynamic production environments,‖ in Proc. 7th IFAC Int. Conf. Fieldbuses and Networks in Industrial and Embedded Systems, 2007, pp. 33–40. [5] ―Ekahau Site Survey—Wi-Fi Planning and Site Survey Tool.‖ [Online]. Available: http://www.ekahau.com/products/ekahau-site-survey/ overview.html [6] T. M. Chan, K. F. Man, K. S. Tang, and S. Kwong, ―A jumping-genes paradigm for optimizing factory WLAN network,‖ IEEE Trans. Ind. Informat., vol. 3, no. 1, pp. 33–43, Feb. 2007. [7] P. Pathak and R. Dutta, ―A survey of network design problems and joint design approaches in wireless mesh networks,‖ IEEE Commun. Surveys Tutorials, vol. 13, no. 3, pp. 396–428, 2011.
    • [8] T. S. Rappaport, Wireless Communications—Principles and Practice, T. S. Rappaport, Ed. Upper Saddle River, NJ: Prentice Hall PTR, 2002.