Wireless Communications and Mobile Computing

Cover image for Vol. 16 Issue 18

Online ISSN: 1530-8677

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Wireless Communications and Mobile Computing is part of an exciting new pilot partnership between Wiley and Hindawi. From 1st January 2017, the journal will become fully open access. Wireless Communications and Mobile Computing will remain a Wiley title but will be published and hosted by Hindawi, and will benefit from Hindawi’s experience and expertise in publishing open access titles. Wireless Communications and Mobile Computing will continue to undergo a rigorous peer review process ensuring that quality remains high. Manuscripts submitted on or after 16 June 2016 and accepted for publication will be published as open access articles, immediately free to read, download and share. Authors or their funder will be required to pay an Article Publication Charge upon acceptance. For further information, please click here.

Recently Published Articles

  1. Clustering hypervisors to minimize failures in mobile cloud computing (pages 3455–3465)

    Berihun Fekade, Taras Maksymyuk and Minho Jo

    Version of Record online: 20 FEB 2017 | DOI: 10.1002/wcm.2770

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    In this paper, we propose a new model using a naive Bayes classifier for hypervisor failure prediction and prevention in mobile cloud computing. We exploit real-time monitoring data in combination with historical maintenance data, which achieve higher accuracy in failure prediction and early failure-risk detection. After detecting hypervisors at risk, we perform live migration of virtual servers within a cluster, which decreases the load and prevents failures in the cloud.

  2. A multi-anchoring approach in mobile IP networks (pages 3423–3438)

    You Wang, Jun Bi and Xiaoke Jiang

    Version of Record online: 27 JAN 2017 | DOI: 10.1002/wcm.2767

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    We argue that single-anchoring approaches for IP mobility have drawbacks when facing various mobility scenarios and offer a novel multi-anchoring approach that allows each mobile node to select an independent mobility anchor for each correspondent node. We show that our proposal gains more performance benefits with an acceptable additional cost by evaluation based on real network topologies. We also demonstrate how our proposal can be integrated into current Mobile IP networks.

  3. Game theory-based global optimization for inter-WBAN interference mitigation (pages 3439–3448)

    Tin-Yu Wu and Wen-Kai Liu

    Version of Record online: 26 JAN 2017 | DOI: 10.1002/wcm.2769

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    The WSN has a coverage problem with interference signals and influence system reliability during data transmission. Some biosignals are very important. The reduction of system reliability due to high latency or packet loss may result in significant loss of important data and can be life-threatening. For this reason, this study makes use of a cooperative non-zero-sum game model for controlling the transmit power of the system as well as reducing the obstructions between simultaneous transmissions and avoiding contention between messages.

  4. Receiver optimization on non-binary joint sparse graph for OFDM system (pages 3360–3376)

    Lei Wen, Tong Wang and Jing Lei

    Version of Record online: 25 JAN 2017 | DOI: 10.1002/wcm.2758

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    A non-binary joint sparse graph is proposed to combine LDS-OFDM and non-binary low density parity check (LDPC) codes, namely non-binary joint sparse graph for OFDM (NJSG–OFDM). Syndrome nodes are added to the non-binary joint sparse graph; consequently, an optimized receiver for the NJSG–OFDM is proposed and analyzed by extrinsic information transfer (EXIT) chart. Simulation results show that the optimized receiver has advantages such as accelerating the convergence rate of the message passing, improving system performance and alleviating near-far effect.

  5. A k-means clustering-based security framework for mobile data mining (pages 3449–3454)

    Sghaier Guizani

    Version of Record online: 23 JAN 2017 | DOI: 10.1002/wcm.2762

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    The k-means clustering shown is finally performed on the resultant data set after the normalization process took place. The analysis is then done on the cluster formed, and data is shown to be organized in a certain pattern.